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  Subjects -> GEOGRAPHY (Total: 493 journals)
Showing 1 - 200 of 277 Journals sorted by number of followers
Geophysical Research Letters     Full-text available via subscription   (Followers: 184)
Journal of Geophysical Research : Space Physics     Full-text available via subscription   (Followers: 159)
Journal of Geophysical Research : Atmospheres     Partially Free   (Followers: 149)
Journal of Geophysical Research : Planets     Full-text available via subscription   (Followers: 143)
Remote Sensing of Environment     Hybrid Journal   (Followers: 96)
Antipode     Hybrid Journal   (Followers: 65)
Journal of Geophysical Research : Earth Surface     Partially Free   (Followers: 60)
Journal of Geophysical Research : Oceans     Partially Free   (Followers: 60)
Progress in Human Geography     Hybrid Journal   (Followers: 60)
Journal of Geophysical Research : Solid Earth     Full-text available via subscription   (Followers: 58)
International Journal of Geographical Information Science     Hybrid Journal   (Followers: 56)
GIScience & Remote Sensing     Open Access   (Followers: 54)
Journal of Water and Climate Change     Open Access   (Followers: 53)
Climate Change Economics     Hybrid Journal   (Followers: 50)
Reviews of Geophysics     Full-text available via subscription   (Followers: 49)
Remote Sensing Letters     Hybrid Journal   (Followers: 45)
Annals of the American Association of Geographers     Hybrid Journal   (Followers: 43)
Economic Geography     Hybrid Journal   (Followers: 42)
Applied Geography     Hybrid Journal   (Followers: 39)
Climate and Development     Hybrid Journal   (Followers: 34)
Urban Geography     Hybrid Journal   (Followers: 34)
Journal of Geophysical Research : Biogeosciences     Full-text available via subscription   (Followers: 34)
Geochemistry, Geophysics, Geosystems     Full-text available via subscription   (Followers: 34)
Annals of GIS     Open Access   (Followers: 31)
Journal of Coastal Research     Hybrid Journal   (Followers: 31)
Cartography and Geographic Information Science     Hybrid Journal   (Followers: 31)
GPS Solutions     Hybrid Journal   (Followers: 28)
Transactions of the Institute of British Geographers     Hybrid Journal   (Followers: 27)
Journal of Urbanism: International Research on Placemaking and Urban Sustainability     Hybrid Journal   (Followers: 24)
Journal of the Middle East and Africa     Hybrid Journal   (Followers: 23)
Dialogues in Human Geography     Hybrid Journal   (Followers: 20)
China : An International Journal     Full-text available via subscription   (Followers: 20)
Urban Research & Practice     Hybrid Journal   (Followers: 20)
Imago Mundi: The International Journal for the History of Cartography     Hybrid Journal   (Followers: 20)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 19)
Atmospheric Measurement Techniques (AMT)     Open Access   (Followers: 19)
Water International     Hybrid Journal   (Followers: 19)
Journal of the American Planning Association     Hybrid Journal   (Followers: 19)
Geography Compass     Hybrid Journal   (Followers: 18)
Journal of Cultural Geography     Hybrid Journal   (Followers: 18)
Cartographica : The International Journal for Geographic Information and Geovisualization     Full-text available via subscription   (Followers: 17)
Professional Geographer     Hybrid Journal   (Followers: 17)
Africa Insight     Full-text available via subscription   (Followers: 16)
Crossings : Journal of Migration & Culture     Hybrid Journal   (Followers: 16)
The Geographical Journal     Hybrid Journal   (Followers: 16)
International Geology Review     Hybrid Journal   (Followers: 16)
Computational Geosciences     Hybrid Journal   (Followers: 15)
Tectonics     Full-text available via subscription   (Followers: 15)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 14)
American Journal of Geographic Information System     Open Access   (Followers: 14)
Annual Review of Marine Science     Full-text available via subscription   (Followers: 13)
Buildings & Landscapes: Journal of the Vernacular Architecture Forum     Full-text available via subscription   (Followers: 13)
Progress in Physical Geography     Hybrid Journal   (Followers: 13)
International Indigenous Policy Journal     Open Access   (Followers: 13)
Geographical Review     Hybrid Journal   (Followers: 13)
Bulletin of Geosciences     Open Access   (Followers: 12)
Geographical Analysis     Hybrid Journal   (Followers: 11)
Journal of Geography     Hybrid Journal   (Followers: 11)
Geosciences Journal     Hybrid Journal   (Followers: 11)
Geographical Research     Hybrid Journal   (Followers: 11)
Canadian Journal of Soil Science     Full-text available via subscription   (Followers: 11)
GeoJournal     Hybrid Journal   (Followers: 11)
American Journal of Human Ecology     Open Access   (Followers: 11)
Geography and Natural Resources     Hybrid Journal   (Followers: 10)
European Spatial Research and Policy     Open Access   (Followers: 9)
Cartographic Journal     Hybrid Journal   (Followers: 9)
Atmospheric Measurement Techniques Discussions (AMTD)     Open Access   (Followers: 9)
Journal of Iberian and Latin American Research     Hybrid Journal   (Followers: 8)
Physical Geography     Hybrid Journal   (Followers: 8)
International Journal of Health Geographics     Open Access   (Followers: 8)
Natural Science     Open Access   (Followers: 8)
Middle East Development Journal     Hybrid Journal   (Followers: 8)
Journal of Borderlands Studies     Hybrid Journal   (Followers: 8)
Journal of Geographical Systems     Hybrid Journal   (Followers: 8)
International Journal of Applied Geospatial Research     Hybrid Journal   (Followers: 8)
Urban History Review / Revue d'histoire urbaine     Full-text available via subscription   (Followers: 7)
Journal of Latin American Geography     Full-text available via subscription   (Followers: 7)
California Italian Studies Journal     Full-text available via subscription   (Followers: 7)
Geo-spatial Information Science     Open Access   (Followers: 7)
Nordic Journal of Migration Research     Open Access   (Followers: 7)
Journal of Maps     Open Access   (Followers: 7)
Social Geography Discussions (SGD)     Open Access   (Followers: 7)
GeoInformatica     Hybrid Journal   (Followers: 7)
Northern Scotland     Hybrid Journal   (Followers: 6)
Asia Policy     Full-text available via subscription   (Followers: 6)
Australian Geographer     Hybrid Journal   (Followers: 6)
Singapore Journal of Tropical Geography     Hybrid Journal   (Followers: 6)
Ocean Science Journal     Hybrid Journal   (Followers: 6)
The Canadian Geographer/le Geographe Canadien     Hybrid Journal   (Followers: 6)
Creativity Studies     Open Access   (Followers: 5)
Australian Antarctic Magazine     Free   (Followers: 5)
Focus on Geography     Partially Free   (Followers: 5)
Journal of Developmental Entrepreneurship     Hybrid Journal   (Followers: 5)
Asian Geographer     Hybrid Journal   (Followers: 5)
Current Research in Geoscience     Open Access   (Followers: 5)
Journal of Australian Studies     Hybrid Journal   (Followers: 5)
ISPRS International Journal of Geo-Information     Open Access   (Followers: 5)
Journal of Map & Geography Libraries     Hybrid Journal   (Followers: 5)
Transmodernity : Journal of Peripheral Cultural Production of the Luso-Hispanic World     Open Access   (Followers: 4)
Applied Geomatics     Hybrid Journal   (Followers: 4)
Latinoamérica. Revista de estudios Latinoamericanos     Open Access   (Followers: 4)
Bulletin of the Ecological Society of America     Open Access   (Followers: 4)
Geografiska Annaler, Series A : Physical Geography     Hybrid Journal   (Followers: 4)
Globe, The     Full-text available via subscription   (Followers: 4)
Genre & histoire     Open Access   (Followers: 4)
Journal of Sedimentary Research     Hybrid Journal   (Followers: 4)
Southeastern Europe     Hybrid Journal   (Followers: 3)
Bulletin of Geography. Socio-economic Series     Open Access   (Followers: 3)
Limnological Review     Open Access   (Followers: 3)
Interaction     Full-text available via subscription   (Followers: 3)
Journal of Western Archives     Open Access   (Followers: 3)
Économie rurale     Open Access   (Followers: 3)
Social Dynamics: A journal of African studies     Hybrid Journal   (Followers: 3)
New Zealand Journal of Geography     Hybrid Journal   (Followers: 3)
Journal of Burma Studies     Full-text available via subscription   (Followers: 3)
South Asian Diaspora     Hybrid Journal   (Followers: 3)
All Earth     Open Access   (Followers: 3)
Lithosphere     Open Access   (Followers: 3)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
Polar Research     Open Access   (Followers: 3)
History of Geo- and Space Sciences     Open Access   (Followers: 2)
Journal of Earthquake and Tsunami     Hybrid Journal   (Followers: 2)
Pastoralism : Research, Policy and Practice     Open Access   (Followers: 2)
Standort - Zeitschrift für angewandte Geographie     Hybrid Journal   (Followers: 2)
Norois     Open Access   (Followers: 2)
Geodesy and Cartography     Open Access   (Followers: 2)
Eastern European Countryside     Open Access   (Followers: 2)
Mineralogia     Open Access   (Followers: 2)
Regions and Cohesion     Open Access   (Followers: 2)
Polar Geography     Hybrid Journal   (Followers: 2)
Southeastern Geographer     Full-text available via subscription   (Followers: 2)
BioRisk     Open Access   (Followers: 2)
Norsk Geografisk Tidsskrift - Norwegian Journal of Geography     Hybrid Journal   (Followers: 2)
Scottish Geographical Journal     Hybrid Journal   (Followers: 2)
Geosphere     Open Access   (Followers: 2)
Études rurales     Open Access   (Followers: 2)
Yearbook of the Association of Pacific Coast Geographers     Full-text available via subscription   (Followers: 2)
Polar Journal     Hybrid Journal   (Followers: 2)
Newfoundland and Labrador Studies     Full-text available via subscription   (Followers: 2)
Regional Science Policy & Practice     Hybrid Journal   (Followers: 2)
Provincial China     Hybrid Journal   (Followers: 2)
Geographical Education     Full-text available via subscription   (Followers: 2)
Cahiers franco-canadiens de l'Ouest     Full-text available via subscription   (Followers: 2)
The South Asianist     Open Access   (Followers: 2)
Reflets : revue d'intervention sociale et communautaire     Full-text available via subscription   (Followers: 2)
Maine Policy Review     Open Access   (Followers: 2)
Revista de Geografía Norte Grande     Open Access   (Followers: 1)
Geoforum Perspektiv     Open Access   (Followers: 1)
Estudios Geográficos     Open Access   (Followers: 1)
PRISM : A Journal of Regional Engagement     Open Access   (Followers: 1)
Norteamérica     Open Access   (Followers: 1)
Amerika     Open Access   (Followers: 1)
L'Année du Maghreb     Open Access   (Followers: 1)
Indiana     Open Access   (Followers: 1)
Les Cahiers d'Outre-Mer     Open Access   (Followers: 1)
Journal of the Southwest     Full-text available via subscription   (Followers: 1)
Revue archéologique du Centre de la France     Open Access   (Followers: 1)
Journal of Terrestrial Observation     Open Access   (Followers: 1)
Méditerranée     Open Access   (Followers: 1)
Journal de la Société des Océanistes     Open Access   (Followers: 1)
Geochronometria     Open Access   (Followers: 1)
South African Geographical Journal     Hybrid Journal   (Followers: 1)
GEM - International Journal on Geomathematics     Hybrid Journal   (Followers: 1)
Terrae Incognitae     Hybrid Journal   (Followers: 1)
International Journal of Bahamian Studies     Open Access   (Followers: 1)
Études internationales     Full-text available via subscription   (Followers: 1)
Recherches sociographiques     Full-text available via subscription   (Followers: 1)
Physio-Géo     Open Access   (Followers: 1)
GEOMATICA     Hybrid Journal   (Followers: 1)
European Countryside     Open Access   (Followers: 1)
PSC Discussion Papers Series     Open Access  
Anales de Geografía de la Universidad Complutense     Open Access  
International Journal of River Basin Management     Hybrid Journal  
Revista Geográfica de América Central     Open Access  
Multiciencias     Open Access  
Investigaciones Geográficas (Esp)     Open Access  
Sociedade & Natureza     Open Access  
Región y Sociedad     Open Access  
Migración y Desarrollo     Open Access  
Migraciones Internacionales     Open Access  
Investigaciones Geográficas     Open Access  
Frontera Norte     Open Access  
Cuadernos de Desarrollo Rural     Open Access  
Boletim de Ciências Geodésicas     Open Access  
Territoire en Mouvement     Open Access  
Quaestiones Geographicae     Open Access  
Limes. Cultural Regionalistics     Open Access  
Preview     Hybrid Journal  
Cuadernos de Geografía : Revista Colombiana de Geografía     Open Access  
Studia Universitatis Babes-Bolyai, Geologia     Open Access  
Recherches amérindiennes au Québec     Full-text available via subscription  
Rabaska : revue d'ethnologie de l'Amérique française     Full-text available via subscription  
Port Acadie : revue interdisciplinaire en études acadiennes / Port Acadie: An Interdisciplinary Review in Acadian Studies     Full-text available via subscription  
Études/Inuit/Studies     Full-text available via subscription  
Aurora Journal     Full-text available via subscription  
Revista de la Asociacion Geologica Argentina     Open Access  
San Francisco Estuary and Watershed Science     Open Access  
Journal of Alpine Research : Revue de géographie alpine     Open Access  
Géocarrefour     Open Access  
Confins     Open Access  

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ISPRS International Journal of Geo-Information
Journal Prestige (SJR): 0.493
Citation Impact (citeScore): 2
Number of Followers: 5  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2220-9964
Published by MDPI Homepage  [84 journals]
  • IJGI, Vol. 11, Pages 458: Extraction of Urban Quality of Life Indicators
           Using Remote Sensing and Machine Learning: The Case of Al Ain City, United
           Arab Emirates (UAE)

    • Authors: Mohamed. M. Yagoub, Yacob T. Tesfaldet, Marwan G. Elmubarak, Naeema Al Hosani
      First page: 458
      Abstract: Urban quality of life (UQoL) study is very important for many applications such as services distribution, urban planning, and socioeconomic analysis. The objective of this study is to create an urban quality of life index map for Al Ain city in the United Arab Emirates (UAE). The research aligns with the United Nations Sustainable Development Goals number ten (reduce inequalities) and eleven (sustainable cities and communities). In this study, remote sensing images and GIS vector datasets were used to extract biophysical and infrastructure facility indicators. The biophysical indicators are normalized difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), enhanced normalized difference impervious surfaces index (ENDISI), normalized difference built-up index (NDBI), land surface temperature (LST), slope, and land use land cover (LULC). In addition, infrastructure facility indicators such as distances to main roads, parks, schools, and hospitals were obtained. Additional infrastructure facility variables namely built-up to green area and build-up to bare soil area ratio were extracted from the LULC map. Machine learning was used to classify satellite images and generate LULC map. Random Forest (RF) was found as the best machine learning classifier for this study. The overall classification and Kappa hat accuracy was 95.3 and 0.92, respectively. Both biophysical and infrastructure facility indicators were integrated using principal component analysis (PCA). The PCA analysis identified four components that explain 75% of the variance among the indicators. The four factors were interpreted as the effect of LULC, infrastructure facility, ecological, and slope. Finally, the components were assigned weights based on the percentage of variance they explained and developed the UQoL map. Overall, the result showed that greenness has a greater effect on the spatial pattern of UQoL in Al Ain city. The study could be of a value to policy makers in urban planning and socioeconomic departments.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-23
      DOI: 10.3390/ijgi11090458
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 459: Assessment of Morphometric Parameters as the
           Basis for Hydrological Inferences in Water Resource Management: A Case
           Study from the Sinú River Basin in Colombia

    • Authors: Alvaro López-Ramos, Juan Pablo Medrano-Barboza, Luisa Martínez-Acosta, Guillermo J. Acuña, John Freddy Remolina López, Alvaro Alberto López-Lambraño
      First page: 459
      Abstract: The geomorphology of a basin makes it possible for us to understand its hydrological pattern. Accordingly, satellite-based remote sensing and geo-information technologies have proven to be effective tools in the morphology analysis at the basin level. Consequently, this present study carried out a morphological analysis of the Sinú river basin, analyzing its geometric characteristics, drainage networks, and relief to develop integrated water resource management. The analyzed zone comprises an area of 13,971.7 km2 with three sub-basins, the upper, the middle, and the lower Sinú sub-basins, where seventeen morphometric parameters were evaluated using remote sensing (RS) and geographical information system (GIS) tools to identify the rainwater harvesting potential index. The Sinú basin has a dendritic drainage pattern, and the results of the drainage network parameters make it possible for us to infer that the middle and lower Sinú areas are the ones mainly affected by floods. The basin geometry parameters indicate an elongated shape, implying a lesser probability of uniform and homogeneous rainfall. Additionally, the hypsometric curve shape indicates that active fluvial and alluvial sedimentary processes are present, allowing us to conclude that much of the material has been eroded and deposited in the basin’s lower zones as it could be confirmed with the geological information available. The obtained results and GIS tools confirm the basin’s geological heterogeneity. Furthermore, they were used to delimit the potential water harvesting zones following the rainwater harvesting potential index (RWHPI) methodology. The research demonstrates that drainage morphometry has a substantial impact on understanding landform processes, soil characteristics, and erosional characteristics. Additionally, the results help us understand the relationship between hydrological variables and geomorphological parameters as guidance and/or decision-making instruments for the competent authorities to establish actions for the sustainable development of the basin, flood control, water supply planning, water budgeting, and disaster mitigation within the Sinú river basin.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-24
      DOI: 10.3390/ijgi11090459
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 460: Web Mapping and Real–Virtual Itineraries
           to Promote Feasible Archaeological and Environmental Tourism in Versilia

    • Authors: Marco Luppichini, Valerio Noti, Danilo Pavone, Marzia Bonato, Francesco Ghizzani Ghizzani Marcìa, Stefano Genovesi, Francesca Lemmi, Lisa Rosselli, Neva Chiarenza, Marta Colombo, Giulia Picchi, Andrea Fontanelli, Monica Bini
      First page: 460
      Abstract: The Versilia plain (NW Italy) experiences forms of tourism that are mainly limited to the beach area and concentrated in the summer season. The area is rich in cultural and natural heritage, not yet adequately enhanced. The presence of four local archaeological museums and a natural park offers a great opportunity to favour feasible archaeological and environmental tourism. The aim of this study is to use a holistic methodology to improve a different type of tourism in the study area. We propose a consilient multidisciplinary approach based on geological, biological and archaeological data in order to enhance the cultural and natural heritage of the Versilia plain. We have based our study on the reconstruction of palaeoenvironment maps showing the evolution of the territory and used them as a leitmotiv to link the archaeological museums and the natural park. We define real and virtual itineraries to create a synergy between the most important archaeological and natural sites and museums. It is possible to promote a different type of tourism in the study area by decreasing human impact and creating a relationship between the fragmented natural and archaeological heritage. Palaeoenvironment maps and real and virtual itineraries can be consulted with the aid of a web application, more specifically web mapping, developed with free and open-source libraries. The web mapping also contains other geological, geomorphological and archaeological datasets, which allow to understand the evolution of the environment and the cultural and natural heritage of the study area. The dataset available on the web mapping is also downloadable.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-28
      DOI: 10.3390/ijgi11090460
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 461: Pattern Recognition and Segmentation of
           Administrative Boundaries Using a One-Dimensional Convolutional Neural
           Network and Grid Shape Context Descriptor

