Subjects -> EARTH SCIENCES (Total: 771 journals)
    - EARTH SCIENCES (527 journals)
    - GEOLOGY (94 journals)
    - GEOPHYSICS (33 journals)
    - HYDROLOGY (29 journals)
    - OCEANOGRAPHY (88 journals)

EARTH SCIENCES (527 journals)            First | 1 2 3     

Showing 201 - 371 of 371 Journals sorted alphabetically
Hydrological Processes     Hybrid Journal   (Followers: 44)
Hydrology and Earth System Sciences     Open Access   (Followers: 38)
ICES Journal of Marine Science: Journal du Conseil     Hybrid Journal   (Followers: 53)
IEEE Geoscience and Remote Sensing Letters     Hybrid Journal   (Followers: 151)
IEEE Geoscience and Remote Sensing Magazine     Hybrid Journal   (Followers: 6)
IEEE Journal of Oceanic Engineering     Hybrid Journal   (Followers: 11)
Indian Geotechnical Journal     Hybrid Journal   (Followers: 4)
Indonesian Journal on Geoscience     Open Access   (Followers: 1)
Inland Waters     Hybrid Journal  
Innovative Infrastructure Solutions     Hybrid Journal  
Interdisciplinary Environmental Review     Hybrid Journal   (Followers: 3)
International Geology Review     Hybrid Journal   (Followers: 17)
International Journal of Advanced Geosciences     Open Access   (Followers: 2)
International Journal of Advanced Remote Sensing and GIS     Open Access   (Followers: 50)
International Journal of Applied Earth Observation and Geoinformation     Open Access   (Followers: 36)
International Journal of Coal Geology     Hybrid Journal   (Followers: 2)
International Journal of Disaster Risk Reduction     Hybrid Journal   (Followers: 18)
International Journal of Earth Sciences     Hybrid Journal   (Followers: 37)
International Journal of Earthquake and Impact Engineering     Hybrid Journal   (Followers: 4)
International Journal of Energetic Materials     Full-text available via subscription  
International Journal of Environment and Geoinformatics     Open Access   (Followers: 4)
International Journal of Geo-Engineering     Open Access   (Followers: 2)
International Journal of Geographical Information Science     Hybrid Journal   (Followers: 55)
International Journal of Geomechanics     Full-text available via subscription   (Followers: 6)
International Journal of Geosciences     Open Access   (Followers: 10)
International Journal of Geosynthetics and Ground Engineering     Full-text available via subscription   (Followers: 3)
International Journal of Geotechnical Earthquake Engineering     Full-text available via subscription   (Followers: 9)
International Journal of Image and Data Fusion     Hybrid Journal   (Followers: 3)
International Journal of Remote Sensing     Hybrid Journal   (Followers: 144)
International Journal of Remote Sensing Applications     Open Access   (Followers: 49)
International Journal of Soil, Sediment and Water     Open Access   (Followers: 4)
International Journal of Speleology     Open Access   (Followers: 3)
Iraqi National Journal of Earth Sciences     Open Access  
iScience     Open Access   (Followers: 1)
Island Arc     Hybrid Journal   (Followers: 5)
ISPRS International Journal of Geo-Information     Open Access   (Followers: 5)
Italian Journal of Geosciences     Open Access  
Izvestiya, Atmospheric and Oceanic Physics     Full-text available via subscription   (Followers: 1)
Izvestiya, Physics of the Solid Earth     Hybrid Journal   (Followers: 2)
Jahresberichte und Mitteilungen des Oberrheinischen Geologischen Vereins     Full-text available via subscription   (Followers: 2)
JETP Letters     Hybrid Journal   (Followers: 3)
Journal of Earth Science & Climatic Change     Open Access   (Followers: 14)
Journal of Advances in Modeling Earth Systems     Open Access   (Followers: 5)
Journal of African Earth Sciences     Hybrid Journal   (Followers: 11)
Journal of Analytical and Numerical Methods in Mining Engineering     Open Access  
Journal of Applied Geophysics     Hybrid Journal   (Followers: 15)
Journal of Applied Volcanology     Open Access   (Followers: 7)
Journal of Arid Land     Hybrid Journal  
Journal of Asian Earth Sciences     Hybrid Journal   (Followers: 15)
Journal of Asian Earth Sciences : X     Open Access  
Journal of Atmospheric and Oceanic Technology     Hybrid Journal   (Followers: 33)
Journal of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 133)
Journal of Big History     Open Access   (Followers: 3)
Journal of Coastal Conservation     Hybrid Journal   (Followers: 6)
Journal of Coastal Research     Hybrid Journal   (Followers: 31)
Journal of Contemporary Physics (Armenian Academy of Sciences)     Hybrid Journal   (Followers: 9)
Journal of Contemporary Water Resource & Education     Hybrid Journal   (Followers: 2)
Journal of Earth Science     Hybrid Journal   (Followers: 12)
Journal of Earth System Science     Open Access   (Followers: 52)
Journal of Earth, Environment and Health Sciences     Open Access   (Followers: 2)
Journal of Earthquake and Tsunami     Hybrid Journal   (Followers: 2)
Journal of Earthquake Engineering     Hybrid Journal   (Followers: 14)
Journal of Environment and Earth Science     Open Access   (Followers: 11)
Journal of Environmental & Engineering Geophysics     Hybrid Journal   (Followers: 2)
Journal of Geodesy     Hybrid Journal   (Followers: 9)
Journal of Geodesy and Geoinformation     Open Access   (Followers: 2)
Journal of Geodynamics     Hybrid Journal   (Followers: 6)
Journal of Geography, Environment and Earth Science International     Open Access  
Journal of Geology     Full-text available via subscription   (Followers: 30)
Journal of Geomorphology     Open Access   (Followers: 3)
Journal of Geophysical Research : Atmospheres     Partially Free   (Followers: 134)
Journal of Geophysical Research : Biogeosciences     Full-text available via subscription   (Followers: 34)
Journal of Geophysical Research : Earth Surface     Partially Free   (Followers: 59)
Journal of Geophysical Research : Oceans     Partially Free   (Followers: 60)
Journal of Geophysical Research : Planets     Full-text available via subscription   (Followers: 116)
Journal of Geophysical Research : Solid Earth     Full-text available via subscription   (Followers: 57)
Journal of Geophysical Research : Space Physics     Full-text available via subscription   (Followers: 136)
Journal of Geophysics and Engineering     Hybrid Journal   (Followers: 2)
Journal of Geoscience Education     Hybrid Journal   (Followers: 1)
Journal of Geoscience, Engineering, Environment, and Technology     Open Access   (Followers: 1)
Journal of Geosciences     Open Access   (Followers: 5)
Journal of Geosciences and Geomatics     Open Access   (Followers: 1)
Journal of Geospatial Applications in Natural Resources     Open Access  
Journal of Geotechnical and Geoenvironmental Engineering     Full-text available via subscription   (Followers: 30)
Journal of Geotechnical Engineering     Full-text available via subscription   (Followers: 4)
Journal of Great Lakes Research     Hybrid Journal   (Followers: 5)
Journal of Hydro-environment Research     Full-text available via subscription   (Followers: 13)
Journal of Hydrologic Engineering     Full-text available via subscription   (Followers: 40)
Journal of International Maritime Safety, Environmental Affairs, and Shipping     Open Access   (Followers: 1)
Journal of Life and Earth Science     Open Access  
Journal of Marine Medical Society     Open Access   (Followers: 1)
Journal of Marine Research     Full-text available via subscription   (Followers: 20)
Journal of Marine Science and Technology     Hybrid Journal   (Followers: 3)
Journal of Marine Systems     Hybrid Journal   (Followers: 9)
Journal of Metamorphic Geology     Hybrid Journal   (Followers: 15)
Journal of Mining Science     Hybrid Journal   (Followers: 2)
Journal of Mountain Science     Hybrid Journal  
Journal of Natural Gas Geoscience     Open Access  
Journal of Ocean and Climate     Open Access   (Followers: 9)
Journal of Oceanology and Limnology     Hybrid Journal   (Followers: 3)
Journal of Petroleum Exploration and Production Technology     Open Access   (Followers: 2)
Journal of Petroleum Science and Engineering     Hybrid Journal   (Followers: 3)
Journal of Petrology     Hybrid Journal   (Followers: 11)
Journal of Plasma Physics     Hybrid Journal   (Followers: 21)
Journal of Population and Sustainability     Open Access  
Journal of Quaternary Science     Hybrid Journal   (Followers: 31)
Journal of Rock Mechanics and Geotechnical Engineering     Open Access   (Followers: 3)
Journal of Sea Research     Hybrid Journal   (Followers: 6)
Journal of Sedimentary Environments     Open Access  
Journal of Seismology     Hybrid Journal   (Followers: 9)
Journal of Spatial Information Science     Open Access   (Followers: 4)
Journal of Structural Geology     Hybrid Journal   (Followers: 26)
Journal of Systematic Palaeontology     Hybrid Journal   (Followers: 7)
Journal of the Atmospheric Sciences     Hybrid Journal   (Followers: 83)
Journal of the Geological Society     Hybrid Journal   (Followers: 17)
Journal of the Royal Society of New Zealand     Hybrid Journal   (Followers: 48)
Journal of the World Aquaculture Society     Hybrid Journal   (Followers: 13)
Journal of Volcanology and Geothermal Research     Hybrid Journal   (Followers: 17)
Journal of Water and Climate Change     Open Access   (Followers: 52)
Journal on Geoinformatics, Nepal     Open Access   (Followers: 2)
Jurnal Ilmiah Perikanan dan Kelautan / Scientific Journal of Fisheries and Marine     Open Access  
Kartografija i geoinformacije (Cartography and Geoinformation)     Open Access  
Lake and Reservoir Management     Hybrid Journal   (Followers: 7)
Landslides     Hybrid Journal   (Followers: 26)
Latin American Journal of Sedimentology and Basin Analysis     Open Access   (Followers: 1)
Lethaia     Hybrid Journal   (Followers: 5)
Letters in Mathematical Physics     Hybrid Journal   (Followers: 4)
Limnologica     Hybrid Journal   (Followers: 4)
Limnology     Hybrid Journal   (Followers: 9)
Lithology and Mineral Resources     Hybrid Journal   (Followers: 3)
Lithos     Hybrid Journal   (Followers: 9)
Malaysian Journal of Geosciences     Open Access  
Marine and Freshwater Research     Hybrid Journal   (Followers: 6)
Marine and Petroleum Geology     Hybrid Journal   (Followers: 21)
Marine Biology Research: New for 2005     Hybrid Journal   (Followers: 2)
Marine Economics and Management     Open Access   (Followers: 3)
Marine Environmental Research     Hybrid Journal   (Followers: 31)
Marine Geodesy     Hybrid Journal   (Followers: 4)
Marine Geology     Hybrid Journal   (Followers: 31)
Marine Geophysical Researches     Hybrid Journal   (Followers: 5)
Marine Georesources & Geotechnology     Hybrid Journal  
Marine Mammal Science     Hybrid Journal   (Followers: 10)
Marine Policy     Hybrid Journal   (Followers: 61)
Mathematical Geosciences     Hybrid Journal   (Followers: 4)
Mathematical Physics, Analysis and Geometry     Hybrid Journal   (Followers: 3)
Mediterranean Geoscience Reviews     Hybrid Journal  
Meteoritics & Planetary Science     Hybrid Journal   (Followers: 18)
Meteorologische Zeitschrift     Full-text available via subscription   (Followers: 4)
Mineralium Deposita     Hybrid Journal   (Followers: 4)
Mineralogia     Open Access   (Followers: 2)
Mineralogy and Petrology     Hybrid Journal   (Followers: 2)
Mineria y Geologia     Open Access  
Mining, Metallurgy & Exploration     Hybrid Journal  
Momona Ethiopian Journal of Science     Open Access   (Followers: 5)
Mongolian Geoscientist     Open Access  
Moscow University Geology Bulletin     Hybrid Journal  
Moscow University Physics Bulletin     Hybrid Journal  
Natural Hazards     Hybrid Journal   (Followers: 53)
Natural Hazards and Earth System Sciences (NHESS)     Open Access   (Followers: 10)
Natural Hazards and Earth System Sciences Discssions     Open Access  
Natural Hazards Research     Open Access  
Natural Hazards Review     Full-text available via subscription   (Followers: 16)
Natural Resources & Engineering     Hybrid Journal  
Natural Resources Research     Hybrid Journal   (Followers: 8)
Nature Geoscience     Full-text available via subscription   (Followers: 161)
Neues Jahrbuch für Geologie und Paläontologie - Abhandlungen     Full-text available via subscription   (Followers: 3)
Neues Jahrbuch für Mineralogie - Abhandlungen     Full-text available via subscription   (Followers: 1)
Newsletters on Stratigraphy     Full-text available via subscription   (Followers: 2)
Nonlinear Processes in Geophysics (NPG)     Open Access  
Nonlinear Processes in Geophysics Discussions     Open Access  
Ocean & Coastal Management     Hybrid Journal   (Followers: 62)
Ocean Development & International Law     Hybrid Journal   (Followers: 15)
Ocean Dynamics     Hybrid Journal   (Followers: 6)
Ocean Engineering     Hybrid Journal   (Followers: 6)
Ocean Modelling     Hybrid Journal   (Followers: 12)
Ocean Science (OS)     Open Access   (Followers: 7)
Ocean Science Journal     Hybrid Journal   (Followers: 6)
Open Geospatial Data, Software and Standards     Open Access   (Followers: 3)
Open Journal of Earthquake Research     Open Access   (Followers: 3)
Open Journal of Soil Science     Open Access   (Followers: 9)
Ore and Energy Resource Geology     Open Access  
Ore Geology Reviews     Hybrid Journal   (Followers: 11)
Organic Geochemistry     Hybrid Journal   (Followers: 4)
Osterreichische Wasser- und Abfallwirtschaft     Hybrid Journal  
Paläontologische Zeitschrift     Hybrid Journal   (Followers: 4)
Papers in Palaeontology     Hybrid Journal  
Permafrost and Periglacial Processes     Hybrid Journal   (Followers: 5)
Perspectives of Earth and Space Scientists i     Open Access   (Followers: 1)
Petroleum Geoscience     Hybrid Journal   (Followers: 5)
Petroleum Science     Open Access  
Petrology     Full-text available via subscription   (Followers: 6)
PFG : Journal of Photogrammetry, Remote Sensing and Geoinformation Science     Hybrid Journal   (Followers: 4)
Photogrammetrie - Fernerkundung - Geoinformation     Full-text available via subscription  
Physical Geography     Hybrid Journal   (Followers: 8)
Physical Science International Journal     Open Access  
Physics of Life Reviews     Hybrid Journal   (Followers: 1)
Physics of Metals and Metallography     Hybrid Journal   (Followers: 18)
Physics of Plasmas     Hybrid Journal   (Followers: 10)
Physics of the Earth and Planetary Interiors     Hybrid Journal   (Followers: 34)
Physics of the Solid State     Hybrid Journal   (Followers: 7)

  First | 1 2 3     

Similar Journals
Journal Cover
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 269: Landslide Susceptibility Assessment Considering
           Spatial Agglomeration and Dispersion Characteristics: A Case Study of
           Bijie City in Guizhou Province, China

    • Authors: Kezhen Yao, Saini Yang, Shengnan Wu, Bin Tong
      First page: 269
      Abstract: Landslide susceptibility assessment serves as a critical scientific reference for geohazard control, land use, and sustainable development planning. The existing research has not fully considered the potential impact of the spatial agglomeration and dispersion of landslides on assessments. This issue may cause a systematic evaluation bias when the field investigation data are insufficient, which is common due to limited human resources. Accordingly, this paper proposes two novel strategies, including a clustering algorithm and a preprocessing method, for these two ignored features to strengthen assessments, especially in high-susceptibility regions. Multiple machine learning models are compared in a case study of the city of Bijie (Guizhou Province, China). Then we generate the optimal susceptibility map and conduct two experiments to test the validity of the proposed methods. The primary conclusions of this study are as follows: (1) random forest (RF) was superior to other algorithms in the recognition of high-susceptibility areas and the portrayal of local spatial features; (2) the susceptibility map incorporating spatial feature messages showed a noticeable improvement over the spatial distribution and gradual change of susceptibility, as well as the accurate delineation of critical hazardous areas and the interpretation of historical hazards; and (3) the spatial distribution feature had a significant positive effect on modeling, as the accuracy increased by 5% and 10% after including the spatial agglomeration and dispersion consideration in the RF model, respectively. The benefit of the agglomeration is concentrated in high-susceptibility areas, and our work provides insight to improve the assessment accuracy in these areas, which is critical to risk assessment and prevention activities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-19
      DOI: 10.3390/ijgi11050269
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 270: A Trade-Off Algorithm for Solving p-Center
           Problems with a Graph Convolutional Network

    • Authors: Haojian Liang, Shaohua Wang, Huilai Li, Huichun Ye, Yang Zhong
      First page: 270
      Abstract: The spatial optimization method between combinatorial optimization problems and GIS has many geographical applications. The p-center problem is a classic NP-hard location modeling problem, which has essential applications in many real-world scenarios, such as urban facility locations (ambulances, fire stations, pipelines maintenance centers, police stations, etc.). This study implements two methods to solve this problem: an exact algorithm and an approximate algorithm. Exact algorithms can get the optimal solution to the problem, but they are inefficient and time-consuming. The approximate algorithm can give the sub-optimal solution of the problem in polynomial time, which has high efficiency, but the accuracy of the solution is closely related to the initialization center point. We propose a new paradigm that combines a graph convolution network and greedy algorithm to solve the p-center problem through direct training and realize that the efficiency is faster than the exact algorithm. The accuracy is superior to the heuristic algorithm. We generate a large amount of p-center problems by the Erdos–Renyi graph, which can generate instances in many real problems. Experiments show that our method can compromise between time and accuracy and affect the solution of p-center problems.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-19
      DOI: 10.3390/ijgi11050270
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 271: WaterSmart-GIS: A Web Application of a Data
           Assimilation Model to Support Irrigation Research and Decision Making

    • Authors: Haoteng Zhao, Liping Di, Ziheng Sun
      First page: 271
      Abstract: Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, to stakeholders. The disseminated datasets include both numerical model results of reanalysis and forecasting from HRLDAS (High-Resolution Land Data Assimilation System), and the remote sensing datasets from NASA SMAP (Soil Moisture Active Passive) and MODIS (Moderate-Resolution Imaging Spectroradiometer). The system aims to quickly and easily create a smart, customized irrigation scheduler for individual fields to relieve the burden on farmers and to significantly reduce wasted water, energy, and equipment due to excessive irrigation. The system is prototyped here with an application in Nebraska, demonstrating its ability to collect and deliver information to end-users via the web application, which provides online analytic functionality such as point-based query, spatial statistics, and timeseries query. Systems such as this will play a critical role in the next few decades to sustain agriculture, which faces great challenges from climate change and increased natural disasters.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-19
      DOI: 10.3390/ijgi11050271
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 272: Investigating Relationships between
           Runoff–Erosion Processes and Land Use and Land Cover Using Remote
           Sensing Multiple Gridded Datasets

    • Authors: Cláudia Adriana Bueno da Fonseca, Nadhir Al-Ansari, Richarde Marques da Silva, Celso Augusto Guimarães Santos, Bilel Zerouali, Daniel Bezerra de Oliveira, Ahmed Elbeltagi
      First page: 272
      Abstract: Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-19
      DOI: 10.3390/ijgi11050272
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 273: An Empirical Study on the Effects of Temporal
           Trends in Spatial Patterns on Animated Choropleth Maps

    • Authors: Paweł Cybulski
      First page: 273
      Abstract: Animated cartographic visualization incorporates the concept of geomedia presented in this Special Issue. The presented study aims to examine the effectiveness of spatial pattern and temporal trend recognition on animated choropleth maps. In a controlled laboratory experiment with participants and eye tracking, fifteen animated maps were used to show a different spatial patterns and temporal trends. The participants’ task was to correctly detect the patterns and trends on a choropleth map. The study results show that effective spatial pattern and temporal trend recognition on a choropleth map is related to participants’ visual behavior. Visual attention clustered in the central part of the choropleth map supports effective spatio-temporal relationship recognition. The larger the area covered by the fixation cluster, the higher the probability of correct temporal trend and spatial pattern recognition. However, animated choropleth maps are more suitable for presenting temporal trends than spatial patterns. Understanding the difficulty in the correct recognition of spatio-temporal relationships might be a reason for implementing techniques that support effective visual searches such as highlighting, cartographic redundancy, or interactive tools. For end-users, the presented study reveals the necessity of the application of a specific visual strategy. Focusing on the central part of the map is the most effective strategy for the recognition of spatio-temporal relationships.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-20
      DOI: 10.3390/ijgi11050273
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 274: Managing Inhomogeneity in the Control Point
           Network during Staking Out Cadastral Boundaries in Austria

    • Authors: Valentin Weber, Gerhard Navratil, Franz Blauensteiner
      First page: 274
      Abstract: The coordinate system of the Austrian cadastre is physically realised through control points provided by the national institution for surveying. Due to historical development over the centuries and changes in measurement technologies, inhomogeneities can occur within the local control point network. These inhomogeneities affect the derived boundary point coordinates. When staking out boundary points in an area with inhomogeneous control points, deviations from the boundary marks in the field can occur that exceed the accuracy requirements of the ordinance for surveying. Examples show that a suitable approach to tackle this issue has to be selected on a case-based strategy. Different situations might require different approaches. This needs to be considered in the legal framework to enable cadastral experts to select the optimal approach.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-21
      DOI: 10.3390/ijgi11050274
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 275: Developing Relative Spatial Poverty Index Using
           Integrated Remote Sensing and Geospatial Big Data Approach: A Case Study
           of East Java, Indonesia

    • Authors: Salwa Rizqina Putri, Arie Wahyu Wijayanto, Anjar Dimara Sakti
      First page: 275
      Abstract: Poverty data are usually collected through on-the-ground household-based socioeconomic surveys. Unfortunately, data collection with such conventional methods is expensive, laborious, and time-consuming. Additional information that can describe poverty with better granularity in scope and at lower cost, taking less time to update, is needed to address the limitations of the currently existing official poverty data. Numerous studies have suggested that the poverty proxy indicators are related to economic spatial concentration, infrastructure distribution, land cover, air pollution, and accessibility. However, the existing studies that integrate these potentials by utilizing multi-source remote sensing and geospatial big data are still limited, especially for identifying granular poverty in East Java, Indonesia. Through analysis, we found that the variables that represent the poverty of East Java in 2020 are night-time light intensity (NTL), built-up index (BUI), sulfur dioxide (SO2), point-of-interest (POI) density, and POI distance. In this study, we built a relative spatial poverty index (RSPI) to indicate the spatial poverty distribution at 1.5 km × 1.5 km grids by overlaying those variables, using a multi-scenario weighted sum model. It was found that the use of multi-source remote sensing and big data overlays has good potential to identify poverty using the geographic approach. The obtained RSPI is strongly correlated (Pearson correlation coefficient = 0.71 (p-value = 5.97×10−7) and Spearman rank correlation coefficient = 0.77 (p-value = 1.58×10−8) to the official poverty data, with the best root mean square error (RMSE) of 3.18%. The evaluation of RSPI shows that areas with high RSPI scores are geographically deprived and tend to be sparsely populated with more inadequate accessibility, and vice versa. The advantage of RSPI is that it is better at identifying poverty from a geographical perspective; hence, it can be used to overcome spatial poverty traps.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-24
      DOI: 10.3390/ijgi11050275
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 276: Spatial Variability and Clustering of Quality of
           Life at Local Level: A Geographical Analysis in Athens, Greece

    • Authors: Antigoni Faka, Kleomenis Kalogeropoulos, Thomas Maloutas, Christos Chalkias
      First page: 276
      Abstract: This paper presents a geographical analysis to evaluate urban quality of life in Athens, Greece, and investigate spatial heterogeneity and potential clustering. The urban environment was examined using composite criteria related to natural, built and socioeconomic environment, housing conditions, public services and infrastructures, and cultural and recreational facilities. Each criterion constructed from a set of mappable sub-criteria/variables. Weighted cartographic overlay was implemented to assess the overall urban quality of life of each spatial unit, based on the importance the residents of the area attributed to each criterion. High levels of quality of life were revealed in the eastern neighborhoods of the municipality, whereas low levels were noticed mainly in the western neighborhoods. The results of the study were validated using the perceived quality of life of the study area’s residents, resulting in substantial agreement. Finally, after spatial autocorrelation analysis, significant clustering of urban quality of life in Athens was revealed. The quality-of-life assessment and mapping at a local scale are efficient tools, contributing to better decision making and policy making.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-26
      DOI: 10.3390/ijgi11050276
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 277: Deep Learning-Assisted Smart Process Planning,
           Robotic Wireless Sensor Networks, and Geospatial Big Data Management
           Algorithms in the Internet of Manufacturing Things

    • Authors: George Lăzăroiu, Mihai Andronie, Mariana Iatagan, Marinela Geamănu, Roxana Ștefănescu, Irina Dijmărescu
      First page: 277
      Abstract: The purpose of our systematic review is to examine the recently published literature on the Internet of Manufacturing Things (IoMT), and integrate the insights it configures on deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Throughout October 2021 and January 2022, a quantitative literature review of aggregators such as ProQuest, Scopus, and the Web of Science was carried out, with search terms including “deep learning-assisted smart process planning + IoMT”, “robotic wireless sensor networks + IoMT”, and “geospatial big data management algorithms + IoMT”. As the analyzed research was published between 2018 and 2022, only 346 sources satisfied the eligibility criteria. A Shiny app was leveraged for the PRISMA flow diagram to comprise evidence-based collected and handled data. Major difficulties and challenges comprised identification of robust correlations among the inspected topics, but focusing on the most recent and relevant sources and deploying screening and quality assessment tools such as the Appraisal Tool for Cross-Sectional Studies, Dedoose, Distiller SR, the Mixed Method Appraisal Tool, and the Systematic Review Data Repository we integrated the core outcomes related to the IoMT. Future research should investigate dynamic scheduling and production execution systems advanced by deep learning-assisted smart process planning, data-driven decision making, and robotic wireless sensor networks.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-27
      DOI: 10.3390/ijgi11050277
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 278: Spatiotemporal Evolution of the Urban Thermal
           Environment Effect and Its Influencing Factors: A Case Study of Beijing,
           China

    • Authors: Ziqi Ren, Zhe Li, Feng Wu, Huiqiang Ma, Zhanjun Xu, Wei Jiang, Shaohua Wang, Jun Yang
      First page: 278
      Abstract: Rapid urbanization has led to significant changes in land surface temperature (LST), which in turn affect the urban thermal environment effect and the health of residents. Exploring the causes of the urban thermal environment effect will provide guidance for promoting sustainable urban development. The spatiotemporal evolution of the urban thermal environment effect within the sixth ring road of Beijing was analyzed by inversion of remote sensing data to obtain the LST in 2004, 2009, 2014, and 2019. In addition, based on multivariate spatial data, we applied the standard deviation ellipse (SDE), spatial principal component analysis (PCA), and other methods to analyze and identify the relationships between the urban thermal environment effect and its influencing factors. The results show that from 2004 to 2019, the spatial distribution of urban development and LST within the sixth ring road of Beijing were closely related, the heat island area showed a small increasing trend, and differences in the thermal environment effect between different administrative regions in different periods were obvious. The main factors affecting the urban thermal environment effect were urban construction intensity, vegetation and water bodies, socioeconomic activities, and geomorphology. It is noteworthy that human factors had a greater impact than natural factors. Among them, the positive effect of the normalized difference impervious surface index (NDBBI) and the negative effect of the fractional vegetation cover (FVC) were the most prominent. This study provides theoretical support for mitigating the urban thermal environment effect and promoting sustainable urban development.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-27
      DOI: 10.3390/ijgi11050278
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 279: Spatial Pattern of the Walkability Index, Walk
           Score and Walk Score Modification for Elderly

    • Authors: Jiri Horak, Pavel Kukuliac, Petra Maresova, Lucie Orlikova, Ondrej Kolodziej
      First page: 279
      Abstract: Contemporary cities require excellent walking conditions to support human physical activity, increase humans’ well-being, reduce traffic, and create a healthy urban environment. Various indicators and metrics exist to evaluate walking conditions. To evaluate the spatial pattern of objective-based indicators, two popular indices were selected—the Walkability Index (WAI), representing environmental-based indicators, and Walk Score (WS), which applies an accessibility-based approach. Both indicators were evaluated using adequate spatial units (circle buffers with radii from 400 m to 2414 m) in two Czech cities. A new software tool was developed for the calculation of WS using OSM data and freely available network services. The new variant of WS was specifically designed for the elderly. Differing gait speeds, and variable settings of targets and their weights enabled the adaptation of WS to local conditions and personal needs. WAI and WS demonstrated different spatial pattern where WAI is better used for smaller radii (up to approx. 800 m) and WS for larger radii (starting from 800 m). The assessment of WS for both cities indicates that approx. 40% of inhabitants live in unsatisfactory walking conditions. A sensitivity analysis discovered the major influences of gait speed and the β coefficient on the walkability assessment.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-27
      DOI: 10.3390/ijgi11050279
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 280: Geographic Approach: Identifying Relatively
           Stable Tibetan Dialect and Subdialect Area Boundaries

    • Authors: Mingyuan Duan, Shangyi Zhou
      First page: 280
      Abstract: Updating dialect maps requires extensive language surveys. Geographic methods can be applied to identify relatively stable boundaries of dialect and subdialect areas, allowing language surveys to focus on boundaries that may change and thereby reduce survey costs. Certain scholars have pointed out that the watershed boundary can be employed as the boundary of Tibetan dialect areas. This paper adds that the lowest-grade road breakpoint line and no-man’s-land boundary can also be used as essential indicators for determining stable (sub)dialect area boundaries. Combined with the revised First Law of Geography and the method of superposition analysis of geographic elements, this study identifies indicators that affect the stability of the Tibetan (sub)dialect area boundaries and evaluates the stability of each boundary segment. Due to the particularity of the study area, most Qinghai–Tibet Plateau (Chinese part) (sub)dialect area boundaries are stable. In addition, boundary inaccuracies caused by defects in the distribution of language survey samples can be identified by geographic approaches.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-27
      DOI: 10.3390/ijgi11050280
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 281: Towards a Core Set of Landscape Metrics of Urban
           Land Use in Wuhan, China

    • Authors: Shiwei Shao, Mengting Yu, Yimin Huang, Yiheng Wang, Jing Tian, Chang Ren
      First page: 281
      Abstract: In this study, we investigate the urban landscape patterns in Wuhan, China based on the land use data in the vector format. Using the approach of landscape metric analysis, we calculate forty-four vector-based landscape metrics and then reduce redundant ones through a combination of Spearman correlation analysis and factor analysis, in order to extract a core set of characterizing landscape metrics. We find that the urban landscape can be depicted by six factors including the overall shape and diversity, mean proximity, overall area variation, fragmentation variation, elongation variation, and mean shape complexity. After analyzing typical patterns indicated by the core metrics and the spatial distribution of land use patterns, we compare our findings with other studies and discuss how the core metrics coincide and differ.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050281
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 282: Assessing Street Space Quality Using Street View
           Imagery and Function-Driven Method: The Case of Xiamen, China