    • Authors: Min Yang, Haoran Huang, Yiqi Zhang, Xiongfeng Yan
      First page: 461
      Abstract: Recognizing morphological patterns in lines and segmenting them into homogeneous segments is critical for line generalization and other applications. Due to the excessive dependence on handcrafted features in existing methods and their insufficient consideration of contextual information, we propose a novel pattern recognition and segmentation method for lines, based on deep learning and shape context descriptors. In this method, a line is divided into a series of consecutive linear units of equal length, termed lixels. A grid shape context descriptor (GSCD) was designed to extract the contextual features for each lixel. A one-dimensional convolutional neural network (1D-U-Net) was constructed to classify the pattern type of each lixel, and adjacent lixels with the same pattern types were fused to obtain segmentation results. The proposed method was applied to administrative boundaries, which were segmented into components with three different patterns. The experiments showed that the lixel classification accuracy of the 1D-U-Net reached 90.42%. The consistency ratio was 92.41%, when compared with the manual segmentation results, which was higher than either of the two existing machine learning-based segmentation methods.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-28
      DOI: 10.3390/ijgi11090461
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 462: OSO-YOLOv5: Automatic Extraction Method of Store
           Signboards in Street View Images Based on Multi-Dimensional Analysis

    • Authors: Jiguang Dai, Yue Gu
      First page: 462
      Abstract: To realize the construction of smart cities, the fine management of various street objects is very important. In dealing with the form of objects, it is considered a pursuit of normativeness and precision. Store signboards are a tangible manifestation of urban culture. However, due to factors such as high spatial heterogeneity, interference from other ground objects, and occlusion, it is difficult to obtain accurate information from store signboards. In this article, in response to this problem, we propose the OSO-YOLOv5 network. Based on the YOLOv5 network, we improve the C3 module in the backbone, and propose an improved spatial pyramid pooling model. Finally, the channel and spatial attention modules are added to the neck structure. Under the constraint of rectangular features, this method integrates location attention and topology reconstruction, realizes automatic extraction of information from store signboards, improves computational efficiency, and effectively suppresses the effect of occlusion. Experiments were carried out on two self-labeled datasets. The quantitative analysis shows that the proposed model can achieve a high level of accuracy in the detection of store signboards. Compared with other mainstream object detection methods, the average precision (AP) is improved by 5.0–37.7%. More importantly, the related procedures have certain application potential in the field of smart city construction.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-28
      DOI: 10.3390/ijgi11090462
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 463: A Lightweight Long-Term Vehicular Motion
           Prediction Method Leveraging Spatial Database and Kinematic Trajectory

    • Authors: Lu Tao, Yousuke Watanabe, Hiroaki Takada
      First page: 463
      Abstract: Long-term vehicular motion prediction is a crucial function for both autonomous driving and advanced driver-assistant systems. However, due to the uncertainties of vehicle dynamics and complexities of surroundings, long-term motion prediction is never trivial work. As they combine effects of humans, vehicles and environments, kinematic trajectory data reflect several aspects of vehicles’ spatial behaviors. In this paper, we propose a novel method that leverages spatial database and kinematic trajectory data to achieve long-term vehicular motion prediction in a lightweight way. In our system, a spatial database system is initially embedded in an extended Kalman filter (EKF) framework. The spatial kinematic trajectory data are managed through the database and directly used in motion prediction; namely, weighted means are derived from the spatially retrieved kinematic data and used to update EKF predictions. The proposed method is validated in the real world. The experiments indicate that different weighting methods make a slight accuracy difference. Our method is not data-and-computation-consumed; its performance is acceptable in the limited data conditions and its prediction accuracy is improved as the size of used data sets increases; our method can predict in real time. The efficiency of an unscented Kalman filter (UKF) is compared with that of the EKF. The results show that the UKF can hardly meet real-time requirements.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-29
      DOI: 10.3390/ijgi11090463
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 464: Uncovering the Relationship between Urban Road
           Network Topology and Taxi Drivers’ Income: A Perspective from
           Spatial Design Network Analysis

    • Authors: Changwei Yuan, Jiannan Zhao, Xinhua Mao, Yaxin Duan, Ningyuan Ma
      First page: 464
      Abstract: Over the past few decades, taxi drivers’ income has received extensive attention from scholars. Previous studies have investigated the factors affecting taxi drivers’ income from multiple perspectives. However, less attention has been paid to road network topology, which has a direct impact on taxis’ operation efficiency and drivers’ income. To fill this gap, this paper examines the relationship between taxi drivers’ income and urban road network topology; we employed various methods, namely, spatial design network analysis (sDNA), bivariate Moran’s I, and geographically weighted regression (GWR). The results show the following. (1) The total order income (TOI) of taxi drivers has a certain degree of positive spatial correlation with closeness and betweenness. (2) The impact of urban road network topology on the average order income (AOI) of taxi drivers is stable. Specifically, closeness and betweenness have significant impacts on the AOI of taxi drivers at the medium and larger scales. (3) Closeness has a negative impact on the AOI of taxi drivers, and betweenness has a positive impact on the AOI of taxi drivers. (4) Compared with betweenness, the impact of closeness on the AOI of taxi drivers is greater and more stable. These findings can provide useful reference values for the development of policies aimed at improving both taxi drivers’ income and urban road network efficiency.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-29
      DOI: 10.3390/ijgi11090464
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 465: A Complete Reinforcement-Learning-Based
           Framework for Urban-Safety Perception

    • Authors: Yaxuan Wang, Zhixin Zeng, Qiushan Li, Yingrui Deng
      First page: 465
      Abstract: Urban-safety perception is crucial for urban planning and pedestrian street preference studies. With the development of deep learning and the availability of high-resolution street images, the use of artificial intelligence methods to deal with urban-safety perception has been considered adequate by many researchers. However, most current methods are based on the feature-extraction capability of convolutional neural networks (CNNs) with large-scale annotated data for training, mainly aimed at providing a regression or classification model. There remains a lack of interpretable and complete evaluation systems for urban-safety perception. To improve the interpretability of evaluation models and achieve human-like safety perception, we proposed a complete decision-making framework based on reinforcement learning (RL). We developed a novel feature-extraction module, a scalable visual computational model based on visual semantic and functional features that could fully exploit the knowledge of domain experts. Furthermore, we designed the RL module—comprising a combination of a Markov decision process (MDP)-based street-view observation environment and an intelligent agent trained using a deep reinforcement-learning (DRL) algorithm—to achieve human-level perception abilities. Experimental results using our crowdsourced dataset showed that the framework achieved satisfactory prediction performance and excellent visual interpretability.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-29
      DOI: 10.3390/ijgi11090465
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 466: Commuting Analysis of the Budapest Metropolitan
           Area Using Mobile Network Data

    • Authors: Gergo Pintér, Imre Felde
      First page: 466
      Abstract: The analysis of human movement patterns based on mobile network data makes it possible to examine a very large population cost-effectively and has led to several discoveries about human dynamics. However, the application of this data source is still not common practice. The goal of this study was to analyze the commuting tendencies of the Budapest Metropolitan Area using mobile network data as a case study and propose an automatized alternative approach to the current, questionnaire-based method, as commuting is predominantly analyzed by the census, which is performed only once in a decade in Hungary. To analyze commuting, the home and work locations of cell phone subscribers were determined based on their appearances during and outside working hours. The detected home locations of the subscribers were compared to census data at a settlement level. Then, the settlement and district level commuting tendencies were identified and compared to the findings of census-based sociological studies. It was found that the commuting analysis based on mobile network data strongly correlated with the census-based findings, even though home and work locations were estimated by statistical methods. All the examined aspects, including commuting from sectors of the agglomeration to the districts of Budapest and the age-group-based distribution of the commuters, showed that mobile network data could be an automatized, fast, cost-effective, and relatively accurate way of analyzing commuting, that could provide a powerful tool for sociologists interested in commuting.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-29
      DOI: 10.3390/ijgi11090466
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 467: Metric, Topological, and Syntactic Accessibility
           in Three-Dimensional Urban Networked Spaces: Modeling Options and

    • Authors: Thi Hong Diep Dao, Jean-Claude Thill
      First page: 467
      Abstract: In this paper, we take the position that cities gain to be represented as three-dimensional spaces populated by scores of micro-scale-built spaces (buildings, rooms, passageways, squares, etc.). Effective algorithms that evaluate place-based accessibility in built structures while considering the indoor spaces’ complexity at a fine granularity are essential for indoor–outdoor seamless urban planning, navigation, way findings, and supporting emergencies. We present a comprehensive set of spatial modeling options and visualizations of indoor accessibility for an entire built structure based on various notions of travel impedance. Notably, we consider the metric length of the paths and their cognitive complexities due to topologic, syntactic, or integrated intricacy within our approaches. Our work presents a comprehensive selection of indoor accessibility analysis with a detailed implemental discussion that can be applied as a solid foundation for smart city applications or seamless urban research and planning. The analysis and visualization techniques presented in this paper can be easily applied to analyze and visualize built interior geographic spaces to study accessibility differentials in cities with vast vertical expansion aimed at achieving (or at avoiding) specific accessibility outcomes.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-29
      DOI: 10.3390/ijgi11090467
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 468: HBIM Meta-Modelling: 50 (and More) Shades of

    • Authors: Martina Attenni, Carlo Bianchini, Marika Griffo, Luca James Senatore
      First page: 468
      Abstract: The paper aims at investigating modelling strategies in HBIM context to identify at what extent the final use of the model might affects, or should affect, the modelling approach itself. Moreover, the discussion wants to shed light on the possibility of connecting in just one digital environment several instances connected to the building. These aims will be discussed presenting and evaluating two different modelling approaches: the “black box” modelling and the “white box” model-ling. The two terms are partially borrowed from computer science to explain two types of testing. The “black box” testing is performed without any preliminary knowledge about the system functionality and internal components; on the contrary, the “white box” testing, implies a full knowledge of the system. These two approaches will be compared to two ways of conceiving a building information model. In conclusion, the paper will investigate the possibility to integrate in just one model, the grey box model, the two ones previously discussed.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-30
      DOI: 10.3390/ijgi11090468
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 469: Heri-Graphs: A Dataset Creation Framework for
           Multi-Modal Machine Learning on Graphs of Heritage Values and Attributes
           with Social Media

    • Authors: Nan Bai, Pirouz Nourian, Renqian Luo, Ana Pereira Roders
      First page: 469
      Abstract: Values (why to conserve) and Attributes (what to conserve) are essential concepts of cultural heritage. Recent studies have been using social media to map values and attributes conveyed by the public to cultural heritage. However, it is rare to connect heterogeneous modalities of images, texts, geo-locations, timestamps, and social network structures to mine the semantic and structural characteristics therein. This study presents a methodological [d=AR]frameworkworkflow for constructing such multi-modal datasets using posts and images on Flickr for graph-based machine learning (ML) tasks concerning heritage values and attributes. After data pre-processing using [d=AR]pre-trainedstate-of-the-art ML models, the multi-modal information of visual contents and textual semantics are modelled as node features and labels, while their social relationships and spatio-temporal contexts are modelled as links in Multi-Graphs. The [d=AR]frameworkworkflow is tested in three cities containing UNESCO World Heritage properties—Amsterdam, Suzhou, and Venice— which yielded datasets with high consistency for semi-supervised learning tasks. The entire process is formally described with mathematical notations, ready to be applied in provisional tasks both as ML problems with technical relevance and as urban/heritage study questions with societal interests. This study could also benefit the understanding and mapping of heritage values and attributes for future research in global cases, aiming at inclusive heritage management practices. Moreover, the proposed framework could be summarized as creating attributed graphs from unstructured social media data sources, ready to be applied in a wide range of use cases.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-30
      DOI: 10.3390/ijgi11090469
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 470: Spatial Prediction of COVID-19 Pandemic Dynamics
           in the United States

    • Authors: Çiğdem Ak, Alex D. Chitsazan, Mehmet Gönen, Ruth Etzioni, Aaron J. Grossberg
      First page: 470
      Abstract: The impact of COVID-19 across the United States (US) has been heterogeneous, with rapid spread and greater mortality in some areas compared with others. We used geographically-linked data to test the hypothesis that the risk for COVID-19 was defined by location and sought to define which demographic features were most closely associated with elevated COVID-19 spread and mortality. We leveraged geographically-restricted social, economic, political, and demographic information from US counties to develop a computational framework using structured Gaussian process to predict county-level case and death counts during the pandemic’s initial and nationwide phases. After identifying the most predictive information sources by location, we applied an unsupervised clustering algorithm and topic modeling to identify groups of features most closely associated with COVID-19 spread. Our model successfully predicted COVID-19 case counts of unseen locations after examining case counts and demographic information of neighboring locations, with overall Pearson’s correlation coefficient and the proportion of variance explained as 0.96 and 0.84 during the initial phase and 0.95 and 0.87 during the nationwide phase, respectively. Aside from population metrics, presidential vote margin was the most consistently selected spatial feature in our COVID-19 prediction models. Urbanicity and 2020 presidential vote margins were more predictive than other demographic features. Models trained using death counts showed similar performance metrics. Topic modeling showed that counties with similar socioeconomic and demographic features tended to group together, and some of these feature sets were associated with COVID-19 dynamics. Clustering of counties based on these feature groups found by topic modeling revealed groups of counties that experienced markedly different COVID-19 spread. We conclude that topic modeling can be used to group similar features and identify counties with similar features in epidemiologic research.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-30
      DOI: 10.3390/ijgi11090470
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 471: A Forest of Forests: A Spatially Weighted and
           Computationally Efficient Formulation of Geographical Random Forests

    • Authors: Stefanos Georganos, Stamatis Kalogirou
      First page: 471
      Abstract: The aim of this paper is to present developments of an advanced geospatial analytics algorithm that improves the prediction power of a random forest regression model while addressing the issue of spatial dependence commonly found in geographical data. We applied the methodology to a simple model of mean household income in the European Union regions to allow easy understanding and reproducibility of the analysis. The results are encouraging and suggest an improvement in the prediction power compared to previous techniques. The algorithm has been implemented in R and is available in the updated version of the SpatialML package in the CRAN repository.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-31
      DOI: 10.3390/ijgi11090471
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 472: HBIM Open Source: A Review

    • Authors: Filippo Diara
      First page: 472
      Abstract: Historic Building Information Modelling (HBIM) methodology has revolutionized the entire cultural heritage documentation panorama since 2009. At the same time, the possibility of creating and managing HBIM projects by using open source solutions opened new research paths in 2016. Different reasons can drive the utilisation of free and open source software (FOSS), however the accessibility of a tailor-made project should be the main purpose. After six years of research on open source HBIM, this paper will review the actual panorama of designed and operative programmes on informative models of historic architecture built with FOSS solutions. Different aspects will be analysed, from open source software setup to parametric modelling and from semantic dimension to data exchange and cloud accessibility. Then, the advantages and drawbacks of open source protocols will be highlighted. Lastly, the next updates, future scenarios and developments on open source HBIM will be estimated.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-31
      DOI: 10.3390/ijgi11090472
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 473: Mapping Climate Parameters over the Territory of
           Botswana Using GMT and Gridded Surface Data from TerraClimate

    • Authors: Polina Lemenkova
      First page: 473
      Abstract: This articles presents a new series of maps showing the climate and environmental variability of Botswana. Situated in southern Africa, Botswana has an arid to semi-arid climate, which significantly varies in its different regions: Kalahari Desert, Makgadikgadi Pan and Okavango Delta. While desert regions are prone to droughts and periods of extreme heat during the summer months, other regions experience heavy downpours, as well as episodic and unpredictable rains that affect agricultural activities. Such climatic variations affect social and economic aspects of life in Botswana. This study aimed to visualise the non-linear correlations between the topography and climate setting at the country’s scale. Variables included T °C min, T °C max, precipitation, soil moisture, evapotranspiration (PET and AET), downward surface shortwave radiation, vapour pressure and vapour pressure deficit (VPD), wind speed and Palmer Drought Severity Index (PDSI). The dataset was taken from the TerraClimate source and GEBCO for topographic mapping. The mapping approach included the use of Generic Mapping Tools (GMT), a console-based scripting toolset, which enables the use of a scripting method of automated mapping. Several GMT modules were used to derive a set of climate parameters for Botswana. The data were supplemented with the adjusted cartographic elements and inspected by the Geospatial Data Abstraction Library (GDAL). The PDSI in Botswana in 2018 shows stepwise variation with seven areas of drought: (1) −3.7 to −2.2. (extreme); (2) −2.2 to −0.8 (strong, southern Kalahari); (3) −0.8 to 0.7 (significant, central Kalahari; (4) 0.7 to 2.1 (moderate); (5) 2.1 to 3.5 (lesser); (6) 3.5 to 4.9 (low); (7) 4.9 to 6.4 (least). The VPD has a general trend towards the south-western region (Kalahari Desert, up to 3.3), while it is lower in the north-eastern region of Botswana (up to 1.4). Other values vary respectively, as demonstrated in the presented 12 maps of climate and environmental inventory in Botswana.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-31
      DOI: 10.3390/ijgi11090473
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 474: Potential Ecological Distributions of Urban
           Adapters and Urban Exploiters for the Sustainability of the Urban Bird

    • Authors: Nurul L. Winarni, Habiburrachman A. H. Fuad, Bhisma G. Anugra, Nabilla Nuril Kaunain, Shania Anisafitri, Mega Atria, Afiatry Putrika
      First page: 474
      Abstract: The bird community in urban areas indicates the species-specific adaptability to urban conditions such as the increase in man-made habitats. Urban adapters and urban exploiters, two groups that make up most of the urban birds, were assessed to determine their suitable habitat and explain their distribution, as well as to determine the environmental predictors for the two bird groups assemblages in Depok, one of Jakarta’s satellite cities. We used the point-count method to survey the birds in three habitat types, green spaces, residentials, and roadside, and then we used Maximum Entropy (MaxEnt) to analyze the species distribution modeling. We also the predicted habitat distributions for the urban adapters and urban exploiters based on several environmental predictors. Our results suggest that both urban adapters and urban exploiters were abundant in residential areas. Eurasian tree sparrows (Passer montanus) and cave swiflets (Collocalia linchi) were the most common species in all three habitat types. On average, canopy cover was most extensive in green spaces followed by residential and roadside areas. Urban exploiters were likely to have a high suitability extent compared to urban adapters. The distributions of both groups were affected by the distance to perennial water, then by land function for the urban adapters, and distance to patches for the urban exploiters. The presence of urban adapters and urban exploiters in residential areas suggests that home gardens supported critical habitats when green spaces were unavailable.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-31
      DOI: 10.3390/ijgi11090474
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 475: Identification of Road Network Intersection
           Types from Vehicle Telemetry Data Using a Convolutional Neural Network

    • Authors: Abdelmajid Erramaline, Thierry Badard, Marie-Pier Côté, Thierry Duchesne, Olivier Mercier
      First page: 475
      Abstract: GPS trajectories collected from automotive telematics for insurance purposes go beyond being a collection of points on the map. They are in fact a powerful data source that we can use to extract map and road network properties. While the location of road junctions is readily available, the information about the traffic control element regulating the intersection is typically unknown. However, this information would be helpful, e.g., for contextualizing a driver’s behavior. Our focus is to use a map-matched GPS OBD-dongle dataset provided by a Canadian insurance company to classify intersections into three classes according to the type of traffic control element present: traffic light, stop sign, or no sign. We design a convolutional neural network (CNN) for classifying intersections. The network takes as entries, for a defined number of trips, the speed and the acceleration profiles over each segment of one meter on a window around the intersection. Our method outperforms two other competing approaches, achieving 99% overall accuracy. Furthermore, our CNN model can infer the three classes even with as few as 25 trips.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-31
      DOI: 10.3390/ijgi11090475
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 476: Spatio-Temporal Sentiment Mining of COVID-19
           Arabic Social Media

    • Authors: Tarek Elsaka, Imad Afyouni, Ibrahim Hashem, Zaher Al Aghbari
      First page: 476
      Abstract: Since the recent outbreak of COVID-19, many scientists have started working on distinct challenges related to mining the available large datasets from social media as an effective asset to understand people’s responses to the pandemic. This study presents a comprehensive social data mining approach to provide in-depth insights related to the COVID-19 pandemic and applied to the Arabic language. We first developed a technique to infer geospatial information from non-geotagged Arabic tweets. Secondly, a sentiment analysis mechanism at various levels of spatial granularities and separate topic scales is introduced. We applied sentiment-based classifications at various location resolutions (regions/countries) and separate topic abstraction levels (subtopics and main topics). In addition, a correlation-based analysis of Arabic tweets and the official health providers’ data will be presented. Moreover, we implemented several mechanisms of topic-based analysis using occurrence-based and statistical correlation approaches. Finally, we conducted a set of experiments and visualized our results based on a combined geo-social dataset, official health records, and lockdown data worldwide. Our results show that the total percentage of location-enabled tweets has increased from 2% to 46% (about 2.5M tweets). A positive correlation between top topics (lockdown and vaccine) and the COVID-19 new cases has also been recorded, while negative feelings of Arab Twitter users were generally raised during this pandemic, on topics related to lockdown, closure, and law enforcement.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-02
      DOI: 10.3390/ijgi11090476
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 477: Spatial Interaction Analysis of Shared Bicycles
           Mobility Regularity and Determinants: A Case Study of Six Main Districts,

    • Authors: Lujin Hu, Zheng Wen, Jian Wang, Jing Hu
      First page: 477
      Abstract: Understanding the regularity and determinants of mobility is indispensable for the reasonable deployment of shared bicycles and urban planning. A spatial interaction network covering streets in Beijing’s six main districts, using bike sharing data, is constructed and analyzed. as Additionally, the exponential random graph model (ERGM) is used to interpret the influencing factors of the network structure and the mobility regularity. The characteristics of the spatial interaction network structure and temporal characteristics between weekdays and weekends show the following: the network structure on weekdays is obvious; the flow edge is always between adjacent blocks; the traffic flow frequently changes and clusters; the network structure on weekends is more complex, showing scattering and seldom changing; and there is a stronger interaction between blocks. Additionally, the predicted result of the ERGM shows that the influencing factors selected in this paper are positively correlated with the spatial interaction network. Among them, the three most important determinants are building density, housing prices and the number of residential areas. Additionally, the determinant of financial services shows greater effects on weekdays than weekends.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-02
      DOI: 10.3390/ijgi11090477
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 478: Early Detection of Suspicious Behaviors for Safe
           Residence from Movement Trajectory Data

    • Authors: Junyi Cheng, Xianfeng Zhang, Xiao Chen, Miao Ren, Jie Huang, Peng Luo
      First page: 478
      Abstract: Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-03
      DOI: 10.3390/ijgi11090478
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 479: Exploring Landscape Composition Using 2D and 3D
           Open Urban Vectorial Data

    • Authors: Frédéric Pedrinis, John Samuel, Manuel Appert, Florence Jacquinod, Gilles Gesquière
      First page: 479
      Abstract: Methods and tools for assessing the visual impact of objects such as high-rises are rarely used in planning, despite the increase in opportunities to develop automated visual assessments, now that 3D urban data are acquired and used by municipalities as well as made available through open data portals. This paper presents a new method for assessing city visibility using a 3D model on a metropolitan scale. This method measures the view composition in terms of city objects visible from a given viewpoint and produces a georeferenced and semantically rich database of those visible objects in order to propose a thematic vision of the city and its urban landscape. As far as computational efficiency is concerned and considering the large amount of data needed, the method relies on a dedicated system of automatic data organization for analyzing visibility over vast areas (hundreds of square kilometers), offering various possibilities for uses on different scales. In terms of operational uses, as shown in our paper, the various results produced by the method (quantitative data, georeferenced databases and 3D schematic images) allow for a wide spectrum of applications in urban planning.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-10
      DOI: 10.3390/ijgi11090479
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 480: Extracting Skeleton Lines from Building
           Footprints by Integration of Vector and Raster Data

    • Authors: Guoqing Chen, Haizhong Qian
      First page: 480
      Abstract: The extraction of skeleton lines of buildings is a key step in building spatial analysis, which is widely performed for building matching and updating. Several methods for vector data skeleton line extraction have been established, including the improved constrained Delaunay triangulation (CDT) and raster data skeleton line extraction methods, which are based on image processing technologies. However, none of the existing studies have attempted to combine these methods to extract the skeleton lines of buildings. This study aimed to develop a building skeleton line extraction method based on vector–raster data integration. The research object was buildings extracted from remote sensing images. First, vector–raster data mapping relationships were identified. Second, the buildings were triangulated using CDT. The extraction results of the Rosenfeld thin algorithm for raster data were then used to remove redundant triangles. Finally, the Shi–Tomasi corner detection algorithm was used to detect corners. The building skeleton lines were extracted by adjusting the connection method of the type three triangles in CDT. The experimental results demonstrate that the proposed method can effectively extract the skeleton lines of complex vector buildings. Moreover, the skeleton line extraction results included a few burrs and were robust against noise.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-10
      DOI: 10.3390/ijgi11090480
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 481: Exploring the Applicability of Self-Organizing
           Maps for Ecosystem Service Zoning of the Guangdong-Hong Kong-Macao Greater
           Bay Area

    • Authors: Yingwei Yan, Yingbin Deng, Ji Yang, Yong Li, Xinyue Ye, Jianhui Xu, Yuyao Ye
      First page: 481
      Abstract: Sustainability is one of the major challenges in the 21st century for humanity. Spatial zoning of ecosystem services is proposed in this study as a solution to meet the demands for the sustainable use of ecosystem services. This study presented a workflow and performed an exploratory analysis using self-organizing maps (SOM) for visualizing the spatial patterns of the ecosystem service value (ESV) of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The zoning was performed based on 11 types of ecosystem services, resulting in 11 ecosystem service zones. Each of the zones derived has its unique characteristics in terms of the dominating ecosystem service types, ESV, land use/land cover patterns, and associated human activity levels. It is recommended that reasonable and effective utilization of the ecosystem services in the GBA should be based on its zonal characteristics rather than haphazard exploitations, which can contribute to the sustainable economy and environment of the region. The applicability of SOM for the GBA ecosystem service zoning has been demonstrated in this study. However, it should be stressed that the method and workflow presented in this study should mainly be used for supporting decision-making rather than used for deriving gold-standard zoning maps.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-13
      DOI: 10.3390/ijgi11090481
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 482: SocialMedia2Traffic: Derivation of Traffic
           Information from Social Media Data

    • Authors: Mohammed Zia, Johannes Fürle, Christina Ludwig, Sven Lautenbach, Stefan Gumbrich, Alexander Zipf
      First page: 482
      Abstract: Traffic prediction is a topic of increasing importance for research and applications in the domain of routing and navigation. Unfortunately, open data are rarely available for this purpose. To overcome this, the authors explored the possibility of using geo-tagged social media data (Twitter), land-use and land-cover point of interest data (from OpenStreetMap) and an adapted betweenness centrality measure as feature spaces to predict the traffic congestion of eleven world cities. The presented framework and workflow are termed as SocialMedia2Traffic. Traffic congestion was predicted at four tile spatial resolutions and compared with Uber Movement data. The overall precision of the forecast for highly traffic-congested regions was approximately 81%. Different data processing steps including ways to aggregate data points, different proxies and machine learning approaches were compared. The lack of a universal definition on a global scale to classify road segments by speed bins into different traffic congestion classes has been identified to be a major limitation of the transferability of the framework. Overall, SocialMedia2Traffic further improves the usability of the tested feature space for traffic prediction. A further benefit is the agnostic nature of the social media platform’s approach.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-13
      DOI: 10.3390/ijgi11090482
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 483: Research on the Dynamic Evolution of the
           Landscape Pattern in the Urban Fringe Area of Wuhan from 2000 to 2020

    • Authors: Yan Long, Shiqi Luo, Xi Liu, Tianyue Luo, Xuejun Liu
      First page: 483
      Abstract: The urban fringe area is a discontinuous spatial phenomenon that refers to the urban-rural interlacing zone which is undergoing urbanization on the fringe of the core built-up area of a large city after the emergence of industrialization. Dynamic, ambiguous, and complex interlacing of various types of lands make urban planners and managers fuzzy about the spatial scope of the urban fringe and it is difficult to control its evolution patterns scientifically. Based on remote sensing data from 2000 to 2020, the range of Wuhan’s urban fringe was extracted from the surface impermeability ratio mutation points, landscape flocculation, and population density. On this basis, the dynamic evolution characteristics of land-use and landscape patterns in the urban fringe area of Wuhan City were analyzed by using dynamic change and landscape pattern index analysis. The results show that: Wuhan City shows a clear “urban core area-urban fringe area-rural hinterland” circle structure, and the urban fringe area continuously extends to the rural hinterland. Moreover, most of the rural hinterland, in the process of moving to the urban core area, has gone through the process of the urban fringe. By comparison with other cities, it is found that the expansion of large cities is generally influenced by policies, topography, and traffic arteries, and gradually shifts from expansion to infill, with the urban core of Wuhan continuously extending and the urban fringe rapidly expanding from 2000 to 2010, and gradually entering a stable development state from 2010 to 2020. The future urban construction of Wuhan should pay attention to the influences of these characteristics on the implementation of urban territorial spatial planning.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-13
      DOI: 10.3390/ijgi11090483
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 484: Soft Integration of Geo-Tagged Data Sets in

    • Authors: Paolo Fosci, Giuseppe Psaila
      First page: 484
      Abstract: The possibility offered by the current technology to collect and store data sets regarding public places located on the Earth globe is posing new challenges, as far as the integration of these data sets is concerned. Analysts usually need to perform such an integration from scratch, without performing complex and long preprocessing or data-cleaning tasks, as well as without performing training activities that require tedious and long labeling of data; furthermore, analysts now have to deal with the popular JSON format and with data sets stored within JSON document stores. This paper demonstrates that a methodology based on soft integration (i.e., data integration performed through soft computing and fuzzy sets) can now be effectively applied from scratch, through the J-CO Framework, which is a stand-alone tool devised to process JSON data sets stored within JSON document stores, possibly by performing soft querying on data sets. Specifically, the paper provides the following contributions: (1) It presents a soft-computing technique for integrating data sets describing public places, without any preliminary pre-processing, cleaning and training, which can be applied from scratch; (2) it presents current capabilities for soft integration of JSON data sets, provided by the J-CO Framework; (3) it demonstrates the effectiveness of the soft integration technique; (4) it shows how a stand-alone tool able to support soft computing (as the J-CO Framework) can be effective and efficient in performing data-integration tasks from scratch.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-13
      DOI: 10.3390/ijgi11090484
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 485: Exploring Spatial Features of Population
           Activities and Functional Facilities in Rail Transit Station Realm Based
           on Real-Time Positioning Data: A Case of Xi’an Metro Line 2

    • Authors: Di Wang, Bart Dewancker, Yaqiong Duan, Meng Zhao
      First page: 485
      Abstract: The rail transit station realm is an important urban spatial node that carries various behavioral activities and multiple functions. In order to accurately identify the spatial and temporal distribution of population activities and functional facilities in the rail transit station realm and understand the dynamic influence relationship between them, this paper takes four different types of stations of Xi’an Metro Line 2 as the research object, using real-time positioning data to represent population activities and points of interest (POIs) to represent functional facilities. An analytical framework combining the spatial point pattern identification technique and ordinary least squares (OLS) regression model is proposed. The results show that (1) there is spatial and temporal heterogeneity in the population activities in the rail transit station realm; the density distribution of population activities in different time periods shows the characteristic of clustering within 500 m of the station, regardless of working days or off days; (2) the distribution of shopping service POI, catering service POI, and living service POI in different station realms shows the feature of clustering around the stations; (3) the catering POI, living POI, shopping POI and transportation POI have positive attraction to population activities in different time periods; the constructed OLS model can basically explain the influence relationship between various functional facilities and population activities in all time periods. The conclusions can help city managers understand the spatial and temporal distribution and intrinsic mechanisms of population activities and functional facilities from a microscopic perspective and provide an effective decision-making basis for optimizing the allocation of functional resources in the station realm.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-14
      DOI: 10.3390/ijgi11090485
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 486: Exploring the Inter-Monthly Dynamic Patterns of
           Chinese Urban Spatial Interaction Networks Based on Baidu Migration Data

    • Authors: Heping Jiang, Shijia Luo, Jiahui Qin, Ruihua Liu, Disheng Yi, Yusi Liu, Jing Zhang
      First page: 486
      Abstract: The rapid development of the economy promotes the increasing of interactions between cities and forms complex networks. Many scholars have explored the structural characteristics of urban spatial interaction networks in China and have conducted spatio-temporal analyzes. However, scholars have mainly focused on the perspective of static networks and have not understood the dynamic spatial interaction patterns of Chinese cities. Therefore, this paper proposes a research framework to explore the urban dynamic spatial interaction patterns. Firstly, we establish a dynamic urban spatial interaction network according to monthly migration data. Then, the dynamic community detection algorithm, combined with the Louvain and Jaccard matching method, is used to obtain urban communities and their dynamic events. We construct event vectors for each urban community and use hierarchical clustering to cluster event vectors to obtain different types of spatial interaction patterns. Finally, we divide the urban dynamic interaction into three urban spatial interaction modes: fixed spatial interaction pattern, long-term spatial interaction pattern, and short-term spatial interaction pattern. According to the results, we find that the cities in well-developed areas (eastern China) and under-developed areas (northwestern China) mostly show fixed spatial interaction patterns and long-term spatial interaction patterns, while the cities in moderately developed areas (central and western China) often show short-term spatial interaction patterns. The research results and conclusions of this paper reveal the inter-monthly urban spatial interaction patterns in China, provide theoretical support for the policy making and development planning of urban agglomeration construction, and contribute to the coordinated development of national and regional cities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-14
      DOI: 10.3390/ijgi11090486
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 487: Road Intersection Recognition via Combining
           Classification Model and Clustering Algorithm Based on GPS Data

    • Authors: Liu, Qing, Zhao, Liao
      First page: 487
      Abstract: Road intersections are essential to road networks. How to precisely recognize road intersections based on GPS data is still challenging in intelligent transportation systems. Road intersection recognition involves detecting intersections and recognizing its scope. There are few works on intersections’ scope recognition. The existing methods always focus on road intersection detection. It includes two parts: one is selecting turning points from GPS data and extracting their geometric features, another is clustering them into center coordinates of road intersections. However, the accuracy of road intersection detection still has improvement room due to two drawbacks: (1) Besides geometric features, spatial features explored from GPS data and the interactions among all features are also important to represent intersections’ semantics more accurately, and (2) How to capture the points around intersections for clustering has great impact on the accuracy of intersection detection. To solve the preceding problems, we propose a novel approach for road intersection recognition via combining a classification model and clustering algorithm based on GPS data, which involves detecting the center coordinate and computing the radius of the intersection. Firstly, we distil geometric features and spatial features from historical GPS points. These features are inputted into the Extreme Deep Factorization Machine (xDeepFM) model which is applied for capturing the GPS points nearby road intersections. Secondly, the preceding points are clustered into center coordinates of road intersections by the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN). Thirdly, we present a new method of radius computing by integrating Delaunay triangulation with circle shape structure. Experiments are carried out on the GPS data of Chengdu, China. Compared with some state-of-the-art methods, our approach achieves higher accuracy on road intersection recognition based on GPS data. The precision, recall, and f-measure of our proposed center coordinates detection method are respectively 99.0%, 92.7%, and 95.8% when the matching area’s radius is 30 m. Moreover, the error of the proposed radius calculation method is less than 26.5%.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-14
      DOI: 10.3390/ijgi11090487
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 488: PM2SFCA: Spatial Access to Urban Parks, Based on
           Park Perceptions and Multi-Travel Modes. A Case Study in Beijing

    • Authors: Shijia Luo, Heping Jiang, Disheng Yi, Ruihua Liu, Jiahui Qin, Yusi Liu, Jing Zhang
      First page: 488
      Abstract: Assessing park accessibility plays an essential role in providing rational recreational services for residents in a city. The perceptions and comments of residents are also important nonspatial factors for accessibility. However, there are few accessibility studies that are combined with public perceptions. Addressing this deficit, this study proposes a perception-based, multi-travel mode, two-step floating catchment area (PM2SFCA) method to calculate park accessibility. First, we quantified the selection probability of residents to parks by integrating the Huff model and the people’s perceptions towards parks. Next, under four travel modes (walking, biking, driving and public transport), we combined the Huff model and the two-step floating catchment area method to compute park accessibility. Furthermore, the Gini coefficient and the Pearson correlation coefficient were used to illustrate the proposed method compared with the traditional E2SFCA method. Based on the above, taking the area of Beijing within the Fifth Ring Road as a study area, this paper facilitated the accessibility computation. The results indicated that the spatial distribution patterns of accessibility differed greatly under the four travel modes. Even under the same travel mode, there was an uneven accessibility distribution. Areas with high accessibility were mainly concentrated in the north, and some marginal areas also presented higher accessibility to parks. The comparative analysis results suggest that our proposed method for accessibility measurements alleviates the underestimation and overestimation of accessibility values obtained by a traditional method such as the center and edge of the study area. The research explores a new research perspective for measuring park accessibility. Furthermore, this study offers better guidance for policymakers trying to optimize park spatial distribution issues.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-15
      DOI: 10.3390/ijgi11090488
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 489: Generalization of Linear and Area Features
           Incorporating a Shape Measure

    • Authors: Natalia Blana, Lysandros Tsoulos
      First page: 489
      Abstract: This article elaborates on the quality issue in cartographic generalization of linear and area features focusing on the assessment of shape preservation. Assessing shape similarity in generalization is still a topic where further research is required. In the study presented here, shape description and matching techniques are investigated and analyzed, a procedure for choosing generalization parameters suitable for line and area features depiction is described and a quality model is developed for the assessment and verification of the generalization results. Based on the procedure developed, cartographers will be confident that the generalization of linear and area features is appropriate for a specific scale of portrayal fulfilling on the same time a basic requirement in generalization, that of shape preservation. The results of the procedure developed are based on the processing and successful generalization of a large number of different line and area features that is supported by a software environment developed in Python programming language.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-16
      DOI: 10.3390/ijgi11090489
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 490: Climate Justice in the City: Mapping
           Heat-Related Risk for Climate Change Mitigation of the Urban and
           Peri-Urban Area of Padua (Italy)