    • Authors: Moyang Wang, Yijun He, Huan Meng, Ye Zhang, Bao Zhu, Joseph Mango, Xiang Li
      First page: 282
      Abstract: Street space quality assessment refers to the extraction and appropriate evaluation of the space quality information of urban streets, which is usually employed to improve the quality of urban planning and management. Compared to traditional approaches relying on expert knowledge, the advances of big data collection and analysis technologies provide an alternative for assessing street space more precisely. With street view imagery (SVI), points of interest (POI) and comment data from social media, this study evaluates street space quality from the perspective of exploring and discussing the relationship among street vitality, service facilities and built environment. Firstly, a transfer-learning-based framework is employed for SVI semantic segmentation to quantify the street built environment. Then, we use POI data to identify different urban functions that streets serve, and comment data are utilized to investigate urban vitality composition and integrate it with different urban functions associated with streets. Finally, a function-driven street space quality assessment approach is established. To examine its applicability and performance, the proposed method is experimented with data from part area in Xiamen, China. The output is compared to results based on expert opinion using the correlation analysis method. Results show that the proposed assessment approach designed in this study is in accordance with the validation data, with the overall R2 value being greater than 0.6. In particular, the proposed method shows better performance in scenic land and mixed functional streets with R2 value being greater than 0.8. This method is expected to be an efficient tool for discovering problems and optimizing urban planning and management.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050282
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 283: Identifying Multiple Scales of Spatial
           Heterogeneity in Housing Prices Based on Eigenvector Spatial Filtering
           Approaches

    • Authors: Zhan Peng, Ryo Inoue
      First page: 283
      Abstract: Interest in studying the urban real estate market, especially in investigating the relationship between house prices and related housing characteristics, is rapidly growing. However, this increasing attention is handicapped by a limited consideration of the multi-scale spatial heterogeneity in these relationships. This study uses the rental price data of 72,466 apartments in the Tokyo metropolitan area to examine spatial heterogeneity in the real estate market at multiple spatial scales. Within the framework of spatially varying coefficient (SVC) modeling, we utilized a random effect eigenvector spatial filtering-based SVC (RE-ESF-SVC) model, an approach not previously employed in real estate studies, and compared it with the traditional ESF-SVC model, which has no random effects. Our results show that: (1) except for one housing characteristic that impacts prices consistently throughout the Tokyo metropolitan area, relationships between other characteristics and prices vary from local to global spatial scales; (2) because of the utilization of random effects, RE-ESF-SVC has the unique advantage of making estimations flexibly while maintaining a high performance.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050283
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 284: Development of Spatial Model for Food Security
           Prediction Using Remote Sensing Data in West Java, Indonesia

    • Authors: Riantini Virtriana, Akhmad Riqqi, Tania Septi Anggraini, Kamal Nur Fauzan, Kalingga Titon Nur Ihsan, Fatwa Cahya Mustika, Deni Suwardhi, Agung Budi Harto, Anjar Dimara Sakti, Albertus Deliar, Budhy Soeksmantono, Ketut Wikantika
      First page: 284
      Abstract: The food crisis is a problem that the world will face. The availability of growing areas that continues to decrease with the increase in food demand will result in a food crisis in the future. Good planning is needed to deal with future food crises. The absence of studies on the development of spatial models in estimating an area’s future food status has made planning for handling the food crisis suboptimal. This study aims to predict food security by integrating the availability of paddy fields with environmental factors to determine the food status in West Java Province. Food status modeling is done by integrating land cover, population, paddy fields productivity, and identifying the influence of environmental factors. The land cover prediction will be developed using the CA-Markov model. Meanwhile, to identify the influence of environmental factors, multivariable linear regression (MLR) was used with environmental factors from remote sensing observations. The data used are in the form of the NDDI (Normalized Difference Drought Index), NDVI (Normalized Difference Vegetation Index), land surface temperature (LST), soil moisture, precipitation, altitude, and slopes. The land cover prediction has an overall accuracy of up to 93%. From the food status in 2005, the flow of food energy in West Java was still able to cover the food needs and obtain an energy surplus of 6.103 Mcal. On the other hand, the prediction of the food energy flow from the food status in 2030 will not cover food needs and obtain an energy deficit of up to 13,996,292.42 Mcal. From the MLR results, seven environmental factors affect the productivity of paddy fields, with the determination coefficient reaching 50.6%. Thus, predicting the availability of paddy production will be more specific if it integrates environmental factors. With this study, it is hoped that it can be used as planning material for mitigating food crises in the future.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050284
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 285: Evaluation of Urban Flood Resilience Enhancement
           Strategies—A Case Study in Jingdezhen City under 20-Year Return
           Period Precipitation Scenario

    • Authors: Jingxuan Zhang, Huimin Wang, Jing Huang, Dianchen Sun, Gaofeng Liu
      First page: 285
      Abstract: Various flood resilience enhancement measures have been proposed to deal with the growing problem of urban flooding. However, there is a lack of evaluation about the applicability of these measures at a community scale. This paper investigates the effects of two types of flood resilience enhancement measures: engineering measures and adaptive measures, in order to explore their effectiveness in different flood-prone communities. A community-scale oriented flood resilience assessment method is used to assess the impact of different types of measures. A case study is applied in three communities that suffer from waterlogging problems in Jingdezhen city, China. Results show that there are spatial differences of flood resilience in three flood-prone communities. Future scenarios present a poorer performance in flood resilience compared to current scenarios due to the effects of urbanization and human activities. Engineering measures are suitable for the old communities with high-density residential areas when sitting alongside the river, for example the communities of Fuliang and Zhushan. On the other hand, adaptive measures exhibit more efficiency in improving flood resilience in all communities, especially effective for the new city town Changjiang where engineering measures are nearly saturated. The findings can help local governments develop appropriate flood resilience enhancement strategies for different types of communities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050285
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 286: GIScience and Historical Visual Sources: A
           Promising Look at Past Scenarios and Sceneries

    • Authors: Motti Zohar
      First page: 286
      Abstract: The discipline of historical geography evolved rapidly in the 20th century [...]
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050286
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 287: A New Graph-Based Fractality Index to
           Characterize Complexity of Urban Form

    • Authors: Lei Ma, Stefan Seipel, Sven Anders Brandt, Ding Ma
      First page: 287
      Abstract: Examining the complexity of urban form may help to understand human behavior in urban spaces, thereby improving the conditions for sustainable design of future cities. Metrics, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index have been proposed to measure this complexity. However, as these indicators are statistical rather than spatial, they result in an inability to characterize the spatial complexity of urban forms, such as building footprints. To overcome this problem, this paper proposes a graph-based fractality index (GFI), which is based on a hybrid of fractal theory and deep learning techniques. First, to quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Next, building footprints in London were used to test the method, where the results showed that the proposed framework performed better than the traditional indices, i.e., the index is capable of differentiating complex patterns. Another advantage is that it seems to assure that the trained deep learning is objective and not affected by potential biases in empirically selected training datasets Furthermore, the possibility to connect fractal theory and deep learning techniques on complexity issues opens up new possibilities for data-driven GIS science.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050287
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 288: A Field Investigation on Gully Erosion and
           Implications for Changes in Sediment Delivery Processes in Some
           Tributaries of the Upper Yellow River in China

    • Authors: Hui Yang, Changxing Shi, Jiansheng Cao
      First page: 288
      Abstract: Erosion and sediment delivery have been undergoing considerable variations in many catchments worldwide owing to climate change and human interference. Monitoring on-site erosion and sediment deposition is crucial for understanding the processes and mechanisms of changes in sediment yield from the catchments. The Ten Kongduis (kongdui is the transliteration of ephemeral creeks in Mongolian) are 10 tributaries of the upper Yellow River. Severe erosion in the upstream hills and gullies and huge aeolian sand input in the middle reaches had made the 10 tributaries one of the main sediment sources of the Yellow River, but the gauged sediment discharge of the tributaries has decreased obviously in recent years. In order to find out the mechanisms of changes in the sediment load of the tributaries, topographic surveys of four typical gullies in 3 of the 10 tributaries were made repeatedly in the field with the terrestrial laser scanning (TLS) technique. The results show that all the monitored gullies were silted with a mean net rate of 587–800 g/m2 from November 2014 to June 2015 and eroded by a mean net rate of 185–24,800 g/m2 from June to November 2015. The monitoring data suggest that the mechanism of interseasonal and interannual sediment storage and release existed in the processes of sediment delivery in the kongduis. The contrast of the low gauged sediment load of the kongduis in recent years against the high surveyed gully erosion indicates the reduction in their sediment delivery efficiency, which can be attributed to the diminution in hyperconcentrated flows caused mainly by the increase in vegetation coverage on slopes and partly by construction of sediment-trapping dams in gullies.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-28
      DOI: 10.3390/ijgi11050288
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 289: Wildland Fires in the Czech
           Republic—Review of Data Spanning 20 Years

    • Authors: Pavel Špulák
      First page: 289
      Abstract: The following article deals with more than 20 years of historical wildland fire data from the Czech Republic, logged in the databases of the operational centers of the Fire and Rescue Service of the Czech Republic (FRS of CR). First, the definition of the term wildland fire is introduced. After that, the locations of wildland fires are discussed, from the point of view of their introduction into the information systems. Next, as the FRS of CR is organized on a regional basis, the number of wildland fires is analyzed regionally. On the basis of this analysis, some advice concerning the preparation for and prevention of wildland fires is provided—for example, focusing fire prevention campaigns in regions where the wildland fire incidence per inhabitant is high, planning aerial firefighting asset coverage with respect to the occurrence of wildland fires, or deploying the necessary fire suppression equipment according to the dominant wildland fire fuel type. Finally, questions concerning the homogeneity of groups of wildland fires which naturally emerge during the process of selection from the emergency database are discussed.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-29
      DOI: 10.3390/ijgi11050289
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 290: GeoSDVA: A Semi-Supervised Dirichlet Variational
           Autoencoder Model for Transportation Mode Identification

    • Authors: Xiaoxi Zhang, Yuan Gao, Xin Wang, Jun Feng, Yan Shi
      First page: 290
      Abstract: Inferring the transportation modes of travelers is an essential part of intelligent transportation systems. With the development of mobile services, it is easy to effectively obtain massive location readings of travelers with GPS-enabled smart devices, such as smartphones. These readings make understanding human activities very convenient. Therefore, how to automatically infer transportation modes from these massive readings has come into the spotlight. The existing methods for transportation mode identification are usually based on supervised learning. However, the raw GPS readings do not contain any labels, and it is expensive and time-consuming to annotate sufficient samples for training supervised learning-based models. In addition, not enough attention is paid to the problem that GPS readings collected in urban areas are affected by surrounding geographic information (e.g., the level of road transportation or the distribution of stations). To solve this problem, a geographic information-fused semi-supervised method based on a Dirichlet variational autoencoder, named GeoSDVA, is proposed in this paper for transportation mode identification. GeoSDVA first fuses the motion features of the GPS trajectories with the nearby geographic information. Then, both labeled and unlabeled trajectories are used to train the semi-supervised model based on the Dirichlet variational autoencoder architecture for transportation mode identification. Experiments on three real GPS trajectory datasets demonstrate that GeoSDVA can train an excellent transportation mode identification model with only a few labeled trajectories.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-29
      DOI: 10.3390/ijgi11050290
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 291: Preservation of Villages in Central Italy:
           Geomatic Techniques’Integration and GIS Strategies for the
           Post-Earthquake Assessment

    • Authors: Fabio Piccinini, Alban Gorreja, Francesco Di Stefano, Roberto Pierdicca, Luis Javier Sanchez Aparicio, Eva Savina Malinverni
      First page: 291
      Abstract: Historical villages represent a highly vulnerable cultural heritage; their preservation can be ensured thanks to technological innovations in the field of geomatics and information systems. Among these, Geographical Information Systems (GISs) allow exploiting heterogeneous data for efficient vulnerability assessment, in terms of both time and usability. Geometric attributes, which currently are mainly inferred by visual inspections, can be extrapolated from data obtained by geomatic technologies. Furthermore, the integration with non-metric data ensures a more complete description of the post-seismic risk thematic mapping. In this paper, a high-performance information system for small urban realities, such as historical villages, is described, starting from the 3D survey obtained through the integrated management of recent innovative geomatic sensors, such as Unmanned Aerial Vehicles (UAVs), Terrestrial Laser Scanners (TLSs), and 360º images. The results show that the proposed strategy of the automatic extraction of the parameters from the GIS can be generalized to other case studies, thus representing a straightforward method to enhance the decision-making of public administrations. Moreover, this work confirms the importance of managing heterogeneous geospatial data to speed up the vulnerability assessment process. The final result, in fact, is an information system that can be used for every village where data have been acquired in a similar way. This information could be used in the field by means of a GIS app that allows updating the geospatial database, improving the work of technicians. This approach was validated in Gabbiano(Pieve Torina), a village in Central Italy affected by earthquakes in 2016 and 2017.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-30
      DOI: 10.3390/ijgi11050291
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 292: Mapping for Awareness of Indigenous Stories

    • Authors: Stephanie Pyne, Melissa Castron, Annita Parish, Peter Farrell, Shawn Johnston
      First page: 292
      Abstract: Joseph Kerski has identified five converging global trends—geo-awareness, geo-enablement, geotechnologies, citizen science, and storytelling—which contribute to the increased relevance of geography for education and society. While these trends are discussed by Kerski in the context of the proliferating significance of geography in teaching and education, they also provide a useful lens for considering the increasing ubiquity of critical approaches to cartography both in general and in the context of teaching and education, where mapping can include participatory collaborations with individuals from a variety of knowledge communities and extend to the mapping of experiences, emotions, and Indigenous perspectives. In this paper, we consider these trends and related ideas such as Kerski’s “geoliteracy” and metaliteracy in light of some relatively current examples and in light of the evolution of research and teaching linked with a series of interrelated map-based projects and courses that take a multidimensional approach to teaching and learning about the Residential Schools Legacy in Canada.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-30
      DOI: 10.3390/ijgi11050292
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 293: Predicting Poverty Using Geospatial Data in
           Thailand

    • Authors: Nattapong Puttanapong, Arturo Martinez Martinez, Joseph Albert Nino Bulan, Mildred Addawe, Ron Lester Durante, Marymell Martillan
      First page: 293
      Abstract: Poverty statistics are conventionally compiled using data from socioeconomic surveys. This study examines an alternative approach to estimating poverty by investigating whether readily available geospatial data can accurately predict the spatial distribution of poverty in Thailand. In particular, the geospatial data examined in this study include the intensity of night-time light (NTL), land cover, vegetation index, land surface temperature, built-up areas, and points of interest. The study also compares the predictive performance of various econometric and machine-learning methods such as generalized least squares, neural network, random forest, and support-vector regression. Results suggest that the intensity of NTL and other variables that approximate population density are highly associated with the proportion of an area’s population that are living in poverty. The random forest technique yielded the highest level of prediction accuracy among the methods considered in this study, primarily due to its capability to fit complex association structures even with small-to-medium-sized datasets. This obtained result suggests the potential applications of using publicly accessible geospatial data and machine-learning methods for timely monitoring of the poverty distribution. Moving forward, additional studies are needed to improve the predictive power and investigate the temporal stability of the relationships observed.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-30
      DOI: 10.3390/ijgi11050293
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 294: An Integrated Graph Model for
           Spatial–Temporal Urban Crime Prediction Based on Attention Mechanism
           

    • Authors: Miaomiao Hou, Xiaofeng Hu, Jitao Cai, Xinge Han, Shuaiqi Yuan
      First page: 294
      Abstract: Crime issues have been attracting widespread attention from citizens and managers of cities due to their unexpected and massive consequences. As an effective technique to prevent and control urban crimes, the data-driven spatial–temporal crime prediction can provide reasonable estimations associated with the crime hotspot. It thus contributes to the decision making of relevant departments under limited resources, as well as promotes civilized urban development. However, the deficient performance in the aspect of the daily spatial–temporal crime prediction at the urban-district-scale needs to be further resolved, which serves as a critical role in police resource allocation. In order to establish a practical and effective daily crime prediction framework at an urban police-district-scale, an “online” integrated graph model is proposed. A residual neural network (ResNet), graph convolutional network (GCN), and long short-term memory (LSTM) are integrated with an attention mechanism in the proposed model to extract and fuse the spatial–temporal features, topological graphs, and external features. Then, the “online” integrated graph model is validated by daily theft and assault data within 22 police districts in the city of Chicago, US from 1 January 2015 to 7 January 2020. Additionally, several widely used baseline models, including autoregressive integrated moving average (ARIMA), ridge regression, support vector regression (SVR), random forest, extreme gradient boosting (XGBoost), LSTM, convolutional neural network (CNN), and Conv-LSTM models, are compared with the proposed model from a quantitative point of view by using the same dataset. The results show that the predicted spatial–temporal patterns by the proposed model are close to the observations. Moreover, the integrated graph model performs more accurately since it has lower average values of the mean absolute error (MAE) and root mean square error (RMSE) than the other eight models. Therefore, the proposed model has great potential in supporting the decision making for the police in the fields of patrolling and investigation, as well as resource allocation.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-30
      DOI: 10.3390/ijgi11050294
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 295: Modeling Health Seeking Behavior Based on
           Location-Based Service Data: A Case Study of Shenzhen, China