    • Authors: Todeschi, Pappalardo, Zanetti, Peroni, De Marchi
      First page: 490
      Abstract: The mitigation of urban heat islands (UHIs) is crucial for promoting the sustainable development of urban areas. Geographic information systems (GISs) together with satellite-derived data are powerful tools for investigating the spatiotemporal distribution of UHIs. Depending on the availability of data and the geographic scale of the analysis, different methodologies can be adopted. Here, we show a complete open source GIS-based methodology based on satellite-driven data for investigating and mapping the impact of the UHI on the heat-related elderly risk (HERI) in the Functional Urban Area of Padua. Thermal anomalies in the territory were mapped by modelling satellite data from Sentinel-3. After a socio-demographic analysis, the HERI was mapped according to five levels of risk. The highest vulnerability levels were localised within the urban area and in three municipalities near Padua, which represent about 20% of the entire territory investigated. In these municipalities, a percentage of elderly people over 20%, a thermal anomaly over 2.4 °C, and a HERI over 0.65 were found. Based on these outputs, it is possible to define nature-based solutions for reducing the UHI phenomenon and promote a sustainable development of cities. Stakeholders can use the results of these investigations to define climate and environmental policies.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-16
      DOI: 10.3390/ijgi11090490
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 491: Design and Application of Multi-Dimensional
           Visualization System for Large-Scale Ocean Data

    • Authors: Teng Lv, Jun Fu, Bao Li
      First page: 491
      Abstract: With the constant deepening of research on marine environment simulation and information expression, there are higher and higher requirements for the sense of the reality of ocean data visualization results and the real-time interaction in the visualization process. Aiming at the challenges of 3D interactive key technology and GPU-based visualization algorithm technology, we developed a visualization system for large-scale 3D marine environmental data. The system realizes submarine terrain rendering, contour line visualization, isosurface visualization, section visualization, volume visualization and flow field visualization. In order to manage and express the data in the system, we developed a data management module, which can effectively integrate a large number of marine environmental data and manage them effectively. We developed a series of data analysis functions for the system, such as point query and line query, local analysis and multi-screen collaboration, etc. These functions can effectively improve the data analysis efficiency of users and meet the data analysis needs in multiple scenarios. The marine environmental data visualization system developed in this paper can efficiently and intuitively simulate and display the nature and changing process of marine water environmental factors.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-16
      DOI: 10.3390/ijgi11090491
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 492: Sensing Tourist Distributions and Their
           Sentiment Variations Using Social Media: Evidence from 5A Scenic Areas in

    • Authors: Jingbo Wang, Yu Xia, Yuting Wu
      First page: 492
      Abstract: The distribution and sentiment characteristics of tourists directly reflect the state of tourism development, and are an important reference for tourists to choose scenic areas. Sensing the tourist distributions and their sentiment variations can provide decision support for the development planning of scenic areas. In this study, we crawled tourist social media data to explore tourist distribution characteristics and the patterns of tourist sentiment variations. First, we used web crawlers to obtain social media data (tourist comment data) and the location data of China’s 5A scenic areas from the Ctrip tourism platform. Second, SnowNLP (Simplified Chinese Text Processing) was optimized and used to classify the sentiment of tourists’ comments and calculate the sentiment value. Finally, we mined the distribution characteristics of tourists in 5A scenic areas and the spatio-temporal variations in tourists’ sentiments. The results show that: (1) There is a negative correlation between the number of tourists to China’s 5A scenic areas and tourist sentiment: the number of tourists is highest in October and lowest in March, while tourist sentiment is highest in March and lowest in October. (2) The spatio-temporal distribution of tourists has obvious aggregation: temporally mainly in July, August and October, spatially mainly in the Yangtze River Delta city cluster, Beijing-Tianjin-Hebei city cluster, and Guanzhong Plain city cluster. (3) Tourist sentiment cold/hot spots vary significantly by city clusters: the Yangtze River Delta city cluster is always a sentiment hot spot; the northern city cluster has more sentiment cold spots; the central city cluster varies significantly during the year; the southwestern city cluster has more sentiment hot spots.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-17
      DOI: 10.3390/ijgi11090492
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 493: MSEN-GRP: A Geographic Relations Prediction
           Model Based on Multi-Layer Similarity Enhanced Networks for Geographic
           Relations Completion

    • Authors: Zongcai Huang, Peiyuan Qiu, Li Yu, Feng Lu
      First page: 493
      Abstract: Geographic relation completion contributes greatly to improving the quality of large-scale geographic knowledge graphs (GeoKGs). However, the internal features of a GeoKG used in large-scale GeoKGs embedding are often limited by the weak connectivity between geographic entities (geo-entities). If there is no proper choice in the method of external semantic enhancement, this will often interfere with the representation and learning of the KG. Therefore, we here propose a geographic relation (geo-relation) prediction model based on multi-layer similarity enhanced networks for geo-relations completion (MSEN-GRP). The MSEN-GRP comprises three parts: enhancer, encoder, and decoder. The enhancer constructs semantic, spatial, structural, and attribute-similarity networks for geo-entities, which can explicitly and effectively enhance the implicit semantic associations between existing geo-entities. The encoder can obtain the long path relation dependency characteristics of geo-entities using a mixed-path sampling strategy and can support different optimization schemes for external semantic enhancement. Geo-relations prediction experiments show that the mean reciprocal ranking of this method is significantly higher than those of the traditional TransE DisMult and methods, and Hits@10 is improved by up to 57.57%. Furthermore, the spatial-similarity network has the most significant enhancement effect on geo-relations prediction. The proposed method provides a new way to perform relation completion in sparse GeoKGs.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-17
      DOI: 10.3390/ijgi11090493
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 494: Reverse Difference Network for Highlighting
           Small Objects in Aerial Images

    • Authors: Huan Ni, Jocelyn Chanussot, Xiaonan Niu, Hong Tang, Haiyan Guan
      First page: 494
      Abstract: The large-scale variation issue in high-resolution aerial images significantly lowers the accuracy of segmenting small objects. For a deep-learning-based semantic segmentation model, the main reason is that the deeper layers generate high-level semantics over considerably large receptive fields, thus improving the accuracy for large objects but ignoring small objects. Although the low-level features extracted by shallow layers contain small-object information, large-object information has predominant effects. When the model, using low-level features, is trained, the large objects push the small objects aside. This observation motivates us to propose a novel reverse difference mechanism (RDM). The RDM eliminates the predominant effects of large objects and highlights small objects from low-level features. Based on the RDM, a novel semantic segmentation method called the reverse difference network (RDNet) is designed. In the RDNet, a detailed stream is proposed to produce small-object semantics by enhancing the output of RDM. A contextual stream for generating high-level semantics is designed by fully accumulating contextual information to ensure the accuracy of the segmentation of large objects. Both high-level and small-object semantics are concatenated when the RDNet performs predictions. Thus, both small- and large-object information is depicted well. Two semantic segmentation benchmarks containing vital small objects are used to fully evaluate the performance of the RDNet. Compared with existing methods that exhibit good performance in segmenting small objects, the RDNet has lower computational complexity and achieves 3.9–18.9% higher accuracy in segmenting small objects.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-18
      DOI: 10.3390/ijgi11090494
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 495: Aquifer and Land Subsidence Interaction
           Assessment Using Sentinel-1 Data and DInSAR Technique

    • Authors: Fatemeh Rafiei, Saeid Gharechelou, Saeed Golian, Brian Alan Johnson
      First page: 495
      Abstract: Climate change and overpopulation have led to an increase in water demands worldwide. As a result, land subsidence due to groundwater extraction and water level decline is causing damage to communities in arid and semiarid regions. The agricultural plain of Samalghan in Iran has recently experienced wide areas of land subsidence, which is hypothesized to be caused by groundwater overexploitation. This hypothesis was assessed by estimating the amount of subsidence that occurred in the Samalghan plain using DInSAR based on an analysis of 25 Sentinel-1 descending SAR images over 6 years. To assess the influence of water level changes on this phenomenon, groundwater level maps were produced, and their relationship with land subsidence was evaluated. Results showed that one major cause of the subsidence in the Samalghan plain was groundwater overexploitation, with the highest average land subsidence occurring in 2019 (34 cm) and the lowest in 2015 and 2018 (18 cm). Twelve Sentinel-1 ascending images were used for relative validation of the DInSAR processing. The correlation value varied from 0.69 to 0.89 (an acceptable range). Finally, the aquifer behavior was studied, and changes in cultivation patterns and optimal utilization of groundwater resources were suggested as practical strategies to control the current situation.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-19
      DOI: 10.3390/ijgi11090495
      Issue No: Vol. 11, No. 9 (2022)
  • IJGI, Vol. 11, Pages 415: Interactive Visualization and Representation
           Analysis Applied to Glacier Segmentation

    • Authors: Minxing Zheng, Xinran Miao, Kris Sankaran
      First page: 415
      Abstract: Interpretability has attracted increasing attention in earth observation problems. We apply interactive visualization and representation analysis to guide the interpretation of glacier segmentation models. We visualize the activations from a U-Net to understand and evaluate the model performance. We built an online interface using the Shiny R package to provide comprehensive error analysis of the predictions. Users can interact with the panels and discover model failure modes. We illustrate an example of how our interface could help guide decisions for improving model performance. Further, we discuss how visualization can provide sanity checks during data preprocessing and model training. By closely examining the problem of glacier segmentation, we are able to discuss how visualization strategies can support the modeling process and the interpretation of prediction results from geospatial deep learning.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-22
      DOI: 10.3390/ijgi11080415
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 416: Identifying Suitable Watersheds across Nigeria
           Using Biophysical Parameters and Machine Learning Algorithms for

    • Authors: Pranay Panjala, Murali Krishna Gumma, Hakeem Ayinde Ajeigbe, Murtala Muhammad Badamasi, Kumara Charyulu Deevi, Ramadjita Tabo
      First page: 416
      Abstract: Identifying suitable watersheds is a prerequisite to operationalizing planning interventions for agricultural development. With the help of geospatial tools, this paper identified suitable watersheds across Nigeria using biophysical parameters to aid agricultural planning. Our study included various critical thematic layers such as precipitation, temperature, slope, land-use/land-cover (LULC), soil texture, soil depth, and length of growing period, prepared and modeled on the Google Earth Engine (GEE) platform. Using expert knowledge, scores were assigned to these thematic layers, and a priority map was prepared based on the combined weighted average score. We also validated priority watersheds. For this, the study area was classified into three priority zones ranging from ‘high’ to ‘low’. Of the 277 watersheds identified, 57 fell in the high priority category, implying that they are highly favorable for interventions. This would be useful for regional-scale water resource planning for agricultural landscape development.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-22
      DOI: 10.3390/ijgi11080416
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 417: Evaluation of Spatial Thinking Ability Based on
           Exposure to Geographical Information Systems (GIS) Concepts in the Context
           of Higher Education

    • Authors: Duarte, Teodoro, Gonçalves
      First page: 417
      Abstract: (1) Background: spatial thinking is indirectly applied in numerous daily activities (e.g., when defining the route when going to school/work) or in scientific areas (e.g., predicting the spatial–temporal spread of contagious diseases), and its ability might be improved using geographical information systems (GIS). The main objective of this study was to perform an analysis of the spatial thinking of students in two curricular units (CUs) that had come from different background areas; (2) Methods: to that end, the Spatial Thinking Ability Test (STAT), composed of 15 multiple choice questions to measure spatial thinking, was given to 83 students before and after exposure to GIS concepts and software. Students’ answers were analyzed question-by-question and as total scores. The statistical analysis was performed using the paired samples t-test, the independent samples t-test or the Mann–Whitney statistical test and the nonparametric Kruskal–Wallis test; (3) Results: an overall significant improvement was observed from the pre- to the post-test. Additionally, total scores were not significantly different between students of different CUs, courses, or genders; (4) Conclusions: this exploratory study can be considered as a support methodology for pedagogical didactics that have been implemented in the CUs and may be readily applied in other academic environments, namely with students from different backgrounds, countries, and teaching strategies, thus promoting the discussion of all such experiences and consequent improvement in geographical education.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-22
      DOI: 10.3390/ijgi11080417
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 418: Mining Type-β Co-Location Patterns on
           Closeness Centrality in Spatial Data Sets

    • Authors: Muquan Zou, Lizhen Wang, Pingping Wu, Vanha Tran
      First page: 418
      Abstract: A co-location pattern is a set of spatial features whose instances are frequently correlated to each other in space. Its mining models always consist of two essential steps. One step is to generate neighbor relationships between spatial instances, and another step is to check the prevalence of candidate patterns on the clique, star or Delaunay triangulation relationships. At least three major issues are addressed in this paper. First, since different spatial regions, different distribution densities, it is difficult to set appropriate parameters to generate ideal neighbor relationships. Second, the clique relationship and the others are so strongly rigid that the users’ personal interests are suppressed ; some interesting patterns are neglected without increasing redundancy. Third, the different strength of correlations among instances are neglected in prevalence calculation. It causes correlations among features to be undifferentiated. Accordingly, the main work of this paper includes: (1) The neighbor relationship generation can be improved on the idea that the distances between an instance and any of its neighbors are not remarkably different. (2) The type-β co-location pattern is defined and checked based on a co-occurrence where the closeness centrality of each instance is not less than a given threshold β. (3) Since the closeness centrality carries strength of correlations among instances, the strength of the correlations between a feature and the other ones in a type-β co-location pattern can be evaluated with prevalence calculation. Finally, experiments on synthetic and real-world spatial data sets are used to assess the effectiveness and efficiency of our works. The results show that fewer spatial neighbor relationships are generated, and more interesting patterns can be discovered by flexibly adjusting β according to the user’s preferences.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-23
      DOI: 10.3390/ijgi11080418
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 419: Exploring the Spatial Heterogeneity and Driving
           Factors of UAV Logistics Network: Case Study of Hangzhou, China

    • Authors: Hongbo He, Huping Ye, Chenchen Xu, Xiaohan Liao
      First page: 419
      Abstract: Urban logistics is an important research topic in human and economic geography; unmanned aerial vehicles (UAVs) are an emerging technology that has huge potential in the field of logistics with the release of control restrictions on low-altitude airspace. The scientific identification of the spatial pattern and impact factors of UAV logistics networks is greatly significant in regards to UAV logistics planning and scheduling. This study considered the urban logistics network of Hangzhou in 2020 as the research topic and used kernel density estimation, a geodetector, and geographic information system (GIS) spatial analysis technology to systematically analyze the spatial patterns and influencing factors at the city and district scales. The study found that a significant spatial pattern was revealed in the UAV logistics network in Hangzhou, and the logistics nodes showed an obvious “core-edge” structure. The urban population, market scale and logistics infrastructure jointly shaped the structure and function of the UAV logistics network, and logistics nodes had a strong coupling relationship with the urban spatial structure. Through interaction detectors, the technical route of urban UAV logistics network construction was analyzed and summarized, and results can provide a scientific basis and case reference for other cities to build and plan UAV logistics networks.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-23
      DOI: 10.3390/ijgi11080419
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 420: Patch-Based Local Climate Zones Mapping and
           Population Distribution Pattern in Provincial Capital Cities of China

    • Authors: Zhou, Ma, Johnson, Yan, Li, Li
      First page: 420
      Abstract: Accurate urban morphology provided by Local Climate Zones (LCZ), a universal surface classification scheme, offers opportunities for studies of urban heat risk, urban ventilation, and transport planning. In recent years, researchers have attempted to generate LCZ maps worldwide with the World Urban Database and Access Portal Tools (WUDAPT). However, the accuracy of LCZ mapping is not satisfactory and cannot fulfill the quality demands of practical usage. Here, we constructed a high-quality sample dataset from Chinese cities and presented a patch-based classification framework that employs chessboard segmentation and multi-seasonal images for LCZ mapping. Compared with the latest WUDAPT method, the overall accuracy for all LCZ types (OA) and urban LCZ types (OAu) of our framework increased by about 10% and 9%, respectively. Furthermore, based on the analysis of population distribution, we first gave the population density of different built-up LCZs of Chinese cities and found a hierarchical effect of population density among built-up LCZs in different size cities. In summary, this study could serve as a valuable reference for producing high-quality LCZ maps and understanding population distribution patterns in built-up LCZ types.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-25
      DOI: 10.3390/ijgi11080420
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 421: Refined Urban Functional Zone Mapping by
           Integrating Open-Source Data

    • Authors: Deng, He
      First page: 421
      Abstract: The determination of a reasonable spatial analysis unit is an essential step in urban functional zone (UFZ) division, which significantly affects the results. However, most studies on the division of functional zones are based on excessively large spatial units, such as blocks or traffic analysis zones (TAZs), which easily overlook the detailed characteristics of urban regions and introduce bias to the research conclusion. To address this issue, a refined zone segmentation method, namely, the Voronoi diagram for the polygon method, was proposed to generate refined spatial analysis units. Afterward, the functional topics of the spatial analysis unit were classified by a multiclass support vector machine (SVM) to produce the final UFZ map, where the functional topics of each spatial unit were obtained by coupling latent Dirichlet allocation (LDA). To verify the effectiveness of the proposed method, experiments were conducted in Beijing, China. The results indicated that the proposed segmentation method can generate fine-scale spatial units and provide fine-grained and higher accuracy UFZs (overall accuracy = 84%; kappa = 0.82).
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-25
      DOI: 10.3390/ijgi11080421
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 422: Global Spatial Suitability Mapping of Wind and
           Solar Systems Using an Explainable AI-Based Approach

    • Authors: Mourtadha Sarhan Sachit, Helmi Zulhaidi Mohd Shafri, Ahmad Fikri Abdullah, Azmin Shakrine Mohd Rafie, Mohamed Barakat A. Gibril
      First page: 422
      Abstract: An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar mapping approach based on eXplainable Artificial Intelligence (XAI). To the best of the author’s knowledge, the current study is the first attempt to create global maps for siting onshore wind and solar power systems and formulate novel weights for decision criteria. A total of 13 conditioning factors (independent variables) defined through a comprehensive literature review and multicollinearity analysis were assessed. Real-world renewable energy experiences (more than 55,000 on-site wind and solar plants worldwide) are exploited to train three machine learning (ML) algorithms, namely Random Forest (RF), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Then, the output of ML models was explained using SHapley Additive exPlanations (SHAP). RF outperformed SVM and MLP in both wind and solar modeling with an overall accuracy of 90% and 89%, kappa coefficient of 0.79 and 0.78, and area under the curve of 0.96 and 0.95, respectively. The high and very high suitability categories accounted for 23.2% (~26.84 million km2) of the site suitability map for wind power plants. In addition, they covered more encouraging areas (24.0% and 19.4%, respectively, equivalent to ~50.31 million km2) on the global map for hosting solar energy farms. SHAP interpretations were consistent with the Gini index indicating the dominance of the weights of technical and economic factors over the spatial assessment under consideration. This study provides support to decision-makers toward sustainable power planning worldwide.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-26
      DOI: 10.3390/ijgi11080422
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 423: The Influence of Data Density and Integration on
           Forest Canopy Cover Mapping Using Sentinel-1 and Sentinel-2 Time Series in
           Mediterranean Oak Forests