    • Authors: Wei Hu, Lin Li, Mo Su
      First page: 295
      Abstract: Understanding residents’ health seeking behavior is crucial for the planning and utilization of healthcare resources. With the support of emerging location-based service (LBS) data, this study proposes a framework for inferring health seeking trips, measuring observed spatial accessibility to healthcare, and interpreting the determinants of health seeking behavior. Taking Shenzhen, China as a case study, a supply–demand ratio calculation method based on observed data is developed to explore basic patterns of health seeking, while health seeking behavior is described using a spatial analysis framework based on the Huff model. A total of 95,379 health seeking trips were identified, and their analysis revealed obvious differences between observed and potential spatial accessibility. In addition to the traditional distance decay effect and number of doctors, the results showed health seeking behavior to be determined by hospital characteristics such as hospital scale, service quality, and popularity. Furthermore, this study also identified differences in health seeking behavior between subgroups with different ages, incomes, and education levels. The findings highlight the need to incorporate actual health seeking behavior when measuring the spatial accessibility of healthcare and planning healthcare resources. The framework and methods proposed in this study can be applied to other contexts and other types of public facilities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-02
      DOI: 10.3390/ijgi11050295
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 296: Exploring the Evolution of the Accessibility of
           Educational Facilities and Its Influencing Factors in Mountainous Areas: A
           Case Study of the Rocky Desertification Area in Yunnan, Guangxi, and
           Guizhou

    • Authors: Lingling Yao, Minjuan Lv, Tao Li, Donghua Wang, Xiaoshu Cao
      First page: 296
      Abstract: The optimal allocation of educational resources has been a hot issue, and exploring the accessibility of educational facilities in poor mountainous areas helps to reasonably plan the layout of educational facilities and promote the balanced development of education. Taking the rocky desertification area in Yunnan, Guangxi, and Guizhou (YGGRD) as the study area, based on the POI data of educational facilities in the YGGRD in 2000, 2010 and 2019, this study explored the evolution of the accessibility of educational facilities in the YGGRD through raster accessibility. And the influencing factors were analyzed by the ordinary least square method (OLS) and geographically weighted regression model (GWR), and evaluated the model through cross validation. The results show that the overall accessibility of educational facilities improved significantly from 2000 to 2019. Educational facilities mainly have good accessibility and average accessibility. Poor accessibility areas are concentrated in the interprovincial border regions, and the boundary effect is significant. County accessibility, population density and rural per capita disposable income have a great impact on the accessibility of educational facilities in the YGGRD. It is suggested to strengthen the construction of educational facilities in the interprovincial border regions, relocate and integrate villages, and improve the education quality of township schools to improve the supply of rural educational resources.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-03
      DOI: 10.3390/ijgi11050296
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 297: A New Urban Space Analysis Method Based on Space
           Syntax and Geographic Information System Using Multisource Data

    • Authors: Zhaolian Xing, Weimin Guo
      First page: 297
      Abstract: With large-scale urban demolition, the spatial pattern of the urban area in many cities has been destroyed, leading to the loss of urban regional identity; therefore, these urban spaces need to be urgently studied and protected. Previous studies on the spatial pattern of urban areas focused on spatial morphology or urban texture. However, due to difficulties in obtaining field survey data, such studies cannot comprehensively analyze the space; thus, the proposed conservation strategies are also more one-sided. In order to study the urban space more scientifically and systematically, and to propose a more operable spatial conservation strategy, this paper conducts a new urban space analysis method based on space syntax and the geographic information system using multisource data. With the help of software such as Depthmap and ArcGIS, as well as theories and methods such as space syntax and regression analysis, this article conducted a visual and quantitative analysis of the spatial information data such as integration of urban road networks, building height, architectural style, points of interest, number of lanes, and maximum road speed. Taking the old city of Wuxi as an example, the method’s feasibility was verified. The regression model analysis revealed that, when the integration of the area was higher, the buildings distributed around were multilane, fast lane, modern buildings, taller buildings, commercial buildings, and vice versa, which gives a scientific basis for the proposed strategy of creating regional characteristics of urban space. This new analysis method of urban space is of great significance for the study of urban problems, the exploration of urban characteristics, and the proposal of urban strategies.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-04
      DOI: 10.3390/ijgi11050297
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 298: Revising Cadastral Data on Land Boundaries Using
           Deep Learning in Image-Based Mapping

    • Authors: Bujar Fetai, Dejan Grigillo, Anka Lisec
      First page: 298
      Abstract: One of the main concerns of land administration in developed countries is to keep the cadastral system up to date. The goal of this research was to develop an approach to detect visible land boundaries and revise existing cadastral data using deep learning. The convolutional neural network (CNN), based on a modified architecture, was trained using the Berkeley segmentation data set 500 (BSDS500) available online. This dataset is known for edge and boundary detection. The model was tested in two rural areas in Slovenia. The results were evaluated using recall, precision, and the F1 score—as a more appropriate method for unbalanced classes. In terms of detection quality, balanced recall and precision resulted in F1 scores of 0.60 and 0.54 for Ponova vas and Odranci, respectively. With lower recall (completeness), the model was able to predict the boundaries with a precision (correctness) of 0.71 and 0.61. When the cadastral data were revised, the low values were interpreted to mean that the lower the recall, the greater the need to update the existing cadastral data. In the case of Ponova vas, the recall value was less than 0.1, which means that the boundaries did not overlap. In Odranci, 21% of the predicted and cadastral boundaries overlapped. Since the direction of the lines was not a problem, the low recall value (0.21) was mainly due to overly fragmented plots. Overall, the automatic methods are faster (once the model is trained) but less accurate than the manual methods. For a rapid revision of existing cadastral boundaries, an automatic approach is certainly desirable for many national mapping and cadastral agencies, especially in developed countries.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-04
      DOI: 10.3390/ijgi11050298
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 299: Digital Soil Mapping of Soil Organic Matter with
           Deep Learning Algorithms

    • Authors: Pengyuan Zeng, Xuan Song, Huan Yang, Ning Wei, Liping Du
      First page: 299
      Abstract: Digital soil mapping has emerged as a new method to describe the spatial distribution of soils economically and efficiently. In this study, a lightweight soil organic matter (SOM) mapping method based on a deep residual network, which we call LSM-ResNet, is proposed to make accurate predictions with background covariates. ResNet not only integrates spatial background information around the observed environmental covariates, but also reduces problems such as information loss, which undermines the integrity of information and reduces prediction uncertainty. To train the model, rectified linear units, mean squared error, and adaptive momentum estimation were used as the activation function, loss/cost function, and optimizer, respectively. The method was tested with Landsat5, the meteorological data from WorldClim, and the 1602 sampling points set from Xinxiang, China. The performance of the proposed LSM-ResNet was compared to a traditional machine learning algorithm, the random forest (RF) algorithm, and a training set (80%) and a test set (20%) were created to test both models. The results showed that the LSM-ResNet (RMSE = 6.40, R2 = 0.51) model outperformed the RF model in both the roots mean square error (RMSE) and coefficient of determination (R2), and the training accuracy was significantly improved compared to RF (RMSE = 6.81, R2 = 0.46). The trained LSM-ResNet model was used for SOM prediction in Xinxiang, a district of plain terrain in China. The prediction maps can be deemed an accurate reflection of the spatial variability of the SOM distribution.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-06
      DOI: 10.3390/ijgi11050299
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 300: How Has the Recent Climate Change Affected the
           Spatiotemporal Variation of Reference Evapotranspiration in a Climate
           Transitional Zone of Eastern China'

    • Authors: Meng Li, Ronghao Chu, Xiuzhu Sha, Abu Reza Md. Towfiqul Islam, Yuelin Jiang, Shuanghe Shen
      First page: 300
      Abstract: Reference evapotranspiration (ET0) is essential for agricultural production and crop water management. The recent climate change affecting the spatiotemporal variation of ET0 in eastern China continues to still be less understood. For this purpose, the latest observed data from 77 meteorological stations in Anhui province were utilized to determine the spatiotemporal variations of ET0 by the use of the Penman–Monteith FAO 56 (PMF-56) model. Furthermore, the Theil–Sen estimator and the Mann–Kendall (M–K) test were adopted to analyze the trends of ET0 and meteorological factors. Moreover, the differential method was employed to explore the sensitivity of ET0 to meteorological factors and the contributions of meteorological factors to ET0 trends. Results show that the ET0 decreased significantly before 1990, and then increased slowly. The ET0 is commonly higher in the north and lower in the south. ET0 is most sensitive to relative humidity (RH), except in summer. However, in summer, net radiation (Rn) is the most sensitive factor. During 1961–1990, Rn was the leading factor annually, during the growing season and summer, while wind speed (u2) played a leading role in others. All meteorological factors provide negative contributions to ET0 trends, which ultimately lead to decreasing ET0 trends. During 1991–2019, the leading factor of ET0 trends changed to the mean temperature (Ta) annually, during the growing season, spring and summer, and then to Rn in others. Overall, the negative contributions from u2 and Rn cannot offset the positive contributions from Ta and RH, which ultimately lead to slow upward ET0 trends. The dramatic drop in the amount of u2 that contributes to the changes in ET0 in Region III is also worth noting.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-06
      DOI: 10.3390/ijgi11050300
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 301: Spatio-Temporal Monitoring of Atmospheric
           Pollutants Using Earth Observation Sentinel 5P TROPOMI Data: Impact of
           Stubble Burning a Case Study

    • Authors: Neeraj K. Maurya, Prem Chandra Pandey, Subhadip Sarkar, Rajesh Kumar, Prashant K. Srivastava
      First page: 301
      Abstract: The problems of atmospheric pollutants are causing significant concern across the globe and in India. The aggravated level of atmospheric pollutants in the surrounding environment poses serious threats to normal living conditions by deteriorating air quality and causing adverse health impacts. Pollutant concentration increases during harvesting seasons of Kharif/Rabi due to stubble burning and is aggravated by other points or mobile sources. The present study is intended to monitor the spatio-temporal variation of the major atmospheric pollutants using Sentinel-5P TROPOMI data through cloud computing. Land Use/Land Cover (LULC-categorization or classification of human activities and natural coverage on the landscape) was utilised to extract the agricultural area in the study site. It involves the cloud computing of MOD64A1 (MODIS Burned monthly gridded data) and Sentinel-5P TROPOMI (S5P Tropomi) data for major atmospheric pollutants, such as CH4, NO2, SOX, CO, aerosol, and HCHO. The burned area output provided information regarding the stubble burning period, which has seen post-harvesting agricultural residue burning after Kharif crop harvesting (i.e., rice from April to June) and Rabi crop harvesting (i.e., wheat from September to November). The long duration of stubble burning is due to variation in farmers’ harvesting and burning stubble/biomass remains in the field for successive crops. This period was used as criteria for considering the cloud computing of the Sentinel-5P TROPOMI data for atmospheric pollutants concentration in the study site. The results showed a significant increase in CH4, SO2, SOX, CO, and aerosol concentration during the AMJ months (stubble burning of Rabi crops) and OND months (stubble burning of Kharif crops) of each year. The results are validated with the ground control station data for PM2.5/PM10. and patterns of precipitation and temperature-gridded datasets. The trajectory frequency for air mass movement using the HYSPLIT model showed that the highest frequency and concentration were observed during OND months, followed by the AMJ months of each year (2018, 2019, 2020, and 2021). This study supports the role and robustness of Earth observation Sentinel-5P TROPOMI to monitor and evaluate air quality and pollutants distribution.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-08
      DOI: 10.3390/ijgi11050301
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 302: Geographical Determinants of Regional Retail
           Sales: Evidence from 12,500 Retail Shops in Qiannan County, China

    • Authors: Wei Wang, Luyao Wang, Xu Wang, Yankun Wang
      First page: 302
      Abstract: The rapid development of the Chinese economy has stimulated consumer demand and brought huge opportunities for the retail industry. Previous studies have emphasized the importance of estimating regional consumption potentiality. However, the determinants of retail sales are yet to be systematically studied, especially at the micro level. As a result, the realization of sustainable development goals in the retail industry is restricted. In this paper, we studied the determinants of retail sales from two aspects—location-based socioeconomic factors and spatial competition between shops. Using 12,500 retail shops as our sample and by adopting a grid-division strategy, we found that regional retail sales can be positively impacted by nearby population, road length, and most non-commercial points of interest (POIs). By contrast, the number of other commercial facilities, such as catering facilities and shopping malls, and the area of geographic barriers often caused negative impacts on retail sales. As to the competition effects, we found that the isolation and decentralization of shops in one area have a marginally positive effect on sales performance within a threshold distance of 226.19 m for a central grid and a threshold distance of 514.85 m for surrounding grids, respectively. This study explores the determinants of micro-level retail sales and provides decision makers with practical and realistic approaches for generating better site selection and marketing strategies, thus realizing the sustainable development goals of the retail industry.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-09
      DOI: 10.3390/ijgi11050302
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 303: Quantifying Urban Expansion from the Perspective
           of Geographic Data: A Case Study of Guangzhou, China

    • Authors: Qingyao Huang, Yihua Liu, Chengjing Chen
      First page: 303
      Abstract: Understanding and quantifying urban expansion is critical to urban management and urban planning. The accurate delineation of built-up areas (BUAs) is the foundation for quantifying urban expansion. To quantify urban expansion simply and efficiently, we proposed a method for delineating BUAs using geographic data, taking Guangzhou as the study area. First, Guangzhou’s natural cities (NCs) in 2014 and 2020 were derived from the point of interest (POI) data. Second, multiple grid maps were combined with NCs to delineate BUAs. Third, the optimal grid map for delineating BUA was determined based on the real BUA data and applying accuracy evaluation indexes. Finally, by comparing the 2014 and 2020 BUAs delineated by the optimal grid maps, we quantified the urban expansion occurring in Guangzhou. The results demonstrated the following. (1) The accuracy score of the BUAs delineated by the 200 m × 200 m grid map reaches a maximum. (2) The BUAs in the central urban area of Guangzhou had a smaller area of expansion, while the northern and southern areas of Guangzhou experienced considerable urban expansion. (3) The BUA expansion was smaller in all spatial orientations in the developed district, while the BUA expansion was larger in all spatial orientations in the developing district. This study provides a new method for delineating BUAs and a new perspective for mapping the spatial distribution of urban BUAs, which helps to better understand and quantify urban expansion.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-10
      DOI: 10.3390/ijgi11050303
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 304: Geo-Enabled Sustainable Municipal Energy
           Planning for Comprehensive Accessibility: A Case in the New Federal
           Context of Nepal

    • Authors: Hari Krishna Dhonju, Bikash Uprety, Wen Xiao
      First page: 304
      Abstract: Energy is a fundamental need of modern society and a basis for economic and social development, and one of the major Sustainable Development Goals (SDG), particularly SDG7. However, the UN’s SDG Report 2021 betrays millions of people living without electricity and one-third of the world’s population deprived of using modern energy cooking services (MECS) through access to electricity. Achieving the SDG7 requires standard approaches and tools that effectively address the geographical, infrastructural, and socioeconomic characteristics of a (rural) municipality of Nepal. Furthermore, Nepal’s Constitution 2015 incorporated a federal system under the purview of a municipality as the local government that has been given the mandate to ensure electricity access and clean energy. To address this, a methodology is developed for local government planning in Nepal in order to identify the optimal mix of electrification options by conducting a detailed geospatial analysis of renewable energy (RE) technologies by exploring accessibility and availability ranging from grid extensions to mini-grid and off-grid solutions, based on (a) life cycle cost and (b) levelized cost of energy. During energy assessment, geospatial and socio-economic data are coupled with household and community level data collected from a mobile survey app, and are exploited to garner energy status-quo and enable local governments to assess the existing situation of energy access/availability and planning. In summary, this paper presents a geo-enabled municipal energy planning method and a comprehensive toolkit to facilitate sustainable energy access to local people.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-10
      DOI: 10.3390/ijgi11050304
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 305: Continuous Monitoring of the Surface Water Area
           in the Yellow River Basin during 1986–2019 Using Available Landsat
           Imagery and the Google Earth Engine

    • Authors: Qingfeng Hu, Chongwei Li, Zhihui Wang, Yang Liu, Wenkai Liu
      First page: 305
      Abstract: The Yellow River Basin (YRB) has been facing severe water shortages; hence, the long-term dynamic monitoring of its surface water area (SWA) is essential for the efficient utilization of its water resources and sustainable socioeconomic development. In order to detect the changing trajectory of the SWA of the YRB and its influencing factors, we used available Landsat images from 1986 through to 2019 and a water and vegetation indices-based method to analyze the spatial–temporal variability of four types of SWAs (permanent, seasonal, maximum and average extents), and their relationship with precipitation (Pre), temperature (Temp), leaf area index (LAI) and surface soil moisture (SM).The multi-year average permanent surface water area (SWA) and seasonal SWA accounted for 46.48% and 53.52% in the Yellow River Basin (YRB), respectively. The permanent and seasonal water bodies were dominantly distributed in the upper reaches, accounting for 70.22% and 48.79% of these types, respectively. The rate of increase of the permanent SWA was 49.82 km2/a, of which the lower reaches contributed the most (34.34%), and the rate of decrease of the seasonal SWA was 79.18 km2/a, of which the contribution of the source region was the highest (25.99%). The seasonal SWA only exhibited decreasing trends in 13 sub-basins, accounting for 15% of all of the sub-basins, which indicates that the decrease in the seasonal SWA was dominantly caused by the change in the SWA in the main river channel region. The conversions from seasonal water to non-water bodies, and from seasonal to permanent water bodies were the dominant trends from 1986 to 2019 in the YRB. The SWA was positively correlated with precipitation, and was negatively correlated with the temperature. Because the permanent and seasonal water bodies were dominantly distributed in the river channel region and sub-basins, respectively, the change in the permanent SWA was significantly affected by the regulation of the major reservoirs, whereas the change in the seasonal SWA was more closely related to climate change. The increase in the soil moisture was helpful in the formation of the permanent water bodies. The increased evapotranspiration induced by vegetation greening played a significant positive role in the SWA increase via the local cooling and humidifying effects, which offset the accelerated water surface evaporation caused by the atmospheric warming.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-10
      DOI: 10.3390/ijgi11050305
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 306: Deducing Flood Development Process Using Social
           Media: An Event-Based and Multi-Level Modeling Approach

    • Authors: Yang Liu, Rui Li, Shunli Wang, Huayi Wu, Zhipeng Gui
      First page: 306
      Abstract: Social media is increasingly being used to obtain timely flood information to assist flood disaster management and situational awareness. However, since data in social media are massive, redundant, and unstructured, it is tricky to intuitively and clearly obtain effective information. To automatically obtain clear flood information and deduce flood development processes from social media, the authors of this paper propose an event-based and multi-level modeling approach including a data model and two methods. Through the hierarchical division of events (division into spatial object, phase, and attribute status), the flood information structure (including time, space, topic, emotion, and disaster condition) is defined. We built an entity construction method and a development process deduction method to achieve the automatic transition from cluttered data to orderly flood development processes. Taking the flooding event of the Yangtze and Huai Rivers in 2020 as an example, we successfully obtained true flood information and development process from social media data, which verified the effectiveness of the model and methods. Meanwhile, spatiotemporal pattern mining was carried out by using entities from different levels. The results showed that the flood was from west to east and the damage level was positively correlated with the number of flood-related social media texts, especially emotional texts. In summary, through the model and methods in this paper, clear flood information and dynamic development processes can be quickly and automatically obtained, and the spatiotemporal patterns of flood entities can be examined. It is beneficial to extract timely flood information and public sentiments towards flood events in order to perform better disaster relief and post-disaster management.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-10
      DOI: 10.3390/ijgi11050306
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 307: Space—Time Surveillance of COVID-19
           Seasonal Clusters: A Case of Sweden

    • Authors: Augustus Aturinde, Ali Mansourian
      First page: 307
      Abstract: While COVID-19 is a global pandemic, different countries have experienced different morbidity and mortality patterns. We employ retrospective and prospective space–time permutation analysis on COVID-19 positive records across different municipalities in Sweden from March 2020 to February 2021, using data provided by the Swedish Public Health Agency. To the best of our knowledge, this is the first study analyzing nationwide COVID-19 space–time clustering in Sweden, on a season-to-season basis. Our results show that different municipalities within Sweden experienced varying extents of season-dependent COVID-19 clustering in both the spatial and temporal dimensions. The reasons for the observed differences could be related to the differences in the earlier exposures to the virus, the strictness of the social restrictions, testing capabilities and preparedness. By profiling COVID-19 space–time clusters before the introduction of vaccines, this study contributes to public health efforts aimed at containing the virus by providing plausible evidence in evaluating which epidemiologic interventions in the different regions could have worked and what could have not worked.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-10
      DOI: 10.3390/ijgi11050307
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 308: Geographic Complexity: Concepts, Theories, and
           Practices

    • Authors: Changxiu Cheng, Samuel A. Cushman, Hung-Chak Ho, Peichao Gao
      First page: 308
      Abstract: Geography is a fundamentally important discipline that provides a framework for understanding the complex surface of our Earth [...]
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-12
      DOI: 10.3390/ijgi11050308
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 309: Scientometric Analysis for Spatial
           Autocorrelation-Related Research from 1991 to 2021

    • Authors: Qing Luo, Kai Hu, Wenxuan Liu, Huayi Wu
      First page: 309
      Abstract: Spatial autocorrelation describes the interdependent relationship between the realizations or observations of a variable that is distributed across a geographical landscape, which may be divided into different units/areas according to natural or political boundaries. Researchers of Geographical Information Science (GIS) always consider spatial autocorrelation. However, spatial autocorrelation research covers a wide range of disciplines, not only GIS, but spatial econometrics, ecology, biology, etc. Since spatial autocorrelation relates to multiple disciplines, it is difficult gain a wide breadth of knowledge on all its applications, which is very important for beginners to start their research as well as for experienced scholars to consider new perspectives in their works. Scientometric analyses are conducted in this paper to achieve this end. Specifically, we employ scientometrc indicators and scientometric network mapping techniques to discover influential journals, countries, institutions, and research communities; key topics and papers; and research development and trends. The conclusions are: (1) journals categorized into ecological and biological domains constitute the majority of TOP journals;(2) northern American countries, European countries, Australia, Brazil, and China contribute the most to spatial autocorrelation-related research; (3) eleven research communities consisting of three geographical communities and eight communities of other domains were detected; (4) hot topics include spatial autocorrelation analysis for molecular data, biodiversity, spatial heterogeneity, and variability, and problems that have emerged in the rapid development of China; and (5) spatial statistics-based approaches and more intensive problem-oriented applications are, and still will be, the trend of spatial autocorrelation-related research. We also refine the results from a geographer’s perspective at the end of this paper.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-12
      DOI: 10.3390/ijgi11050309
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 310: Analysis of Spatiotemporal Data Imputation
           Methods for Traffic Flow Data in Urban Networks

    • Authors: Endra Joelianto, Muhammad Farhan Fathurrahman, Herman Yoseph Sutarto, Ivana Semanjski, Adiyana Putri, Sidharta Gautama
      First page: 310
      Abstract: The increase in traffic in cities world-wide has led to a need for better traffic management systems in urban networks. Despite the advances in technology for traffic data collection, the collected data are still suffering from significant issues, such as missing data, hence the need for data imputation methods. This paper explores the spatiotemporal probabilistic principal component analysis (PPCA) based data imputation method that utilizes traffic flow data from vehicle detectors and focuses specifically on detectors in urban networks as opposed to a freeway setting. In the urban context, detectors are in a complex network, separated by traffic lights, measuring different flow directions on different types of roads. Different constructions of a spatial network are compared, from a single detector to a neighborhood and a city-wide network. Experiments are conducted on data from 285 detectors in the urban network of Surabaya, Indonesia, with a case study on the Diponegoro neighborhood. Methods are tested against both point-wise and interval-wise missing data in various scenarios. Results show that a spatial network adds robustness to the system and the choice of the subset has an impact on the imputation error. Compared to a single detector, spatiotemporal PPCA is better suited for interval-wise errors and more robust against outliers and extreme missing data. Even in the case where an entire day of data is missing, the method is still able to impute data accurately relying on other vehicle detectors in the network.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-12
      DOI: 10.3390/ijgi11050310
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 311: Few-Shot Building Footprint Shape Classification
           with Relation Network

    • Authors: Yaohui Hu, Chun Liu, Zheng Li, Junkui Xu, Zhigang Han, Jianzhong Guo
      First page: 311
      Abstract: Buildings are important entity objects of cities, and the classification of building shapes plays an indispensable role in the cognition and planning of the urban structure. In recent years, some deep learning methods have been proposed for recognizing the shapes of building footprints in modern electronic maps. Furthermore, their performance depends on enough labeled samples for each class of building footprints. However, it is impractical to label enough samples for each type of building footprint shapes. Therefore, the deep learning methods using few labeled samples are more preferable to recognize and classify the building footprint shapes. In this paper, we propose a relation network based method for the recognization of building footprint shapes with few labeled samples. Relation network, composed of embedding module and relation module, is a metric based few-shot method which aims to learn a generalized metric function and predict the types of the new samples according to their relation with the prototypes of these few labeled samples. To better extract the shape features of the building footprints in the form of vector polygons, we have taken the TriangleConv embedding module to act as the embedding module of the relation network. We validate the effectiveness of our method based on a building footprint dataset with 10 typical shapes and compare it with three classical few-shot learning methods in accuracy. The results show that our method performs better for the classification of building footprint shapes with few labeled samples. For example, the accuracy reached 89.40% for the 2-way 5-shot classification task where there are only two classes of samples in the task and five labeled samples for each class.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-14
      DOI: 10.3390/ijgi11050311
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 312: Spatial Concept Query Based on Lattice-Tree

    • Authors: Aopeng Xu, Zhiyuan Zhang, Xiaqing Ma, Zixiang Zhang, Tao Xu
      First page: 312
      Abstract: As a basic method of spatial data operation, spatial keyword query can provide meaningful information to meet user demands by searching spatial textual datasets. How to accurately understand users’ intentions and efficiently retrieve results from spatial textual big data are always the focus of research. Spatial textual big data and their complex correlation between textual features not only enrich the connotation of spatial objects but also bring difficulties to the efficient recognition and retrieval of similar spatial objects. Because there are a lot of many-to-many relationships between massive spatial objects and textual features, most of the existing research results that employ tree-like and table-like structures to index spatial data and textual data are inefficient in retrieving similar spatial objects. In this paper, firstly, we define spatial textual concept (STC) as a group of spatial objects with the same textual keywords in a limited spatial region in order to present the many-to-many relationships between spatial objects and textual features. Then we attempt to introduce the concept lattice model to maintain a group of related STCs and propose a hybrid tree-like spatial index structure, the lattice-tree, for spatial textual big data. Lattice-tree employs R-tree to index the spatial location of objects, and it embeds a concept lattice structure into specific tree nodes to organize the STC set from a large number of textual keywords of objects and their relationships. Based on this, we also propose a novel spatial keyword query, named Top-k spatial concept query (TkSCQ), to answer STC and retrieve similar spatial objects with multiple textual features. The empirical study is carried out on two spatial textual big data sets from Yelp and Amap. Experiments on the lattice-tree verify its feasibility and demonstrate that it is efficient to embed the concept lattice structure into tree nodes of 3 to 5 levels. Experiments on TkSCQ evaluate lattice from results, keywords, data volume, and so on, and two baseline index structures based on IR-tree and Fp-tree, named the inverted-tree and Fpindex-tree, are developed to compare with the lattice-tree on data sets from Yelp and Amap. Experimental results demonstrate that the Lattice-tree has the better retrieval efficiency in most cases, especially in the case of large amounts of data queries, where the retrieval performance of the lattice-tree is much better than the inverted-tree and Fpindex-tree.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-15
      DOI: 10.3390/ijgi11050312
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 313: Sustainable Urban Land-Use Optimization Using
           GIS-Based Multicriteria Decision-Making (GIS-MCDM) Approach

    • Authors: Md. Mostafizur Rahman, György Szabó
      First page: 313
      Abstract: Land-use optimization is an effective technique to produce optimal benefits in urban land-use planning. There are many approaches and methods to optimize land-use allocation. However, the focus on addressing urban sustainability in land-use optimization is very limited. In this study, we presented a GIS-based multicriteria decision-making (GIS-MCDM) approach to optimize the location of a new residential development considering sustainability dimensions (social, economic, and environmental benefits). Rajshahi City in Bangladesh was taken as a case study. Different types of data, including land use, land cover, ecosystem service value, land surface temperature, and carbon storage, were used to define sustainability criteria. Five physical criteria, three sustainability criteria, and two constraints were used to optimize residential land. Fuzzy membership functions were used to standardize the criteria. The ordered weighted averaging (OWA) was used to produce a residential suitability map. Finally, the multiobjective land allocation (MOLA) module of TerrSet v 19.0 was used to generate optimal locations under an alternative decision scenario. The findings suggest that about 9.00% more sustainability benefits can be achieved using our approach. Using our proposed approach, we also generated six alternative decision scenarios. Among the alternative decision strategies, “high risk–no trade-off” proved to be the most optimal decision strategy that generated the highest sustainability benefit in our case.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-15
      DOI: 10.3390/ijgi11050313
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 314: Applying Check-In Data and User Profiles to
           Identify Optimal Store Locations in a Road Network

    • Authors: Yen-Hsun Lin, Yi-Chung Chen, Sheng-Min Chiu, Chiang Lee, Fu-Cheng Wang
      First page: 314
      Abstract: Spatial information analysis has gained increasing attention in recent years due to its wide range of applications, from disaster prevention and human behavioral patterns to commercial value. This study proposes a novel application to help businesses identify optimal locations for new stores. Optimal store locations are close to other stores with similar customer groups. However, they are also a suitable distance from stores that might represent competition. The style of a new store also exerts a significant effect. In this paper, we utilized check-in data and user profiles from location-based social networks to calculate the degree of influence of each store in a road network on the query user to identify optimal new store locations. As calculating the degree of influence of every store in a road network is time-consuming, we added two accelerating algorithms to the proposed baseline. The experiment results verified the validity of the proposed approach.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-16
      DOI: 10.3390/ijgi11050314
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 315: The Impact of Built Environment Factors on
           Elderly People’s Mobility Characteristics by Metro System
           Considering Spatial Heterogeneity

    • Authors: Hong Yang, Zehan Ruan, Wenshu Li, Huanjie Zhu, Jie Zhao, Jiandong Peng
      First page: 315
      Abstract: This study used metro smart-card data from Wuhan, China, and explored the impact of the built environment on the metro ridership and station travel distance of elderly people using geographically weighted regression (GWR). First, our results show that elderly ridership at transfer stations is significantly higher than that at non-transfer stations. The building floor area ratio and the number of commercial facilities positively impact elderly ridership, while the number of road intersections and general hospitals has the opposite impact, of which factors show significant heterogeneity. Second, our results show that the average travel distance of terminal stations is significantly higher than that of non-terminal stations, and the average travel distance of non-transfer stations is higher than that of transfer stations. The distance of stations from the subcenter and building volume ratio have a positive effect, while station opening time and betweenness centrality have a negative effect. Our findings may provide insights for the optimization of land use in the built environment of age-friendly metros, help in the formulation of relevant policies to enhance elderly mobility, and provide a reference for other similar cities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-19
      DOI: 10.3390/ijgi11050315
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 316: Development of a Conceptual Data Model for 3D
           Geospatial Road Management Based on LandInfra Standard: A Case Study of
           Korea