    • Authors: Nasiri, Sadeghi, Moradi, Afshari, Deljouei, Griess, Maftei, Borz
      First page: 423
      Abstract: Forest canopy cover (FCC) is one of the most important forest inventory parameters and plays a critical role in evaluating forest functions. This study examines the potential of integrating Sentinel-1 (S-1) and Sentinel-2 (S-2) data to map FCC in the heterogeneous Mediterranean oak forests of western Iran in different data densities (one-year datasets vs. three-year datasets). This study used very high-resolution satellite images from Google Earth, gridded points, and field inventory plots to generate a reference dataset. Based on it, four FCC classes were defined, namely non-forest, sparse forest (FCC = 1–30%), medium-density forest (FCC = 31–60%), and dense forest (FCC > 60%). In this study, three machine learning (ML) models, including Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), were used in the Google Earth Engine and their performance was compared for classification. Results showed that the SVM produced the highest accuracy on FCC mapping. The three-year time series increased the ability of all ML models to classify FCC classes, in particular the sparse forest class, which was not distinguished well by the one-year dataset. Class-level accuracy assessment results showed a remarkable increase in F-1 scores for sparse forest classification by integrating S-1 and S-2 (10.4% to 18.2% increased for the CART and SVM ML models, respectively). In conclusion, the synergetic use of S-1 and S-2 spectral temporal metrics improved the classification accuracy compared to that obtained using only S-2. The study relied on open data and freely available tools and can be integrated into national monitoring systems of FCC in Mediterranean oak forests of Iran and neighboring countries with similar forest attributes.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-26
      DOI: 10.3390/ijgi11080423
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 424: Object-Based Automatic Mapping of Winter Wheat
           Based on Temporal Phenology Patterns Derived from Multitemporal Sentinel-1
           and Sentinel-2 Imagery

    • Authors: Wang, Jin, Xiong, Zhang, Wu
      First page: 424
      Abstract: Although winter wheat has been mapped by remote sensing in several studies, such mapping efforts did not sufficiently utilize contextual information to reduce the noise and still depended heavily on optical imagery and exhausting classification approaches. Furthermore, the influence of similarity measures on winter wheat identification remains unclear. To overcome these limitations, this study developed an object-based automatic approach to map winter wheat using multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. First, after S1 and S2 images were preprocessed, the Simple Non-Iterative Clustering (SNIC) algorithm was used to conduct image segmentation to obtain homogeneous spatial objects with a fusion of S1 and S2 bands. Second, the temporal phenology patterns (TPP) of winter wheat and other typical land covers were derived from object-level S1 and S2 imagery based on the collected ground truth samples, and two improved distance measures (i.e., a composite of Euclidean distance and Spectral Angle Distance, (ESD) and the difference–similarity factor distance (DSF)) were built to evaluate the similarity between two TPPs. Third, winter wheat objects were automatically identified from the segmented spatial objects by the maximum between-class variance method (OTSU) with distance measures based on the unique TPP of winter wheat. According to ground truth data, the DSF measure was superior to other distance measures in winter wheat mapping, since it achieved the best overall accuracy (OA), best kappa coefficient (Kappa) and more spatial details for each feasible band (i.e., NDVI, VV, and VH/VV), or it obtained results comparable to those for the best one (e.g., NDVI + VV). The resultant winter wheat maps derived from the NDVI band with the DSF measure achieved the best accuracy and more details, and had an average OA and Kappa of 92% and 84%, respectively. The VV polarization with the DSF measure produced the second best winter wheat maps with an average OA and Kappa of 91% and 80%, respectively. The results indicate the great potential of the proposed object-based approach for automatic winter wheat mapping for both optical and Synthetic Aperture Radar (SAR) imagery.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-26
      DOI: 10.3390/ijgi11080424
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 425: Domain-Specific Language for Land Administration
           System Transactions

    • Authors: Đorđe Pržulj, Igor Dejanović, Miroslav Stefanović, Teodora Lolić, Srđan Sladojević
      First page: 425
      Abstract: The Land Administration System (LAS) records real estates, owners, and rights information. Changes that take place in the real world are recorded as transactions in LAS. This paper discusses various data-integrity constraints that have to be taken into account so that LAS data will be correct and consistent after the execution of LAS transactions. Those transactions are executed by system users, typically through some graphical user interface (GUI) applications. Domain-specific languages (DSLs) provide the possibility for domain experts to write statements that can be interpreted and executed on respective software systems. In the case of LAS, DSL for LAS transactions could enable land administration experts to write statements that would execute transactions and keep LAS data up to date with real world changes. Two types of LAS transactions are considered: legal transactions, which result in ownership changes, and survey transactions, which change the real estate geometry data. In this paper, a possible DSL solution for transactions in the LAS domain is proposed. A system architecture that could enable the efficient writing, validation, verification, execution, and storage of DSL statements is also proposed. A possible DSL for LAS transaction implementation is presented, and examples of legal and survey transactions are explained. The advantages and possible challenges of the proposed solution’s implementation are also discussed in this paper.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-27
      DOI: 10.3390/ijgi11080425
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 426: Vegetation Greenness Trend in Dry Seasons and
           Its Responses to Temperature and Precipitation in Mara River Basin, Africa

    • Authors: Wanyi Zhu, Zhenke Zhang, Shuhe Zhao, Xinya Guo, Priyanko Das, Shouming Feng, Binglin Liu
      First page: 426
      Abstract: The Mara River Basin of Africa has a world-famous ecosystem with vast vegetation, which is home to many wild animals. However, the basin is experiencing vegetation degradation and bad climate change, which has caused conflicts between people and wild animals, especially in dry seasons. This paper studied the vegetation greenness (VG), vegetation greenness trends (VGT), and their responses to climate change in dry seasons in the Mara River Basin, Africa. Firstly, based on Google Earth Engine (GEE) platform and Sentinel-2 images, the vegetation distribution map of the Mara River Basin was drawn. Then dry seasons MODIS NDVI data (January to February and June to September) were used to analyze the VGT. Finally, a random forest regression algorithm was used to evaluate the response of VG and VGT to temperature and precipitation derived from ERA5 from 2000 to 2019 at a resolution of 250 m. The results showed that the VGT was fluctuating in dry seasons, and the spatial differentiation was obvious. The greenness increasing trends both upstream and downstream were significantly larger than that of in the midstream. The responses of VG to precipitation were almost twice larger than temperature, and the responses of VGT to temperature were about 1.5 times larger than precipitation. The climate change trend of rising temperature and falling precipitation will lead to the degradation of vegetation and the reduction of crop production. There will be a vegetation degradation crisis in dry seasons in the Mara River Basin in the future. Identifying the spatiotemporal changes of VGT in dry seasons will be helpful to understand the response of VG and VGT to climate change and could also provide technical support to cope with climate-change-related issues for the basin.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-28
      DOI: 10.3390/ijgi11080426
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 427: Grid-Scale Regional Risk Assessment of
           Potentially Toxic Metals Using Multi-Source Data

    • Authors: Chen, Cai, Wang, Lei
      First page: 427
      Abstract: Understanding the risks posed by potentially toxic metals (PTMs) in large regions is important for environmental management. However, regional risk assessment that relies on traditional field sampling or administrative statistical data is labor-intensive, time-consuming, and coarse. Internet data, remote sensing data, and multi-source data, have the advantage of high speed of collection, and can, thereby, overcome time lag challenges and traditional evaluation inefficiencies, although, to date, they are rarely applied. To evaluate their effectiveness, the current study used multi-source data to conduct a 1 km scale assessment of PTMs in Yunnan Province, China. In addition, a novel model to simulate potentially hazardous areas, based on atmospheric deposition, was also proposed. Assessments reveal that risk areas are mainly distributed in the east, which is consistent with the distribution of mineral resources in the province. Approximately 3.6% of the cropland and 1.4% of the sensitive population are threatened. The risk areas were verified against those reported by the government and the existing literature. The verification exercise confirmed the reliability of multi-source data, which are cost-effective, efficient, and generalizable for assessing pollution risks in large areas, particularly when there is little to no site-specific contamination information.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-28
      DOI: 10.3390/ijgi11080427
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 428: Explore the Correlation between Environmental
           Factors and the Spatial Distribution of Property Crime

    • Authors: Sun, Zhang, Zhao, Ji, Gu, Sun, Li
      First page: 428
      Abstract: Comprehensively understanding the factors influencing crime is a prerequisite for preventing and combating crime. Although some studies have investigated the relationship between environmental factors and property crime, the interaction between factors was not fully considered in these studies, and the explanation of complex factors may be insufficient. This paper explored the influence of environmental factors on property crime using factor regression and factor interaction based on data from the central city of Lanzhou, China. Our findings showed that: (1) The distribution of crime cases showed the pattern of a local multi-center. Shop density, hotel density, entertainment density and house price were the four dominant environmental drivers of property crime; (2) The relationship between the light intensity and property crime had different correlation explanations in temporal projection and spatial projection. There was a normal distribution curve between the number of property crimes and the Price-to-Earnings Ratio (PE Ratio) of the community house price; and (3) The results of the factor interaction indicated that the effect of all factors on crime showed a two-factor enhancement. As an important catalyst, shop density had the strongest interaction with other factors. Shop density gradient influenced the degree of interpretation of spatial heterogeneity of property crime.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-28
      DOI: 10.3390/ijgi11080428
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 429: Beyond Accessibility: A Multidimensional
           Evaluation of Urban Park Equity in Yangzhou, China

    • Authors: Li, Liang, Feng, Fan
      First page: 429
      Abstract: Evaluating park equity can help guide the advancement of sustainable and equitable space policies. Previous studies have mainly considered accessibility when evaluating park equity while ignoring the selectivity and convenience of entering parks and residents’ recognition of parks. Measuring equity based mainly on spatial thinking has resulted in the social aspects of parks receiving insufficient attention. In this study, we therefore integrated the spatial and social equity of parks and developed a multidimensional framework to evaluate park equity in four dimensions: accessibility (Ai), diversity (Di), convenience (Ci), and satisfaction (Si). Empirical analysis from Yangzhou, China showed that: (1) in Yangzhou’s built-up districts, 23.43% of the communities received high- or relatively high-level park access but 17.72% received little or no park access. (2) The Gini coefficient indicated that all three dimensions showed a mismatch with population distribution, except for satisfaction (Si), which showed a relatively reasonable match. (3) Park access was generally better in communities with better locations, environments, and facilities. High-income groups enjoyed significantly better park access than low- and middle-income groups. These findings could help urban planners and policymakers develop effective policies to reduce inequality in park access.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-29
      DOI: 10.3390/ijgi11080429
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 430: Adaptive Geometric Interval Classifier

    • Authors: Shuang Li, Jie Shan
      First page: 430
      Abstract: Quantile, equal interval, and natural breaks methods are widely used data classification methods in geospatial analysis and cartography. However, when applied to data with skewed distributions, they can only reveal the variations of either high frequent values or extremes, which often leads to undesired and biased classification results. To handle this problem, Esri provided a compromise method, named geometric interval classification (GIC). Although GIC performs well for various classification tasks, its mathematics and solution process remain unclear. Moreover, GIC is theoretically only applicable to single-peak (single-modal), one-dimensional data. This paper first mathematically formulates GIC as a general optimization problem subject to equality constraint. We then further adapt such formulated GIC to handle multi-peak and multi-dimensional data. Both thematic data and remote sensing images are used in this study. The comparison with other classification methods demonstrates the advantage of GIC being able to highlight both middle and extreme values. As such, it can be regarded as a general data classification approach for thematic mapping and other geospatial applications.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-31
      DOI: 10.3390/ijgi11080430
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 431: Uncovering Factors Affecting Taxi Income from
           GPS Traces at the Directional Road Segment Level

    • Authors: Shuxin Jin, Zhouhao Wu, Tong Shen, Di Wang, Ming Cai
      First page: 431
      Abstract: Nowadays, the market demand for taxis is still intense. However, there exist lots of issues affecting the healthy development of the taxi industry, such as an increasing difficulty in hailing taxis, detouring behavior etc., and especially, the low incomes of taxi drivers. This paper establishes a multi-layer road index (MRI) system of 7862 directional road segments (DRSs), and collects over 194 million occupied GPS points within a week, revealing the factors affecting taxi drivers’ incomes in Shenzhen, China. The income differences has been identified on different DRSs, which accordingly have been categorized into two levels. Four categories of DRS factors, i.e., road attributes, traffic dynamics, points of interest (POIs), and taxi operation strategies, are defined as the impact factors affecting income levels. The selected sample-based binomial logit (SBL) model has been proposed to reveal the significance of these influencing factors. The results indicate that the road segments with different features have different incomes over different time periods. The main factors in income analysis are the factors used to represent taxi operation strategies. Highly rewarding pick-up road segments can be identified, which could contribute to drivers’ income improvements, and can further contribute to the development of the taxi market.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-31
      DOI: 10.3390/ijgi11080431
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 432: Visualization and Analysis of Transport
           Accessibility Changes Based on Time Cartograms

    • Authors: Lina Wang, Xiang Li, Linfang Ding, Xinkai Yu, Tao Hu
      First page: 432
      Abstract: Visualization of the spatial distribution pattern of transport accessibility and its changes can be crucial for understanding and assessing the performance of transportation systems. Compared to traditional maps representing geographic space, time cartograms modify geographic locations and spatial relationships to suit travelling times and thereby emphasize time–distance relationships in time-space. This study aims to facilitate a better understanding of the evolution of the spatial distribution pattern of accessibility by presenting a novel visualization and analysis methodology based on time cartograms. This is achieved by combining a visual qualitative display with a quantitative indicator analysis from multiple perspectives to show transport accessibility changes. Two indicators, namely, the shortest railway travel time (STRT) and spatiotemporal con-version parameter (STCP), are proposed to measure accessibility changes. Our work consists of the construction of time cartograms, the analysis of indicators, and the use of multiple views to show changes in transportation accessibility from multiple perspectives. The methodology is applied on the railway data of Beijing and selected 226 cities in China and to analyze changes in railway accessibility in 1996, 2003, 2009 and 2016. The results show that the development of transportation technology has continuously shortened the travel time, the time-space is gradually compressed, However, the difference in transport accessibility is getting bigger and bigger because of the uneven transportation development speeds between the regions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-01
      DOI: 10.3390/ijgi11080432
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 433: Effects of Climate Change on Corn Yields:
           Spatiotemporal Evidence from Geographically and Temporally Weighted
           Regression Model

    • Authors: Bing Yang, Sensen Wu, Zhen Yan
      First page: 433
      Abstract: Food security has been one of the greatest global concerns facing the current complicated situation. Among these, the impact of climate change on agricultural production is dynamic over time and space, making it a major challenge to food security. Taking the U.S. Corn Belt as an example, we introduce a geographically and temporally weighted regression (GTWR) model that can handle both temporal and spatial non-stationarity in the relationship between corn yield and meteorological variables. With a high fitting performance (adjusted R2 at 0.79), the GTWR model generates spatiotemporally varying coefficients to effectively capture the spatiotemporal heterogeneity without requiring completion of the unbalanced data. This model makes it possible to retain original data to the maximum possible extent and to estimate the results more reliably and realistically. Our regression results showed that climate change had a positive effect on corn yield over the past 40 years, from 1981 to 2020, with temperature having a stronger effect than precipitation. Furthermore, a fuzzy c-means algorithm was used to cluster regions based on spatiotemporally changing trends. We found that the production potential of regions at high latitudes was higher than that of regions at low latitudes, suggesting that the center of productive regions may migrate northward in the future.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-01
      DOI: 10.3390/ijgi11080433
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 434: Optimal Routing of Wide Multi-Modal Energy and
           Infrastructure Corridors

    • Authors: Mehdi Salamati, Xin Wang, Jennifer Winter, Hamidreza Zareipour
      First page: 434
      Abstract: A multi-modal corridor accommodates multiple modes of energy and transportation infrastructure within the same right-of-way. The existing literature on corridor routing in raster space often focuses on one mode with no consideration of the width. This is not a realistic assumption, especially if multiple modes are to co-exist within the same wide right-of-way. Moreover, newer routing methods that consider corridor width cannot take into account multi-modality and the arrangement of modes within a corridor. We developed two multi-modal wide-corridor routing methods using raster data. In the first method, the cost rasters of all modes are weighted and aggregated into a single composite on which a wide LCP is found. This wide LCP is then divided among the modes based on the desired arrangement. The second method uses a directed transformed graph in which the weight of each edge is calculated using different layers of cost data based on the edge direction, the desired widths and arrangement of the modes. Comparative analyses using synthetic datasets show the superior performance of the second proposed method in finding a muti-modal corridor in comparison with the first mode, and in finding a single-modal corridor when compared to the existing methods.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-01
      DOI: 10.3390/ijgi11080434
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 435: Identification of Urban Functional Zones Based
           on the Spatial Specificity of Online Car-Hailing Traffic Cycle

    • Authors: Zhicheng Deng, Xiangting You, Zhaoyang Shi, Hong Gao, Xu Hu, Zhaoyuan Yu, Linwang Yuan
      First page: 435
      Abstract: The study of urban functional zoning is not only important for analyzing urban spatial structure but also for optimizing urban management and promoting scientific urban planning. Different areas undertaking different urban functions correspond to different traffic patterns and specific cycles. Here, a method named Urban Functional Zoning based on the Spatial Specificity (UFZ-SS) is proposed. The core of this method is to obtain urban spatial zoning through the specific cycles of traffic flows. First, UFZ-SS uses the Ensemble Empirical Modal Decomposition (EEMD) method to extract the specific periodic signal characteristics of traffic flows. Second, UFZ-SS calculates the contribution of online car-hailing traffic of different cycles in each zone. Then, the Gaussian Mixture Model (GMM) is utilized to classify all spatial zones into different spatial partitions based on the contribution of each periodic signal. Finally, this study validates UFZ-SS with the online car-hailing traffic volume in northeast Chengdu, China. The results show that the periodic characteristics of traffic can be effectively extracted and analyzed by the EEMD method, and highly distinct and accurate urban spatial partitioning results can be derived by spatial clustering based on the measures of specific cycles. Moreover, with the assistance of Point of Interest (POI) data, we verify the functional zones and structural patterns, which further demonstrates the validity and rationality of urban functional zones identified by UFZ-SS. This study provides a new potential perspective for the identification of urban functional zones, which may lead to a better understanding of the urban spatial structure and even urban planning.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-01
      DOI: 10.3390/ijgi11080435
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 436: Improving the Spatial Accessibility of
           Community-Level Healthcare Service toward the ‘15-Minute City’
           Goal in China

    • Authors: Genxin Song, Xinxin He, Yunfeng Kong, Ke Li, Hongquan Song, Shiyan Zhai, Jingjing Luo
      First page: 436
      Abstract: Background: The recent global COVID-19 pandemic serves as another reminder that people in different urban neighborhoods need equal access to basic medical services. This study aims to improve the spatial accessibility of healthcare services toward the ‘15-minute city’ goal. Methods: We chose Zhengzhou, China, as a case study. To improve spatial accessibility, two optimization models of optimal supply-demand allocation (OSD) and the capacitated p-medina problem (CPMP) were used. Spatial accessibility in this study is defined as the walking time from the communities to healthcare centers. Results: For the current status of healthcare services at the community level, the mean travel time is 18.3 min, and 39.6% of residents can access healthcare services within a 15-minute travel time. Population coverage within a 15-minute walking time is significantly lower than the national target of 80%. After redefining the service areas through OSD allocation, the mean travel time was reduced to 16.5 min, and 45.1% of the population could reach services. Furthermore, the 60 newly proposed healthcare centers selected by the CPMP model could potentially increase by 35.0% additional population coverage. The average travel time was reduced to 10 min. Conclusions: Both the redefinition of the service areas and the opening of new service centers are effective ways to improve the spatial accessibility of healthcare services. Two methods of this study have implications for urban planning practices towards the 15-minute city.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-01
      DOI: 10.3390/ijgi11080436
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 437: Integrating Post-Processing Kinematic
           (PPK)–Structure-from-Motion (SfM) with Unmanned Aerial Vehicle (UAV)
           Photogrammetry and Digital Field Mapping for Structural Geological