    • Authors: Munkhbaatar Buuveibaatar, Kangjae Lee, Wonhee Lee
      First page: 316
      Abstract: In practice, road management data are typically managed in two-dimensional (2D) geospatial forms. However, 2D geographic information system (GIS)-based road infrastructure management data have limitations in their representation of complex roads, such as interchanges, bridges, and tunnels. As such, complex and large road network management data cannot be adequately managed in a 2D GIS-based form. This study discusses the use of the LandInfra standard for road infrastructure management in Korea, considering its focus on land and civil engineering infrastructure facilities. To facilitate the transition from 2D to 3D GIS, we analyzed existing road management models of road pavement and road register information and created Unified Modeling Language (UML) class diagrams depicting these models. Then, existing road management classes and LandInfra classes were mapped. Based on the results, we propose a road management model based on the Facility, Alignment, and Road parts of LandInfra. For its implementation, several classes of the proposed data model were encoded into InfraGML using real-world data input. Taken together, this study shows how the LandInfra standard can be extended and applied to the field of road infrastructure management in Korea, supporting the transition from a 2D to a 3D GIS-based model.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-05-21
      DOI: 10.3390/ijgi11050316
      Issue No: Vol. 11, No. 5 (2022)
       
  • IJGI, Vol. 11, Pages 217: A Knowledge Discovery Method for Landslide
           Monitoring Based on K-Core Decomposition and the Louvain Algorithm

    • Authors: Ping Wang, Xingdong Deng, Yang Liu, Liang Guo, Jun Zhu, Lin Fu, Yakun Xie, Weilian Li, Jianbo Lai
      First page: 217
      Abstract: Landslide monitoring plays an important role in predicting, forecasting and preventing landslides. Quantitative explorations at the subject level and fine-scale knowledge in landslide monitoring research can be used to provide information and references for landslide monitoring status analysis and disaster management. In the context of the large amount of network information, it is difficult to clearly determine and display the domain topic hierarchy and knowledge structure. This paper proposes a landslide monitoring knowledge discovery method that combines K-core decomposition and Louvain algorithms. In this method, author keywords are used as nodes to construct a weighted co-occurrence network, and a pruning standard value is defined as K. The K-core approach is used to decompose the network into subgraphs. Combined with the unsupervised Louvain algorithm, subgraphs are divided into different topic communities by setting a modularity change threshold, which is used to establish a topic hierarchy and identify fine-scale knowledge related to landslide monitoring. Based on the Web of Science, a comparative experiment involving the above method and a high-frequency keyword subgraph method for landslide monitoring knowledge discovery is performed. The results show that the run time of the proposed method is significantly less than that of the traditional method.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-22
      DOI: 10.3390/ijgi11040217
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 218: A Generalized 9-Intersection Model for
           Topological Relations between Regions with Holes

    • Authors: Liang Leng, Fengyan Wang, Mingchang Wang, Guodong Yang, Xuefeng Niu, Xuqing Zhang
      First page: 218
      Abstract: Current models cannot distinguish detailed topological relations between regions with holes. In order to solve this problem, a new detailed representation model for topological relations is proposed in this study. In this model, the key is to describe topological relations caused by multi-holes. Definitions of regions with holes and real objects expressed by elements of regions with holes are comprehensively analyzed, and then a practical definition of regions with holes is presented. Based on the 9-intersection model, a generalized 9-intersection model is proposed, which can completely describe detailed topological relations between regions with holes. For showing the description ability, the model is applied to describe detailed topological relations between regions with holes and detailed topological relations between objects of different complexities with the same major categories of topological relations, and a case study is designed to show the practicality of the model. The results show that the model is valid.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-23
      DOI: 10.3390/ijgi11040218
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 219: Daily Human Mobility: A Reproduction Model and
           Insights from the Energy Concept

    • Authors: Wang, Osaragi
      First page: 219
      Abstract: Human movements have raised broad attention, and many models have been developed to reproduce them. However, most studies focus on reproducing the statistical properties of human mobility, such as the travel distance and the visiting frequency. In this paper, a two-step Markov Chain model is proposed to generate daily human movements, and spatial and spatiotemporal attributes of reproduced mobility are examined. In the first step, people’s statuses in the next time slot are conditioned on their previous travel patterns; and in the second step, individual location in such a slot is probabilistically determined based on his/her status. Our model successfully reproduces the spatial and spatiotemporal characteristics of human daily movements, and the result indicates that people’s future statuses can be inferred based on travel patterns they made, regardless of exactly where they have traveled, and when trips happen. We also revisit the energy concept, and show that the energy expenditure is stable over years. This idea is further used to predict the proportion of long-distance trips for each year, which gives insights into the probabilities of statuses in the next time slot. Finally, we interpret the constant energy expenditure as the constant ‘cost’ over years.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-23
      DOI: 10.3390/ijgi11040219
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 220: A Novel Composite Index to Measure Environmental
           Benefits in Urban Land Use Optimization Problems

    • Authors: Md. Mostafizur Rahman, György Szabó
      First page: 220
      Abstract: In urban land use optimization problems, different conflicting objectives are applied. One of the most significant goals in urban land use optimization problems is to maximize environmental benefits. To quantify environmental benefits in land use optimization, many researchers have employed a variety of methodologies. According to previous studies, there is no standard approach for calculating environmental benefits in urban land use allocation problems. Against this background, this study aims to (a) identify indicators of environmental benefits and (b) propose a novel composite index to measure environmental benefits in urban land use optimization problems. This study identified four indicators as a measure of environmental benefits based on a literature assessment and expert opinion. These are spatial compactness, land surface temperature, carbon storage, and ecosystem service value. In this work, we proposed a novel composite environmental benefits index (EBI) to quantify environmental benefits in urban land use allocation problems using an ordered weighted averaging (OWA) method. The study results showed that land surface temperature (LST) is the most influential indicator of environmental benefit while carbon storage is the least important factor. Finally, the proposed method was applied in Rajshahi city in Bangladesh. This study identified that, in an average-risk decision, most of the land (64.55%) of the study area falls within the low-environmental-benefit zone due to a lack of vegetated land cover. The result suggests the potential of using EBI in the land use allocation problem to ensure environmental benefits.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-23
      DOI: 10.3390/ijgi11040220
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 221: Accuracy Issues for Spatial Update of Digital
           Cadastral Maps

    • Authors: David Pullar, Stephen Donaldson
      First page: 221
      Abstract: All geospatial data are updated periodically. Cadastral parcel mapping, however, has special update requirements that set it apart from other geospatial data. Mapped boundaries change continuously to fit with new survey plans. Additionally, new parcels have to be fitted and aligned with adjoining parcels to merge them into existing cadastral mapping. This is preferably performed by a spatial adjustment approach to systematically improve its accuracy over time. This paper adapts methods for analysis and adjustment of survey networks to improve the accuracy of cadastral mapping with better coordinate positioning and survey plan dimensions. Case studies for both hypothetical and real cadastral mapping are used to illustrate the issues and spatially resolve errors. Adjustment results achieve an accuracy consistent with other GIS layers and boundary features visible in high-resolution orthoimagery. Graphical charts based on stress–strain relationships provide a simplified means to interpret post-adjustment results to identify and fix potential errors.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-24
      DOI: 10.3390/ijgi11040221
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 222: Utilizing Geospatial Data for Assessing Energy
           Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles
           and Deep Learning

    • Authors: Simiao Ren, Jordan Malof, Rob Fetter, Robert Beach, Jay Rineer, Kyle Bradbury
      First page: 222
      Abstract: Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, as well as tracking the progress of universal electrification goals, is shared access to high-quality data on individual SHS installations including information such as location and power capacity. Though recent studies utilizing satellite imagery and machine learning to detect solar panels have emerged, they struggle to accurately locate many SHS due to limited image resolution (some small solar panels only occupy several pixels in satellite imagery). In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery. More specifically, we explore three questions: (i) what is the detection performance of SHS using drone imagery; (ii) how expensive is the drone data collection, compared to satellite imagery; and (iii) how well does drone-based SHS detection perform in real-world scenarios' To examine these questions, we collect and publicly-release a dataset of high-resolution drone imagery encompassing SHS imaged under a variety of real-world conditions and use this dataset and a dataset of imagery from Rwanda to evaluate the capabilities of deep learning models to recognize SHS, including those that are too small to be reliably recognized in satellite imagery. The results suggest that UAV imagery may be a viable alternative to identify very small SHS from perspectives of both detection accuracy and financial costs of data collection. UAV-based data collection may be a practical option for supporting electricity access planning strategies for achieving sustainable development goals and for monitoring the progress towards those goals.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-24
      DOI: 10.3390/ijgi11040222
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 223: Perspective Charts in a Multi-Foci Globe-Based
           Visualization of COVID-19 Data

    • Authors: Mia MacTavish, Lakin Wecker, Faramarz Samavati
      First page: 223
      Abstract: The use of perspective projection in data visualization has been shown to potentially aid with the perception of small values in datasets with important variations at multiple scales. We integrate perspective charts, which use perspective projection in their designs, into a geospatial visualization application for global COVID-19 data. We perform an evaluation through Amazon Mechanical Turk to evaluate the readability of these visualizations compared to traditional methods, when tools such as interactive techniques are used. Results of our evaluation show that participants more accurately retrieved small values from perspective chart visualizations than traditional bar charts on the globe. The use of perspective projection in an interactive system allows for users to read data with important variations at multiple scales without affecting the overall perception of scale in datasets.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-24
      DOI: 10.3390/ijgi11040223
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 224: A Robustness Study for the Extraction of
           Watertight Volumetric Models from Boundary Representation Data

    • Authors: Markus Wilhelm Jahn, Patrick Erik Bradley
      First page: 224
      Abstract: Geometrically induced topology plays a major role in applications such as simulations, navigation, spatial or spatio-temporal analysis and many more. This article computes geometrically induced topology useful for such applications and extends previous results by presenting the unpublished used algorithms to find inner disjoint (d+1)-dimensional simplicial complexes from a set of intersecting d-dimensional simplicial complexes which partly shape their B-Reps (Boundary Representations). CityGML has been chosen as the input data format for evaluation purposes. In this case, the input data consist of planar segment complexes whose triangulated polygons serve as the set of input triangle complexes for the computation of the tetrahedral model. The creation of the volumetric model and the computation of its geometrically induced topology is partly parallelized by decomposing the input data into smaller pices. A robustness analysis of the implementations is given by varying the angular precision and the positional precision of the epsilon heuristic inaccuracy model. The results are analysed spatially and topologically, summarised and presented. It turns out that one can extract most, but not all, volumes and that the numerical issues of computational geometry produce failures as well as a variety of outcomes.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-26
      DOI: 10.3390/ijgi11040224
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 225: Using TanDEM-X Global DEM to Map Coastal
           Flooding Exposure under Sea-Level Rise: Application to Guinea-Bissau

    • Authors: Morto Baiém Fandé, Cristina Ponte Ponte Lira, Gil Penha-Lopes
      First page: 225
      Abstract: The increased exposure to coastal flooding in low-lying coastal areas is one of the consequences of sea-level rise (SLR) induced by climate changes. The coastal zone of Guinea-Bissau contains significant areas of low elevation and is home to most of the population and economic activity, making it already vulnerable to coastal flooding, especially during spring tides and storm surges (SS). Coastal flooding will tend to intensify with the expected SLR in the coming decades. This study aimed at quantifying and mapping the area exposed to the coastal flooding hazard using SLR scenarios by the years 2041, 2083, and 2100. The study analyzes and discusses the application of a the simple “bathtub” model coupled with a high-precision global digital elevation models (TanDEM-X DEM) to areas where no other data are available. Therefore, three coastal hazards hot-spots of Guinea-Bissau: Bissau, Bubaque, and Suzana, were used as case study. At each site, the area potentially exposed to coastal flooding was evaluated in a geographic information systems (GIS) environment, by estimating the Total Water Levels for each SLR scenario. For all areas, land exposed to coastal flooding hazard increases significantly and progressively with increasing SLR scenarios. Bissau and Suzana, where housing, infrastructure, and agricultural land are low-lying, presented the greatest flood exposure, while Bubaque, where housing and infrastructure are located in relatively high-lying land and rain-fed agriculture is practiced, present lesser flood exposure. The methodology presented is simple to use but powerful in identifying potentially vulnerable places to coastal flooding hazard, and its results can aid low developed countries to assess their exposure to coastal risks, thus supporting risk awareness and mitigation measures.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-26
      DOI: 10.3390/ijgi11040225
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 226: The Use of Machine Learning Algorithms in Urban
           Tree Species Classification

    • Authors: Zehra Cetin, Naci Yastikli
      First page: 226
      Abstract: Trees are the key components of urban vegetation in cities. The timely and accurate identification of existing urban tree species with their location is the most important task for improving air, water, and land quality; reducing carbon accumulation; mitigating urban heat island effects; and protecting soil and water balance. Light detection and ranging (LiDAR) is frequently used for extracting high-resolution structural information regarding tree objects. LiDAR systems are a cost-effective alternative to the traditional ways of identifying tree species, such as field surveys and aerial photograph interpretation. The aim of this work was to assess the usage of machine learning algorithms for classifying the deciduous (broadleaf) and coniferous tree species from 3D raw LiDAR data on the Davutpasa Campus of Yildiz Technical University, Istanbul, Turkey. First, ground, building, and low, medium, and high vegetation classes were acquired from raw LiDAR data using a hierarchical-rule-based classification method. Next, individual tree crowns were segmented using a mean shift clustering algorithm from high vegetation points. A total of 25 spatial- and intensity-based features were utilized for support vector machine (SVM), random forest (RF), and multi-layer perceptron (MLP) classifiers to discriminate deciduous and coniferous tree species in the urban area. The machine learning-based classification’s overall accuracies were 80%, 83.75%, and 73.75% for the SVM, RF, and MLP classifiers, respectively, in split 70/30 (training/testing). The SVM and RF algorithms generally gave better classification results than the MLP algorithm for identifying the urban tree species.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-26
      DOI: 10.3390/ijgi11040226
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 227: Similarity Analysis: Revealing the Regional
           Difference in Geomorphic Development in Areas with High and Coarse
           Sediment Yield of the Loess Plateau in China

    • Authors: Hui Yang, Jinhong Liu, Leichao Bai, Mingliang Luo
      First page: 227
      Abstract: The development of loess landforms is controlled by underlying, pre-existing paleotopography. Previous studies have focused on the inheritance of loess landform and the control of underlying paleotopography on modern terrain based on the digital elevation model (DEM), while the similarities and differences between modern terrain and underlying paleotophotography have not been directly spatialized. In this study, areas with high and coarse sediment yield (AHCSY) in the Loess Plateau of China were selected to form the study area, and the DEM of the study area’s underlying paleotophotography was reconstructed using detailed geological maps, loess thickness maps, and underlying paleotopographic information. The hypsometric integral (HI) and spatial similarity analysis methods were used to compare the spatialized difference between underlying and modern terrain of the Loess Plateau from the perspectives of the landform development stage and surface elevation, respectively. The results of the HI method demonstrate that essentially, there are similarities between the geomorphologic development stages of underlying and modern terrain, and only some local differences exist in some special areas. The results regarding the spatialized coefficient of relative difference and the Jensen–Shannon divergence demonstrate that the thicker the loess is, the weaker the similarity is, and vice versa. Meanwhile, according to the present loess landform division, the order of regional similarity from low to high is as follows: loess tableland, broken loess tableland, hilly regions, dunes, and the Yellow River Trunk. The use of the similarity analysis method to analyze similarities between underlying and modern terrain plays an important role in revealing the inheritance of loess landforms.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-28
      DOI: 10.3390/ijgi11040227
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 228: Identification and Classification of Routine
           Locations Using Anonymized Mobile Communication Data