    • Authors: Daniele Cirillo, Francesca Cerritelli, Silvano Agostini, Simone Bello, Giusy Lavecchia, Francesco Brozzetti
      First page: 437
      Abstract: We studied some exposures of the Roccacaramanico Conglomerate (RCC), a calcareous-clastic mega-bed intercalated within the Late Messinian–Early Pliocene pelitic succession of the La Queglia and Maiella tectonic units (central Apennines). The outcrops, localized in the overturned limb of a kilometric-scale syncline, show a complex array of fractures, including multiple systems of closely spaced cleavages, joints, and mesoscopic faults, which record the progressive deformation associated with the Late Pliocene thrusting. Due to the extent of the investigated sites and a large amount of data to collect, we applied a multi-methodology survey technique integrating unmanned aerial vehicle (UAV) technologies and digital mapping in the field. We reconstructed the 3D digital outcrop model of the RCC in the type area and defined the 3D pattern of fractures and their time–space relationships. The field survey played a pivotal role in determining the various sets of structures, their kinematics, the associated displacements, and relative chronology. The results unveiled the investigated area’s tectonic evolution and provide a deformation model that could be generalized in similar tectonic contexts. Furthermore, the methodology allows for evaluating the reliability of the applied remote survey techniques (i.e., using UAV) compared to those based on the direct measurements of structures using classic devices. Our purpose was to demonstrate that our multi-methodology approach can describe the tectonic evolution of the study area, providing consistent 3D data and using a few ground control points. Finally, we propose two alternative working methods and discuss their different fields of application.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-02
      DOI: 10.3390/ijgi11080437
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 438: Measure of Utilizing Space Database Information
           for Improvement of Efficient Disaster Management (Focusing on Nuclear
           Power Plant Accidents)

    • Authors: Bomi Lee, Aetti Kang, Sungil Ham
      First page: 438
      Abstract: The damage caused by disasters is increasing worldwide, with hundreds of thousands of deaths due to the occurrence of complex large-scale disasters such as the 2010 Haiti earthquake and the 2004 Indian tsunami. South Korea has also experienced human casualties and damage to property caused by large-scale disasters in the past 10 years. Accordingly, a disaster-appropriate response measure is needed. Thus, we conducted this study to present a measure of utilizing spatial database and image information to improve the efficiency of disaster management that is operated based on the country’s existing national disaster management system. We present an efficient disaster response measure that differs from the existing collection-, reporting-, and propagation-oriented operating methods of disaster information through the use of spatial database and image-based information that can be combined with mandatory information with regard to nuclear power plant accidents. Thus, this study contributes to deriving a system that could collect and provide information rapidly at the time of disaster by defining the attribute and spatial information required at the time of disaster during nuclear power plant accidents and by deriving available systems and providing institutions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-02
      DOI: 10.3390/ijgi11080438
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 439: Raster Map Line Element Extraction Method Based
           on Improved U-Net Network

    • Authors: Wenjing Ran, Jiasheng Wang, Kun Yang, Ling Bai, Xun Rao, Zhe Zhao, Chunxiao Xu
      First page: 439
      Abstract: To address the problem of low accuracy in line element recognition of raster maps due to text and background interference, we propose a raster map line element recognition method based on an improved U-Net network model, combining the semantic segmentation algorithm of deep learning, the attention gates (AG) module, and the atrous spatial pyramid pooling (ASPP) module. In the proposed network model, the encoder extracts image features, the decoder restores the extracted features, the features of different scales are extracted in the dilated convolution module between the encoder and the decoder, and the attention mechanism module increases the weight of line elements. The comparison experiment was carried out through the constructed line element recognition dataset. The experimental results show that the improved U-Net network accuracy rate is 93.08%, the recall rate is 92.29%, the DSC accuracy is 93.03%, and the F1-score is 92.68%. In the network robustness test, under different signal-to-noise ratios (SNRs), comparing the improved network structure with the original network structure, the DSC improved by 13.18–17.05%. These results show that the network model proposed in this paper can effectively extract raster map line elements.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-03
      DOI: 10.3390/ijgi11080439
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 440: Using Attributes Explicitly Reflecting User
           Preference in a Self-Attention Network for Next POI Recommendation

    • Authors: Ruijing Li, Jianzhong Guo, Chun Liu, Zheng Li, Shaoqing Zhang
      First page: 440
      Abstract: With the popularity of location-based social networks such as Weibo and Twitter, there are many records of points of interest (POIs) showing when and where people have visited certain locations. From these records, next POI recommendation suggests the next POI that a target user might want to visit based on their check-in history and current spatio-temporal context. Current next POI recommendation methods mainly apply different deep learning models to capture user preferences by learning the nonlinear relations between POIs and user preference and pay little attention to mining or using the information that explicitly reflects user preference. In contrast, this paper proposes to utilize data that explicitly reflect user preference and include these data in a deep learning-based process to better capture user preference. Based on the self-attention network, this paper utilizes the attributes of the month of the check-ins and the categories of check-ins during this time, which indicate the periodicity of the user’s work and life and can reflect the habits of users. Moreover, considering that distance has a significant impact on a user’s decision of whether to visit a POI, we used a filter to remove candidate POIs that were more than a certain distance away when recommending the next POIs. We use check-in data from New York City (NYC) and Tokyo (TKY) as datasets, and experiments show that these improvements improve the recommended performance of the next POI. Compared with the state-of-the-art methods, the proposed method improved the recall rate by 7.32% on average.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-04
      DOI: 10.3390/ijgi11080440
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 441: High-Precision Dynamic Traffic Noise Mapping
           Based on Road Surveillance Video

    • Authors: Yanjie Sun, Mingguang Wu, Xiaoyan Liu, Liangchen Zhou
      First page: 441
      Abstract: High-precision dynamic traffic noise maps can describe the spatial and temporal distributions of noise and are necessary for actual noise prevention. Existing monitoring point-based methods suffer from limited spatial adaptability, and prediction model-based methods are limited by the requirements for traffic and environmental parameter specifications. Road surveillance video data are effective for computing and analyzing dynamic traffic-related factors, such as traffic flow, vehicle speed and vehicle type, and environmental factors, such as road material, weather and vegetation. Here, we propose a road surveillance video-based method for high-precision dynamic traffic noise mapping. First, it identifies dynamic traffic elements and environmental elements from videos. Then, elements are converted from image coordinates to geographic coordinates by video calibration. Finally, we formalize a dynamic noise mapping model at the lane level. In an actual case analysis, the average error is 1.53 dBA. As surveillance video already has a high coverage rate in most cities, this method can be deployed to entire cities if needed.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-04
      DOI: 10.3390/ijgi11080441
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 442: Prediction of Urban Sprawl by Integrating
           Socioeconomic Factors in the Batticaloa Municipal Council, Sri Lanka

    • Authors: Mathanraj Seevarethnam, Noradila Rusli, Gabriel Hoh Teck Ling
      First page: 442
      Abstract: Due to extensive population growth, urbanization increases urban development and sprawl in the world’s cities. Urban sprawl is a socioeconomic phenomenon that has not extensively incorporated socioeconomic factors in the prediction of most of the urban sprawl models. This study aimed to predict the urban sprawl pattern in 2030 by integrating socioeconomic and biophysical factors. NDBI, Cramer’s V, logistic regression, and CA-Markov analyses were used to classify and predict built-up patterns. The built-up area is the dominant land use, which had a gradual growth from 1990 to 2020. A total of 20 socioeconomic and biophysical factors were identified as potentials in the municipality, affecting the urban sprawl. Policy regulation was the most attractive driver with a positive association, and land value had a high inverse association. Three prediction scenarios for urban sprawl were achieved for 2030. Higher sprawling growth is expected in scenario 3, compared with scenarios 1 and 2. Scenario 3 was simulated with biophysical and socioeconomic factors. This study aids in addressing urban sprawl at different spatial and temporal scales and helps urban planners and decision makers enhance the development strategies in the municipality. Predicted maps with different scenarios can support evaluating future sprawling growth and be used to develop sustainable planning for the city.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-04
      DOI: 10.3390/ijgi11080442
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 443: Exploring the Impact of Floating Population with
           Different Household Registration on Theft

    • Authors: Chong Xu, Xi Chen, Jianguo Chen, Debao Chen
      First page: 443
      Abstract: The floating population is frequently treated as a homogeneous whole to explore its impact on crime in numerous crime studies in China. However, there are different compositions within the floating population and significant differences in the effects on crime. In this study, the floating population was divided into three types based on household registration (i.e., Hukou): the floating population from other districts in the same city (FPFOD), the floating population from other cities in the same province (FPFOC) and the floating population from other provinces (FPFOP). The Moran index was used to analyze their spatial distribution patterns and aggregation, respectively, and several negative binomial regression models were constructed to explore the influence of different types of floating populations on theft. The results show that the three types of floating populations are mainly distributed in different urban areas, implying differences in their impact on theft. Among them, the proportion of the FPFOD shows insignificant negative correlation on theft, while the proportion of the FPFOC and the FPFOP present a significant positive correlation. Meanwhile, the proportion of the FPFOP creates a stronger effect on theft than the proportion of entire floating population. Overall, the model performs best when variables of the proportion of the FPFOC and the FPFOP are included. The research conclusions can provide a meaningful reference for precisely measuring the floating population in crime research.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-07
      DOI: 10.3390/ijgi11080443
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 444: Correction: Shi et al. Spatio-Temporal Variation
           Analysis of the Biological Boundary Temperature Index Based on Accumulated
           Temperature: A Case Study of the Yangtze River Basin. ISPRS Int. J.
           Geo-Inf. 2021, 10, 675

    • Authors: Guangxun Shi, Peng Ye, Xianwu Yang
      First page: 444
      Abstract: The authors wish to make the following corrections to their paper [...]
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-10
      DOI: 10.3390/ijgi11080444
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 445: VHRShips: An Extensive Benchmark Dataset for
           Scalable Deep Learning-Based Ship Detection Applications

    • Authors: Serdar Kızılkaya, Ugur Alganci, Elif Sertel
      First page: 445
      Abstract: The classification of maritime boats and ship targets using optical satellite imagery is a challenging subject. This research introduces a unique and rich ship dataset named Very High-Resolution Ships (VHRShips) from Google Earth images, which includes diverse ship types, different ship sizes, several inshore locations, and different data acquisition conditions to improve the scalability of ship detection and mapping applications. In addition, we proposed a deep learning-based multi-stage approach for ship type classification from very high resolution satellite images to evaluate the performance of the VHRShips dataset. Our “Hierarchical Design (HieD)” approach is an end-to-end structure that allows the optimization of the Detection, Localization, Recognition, and Identification (DLRI) stages, independently. We focused on sixteen parent ship classes for the DLR stages, and specifically considered eight child classes of the navy parent class at the identification stage. We used the Xception network in the DRI stages and implemented YOLOv4 for the localization stage. Individual optimization of each stage resulted in F1 scores of 99.17%, 94.20%, 84.08%, and 82.13% for detection, recognition, localization, and identification, respectively. The end-to-end implementation of our proposed approach resulted in F1 scores of 99.17%, 93.43%, 74.00%, and 57.05% for the same order. In comparison, end-to-end YOLOv4 yielded F1-scores of 99.17%, 86.59%, 68.87%, and 56.28% for DLRI, respectively. We achieved higher performance with HieD than YOLOv4 for localization, recognition, and identification stages, indicating the usability of the VHRShips dataset in different detection and classification models. In addition, the proposed method and dataset can be used as a benchmark for further studies to apply deep learning on large-scale geodata to boost GeoAI applications in the maritime domain.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-10
      DOI: 10.3390/ijgi11080445
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 446: Factors That Affect Spatial Data Sharing in

    • Authors: Qasim Hamakhurshid Hamamurad, Normal Mat Jusoh, Uznir Ujang
      First page: 446
      Abstract: This paper examines the phenomena of the local government’s inadequate reaction to the national programme of geographical infrastructure for the effective sharing of spatial data in Malaysia. We investigate the determinants of sharing data for Malaysia’s spatial data infrastructure (SDI) and aim to define the model for spatial data-sharing of Malaysia’s local SDI. The main contribution of this paper is an explanation of the novel methodology to study factors that affect spatial data sharing including a new qualitative analysis method through an interview with people concerned in this field, including engineers, technicians and academics, which was undertaken in Kuala Lumpur, and a new methodology to identify the necessary approach that affects spatial data sharing. An interview and a questionnaire were used in this study as part of a sequential exploratory approach. Among land use, Plan Malaysia, and Telekom Malaysia Berhad TMOne, 15 participants were interviewed in-depth to obtain their responses, and 83 individuals took part in the survey questionnaires. Interview data were measured by content analysis, while questionnaire data were measured by partial least squares analysis. In the structural model analysis, Smart PLS was used to choose the fit items based on validity and reliability measurements. According to the hypothesis measurement, technology and organisation both significantly affect the practice of spatial data sharing, but human resources and spatial data do not significantly affect it. All R-Squared values represent a value above 56 per cent for the human resource aspect, technology aspect and spatial data aspect. However, the R-Square value for spatial data sharing is 47%. Spatial data and human resources have a less substantial impact on spatial data sharing; hence, this study proposes a national awareness programme and mentoring to improve local SDI support for spatial data sharing.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-11
      DOI: 10.3390/ijgi11080446
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 447: Characterizing
           Production–Living–Ecological Space Evolution and Its Driving

    • Authors: Ruyi Zhang, Songnian Li, Baojing Wei, Xu Zhou
      First page: 447
      Abstract: The division of the territorial space functional area is the primary method to study the rational exploitation and use of land space. The research on the Production–Living–Ecological Space (PLES) change and its motivating factors has major implications for managing and optimizing spatial planning and may open up a new research direction for inquiries into environmental change on a global scale. In this study, the transfer matrix and landscape pattern index methods were used to analyze the temporal changes as well as the evolution features of the landscape pattern of the PLES in the Chaohu Lake Basin from 2000 to 2020. Using principal component analysis and grey correlation analysis, the primary driving indicators of the spatial changes of the PLES in the Chaohu Lake Basin and the degree of the influence of various driving factors on various spatial types were determined. The study concluded with a few findings. First, from the standpoint of landscape structure, the Chaohu Lake Basin’s agricultural production space (APS) makes up more than 60% of the total area, and it and urban living space (ULS) are the two most visible spatial categories. Second, the pattern of the landscape demonstrates that the area used for agricultural production holds a significant advantage within the overall structure of the landscape. Although there is less connectedness between different landscape types, less landscape dominance, and more landscape fragmentation, the structure of different landscape types tends to be more varied. Third, the findings of the driving analysis demonstrate that the natural climate, population structure of agricultural development, and industrial structure of economic development are the three driving indicators of the change of the PLES. Finally, in order to promote the formation of a territorial space development pattern with intensive and efficient production space, appropriate living space, and beautiful ecological space, it is proposed to carry out land regulation according to natural factors, economic development, national policies, and other actual conditions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-11
      DOI: 10.3390/ijgi11080447
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 448: RDQS: A Geospatial Data Analysis System for
           Improving Roads Directionality Quality

    • Authors: Abdulrahman Salama, Cordel Hampshire, Josh Lee, Adel Sabour, Jiawei Yao, Eyhab Al-Masri, Mohamed Ali, Harsh Govind, Ming Tan, Vashutosh Agrawal, Egor Maresov, Ravi Prakash
      First page: 448
      Abstract: With the increasing availability of smart devices, billions of users are currently relying on map services for many fundamental daily tasks such as obtaining directions and getting routes. It is becoming more and more important to verify the quality and consistency of route data presented by different map providers. However, verifying this consistency manually is a very time-consuming task. To address this problem, in this paper we introduce a novel geospatial data analysis system that is based on road directionality. We investigate our Road Directionality Quality System (RDQS) using multiple map providers, including: Bing Maps, Google Maps, and OpenStreetMap. Results from the experiments conducted show that our detection neural network is able to detect an arrow’s position and direction in map images with >90% F1-Score across each of the different providers. We then utilize this model to analyze map images in six different regions. Our findings show that our approach can reliably assess map quality and discover discrepancies in road directionality across the different providers. We report the percentage of discrepancies found between map providers using this approach in a proposed study area. These results can help determine areas needs to be revised and prioritized to improve the overall quality of the data within maps.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-14
      DOI: 10.3390/ijgi11080448
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 449: Measuring COVID-19 Vulnerability for Northeast
           Brazilian Municipalities: Social, Economic, and Demographic Factors Based
           on Multiple Criteria and Spatial Analysis

    • Authors: Ciro José Jardim de Figueiredo, Caroline Maria de Miranda Mota, Kaliane Gabriele Dias de Araújo, Amanda Gadelha Ferreira Rosa, Arthur Pimentel Gomes de Souza
      First page: 449
      Abstract: COVID-19 has brought several harmful consequences to the world from many perspectives, including social, economic, and well-being in addition to health issues. However, these harmful consequences vary in intensity in different regions. Identifying which cities are most vulnerable to COVID-19 and understanding which variables could be associated with the advance of registered cases is a challenge. Therefore, this study explores and builds a spatial decision model to identify the characteristics of the cities that are most vulnerable to COVID-19, taking into account social, economic, demographic, and territorial aspects. Hence, 18 features were separated into the four groups mentioned. We employed a model joining the dominance-based rough set approach to aggregate the features (multiple criteria) and spatial analysis (Moran index, and Getis and Ord) to obtain final results. The results show that the most vulnerable places have characteristics with high population density and poor economic conditions. In addition, we conducted subsequent analysis to validate the results. The case was developed in the northeast region of Brazil.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-16
      DOI: 10.3390/ijgi11080449
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 450: House Price Valuation Model Based on
           Geographically Neural Network-Weighted Regression: The Case Study of
           Shenzhen, China

    • Authors: Zimo Wang, Yicheng Wang, Sensen Wu, Zhenhong Du
      First page: 450
      Abstract: Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographical neural network-weighted regression (GNNWR) to improve the accuracy of real estate appraisal with the help of neural networks. Based on the Shenzhen house price dataset, this work conspicuously captures the variable spatial regression relationships at different regions of different variables, which GWR has difficulty realizing. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, and refine the experiment process with 10-fold cross-validation. In contrast with the ordinary least squares (OLS) model, our model achieves an improvement of about 50% on most of the metrics. Compared with the best GWR model, our thorough experiments reveal that our model improves the mean absolute error (MAE) by 13.5% and attains a decrease of the mean absolute percentage error (MAPE) by 13.0% in the evaluation on the validation dataset. It is a practical and powerful way to assess house prices, and we believe our model could be applied to other valuation problems concerning geographical data to promote the prediction accuracy of socioeconomic phenomena.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-18
      DOI: 10.3390/ijgi11080450
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 451: Assessing Multi-Temporal Global Urban Land-Cover
           Products Using Spatio-Temporal Stratified Sampling

    • Authors: Yali Gong, Huan Xie, Yanmin Jin, Xiaohua Tong
      First page: 451
      Abstract: In recent years, the availability of multi-temporal global land-cover datasets has meant that they have become a key data source for evaluating land cover in many applications. Due to the high data volume of the multi-temporal land-cover datasets, probability sampling is an efficient method for validating multi-temporal global urban land-cover maps. However, the current accuracy assessment methods often work for a single-epoch dataset, and they are not suitable for multi-temporal data products. Limitations such as repeated sampling and inappropriate sample allocation can lead to inaccurate evaluation results. In this study, we propose the use of spatio-temporal stratified sampling to assess thematic mappings with respect to the temporal changes and spatial clustering. The total number of samples in the two stages, i.e., map and pixel, was obtained by using a probability sampling model. Since the proportion of the area labeled as no change is large while that of the area labeled as change is small, an optimization algorithm for determining the sample sizes of the different strata is proposed by minimizing the sum of variance of the user’s accuracy, producer’s accuracy, and proportion of area for all strata. The experimental results show that the allocation of sample size by the proposed method results in the smallest bias in the estimated accuracy, compared with the conventional sample allocation, i.e., equal allocation and proportional allocation. The proposed method was applied to multi-temporal global urban land-cover maps from 2000 to 2010, with a time interval of 5 years. Due to the spatial aggregation characteristics, the local pivotal method (LPM) is adopted to realize spatially balanced sampling, leading to more representative samples for each stratum in the spatial domain. The main contribution of our research is the proposed spatio-temporal sampling approach and the accuracy assessment conducted for the multi-temporal global urban land-cover product.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-19
      DOI: 10.3390/ijgi11080451
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 452: Coupling a Physical Replica with a Digital Twin:
           A Comparison of Participatory Decision-Making Methods in an Urban Park

    • Authors: Junjie Luo, Pengyuan Liu, Lei Cao
      First page: 452
      Abstract: Public participation is crucial in promoting built environment quality. By using Nancuiping park in China as a case study, this research brings attention to the digital twin park compared to the physical replica in a participatory workshop. Using UAV oblique photography, we created a digital twin model of this park and divided it into six layers to better manage and analyze the environment. Bracing the `bottom-up’ design philosophy, in the workshop, we analyzed existing issues in the park and simulated built environment changes, taking suggestions and comments from participants into account to support the decision-making of the park’s optimization. Our digital twin model and physical replica were assessed through a questionnaire in which 59 participants used 3 defined indicators: usability, interactivity, and scenario simulation and visualization quality. The results suggest that the physical replica is easier to use in the participatory design. However, the digital twin model can provide better interactivity and efficient scene simulation and visualization quality. The statistical analysis of the relationship between participants’ feedback on the two models and their sociodemographics (age, gender, and education background) shows that age is a barrier to promoting digital twins for older participants. Meanwhile, the digital twin’s highly interactive features and high-resolution visualization capability were attractive to the younger and well-educated participants. Our study indicates future directions to improve the urban digital twin by incorporating human feedback into the urban model, thus establishing a two-way interaction between the digital system, the physical environment, and human perceptions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-19
      DOI: 10.3390/ijgi11080452
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 453: Modelling the Mobility Changes Caused by
           Perceived Risk and Policy Efficiency

    • Authors: Sijin Wu, Susan Grant-Muller, Lili Yang
      First page: 453
      Abstract: In many countries, governments have implemented non-pharmaceutical techniques to limit COVID-19 transmission. Restricting human mobility is one of the most common interventions, including lockdown, travel restrictions, working from home, etc. However, due to the strong transmission ability of the virus variants, further rounds of interventions, including a strict lockdown, are not considered as effective as expected. The paper aims to understand how the lockdown policy and pandemics changed human mobility in the real scenario. Here we focus on understanding the mobility changes caused by compliance with restrictions and risk perceptions, using a mobility index from the Google report during three strict lockdown periods in Leeds, the largest city in the county of West Yorkshire, England, from March 2020 to March 2021. The research uses time-varying z-scores and Principal Component Analysis (PCA) to simulate how local people dynamically process and perceive health risks based on multi-dimensional daily COVID-19 reports first. Further modelling highlights exponentially increasing policy non-compliance through the duration of lockdown, probably attributable to factors such as mental anxiety and economic pressures. Finally, the proposed nonlinear regression model examines the mobility changes caused by the population’s dynamic risk perceptions and lockdown duration. The case study model in Leeds shows a good fit to the empirical mobility data and indicates that the third lockdown policy took effect much slower than the first. At the same time, the negative impact of the epidemic on population mobility decayed by 40% in the third lockdown period in contrast with the first lockdown. The risk perception estimation methods could reflect that the local population became increasingly accustomed to the COVID-19 situation, and local people rationally evaluated the risks of COVID in the third lockdown period. The results demonstrate that simulated risk perceptions and policy decay could explain urban mobility behaviour during lockdown periods, which could be a reference for future decision-making processes.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-20
      DOI: 10.3390/ijgi11080453
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 454: Discovering Spatio-Temporal Co-Occurrence
           Patterns of Crimes with Uncertain Occurrence Time

    • Authors: Yuanfang Chen, Jiannan Cai, Min Deng
      First page: 454
      Abstract: The discovery of spatio-temporal co-occurrence patterns (STCPs) among multiple types of crimes whose events frequently co-occur in neighboring space and time is crucial to the joint prevention of crimes. However, the crime event occurrence time is often uncertain due to a lack of witnesses. This occurrence time uncertainty further results in the uncertainty of the spatio-temporal neighborhood relationships and STCPs. Existing methods have mostly modeled the uncertainty of events under the independent and identically distributed assumption and utilized one-sided distance information to measure the distance between uncertain events. As a result, STCPs detected from a dataset with occurrence time uncertainty (USTCPs) are likely to be erroneously assessed. Therefore, this paper proposes a probabilistic-distance-based USTCP discovery method. First, the temporal probability density functions of crime events with uncertain occurrence times are estimated by considering the temporal dependence. Second, the spatio-temporal neighborhood relationships are constructed based on the spatial Euclidean distance and the proposed temporal probabilistic distance. Finally, the prevalent USTCPs are identified. Experimental comparisons performed on twelve types of crimes from X City Public Security Bureau in China demonstrate that the proposed method can more objectively express the occurrence time of crimes and more reliably identify USTCPs.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-20
      DOI: 10.3390/ijgi11080454
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 455: Spatial Pattern and Formation Mechanism of Rural
           Tourism Resources in China: Evidence from 1470 National Leisure Villages

    • Authors: Yuchen Xie, Xiangzhuang Meng, Jeremy Cenci, Jiazhen Zhang
      First page: 455
      Abstract: Rural tourism development has been an essential driving force behind China’s promotion of integrated urban–rural development, sustainable rural development and rural revitalization in the new era. This study included 1470 villages on the national list of beautiful leisure villages in China (BLVCs) from 2010 to 2021. We explored the distribution characteristics and influencing factors based on mathematical statistics and spatial analysis in ArcGIS to provide a theoretical reference for promoting the development of leisure village agriculture and rural tourism. The results show that the distribution of BLVC presents a clustered state, showing a distribution pattern of a dual core, seven centers and multiple scattered points. BLVCs are mainly distributed in areas with flat terrain and sufficient water resources, which are conducive to agricultural production and life. Having convenient transportation and rich tourism resources aids the promotion of rural tourism development. The resulting gap in regional development is balanced to some extent by government support. The research results provide a reference value for future rural spatial optimization and sustainable development. This paper summarizes the law of rural development and clarifies the factors influencing the development of rural tourism, and it provides the Chinese experience as a model for a rural renaissance empowered by rural tourism.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-20
      DOI: 10.3390/ijgi11080455
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 456: Identification of Urban Agglomeration Spatial
           Range Based on Social and Remote-Sensing Data—For Evaluating
           Development Level of Urban Agglomeration

    • Authors: Shuai Zhang, Hua Wei
      First page: 456
      Abstract: The accurate identification of urban agglomeration spatial area is helpful in understanding the internal spatial relationship under urban expansion and in evaluating the development level of urban agglomeration. Previous studies on the identification of spatial areas often ignore the functional distribution and development of urban agglomerations by only using nighttime light data (NTL). In this study, a new method is firstly proposed to identify the accurate spatial area of urban agglomerations by fusing night light data (NTL) and point of interest data (POI); then an object-oriented method is used by this study to identify the spatial area, finally the identification results obtained by different data are verified. The results show that the accuracy identified by NTL data is 82.90% with the Kappa coefficient of 0.6563, the accuracy identified by POI data is 81.90% with the Kappa coefficient of 0.6441, and the accuracy after data fusion is 90.70%, with the Kappa coefficient of 0.8123. The fusion of these two kinds of data has higher accuracy in identifying the spatial area of urban agglomeration, which can play a more important role in evaluating the development level of urban agglomeration; this study proposes a feasible method and path for urban agglomeration spatial area identification, which is not only helpful to optimize the spatial structure of urban agglomeration, but also to formulate the spatial development policy of urban agglomeration.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-21
      DOI: 10.3390/ijgi11080456
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 457: Make It Simple: Effective Road Selection for
           Small-Scale Map Design Using Decision-Tree-Based Models

    • Authors: Izabela Karsznia, Karolina Wereszczyńska, Robert Weibel
      First page: 457
      Abstract: The complexity of a road network must be reduced after a scale change, so that the legibility of the map can be maintained. However, deciding whether to show a particular road section on the map is a very complex process. This process, called selection, constitutes the first step in a sequence of further generalization operations and it is a prerequisite to effective road network generalization. So far, not many comprehensive solutions have been developed for effective road selection specifically at small scales as the studies have mainly dealt with large-scale maps. The paper presents an experiment using machine learning (ML), specifically decision-tree-based (DT) models, to optimize the selection of the roads from 1:250,000 to 1:500,000 and 1:1,000,000 scales. The scope of this research covers designing and verifying road selection models on the example of three selected districts in Poland. The aim is to consider the problem of road generalization holistically, including numerous semantic, geometric, topological, and statistical road characteristics. The research resulted in a list of measurable road attributes that comprehensively describe the rank of a particular road section. The outcome also includes attribute weights, attribute correlation calculated for roads, and machine learning models designed for automatic road network selection. The performance of the machine learning models is very high and ranges from 80.94% to 91.23% for the 1:500,000 target scale and 98.21% to 99.86% for the 1:1,000,000 scale.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-08-22
      DOI: 10.3390/ijgi11080457
      Issue No: Vol. 11, No. 8 (2022)
  • IJGI, Vol. 11, Pages 412: Similarity Search on Semantic Trajectories Using
           Text Processing

    • Authors: Damião Ribeiro de Almeida, Cláudio de Souza Baptista, Fabio Gomes de Andrade
      First page: 412
      Abstract: The use of location-based sensors has increased exponentially. Tracking moving objects has become increasingly common, consolidating a new field of research that focuses on trajectory data management. Such trajectories may be semantically enriched using sensors and social media. This enables a detailed analysis of trajectory behavior patterns. One of the problems in this field is the search for a semantic trajectory database that is flexible and adaptable; flexibility in the sense of retrieving trajectories that are closest to the user’s query and not just based on exact matching. Adaptability refers to adjusting to different types of semantic trajectories. This article proposes a new approach for representing and querying semantic trajectories based on text-processing techniques. Furthermore, we describe a framework, called SETHE (SEmantic Trajectory HuntEr), that performs similarity queries on semantically enriched trajectory databases. SETHE can be adapted according to the aspect types posed in user queries. We also presented an evaluation of the proposed framework using a real dataset, and compare our results with those of state-of-the-art approaches.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-21
      DOI: 10.3390/ijgi11070412
      Issue No: Vol. 11, No. 7 (2022)
  • IJGI, Vol. 11, Pages 413: The Reflection of Income Segregation and
           Accessibility Cleavages in Sydney’s House Prices

    • Authors: Matthew Kok Ming Ng, Josephine Roper, Chyi Lin Lee, Christopher Pettit
      First page: 413
      Abstract: Cities often show residential income segregation, and the price of housing is generally related to employment accessibility, but how do these factors intersect' We analyse Greater Sydney, Australia, a metropolitan area of 5 million people. Sydney is found to have reasonably even employment accessibility by car, reflecting the increasingly polycentric nature of the modern city; however, it also shows considerable income segregation and variance in property prices between different parts of the city. Entropy is used to examine diversity and mixing of different income groups. Finally, hedonic price models using ordinary-least squares and geographically-weighted regression techniques show the differing effects of employment accessibility on house prices in different parts of the city. The results show that accessibility has small to negative effects on prices in the most valuable areas, suggesting that other effects such as recreational access and employment type/quality may be more important determinants of house prices in these areas.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-21
      DOI: 10.3390/ijgi11070413
      Issue No: Vol. 11, No. 7 (2022)
  • IJGI, Vol. 11, Pages 414: An Assessment of the Accessibility of Multiple
           Public Service Facilities and Its Correlation with Housing Prices Using an
           Improved 2SFCA Method—A Case Study of Jinan City, China

    • Authors: Luoan Yang, Shumin Zhang, Mei Guan, Jianfei Cao, Baolei Zhang
      First page: 414
      Abstract: The spatial distribution and accessibility of urban public service facilities affect socioeconomic factors in the lives of residents, especially housing prices. Given that most previous studies focus on the accessibility of a certain, single type of facility and its impact on housing prices, this research uses improved two-step floating catchment area (2SFCA) methods by considering the differences in the service capacity of different types of public service facilities in real life to evaluate their accessibility to residential communities in Jinan city based on 3117 facilities covering 11 different kinds of facilitates. Then, we assess the spatial distribution of the impact of the accessibility of different public service facilities on housing prices in Jinan city through a local indicator of a spatial association (LISA) cluster diagram generated based on the bivariate local Moran’s index. Our objectives are to assess the accessibility of multiple public service facilities using an improved 2SFCA method and to explore the spatial correlations between the accessibility of public service facilities and housing prices. The results show that the housing prices in Jinan are clustered and that the areas with high housing prices are mainly concentrated in the Lixia District and the center of the downtown area. The accessibility of medical, shopping, educational and bus stop facilities in the Lixia District is better than that in other districts. The accessibility of shopping, medical and tourist attraction facilities has the most significant impact on housing prices, and the number of communities in which the accessibility of these public service facilities and housing prices form a positive correlation cluster accounts for 50.5%, 47.9% and 45.8% of all communities, respectively. On the other hand, educational accessibility and bus stop accessibility have nothing to do with housing prices, and the number of communities in which the accessibility of these public service facilities forms a not-significant cluster with housing prices accounting for 51.1% and 56.5% of the total, respectively. In this study, the combined 2SFCA method is used to improve the method for evaluating the accessibility of a variety of public service facilities, and its applicability is verified by practical application. By analyzing the spatial correlation between accessibility and housing prices, we expand our understanding of accessibility and show that it plays a central role in housing prices, which will help to improve the spatial pattern of urban public places in the future, provide support for decision makers and provide a reference for the government and real estate developers.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-07-21
      DOI: 10.3390/ijgi11070414
      Issue No: Vol. 11, No. 7 (2022)
  • IJGI, Vol. 11, Pages 496: The Design and Implementation of Geospatial
           Information Verification Middle Platform for Natural Resources Government

    • Authors: Fanrong Meng, Junjie Zhou, Dengkui Kong, Min Yao, Kongyi Wu, Xuchun Liu, Xuefei Wang, Yike Guo
      First page: 496
      Abstract: Geospatial Information Verification Mid-End Platform for Natural Resource Administration is designed in response to issues such as repeated development, low scalability, and inconsistent verification rules in existing approval and supervision application systems. We first discussed the architecture of the middle platform and micro-services and also examined the business requirements. Secondly, we presented the architecture of the spatial information verification platform. Finally, the application method in the construction land approval business is introduced. Practical applications proved that the spatial information verification platform is highly scalable and maintainable, with reusable business components and data services for a variety of government affairs application systems.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-21
      DOI: 10.3390/ijgi11100496
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 497: Using Flickr Data to Understand Image of Urban
           Public Spaces with a Deep Learning Model: A Case Study of the Haihe River
           in Tianjin

    • Authors: Chenghao Yang, Tongtong Liu, Shengtian Zhang
      First page: 497
      Abstract: Understanding public perceptions of images of urban public spaces can guide efforts to improve urban vitality and spatial diversity. The rise of social media data and breakthroughs in deep learning frameworks for computer vision provide new opportunities for studying public perceptions in public spaces. While social media research methods already exist for extracting geo-information on public preferences and emotion analysis findings from geodata, this paper aims at deep learning analysis by building a VGG-16 image classification method that enhanced the research content of images without geo-information. In this study, 1940 Flickr images of the Haihe River in Tianjin were identified in multiple scenes with deep learning. The regularized VGG-16 architecture showed high accuracies of 81.75% for the TOP-1 and 96.75% for the TOP-5 and Grad-CAM visualization modules for the interpretation of classification results. The result of the present work indicate that images of the Haihe River are dominated by skyscrapers, bridges, promenades, and urban canals. After using kernel density to visualize the spatial distribution of Flickr images with geodata, it was found that there are three vitality areas in Haihe River. However, the kernel density result also shows that judging spatial visualization based solely on geodata is incomplete. The spatial distribution can be used as an assistant function in the case of the under-representation of geodata. Collectively, the field of how to apply computer vision to urban design research was explored and extended in this trial study.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-21
      DOI: 10.3390/ijgi11100497
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 498: A GloVe Model for Urban Functional Area
           Identification Considering Nonlinear Spatial Relationships between Points
           of Interest

    • Authors: Yue Chen, Haizhong Qian, Xiao Wang, Di Wang, Lijian Han
      First page: 498
      Abstract: As cities continue to grow, the functions of urban areas change and problems arise from previously constructed urban planning schemes. Hence, the actual distribution of urban functional areas needs to be confirmed. POI data, as a representation of urban facilities, can be used to mine the spatial correlation within the city. Therefore it has been widely used for urban functional area extraction. Previous studies are mostly devoted to mining POI linear location relationships and do not comprehensively mine POI spatial information, such as spatial interaction information. This results in less accurate modeling of the relationship between POI-based and urban function types. In addition, they all use Euclidean distance for proximity assessment, which is not realistic. This paper proposes an urban functional area identification method that considers the nonlinear spatial relationship between POIs. First, POI adjacency is determined according to road network constraints, which forms the basis of a co-occurrence matrix. Then, a Global Vectors (GloVe) model is used to train POI category vectors and the feature vectors for each basic research unit are obtained using weighted averages. This is followed by clustering analysis, which is realized by a K-Means++ algorithm. Lastly, the functional areas are labeled according to the POI category ratio, enrichment factors, and mobile phone signal heat data. The model was tested experimentally, using core areas of Zhengzhou City in China as an example. When the results were compared with a Baidu map, we confirmed that making full use of nonlinear spatial relationships between POIs delivers high levels of identification accuracy for urban functional areas.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-24
      DOI: 10.3390/ijgi11100498
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 499: Spatial Modeling of COVID-19 Prevalence Using
           Adaptive Neuro-Fuzzy Inference System