    • Authors: Gonçalo Ferreira, Ana Alves, Marco Veloso, Carlos Bento
      First page: 228
      Abstract: Digital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of places, thus failing to model and enhance the understanding of the motivations behind people’s mobility. In this paper, we applied a methodology for identifying individual users’ routine locations and propose an approach for attaching semantic meaning to these locations. Specifically, we used circular sectors that correspond to cellular antennas’ signal areas. In those areas, we found that all contained points of interest (POIs), extracted their most important attributes (opening hours, check-ins, category) and incorporated them into the classification. We conducted experiments with real-world data from Coimbra, Portugal, and the initial experimental results demonstrate the effectiveness of the proposed methodology to infer activities in the user’s routine areas.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-29
      DOI: 10.3390/ijgi11040228
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 229: Moderating Effect of a Cross-Level Social
           Distancing Policy on the Disparity of COVID-19 Transmission in the United
           States

    • Authors: Zhenwei Luo, Lin Li, Jianfang Ma, Zhuo Tang, Hang Shen, Haihong Zhu, Bin Wu
      First page: 229
      Abstract: Currently, coronavirus disease 2019 (COVID-19) remains a global pandemic, but the prevention and control of the disease in various countries have also entered the normalization stage. To achieve economic recovery and avoid a waste of resources, different regions have developed prevention and control strategies according to their social, economic, and medical conditions and culture. COVID-19 disparities under the interaction of various factors, including interventions, need to be analyzed in advance for effective and precise prevention and control. Considering the United States as the study case, we investigated statistical and spatial disparities based on the impact of the county-level social vulnerability index (SVI) on the COVID-19 infection rate. The county-level COVID-19 infection rate showed very significant heterogeneity between states, where 67% of county-level disparities in COVID-19 infection rates come from differences between states. A hierarchical linear model (HLM) was adopted to examine the moderating effects of state-level social distancing policies on the influence of the county-level SVI on COVID-19 infection rates, considering the variation in data at a unified level and the interaction of various data at different levels. Although previous studies have shown that various social distancing policies inhibit COVID-19 transmission to varying degrees, this study explored the reasons for the disparities in COVID-19 transmission under various policies. For example, we revealed that the state-level restrictions on the internal movement policy significantly attenuate the positive effect of county-level economic vulnerability indicators on COVID-19 infection rates, indirectly inhibiting COVID-19 transmission. We also found that not all regions are suitable for the strictest social distancing policies. We considered the moderating effect of multilevel covariates on the results, allowing us to identify the causes of significant group differences across regions and to tailor measures of varying intensity more easily. This study is also necessary to accomplish targeted preventative measures and to allocate resources.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-30
      DOI: 10.3390/ijgi11040229
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 230: OSM Science—The Academic Study of the
           OpenStreetMap Project, Data, Contributors, Community, and Applications

    • Authors: A. Yair Grinberger, Marco Minghini, Levente Juhász, Godwin Yeboah, Peter Mooney
      First page: 230
      Abstract: This paper is an Editorial for the Special Issue titled “OpenStreetMap as a multidisciplinary nexus: perspectives, practices and procedures”. The Special Issue is largely based on the talks presented in the 2019 and 2020 editions of the Academic Track at the State of the Map conferences. As such, it represents the most pressing and relevant issues and topics considered by the academic community in relation to OpenStreetMap (OSM)—a global project and community aimed to create and maintain a free and editable database and map of the world. In this Editorial, we survey the papers included in the Special Issue, grouping them into three research perspectives: applications of OSM for studies within other disciplines, OSM data quality, and dynamics in OSM. This survey reveals that these perspectives, while being distinct, are also interrelated. This calls for the formalization of an ‘OSM science’ that will provide the conceptual grounds to advance the scientific study of OSM, not as a set of individualized efforts but as a unified approach.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-30
      DOI: 10.3390/ijgi11040230
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 231: An Occupancy Information Grid Model for Path
           Planning of Intelligent Robots

    • Authors: Jinming Zhang, Xun Wang, Lianrui Xu, Xin Zhang
      First page: 231
      Abstract: Commonly used robot map models include occupancy grid maps, topological maps, and semantic maps. Among these, an occupancy grid map is mainly represented as a quadrilateral grid. This paper proposes an occupancy information grid for intelligent robots by exploiting the advantages of the occupancy grid map and spatial information grid. In terms of geometric structure, a regular hexagonal grid is used instead of a regular quadrilateral grid. In terms of attribute structure, the single obstacle attribute is replaced by the grid terrain characteristics, grid element attributes, and grid edge attributes. Thus, the occupancy information grid model is transformed into a new data structure describing the spatial environment, and it can be effectively applied to map construction and path planning of intelligent robots. For the map construction application of intelligent robots, this paper describes the basic process of laser sensor-based grid model construction. For the path planning application of intelligent robots, this paper extends the A* algorithm based on a regular hexagonal grid. Additionally, map construction and path planning applications for intelligent robots are experimentally verified. Several experimental results were obtained. First, the experimental results confirmed the theoretical conclusion that the minimum sampling density of the hexagonal structure was 13.4% lower than that of the quadrilateral structure. Second, the regular hexagonal grid is clearly more advantageous in describing environmental scenes, which can ameliorate the "undercompleteness" phenomenon. Third, there were large differences in the planning paths based on two types of grids, as shown by the fact that the distance of the planning paths obtained by the regular hexagonal grid was reduced by at least 10.8% and at most 15.6% compared with the regular quadrilateral grid.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-31
      DOI: 10.3390/ijgi11040231
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 232: EU Net-Zero Policy Achievement Assessment in
           Selected Members through Automated Forecasting Algorithms

    • Authors: Cristiana Tudor, Robert Sova
      First page: 232
      Abstract: The European Union (EU) has positioned itself as a frontrunner in the worldwide battle against climate change and has set increasingly ambitious pollution mitigation targets for its members. The burden is heavier for the more vulnerable economies in Central and Eastern Europe (CEE), who must juggle meeting strict greenhouse gas emission (GHG) reduction goals, significant fossil-fuel reliance, and pressure to respond to current pandemic concerns that require an increasing share of limited public resources, while facing severe repercussions for non-compliance. Thus, the main goals of this research are: (i) to generate reliable aggregate GHG projections for CEE countries; (ii) to assess whether these economies are on track to meet their binding pollution reduction targets; (iii) to pin-point countries where more in-depth analysis using spatial inventories of GHGs at a finer resolution is further needed to uncover specific areas that should be targeted by additional measures; and (iv) to perform geo-spatial analysis for the most at-risk country, Poland. Seven statistical and machine-learning models are fitted through automated forecasting algorithms to predict the aggregate GHGs in nine CEE countries for the 2019–2050 horizon. Estimations show that CEE countries (except Romania and Bulgaria) will not meet the set pollution reduction targets for 2030 and will unanimously miss the 2050 carbon neutrality target without resorting to carbon credits or offsets. Austria and Slovenia are the least likely to meet the 2030 emissions reduction targets, whereas Poland (in absolute terms) and Slovenia (in relative terms) are the farthest from meeting the EU’s 2050 net-zero policy targets. The findings thus stress the need for additional measures that go beyond the status quo, particularly in Poland, Austria, and Slovenia. Geospatial analysis for Poland uncovers that Krakow is the city where pollution is the most concentrated with several air pollutants surpassing EU standards. Short-term projections of PM2.5 levels indicate that the air quality in Krakow will remain below EU and WHO standards, highlighting the urgency of policy interventions. Further geospatial data analysis can provide valuable insights into other geo-locations that require the most additional efforts, thereby, assisting in the achievement of EU climate goals with targeted measures and minimum socio-economic costs. The study concludes that statistical and geo-spatial data, and consequently research based on these data, complement and enhance each other. An integrated framework would consequently support sustainable development through bettering policy and decision-making processes.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-03-31
      DOI: 10.3390/ijgi11040232
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 233: Method to Determine the Centroid of
           Non-Homogeneous Polygons Based on Suspension Theory

    • Authors: Jianhua Ni, Jie Chen, Yanlan Wu, Zihao Chen, Ming Liang
      First page: 233
      Abstract: The centroid is most often used to describe the average position of an object’s mass and has very important applications in computational geometry, applied physics, and spatial information fields, amongst others. Based on the suspension theory of physics, this paper proposes a new method to determine the centroid of a non-homogeneous polygon by the intersection of the two balance lines. By considering the inside point value and distance to the balance line, the proposed method overcomes the traditional method’s limitation of only considering the geometric coordinates of the boundary points of the polygon. The results show that the consideration of grid distance and grid value is logical and consistent with the calculation of the centroid of a non-homogeneous polygon. While using this method, a suitable value for relative parameters needs to be established according to specific application instances. The proposed method can be applied to aid in solving specific problems such as location assessment, allocation of resources, spatial optimization, and other relative uses.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-02
      DOI: 10.3390/ijgi11040233
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 234: A Vector Data Model for Efficiently Rendering
           Large Vector Maps on Global 3D Terrain Surfaces

    • Authors: Ganlin Wang, Jing Chen
      First page: 234
      Abstract: Visualizing vector data on 3D terrain surfaces is a basic and essential function in 3D GIS. However, due to the complexity of vector data structures, efficient and effective organization of the vector data is a key issue for the efficient display of vector data in 3D. In this paper, we present a new Vector Tiled Pyramid Model to organize and manage vector data so that they can be visualized on 3D terrain surfaces more effectively. In the Vector Tiled Pyramid Model, vector data at different scales within the same geographical extent are stored as separate levels. Each vector level in our proposed model is divided into vector tiles of fixed sizes organized in a grid index. This improves the efficiency of visualizing vector data on 3D terrain surfaces. Preliminary experimental results suggest that the proposed Vector tiled Pyramid Model, compared with the traditional vector database scheme, can help us to visualize vector data on 3D terrain surfaces more efficiently. In addition, this advantage is more evident when a vector tile at a lower level (large-scale) is requested and visualized on 3D terrain surfaces.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-04
      DOI: 10.3390/ijgi11040234
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 235: Using GIS to Understand Healthcare Access
           Variations in Flood Situation in Surabaya

    • Authors: Nurwatik Nurwatik, Jung-Hong Hong, Lalu Muhamad Jaelani, Hepi Hapsari Handayani, Agung Budi Cahyono, Mohammad Rohmaneo Darminto
      First page: 235
      Abstract: This paper proposes to identify the variation of accessibility to healthcare facilities based on vulnerability assessments of floods by using open source data. The open source data comprises Open Street Map (OSM), world population, and statistical data. The accessibility analysis is more focused on vulnerable populations that might be affected by floods. Therefore, a vulnerability assessment is conducted beforehand to identify the location where the vulnerable population is located. A before and after scenario of floods is applied to evaluate the changes of healthcare accessibility. A GIS Network Analyst is chosen as the accessibility analysis tool. The results indicate that the most vulnerable population lives in the Asemrowo district. The service area analysis showed that 94% of the West of Surabaya was well-serviced in the before scenario. Otherwise, the decrement of service area occurs at the city center in the after scenario. Thus, the disaster manager can understand which vulnerable area is to be more prioritized in the evacuation process.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-04
      DOI: 10.3390/ijgi11040235
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 236: Hydrological Web Services for Operational Flood
           Risk Monitoring and Forecasting at Local Scale in Niger

    • Authors: Tiziana De Filippis, Leandro Rocchi, Giovanni Massazza, Alessandro Pezzoli, Maurizio Rosso, Mohamed Housseini Ibrahim, Vieri Tarchiani
      First page: 236
      Abstract: Emerging hydrological services provide stakeholders and political authorities with useful and reliable information to support the decision-making process and develop flood risk management strategies. Most of these services adopt the paradigm of open data and standard web services, paving the way to increase distributed hydrometeorological services’ interoperability. Moreover, sharing of data, models, information, and the use of open-source software, greatly contributes to expanding the knowledge on flood risk and to increasing flood preparedness. Nevertheless, services’ interoperability and open data are not common in local systems implemented in developing countries. This paper presents the web platform and related services developed for the Local Flood Early Warning System of the Sirba River in Niger (SLAPIS) to tailor hydroclimatic information to the user’s needs, both in content and format. Building upon open-source software components and interoperable web services, we created a software framework covering data capture and storage, data flow management procedures from several data providers, real-time web publication, and service-based information dissemination. The geospatial infrastructure and web services respond to the actual and local decision-making context to improve the usability and usefulness of information derived from hydrometeorological forecasts, hydraulic models, and real-time observations. This paper presents also the results of the three years of operational campaigns for flood early warning on the Sirba River in Niger. Semiautomatic flood warnings tailored and provided to end users bridge the gap between available technology and local users’ needs for adaptation, mitigation, and flood risk management, and make progress toward the sustainable development goals.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-05
      DOI: 10.3390/ijgi11040236
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 237: Revealing Dynamic Spatial Structures of Urban
           Mobility Networks and the Underlying Evolutionary Patterns

    • Authors: Chun Liu, Li Chen, Quan Yuan, Hangbin Wu, Wei Huang
      First page: 237
      Abstract: Urban space exhibits rich and diverse organizational structures, which is difficult to characterize and interpret. Modelling urban spatial structures in the context of mobility and revealing their underlying patterns in dynamic networks are key to understanding urban spatial structures and how urban systems work. Most existing methods overlook its temporal dimension and oversimplify its spatial heterogeneity, and it is challenging to address these complex properties using one single method. Therefore, we propose a framework based on temporal networks for modeling dynamic urban mobility structures. First, we cast aggregated traffic flows into a compact and informative temporal network for structure representation. Then, we explore spatial cluster substructures and temporal evolution patterns to acquire evolution regularities. Last, the capability of the proposed framework is examined by an empirical analysis based on taxi mobility networks. The experiment results enable to quantitatively depict urban space dynamics and effectively detect spatiotemporal heterogeneity in mobility networks.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-05
      DOI: 10.3390/ijgi11040237
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 238: Digitalization and Classification of Cesare
           Battisti’s Atlas of 1915

    • Authors: Paolo Zatelli, Nicola Gabellieri, Angelo Besana
      First page: 238
      Abstract: The paper deals with an automated methodology for the digital acquisition of thematic information from historical maps in order to use them for spatial analysis in a GIS software. This methodology has been applied to an early XIX c. map in order to assess the historical changes in the forest coverage in Trentino. Specifically, a tailored Object Based Image Analysis (OBIA) and filtering procedure has been applied to digitize and georeference Cesare Battisti’s map of forest density published in his atlas “Il Trentino. Economic Statistical Illustration” from 1915. According to the historical ecology approach, forest history can be analyzed and evaluated with the use of historical documentary sources. Following this approach, historical cartography is a precious information tool, and in many respects unique, through which it is possible to reconstruct the evolution of the forest cover of a given territory. Trentino, in particular, has a rich heritage of historical maps from which to draw useful information for the construction of a qualitative and quantitative diachronic picture of the evolutionary dynamics of wooded areas. In these territories, forest management is a topic of great importance both for its socio-economic implications and for the more strictly environmental ones, connected to the increasingly urgent need to implement mitigation and adaptation policies towards climate change. Thus, the paper presents the historical maps and illustrates the methodology used for the digitisation. Data extracted by the historical sources have been compared with the current one in order to identify changes in forest density in the last century.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-05
      DOI: 10.3390/ijgi11040238
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 239: Towards a Sensitive Urban Wind Representation in
           Virtual Reality

    • Authors: Gabriel Giraldo, Myriam Servières, Guillaume Moreau
      First page: 239
      Abstract: Wind can influence people’s behavior and their way of inhabiting an architectural or urban space. Furthermore, virtual reality (VR) enables the simulation of different physical and sensitive phenomena such as the wind. We aim to analyze the effects of different wind representations in terms of perception of its properties and sense of presence in VR. We carry out two within-subject studies aiming at evaluating different wind representation suggestions (including audiovisual and tactile stimuli) to identify their effects on wind properties’ perception and sense of presence in the VR scene. Our analysis showed significant effects of tactile restitution over the visual effects used in the study, both for understanding wind properties and for increasing the sense of presence in the VR scene. The tactile condition (T) reduced the estimation error of wind direction by 27% compared to the visual condition (V). The wind force error was reduced by 9.8% using (T) with (V). (T) increased the sense of presence by 12.2% compared to (V). Our second experiment showed an overestimation of the wind force perceived compared to the reference value of the Beaufort scale. For the maximum force value evaluated, the average result was 91% higher than the reference value, while for the lower, the average answer was 77% higher than the reference value. Previous studies have evaluated wind rendering in virtual reality, and others have studied the visualization of wind simulation results. To our knowledge, our study is the first to compare the perception of these two types of representations as well as the effects of wind on elements of the context. We also compared the wind perception to a reference-based method, the Beaufort scale.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-06
      DOI: 10.3390/ijgi11040239
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 240: Virtual 3D City Models