    • Authors: Mohammad Tabasi, Ali Asghar Alesheikh, Mohsen Kalantari, Elnaz Babaie, Abolfazl Mollalo
      First page: 499
      Abstract: This study is dedicated to modeling the spatial variation in COVID-19 prevalence using the adaptive neuro-fuzzy inference system (ANFIS) when dealing with nonlinear relationships, especially useful for small areas or small sample size problems. We compiled a broad range of socio-demographic, environmental, and climatic factors along with potentially related urban land uses to predict COVID-19 prevalence in rural districts of the Golestan province northeast of Iran with a very high-case fatality ratio (9.06%) during the first year of the pandemic (2020–2021). We also compared the ANFIS and principal component analysis (PCA)-ANFIS methods for modeling COVID-19 prevalence in a geographical information system framework. Our results showed that combined with the PCA, the ANFIS accuracy significantly increased. The PCA-ANFIS model showed a superior performance (R2 (determination coefficient) = 0.615, MAE (mean absolute error) = 0.104, MSE (mean square error) = 0.020, and RMSE (root mean square error) = 0.139) than the ANFIS model (R2 = 0.543, MAE = 0.137, MSE = 0.034, and RMSE = 0.185). The sensitivity analysis of the ANFIS model indicated that migration rate, employment rate, the number of days with rainfall, and residential apartment units were the most contributing factors in predicting COVID-19 prevalence in the Golestan province. Our findings indicated the ability of the ANFIS model in dealing with nonlinear parameters, particularly for small sample sizes. Identifying the main factors in the spread of COVID-19 may provide useful insights for health policymakers to effectively mitigate the high prevalence of the disease.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-24
      DOI: 10.3390/ijgi11100499
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 500: Placial-Discursive Topologies of Violence:
           Volunteered Geographic Information and the Reproduction of Violent Places
           in Recife, Brazil

    • Authors: Cléssio Moura de Souza, Dominik Kremer, Blake Byron Walker
      First page: 500
      Abstract: Knowledge and experiences of violence transform the ways in which individuals perceive the urban landscape, construct and reproduce (un)safety, and make everyday decisions regarding mobility and the use of space. This knowledge and these experiences are placially anchored and are shaped by everyday regionalisations. In the context of interpersonal violence in Recife, Brazil, we examine the ways in which volunteered geographic information (VGI), formal and informal information exchange networks, and individual experience contribute to the reproduction of violent spaces. During interviews with civilian residents and police officers, we explore the knowledge and information flows and their spatial anchorings before and after presenting informants with a VGI-based map of firearms violence. Following coding, interviews were also analysed using a novel, semiautomated text mining algorithm to produce context-sensitive co-occurrence graphs of key arguments within participant narratives. The results indicate strong differences in the placial anchorings between police officers and civilians, and highlight key dynamics in the flows of VGI amongst residents and local news organisations, as well as through social media. These forms of placial knowledge exchange are in constant negotiation with individuals’ perceptions and experiences of the study area and reflect cognitive-discursive reproductions of everyday geographies of (un)safety.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-25
      DOI: 10.3390/ijgi11100500
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 501: Research into the Optimal Regulation of the
           Groundwater Table and Quality in the Southern Plain of Beijing Using
           Geographic Information Systems Data and Machine Learning Algorithms

    • Authors: Chen Li, Baohui Men, Shiyang Yin, Teng Zhang, Ling Wei
      First page: 501
      Abstract: The purpose of this paper is to provide new ideas and methods for the sustainable use of groundwater in areas with serious groundwater overexploitation and serious groundwater pollution. Geographic information systems (GIS) were combined with machine learning algorithms, water resources optimization technology, and groundwater numerical simulation to optimize the regulation of the groundwater table and quality beneath the Daxing District in the southern plain of Beijing. By collecting local consumption and supply data and observations of the groundwater table and quality in the connected aquifer beneath Daxing for the years 2006–2020, the corresponding water demands and groundwater impact were extrapolated for the years 2021–2025 based on the basis of the existing development model. Through the combination of GIS and machine learning algorithms, the NO3-N concentration of local groundwater monitoring points in wet years, normal years, and dry years were predicted. With respect to NO3-N pollution, three new groundwater exploitation regimes were devised, which we numbered 1 to 3. The optimal allocation of water resources was then calculated for wet year, typical year, and dry year scenarios for the year 2025. By comparing the water shortage, groundwater utilization rate, and NO3-N pollution under the new groundwater exploitation regimes, the optimal groundwater exploitation mode for the three different types of hydrological year was determined. The results indicate that NO3-N pollution was greatly reduced after the adoption of the optimal regimes and that the groundwater table demonstrated rapid recovery. These results can be of great help in realizing the management, supervision, and regulation of groundwater by combining GIS with machine learning algorithms.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-26
      DOI: 10.3390/ijgi11100501
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 502: Incremental Road Network Update Method with
           Trajectory Data and UAV Remote Sensing Imagery

    • Authors: Jianxin Qin, Wenjie Yang, Tao Wu, Bin He, Longgang Xiang
      First page: 502
      Abstract: GPS trajectory and remote sensing data are crucial for updating urban road networks because they contain critical spatial and temporal information. Existing road network updating methods, whether trajectory-based (TB) or image-based (IB), do not integrate the characteristics of both types of data. This paper proposed and implemented an incremental update method for rapid road network checking and updating. A composite update framework for road networks is established, which integrates trajectory data and UAV remote sensing imagery. The research proposed utilizing connectivity between adjacent matched points to solve the problem of updating problematic road segments in networks based on the features of the Hidden Markov Model (HMM) map-matching method in identifying new road segments. Deep learning is used to update the local road network in conjunction with the flexible and high-precision characteristics of UAV remote sensing. Additionally, the proposed method is evaluated against two baseline methods through extensive experiments based on real-world trajectories and UAV remote sensing imagery. The results show that our method has higher extraction accuracy than the TB method and faster updates than the IB method.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-27
      DOI: 10.3390/ijgi11100502
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 503: ICD: VHR-Oriented Interactive Change-Detection

    • Authors: Zhuoran Jiang, Xinxin Zhou, Wei Cao, Zaihong Sun, Changbin Wu
      First page: 503
      Abstract: In recent years, deep learning has become the mainstream development direction in the change-detection field, and its accuracy and speed have also reached a high level. However, the change-detection method based on deep learning cannot predict all the change areas accurately, and its application is limited due to local prediction defects. For this reason, we propose an interactive change-detection network (ICD) for very high resolution (VHR) based on a deep convolution neural network. The network integrates positive- and negative-click information in the distance layer of the change-detection network, and users can correct the prediction defects by adding clicks. We carried out experiments on the open source dataset WHU and LEVIR-CD. By adding clicks, their F1-scores can reach 0.920 and 0.912, respectively, which are 4.3% and 4.2% higher than the original network. To better evaluate the correction ability of clicks, we propose a set of evaluation indices—click-correction ranges, which is suitable for evaluating clicks, and we carry out experiments on the above models. The results show that the method of adding clicks can effectively correct the prediction defects and improve the result accuracy.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-27
      DOI: 10.3390/ijgi11100503
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 504: How Are Macro-Scale and Micro-Scale Built
           Environments Associated with Running Activity' The Application of
           Strava Data and Deep Learning in Inner London

    • Authors: Hongchao Jiang, Lin Dong, Bing Qiu
      First page: 504
      Abstract: Running can promote public health. However, the association between running and the built environment, especially in terms of micro street-level factors, has rarely been studied. This study explored the influence of built environments at different scales on running in Inner London. The 5Ds framework (density, diversity, design, destination accessibility, and distance to transit) was used to classify the macro-scale features, and computer vision (CV) and deep learning (DL) were used to measure the micro-scale features. We extracted the accumulated GPS running data of 40,290 sample points from Strava. The spatial autoregressive combined (SAC) model revealed the spatial autocorrelation effect. The result showed that, for macro-scale features: (1) running occurs more frequently on trunk, primary, secondary, and tertiary roads, cycleways, and footways, but runners choose tracks, paths, pedestrian streets, and service streets relatively less; (2) safety, larger open space areas, and longer street lengths promote running; (3) streets with higher accessibility might attract runners (according to a spatial syntactic analysis); and (4) higher job density, POI entropy, canopy density, and high levels of PM 2.5 might impede running. For micro-scale features: (1) wider roads (especially sidewalks), more streetlights, trees, higher sky openness, and proximity to mountains and water facilitate running; and (2) more architectural interfaces, fences, and plants with low branching points might hinder running. The results revealed the linkages between built environments (on the macro- and micro-scale) and running in Inner London, which can provide practical suggestions for creating running-friendly cities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-27
      DOI: 10.3390/ijgi11100504
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 505: Variable-Scale Visualization of High-Density
           Polygonal Buildings on a Tile Map

    • Authors: Zhixiong Chen, Yilang Shen, Xinlin Lv, Qiaolin Qin, Xin Chen
      First page: 505
      Abstract: To better satisfy user’s needs for the accurate visualization of massive amounts of geographic data, the variable-scale expression of map content based on multilevel data organization has attracted increasing attention. Traditional methods based on vector data usually cannot handle tile data in the form of a grid on the network. Therefore, this paper proposes a variable-scale visualization method for high-density buildings based on a raster tile map. First, the buildings on a tile map are typified on the basis of linear spectral clustering (LSC) superpixel segmentation to reduce the number of buildings. Then, the shapes of buildings are simplified using the minimum bounding rectangle method. Lastly, the designed focus + glue + context (F + G + C) variable-scale model is used for visual output. The OpenStreetMap tile data are used to perform experiments. Compared with traditional methods, the proposed variable-scale visualization method in this paper considers the spatial distribution, quantity, and shape characteristics of buildings, reduces the clutter of data, and has a better (average value of building quantity, area and density is 57%) visual effect. Variable-scale visualization can be applied to unstructured map data sources and extended to grid data sources to improve the readability and recognizability of high-density buildings.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-28
      DOI: 10.3390/ijgi11100505
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 506: HiPDERL: An Improved Implementation of the PDERL
           Viewshed Algorithm and Accuracy Analysis

    • Authors: Haozhe Cheng, Dou Wanfeng
      First page: 506
      Abstract: Terrain viewshed analysis based on the digital elevation model (DEM) is of significant application value. A lot of viewshed analysis algorithms have been proposed, including R3 as the accurate one and others as efficient ones. The R3 algorithm is accurate because of its comprehensive but time-consuming computation, while the others are efficient due to proper approximation. However, no algorithm is capable of taking advantage of both until one algorithm is proposed, which is based on a ‘proximity-direction-elevation’ (PDE) coordinate system and named the PDE spatial reference line (PDERL) algorithm. The original research proves the PDERL algorithm is perfectly accurate by theory and experimental results, in comparison with R3 as standard, and even more efficient than R3. However, the original research does not mention the cases where the observer is placed on grid points, and the original implementation does not produce very accurate results in practice. It is important to find out and correct the errors. In this paper, a checking algorithm for PDERL is proposed to allow further investigation of errors. With the fundamental ideas of PDERL unchallenged, an improved implementation of the PDERL algorithm is proposed, named HiPDERL. By experimental results, this paper proves HiPDERL utilizes the potential of PDERL on accuracy at the cost of a little efficiency when the observer is placed on grid points.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-28
      DOI: 10.3390/ijgi11100506
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 507: Exploring the Association of Spatial Capital and
           Economic Diversity in the Tourist City of Surat Thani, Thailand

    • Authors: Manat Srivanit, Chompoonut Kongphunphin, Damrongsak Rinchumphu
      First page: 507
      Abstract: Diversity in economic activity can be found at different spatial scales in cities’ urban morphology. Spatial capital is defined as the area’s physical appearance, which is important for enhancing economic activities in urban areas. It addresses how urban form, as a result of urban design, influences urban life—that is, how it supports and creates the potential for variations of urbanity and spatial diversity. The aims of this study are (i) to measure the economic diversity based on Simpson’s diversity index by using points of interest (POI) data, which can reflect economic activity functions in the tourist city of Surat Thani, which is mainly used as a jumping off point for land travel to other islands off the east coast of Thailand; (ii) to explore the space syntax to measure the values of urban morphology by integrations with DepthMapX Software; and (iii) to investigate the relationship between measures of the degree of spatial morphology configuration and patterns of spatial diversity of economic activities using the Pearson’s correlation coefficient. The study found that measuring the values of urban morphology can generate variations in spatial accessibility that are positively related to the variety of economic diversity, especially in terms of the availability of convenience stores, shops, and bank branches. This research is beneficial to planners in identifying important economic areas of the city, whose complex spatial interactions between commerce and urban morphology influence the current demand for economic space.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-28
      DOI: 10.3390/ijgi11100507
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 508: A GIS Pipeline to Produce GeoAI Datasets from
           Drone Overhead Imagery

    • Authors: John R. Ballesteros, German Sanchez-Torres, John W. Branch-Bedoya
      First page: 508
      Abstract: Drone imagery is becoming the main source of overhead information to support decisions in many different fields, especially with deep learning integration. Datasets to train object detection and semantic segmentation models to solve geospatial data analysis are called GeoAI datasets. They are composed of images and corresponding labels represented by full-size masks typically obtained by manual digitizing. GIS software is made of a set of tools that can be used to automate tasks using geo-referenced raster and vector layers. This work describes a workflow using GIS tools to produce GeoAI datasets. In particular, it mentions the steps to obtain ground truth data from OSM and use methods for geometric and spectral augmentation and the data fusion of drone imagery. A method semi-automatically produces masks for point and line objects, calculating an optimum buffer distance. Tessellation into chips, pairing and imbalance checking is performed over the image–mask pairs. Dataset splitting into train–validation–test data is done randomly. All of the code for the different methods are provided in the paper, as well as point and road datasets produced as examples of point and line geometries, and the original drone orthomosaic images produced during the research. Semantic segmentation results performed over the point and line datasets using a classical U-Net show that the semi-automatically produced masks, called primitive masks, obtained a higher mIoU compared to other equal-size masks, and almost the same mIoU metric compared to full-size manual masks.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-30
      DOI: 10.3390/ijgi11100508
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 509: Passenger Flow Prediction of Scenic Spots in
           Jilin Province Based on Convolutional Neural Network and Improved Quantile
           Regression Long Short-Term Memory Network

    • Authors: Xiwen Qin, Dongmei Yin, Xiaogang Dong, Dongxue Chen, Shuang Zhang
      First page: 509
      Abstract: Passenger flow is an important benchmark for measuring tourism benefits, and accurate tourism passenger flow prediction is of great significance to the government and related tourism enterprises and can promote the sustainable development of China’s tourism industry. For daily passenger flow time series data, a passenger flow forecasting method based on convolutional neural network (CNN) and improved quantile regression long short-term memory network (QRLSTM), denoted as CNN-IQRLSTM, is proposed with reconstructed correlation features and in the form of sliding windows as inputs. First, four discrete variables such as whether the day is a weekend and holiday are created by time; then, a sliding window of width 42 is used to pass the passenger flow data into the network sequentially; finally, the loss function of the sparse Laplacian improved QRLSTM is introduced for passenger flow prediction, and the point prediction and interval prediction results under different quartiles are obtained. The application of quantile regression captures the overall picture of the data, enhances the robustness, fit, predictive power and nonlinear processing capability of neural networks, and fills the gap between quantile regression and neural network methods in the field of passenger flow prediction. CNN can effectively handle complex input data, and the improved nonlinear QR model can provide passenger flow quantile prediction information. The method is applied to the tourism traffic prediction of four 5A scenic spots in Jilin Province, and the effectiveness of the method is verified. The results show that the method proposed in this paper fits best in point prediction and has higher prediction accuracy. The MAPE of the Changbai Mountain dataset was 0.07, the MAPE of the puppet palace museum dataset was 0.05, the fit of the Sculpture Park dataset reached 93%, and the fit of the net moon lake dataset was as high as 99%. Meanwhile, the interval prediction results show that the method has a larger interval coverage as well as a smaller interval average width, which improves the prediction efficiency. In 95% of the interval predictions, the interval coverage of Changbai Mountain data is 99% and the interval average width is 0.49. It is a good reference value for the management of different scenic spots.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-30
      DOI: 10.3390/ijgi11100509
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 510: Identification of Paddy Varieties from Landsat 8
           Satellite Image Data Using Spectral Unmixing Method in Indramayu Regency,

    • Authors: Iqbal Maulana Cipta, Lalu Muhamad Jaelani, Hartanto Sanjaya
      First page: 510
      Abstract: Indramayu Regency is the highest rice producer in West Java province, Indonesia. According to the Central Statistics Agency (BPS), in 2021, rice production in 2020 reached 1,365,435.39 tons of GKG (milled dry grain). Technological developments in the food sector produce various kinds of premium quality rice and rice varieties resistant to climate change, such as Ciherang, Inpari 32 HDB and IR 64. The regular monitoring of specific rice varieties over large areas effectively maintains the quality and quantity of rice production. This study used remote sensing data to monitor rice conditions and distribution based on the spectral unmixing method. The spectral unmixing method was used to identify the percentage of the presence of a pure object in a pixel. The results obtained in this study were images of the endmember fractions of rice varieties and areas of dominant rice varieties used in the Indramayu district. The dominant variety detected with the processing results was the Inpari 32 HDB variety, with an area of 30,738.64 hectares. In comparison, varieties other than Inpari 32 HDB were also detected in several areas in the Indramayu district, with an area of 12,192.68 hectares.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-30
      DOI: 10.3390/ijgi11100510
      Issue No: Vol. 11, No. 10 (2022)
  • IJGI, Vol. 11, Pages 511: Quantify the Potential Spatial Reshaping Utility
           of Urban Growth Boundary (UGB): Evidence from the Constrained Scenario
           Simulation Model

    • Authors: Shifa Ma, Haiyan Jiang, Xiwen Zhang, Dixiang Xie, Yunnan Cai, Yabo Zhao, Guanwei Wang
      First page: 511
      Abstract: Many countries, including China, have implemented the spatial government policy widely known as urban growth boundary (UGB) for managing future urban growth. However, few studies have asked why we need UGB, especially pre-evaluating the utility of UGB for reshaping the future spatial patterns of cities. In this research, we proposed a constrained urban growth simulation model (CUGSM) which coupled Markov chain (MC), random forest (RF), and patch growth based cellular automata (Patch-CA) to simulate urban growth. The regulatory effect of UGB was coupled with CUGSM based on a random probability game method. Guangzhou city, a metropolitan area located in the Peral River Delta of China, was taken as a case study. Historical urban growth from 1995 to 2005 and random forests were used to calibrate the conversion rules of Patch-CA, and the urban patterns simulated and observed in 2015 were used to identify the simulation accuracy. The results showed that the Kappa and figure of merit (FOM) indices of the unconstrained Patch-CA were just 0.7914 and 0.1930, respectively, which indicated that the actual urban growth was reshaped by some force beyond what Patch-CA has learned. We further compared the simulation scenarios in 2035 with and without considering the UGB constraint, and the difference between them is as high as 21.14%, which demonstrates that UGB plays an important role in the spatial reshaping of future urban growth. Specifically, the newly added urban land outside the UGB has decreased from 25.13% to 16.86% after considering the UGB constraint; particularly, the occupation of agricultural space and ecological space has been dramatically reduced. This research has demonstrated that the utility of UGB for reshaping future urban growth is pronounced, and it is necessary for the Chinese government to further strengthen UGB policy to promote sustainable urban growth.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-09-30
      DOI: 10.3390/ijgi11100511
      Issue No: Vol. 11, No. 10 (2022)
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