    • Authors: Rudi Stouffs
      First page: 240
      Abstract: Virtual 3D city models, in varying forms of extent and detail, are becoming more common, yet their usage might still be limited [...]
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-06
      DOI: 10.3390/ijgi11040240
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 241: Evaluation of Street Space Quality Using
           Streetscape Data: Perspective from Recreational Physical Activity of the
           Elderly

    • Authors: Ying Du, Wei Huang
      First page: 241
      Abstract: The quality of street space has attracted attention. It is important to understand the needs of different population groups for street space quality, especially the rapidly growing elderly group. Improving the quality of street space is conducive to promoting the physical leisure activities of the elderly to benefit to their health. Therefore, it is important to evaluate street space quality for the elderly. The existing studies, on the one hand, are limited by the sample size of traditional survey data, which is hard to apply on a large scale; on the other hand, there is a lack of consideration for factors that reveal the quality of street space from the perspective of the elderly. This paper takes Guangzhou as an example to evaluate the quality of street space. First, the sample street images were scored by the elderly on a small scale; then the regression analysis was used to extract the street elements that the elderly care about. Last, the street elements were put into the random forest model to assess street space quality io a large scale. It was found that the green view rate and sidewalks are positively correlated with satisfaction, and the positive effect increases in that order. Roads, buildings, sky, vehicles, walls, ceilings, glass windows, runways, railings, and rocks are negatively correlated with satisfaction, and the negative effect increases in that order. The mean satisfaction score of the quality of street space for the elderly’s recreational physical activities in three central districts of Guangzhou (Yuexiu, Liwan, and Haizhu) is 2.6, among which Xingang street gets the highest quality score (2.92), and Hailong street has the lowest quality score (2.32). These findings are useful for providing suggestions to governors and city designers for street space optimization.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-07
      DOI: 10.3390/ijgi11040241
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 242: Incorporating Spatial Autocorrelation in Machine
           Learning Models Using Spatial Lag and Eigenvector Spatial Filtering
           Features

    • Authors: Liu, Kounadi, Zurita-Milla
      First page: 242
      Abstract: Applications of machine-learning-based approaches in the geosciences have witnessed a substantial increase over the past few years. Here we present an approach that accounts for spatial autocorrelation by introducing spatial features to the models. In particular, we explore two types of spatial features, namely spatial lag and eigenvector spatial filtering (ESF). These features are used within the widely used random forest (RF) method, and their effect is illustrated on two public datasets of varying sizes (Meuse and California housing datasets). The least absolute shrinkage and selection operator (LASSO) is used to determine the best subset of spatial features, and nested cross-validation is used for hyper-parameter tuning and performance evaluation. We utilize Moran’s I and local indicators of spatial association (LISA) to assess how spatial autocorrelation is captured at both global and local scales. Our results show that RF models combined with either spatial lag or ESF features yield lower errors (up to 33% different) and reduce the global spatial autocorrelation of the residuals (up to 95% decrease in Moran’s I) compared to the RF model with no spatial features. The local autocorrelation patterns of the residuals are weakened as well. Compared to benchmark geographically weighted regression (GWR) models, the RF models with spatial features yielded more accurate models with similar levels of global and local autocorrelation in the prediction residuals. This study reveals the effectiveness of spatial features in capturing spatial autocorrelation and provides a generic machine-learning modelling workflow for spatial prediction.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-07
      DOI: 10.3390/ijgi11040242
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 243: Precise Indoor Path Planning Based on Hybrid
           Model of GeoSOT and BIM

    • Authors: Zhang, Li
      First page: 243
      Abstract: With the improvement of urban infrastructure and the increase in the coverage of high-rise buildings, the demand for location information services inside buildings is becoming more and more urgent. Moreover, indoor path planning, as a prerequisite and basis for realizing path guidance inside buildings, has become a research focus in the field of location services. This makes the accurate planning of indoor paths an urgent problem to be solved at present. This requires dynamic and precise planning from static fuzzy planning, and the corresponding scene converted from a two-dimensional plane to a three-dimensional one. However, most of the existing indoor path planning methods focus on the use of two-dimensional floor plans in buildings to build indoor maps and rely on traditional path search algorithms for pathfinding, which lack in the efficient use of the building’s own geometric and attribute information and lack consideration of the internal spatial topology of the building, making it difficult to meet the needs of indoor multi-layer continuous space path planning. Considering this relationship, it is difficult to meet the path planning needs of indoor multi-layer continuous spaces. In addition, the two-dimensional expression dominated by arrows and line drawings also greatly reduces the intuitiveness and interactivity of path expression. Regarding this, this paper combines the GeoSOT grid with accurate real geographic information and the BIM model and proposes an accurate indoor path planning method. Finally, using Guanlan Commercial Street in Baiyin City as the experimental object, the precise planning and generation of indoor paths and the interaction of visual displays on the web page are realized. It has been verified that the method has certain reference and application values for meeting the demand of location information services in buildings and building an integrated indoor–outdoor navigation service platform.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-08
      DOI: 10.3390/ijgi11040243
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 244: Visual Analysis of Vessel Behaviour Based on
           Trajectory Data: A Case Study of the Yangtze River Estuary

    • Authors: Li, Ren
      First page: 244
      Abstract: The widespread of shipborne Automatic Identification System (AIS) equipment will continue to produce a large amount of spatiotemporal trajectory data. In order to explore and understand the hidden behaviour patterns in the data, an interactive visual analysis method combining multiple views is proposed. The method mainly includes four parts: using a trajectory compression algorithm that takes into account the vessel motion characteristics to preprocess the vessel trajectory data; displaying and replaying vessel trajectories based on Electronic Chart System (ECS), and proposing a detection algorithm for vessel stay points based on the principle of spatiotemporal density to semantically label vessel trajectories; using the Fast Dynamic Time Warping (FastDTW) similarity measurement algorithm and the Ordering Points to Identify the Clustering Structure (OPTICS) clustering algorithm to cluster vessel trajectories to show the differences and similarities between vessel traffic flows; and showing the distribution of vessels and the variation trend of vessel density based on the vessel heatmap. Based on the AIS data of the Yangtze River Estuary, three cases are used to prove the usefulness and effectiveness of the system in vessel behaviour analysis.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-09
      DOI: 10.3390/ijgi11040244
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 245: Automatic Classification of Photos by Tourist
           

    • Authors: Jiyeon Kim, Youngok Kang
      First page: 245
      Abstract: With the rise of social media platforms, tourists tend to share their experiences in the form of texts, photos, and videos on social media. These user-generated contents (UGC) play an important role in shaping tourism destination images (TDI) and directly affect the decision-making process of tourists. Among UGCs, photos represent tourists’ visual preferences for a specific area. Paying attention to the value of photos, several studies have attempted to analyze them using deep learning technology. However, the research methods that analyze tourism photos using recent deep learning technology have a limitation in that they cannot properly classify unique photos appearing in specific tourist attractions with predetermined photo categories such as Places365 or ImageNet dataset or it takes a lot of time and effort to build a separate training dataset to train the model and to generate a tourism photo classification category according to a specific tourist destination. The purpose of this study is to propose a method of automatically classifying tourist photos by tourist attractions by applying the methods of the image feature vector clustering and the deep learning model. To this end, first, we collected photos attached to reviews posted by foreign tourists on TripAdvisor. Second, we embedded individual images as 512-dimensional feature vectors using the VGG16 network pre-trained with Places365 and reduced them to two dimensions with t-SNE(t-Distributed Stochastic Neighbor Embedding). Then, clusters were extracted through HDBSCAN(Hierarchical Clustering and Density-Based Spatial Clustering of Applications with Noise) analysis and set as a regional image category. Finally, the Siamese Network was applied to remove noise photos within the cluster and classify photos according to the category. In addition, this study attempts to confirm the validity of the proposed method by applying it to two representative tourist attractions such as ‘Gyeongbokgung Palace’ and ‘Insadong’ in Seoul. As a result, it was possible to identify which visual elements of tourist attractions are attractive to tourists. This method has the advantages in that it is not necessary to create a classification category in advance, it is possible to flexibly extract categories for each tourist destination, and it is able to improve classification performance even with a rather small volume of a dataset.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-10
      DOI: 10.3390/ijgi11040245
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 246: Urban Change Detection from Aerial Images Using
           Convolutional Neural Networks and Transfer Learning

    • Authors: Tautvydas Fyleris, Andrius Kriščiūnas, Valentas Gružauskas, Dalia Čalnerytė, Rimantas Barauskas
      First page: 246
      Abstract: Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of indicators from large-scale orthoimages of different resolution with practically acceptable accuracy after a short training process. Remote sensing data can be used to detect changes in number of buildings, forest areas, and other landscape objects. In this paper, aerial images of a digital raster orthophoto map at scale 1:10,000 of the Republic of Lithuania (ORT10LT) of three periods (2009–2010, 2012–2013, 2015–2017) were analyzed. Because of the developing technologies, the quality of the images differs significantly and should be taken into account while preparing the dataset for training the semantic segmentation model DeepLabv3 with a ResNet50 backbone. In the data preparation step, normalization techniques were used to ensure stability of image quality and contrast. Focal loss for the training metric was selected to deal with the misbalanced dataset. The suggested model training process is based on the transfer learning technique and combines using a model with weights pretrained in ImageNet with learning on coarse and fine-tuning datasets. The coarse dataset consists of images with classes generated automatically from Open Street Map (OSM) data and the fine-tuning dataset was created by manually reviewing the images to ensure that the objects in images match the labels. To highlight the benefits of transfer learning, six different models were trained by combining different steps of the suggested model training process. It is demonstrated that using pretrained weights results in improved performance of the model and the best performance was demonstrated by the model which includes all three steps of the training process (pretrained weights, training on coarse and fine-tuning datasets). Finally, the results obtained with the created machine learning model enable the implementation of different approaches to detect, analyze, and interpret urban changes for policymakers and investors on different levels on a local map, grid, or municipality level.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-10
      DOI: 10.3390/ijgi11040246
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 247: An Efficient Plane-Segmentation Method for
           Indoor Point Clouds Based on Countability of Saliency Directions

    • Authors: Xuming Ge, Jingyuan Zhang, Bo Xu, Hao Shu, Min Chen
      First page: 247
      Abstract: This paper proposes an efficient approach for the plane segmentation of indoor and corridor scenes. Specifically, the proposed method first uses voxels to pre-segment the scene and establishes the topological relationship between neighboring voxels. The voxel normal vectors are projected onto the surface of a Gaussian sphere based on the corresponding directions to achieve fast plane grouping using a variant of the K-means approach. To improve the segmentation integration, we propose releasing the points from the specified voxels and establishing second-order relationships between different primitives. We then introduce a global energy-optimization strategy that considers the unity and pairwise potentials while including high-order sequences to improve the over-segmentation problem. Three benchmark methods are introduced to evaluate the properties of the proposed approach by using the ISPRS benchmark datasets and self-collected in-house. The results of our experiments and the comparisons indicate that the proposed method can return reliable segmentation with precision over 72% even with the low-cost sensor, and provide the best performances in terms of the precision and recall rate compared to the benchmark methods.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-10
      DOI: 10.3390/ijgi11040247
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 248: Metric Rectification of Spherical Images

    • Authors: Luigi Barazzetti
      First page: 248
      Abstract: This paper describes a method for metric recording based on spherical images, which are rectified to document planar surfaces. The proposed method is a multistep workflow in which multiple rectilinear images are (i) extracted from a single spherical projection and (ii) used to recover metric properties. The workflow is suitable for documenting buildings with small and narrow rooms, i.e., documentation projects where the acquisition of 360 images is faster than the traditional acquisition of several photographs. Two different rectification procedures were integrated into the current implementation: (i) an analytical method based on control points and (ii) a geometric procedure based on two sets of parallel lines. Constraints based on line parallelism can be coupled with the focal length of the rectified image to estimate the rectifying transformation. The calculation of the focal length does not require specific calibrations projects. It can be derived from the spherical image used during the documentation project, obtaining a rectified image with just an overall scale ambiguity. Examples and accuracy evaluation are illustrated and discussed to show the pros and cons of the proposed method.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-11
      DOI: 10.3390/ijgi11040248
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 249: Quantification of Spatial Association between
           Commercial and Residential Spaces in Beijing Using Urban Big Data

    • Authors: Lei Zhou, Ming Liu, Zhenlong Zheng, Wei Wang
      First page: 249
      Abstract: Commercial and residential spaces are two core types of geographical objects in urban areas. However, these two types of spaces are not independent of each other. Spatial associations exist between them, and a thorough understanding of this spatial association is of great significance for improving the efficiency of urban spatial allocation and realizing scientific spatial planning and governance. Thus, in this paper, the spatial association between commercial and residential spaces in Beijing is quantified with GIS spatial analysis of the average nearest neighbor distance, kernel density, spatial correlation, and honeycomb grid analysis. Point-of-interest (POI) big data of the commercial and residential spaces is used in the quantification since this big data represents a comprehensive sampling of these two spaces. The results show that the spatial distributions of commercial and residential spaces are highly correlated, maintaining a relatively close consumption spatial association. However, the degrees of association between different commercial formats and residential spaces vary, presenting the spatial association characteristics of “integration of daily consumption and separation of nondaily consumption”. The commercial formats of catering services, recreation and leisure services, specialty stores, and agricultural markets are strongly associated with the residential spaces. However, the development of frequently used commercial formats of daily consumption such as living services, convenience stores, and supermarkets appears to lag behind the development of residential spaces. In addition, large-scale comprehensive and specialized commercial formats such as shopping malls, home appliances and electronics stores, and home building materials markets are lagging behind the residential spaces over a wide range. This paper is expected to provide development suggestions for the transformation of urban commercial and residential spaces and the construction of “people-oriented” smart cities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-11
      DOI: 10.3390/ijgi11040249
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 250: A Tourist Behavior Analysis Framework Guided by
           Geo-Information Tupu Theory and Its Application in Dengfeng City, China

    • Authors: Zhihui Tian, Yi Liu, Yongji Wang, Lili Wu
      First page: 250
      Abstract: With the development of tourism and the change in urban functions, the analysis of the spatial pattern of urban tourist flows has become increasingly important. Existing studies have explored and analyzed tourist behavior well, using the appropriate digital footprint data and research methods. However, most studies have ignored internal mechanisms analysis and tourism decision making. This paper proposed a novel framework for tourist behavior analysis inspired by geo-information Tupu, including three modules of the spatiotemporal database, symptom, diagnosis, and implementation. The spatiotemporal database module is mainly used for data acquisition and data cleaning of the digital footprint of tourists. The symptom module is mainly used for revealing the spatial patterns and network structures of tourist flows. The diagnosis and implementation module is mainly used for internal mechanism analysis and tourism decision making under different tourist flow patterns. This paper applied the proposed research framework to Dengfeng City, China, using online travel diaries as the source of digital footprint data, to analyze its tourist behavior. The results were as follows: tourist flows of Dengfeng were unevenly distributed, thus forming an obvious core–periphery structure with intense internal competition and unbalanced power. The difference in tourism resources between its northern and southern areas remains a challenge for future tourism development in Dengfeng.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-11
      DOI: 10.3390/ijgi11040250
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 251: Bibliometric Analysis of OGC Specifications
           between 1994 and 2020 Based on Web of Science (WoS)

    • Authors: Mingrui Huang, Xiangtao Fan, Hongdeng Jian, Hongyue Zhang, Liying Guo, Liping Di
      First page: 251
      Abstract: The Open Geospatial Consortium (OGC) is an international non-profit standards organization. Established in 1994, OGC aims to make geospatial information and services FAIR-Findable, Accessible, Interoperable, and Reusable. OGC specifications have greatly facilitated interoperability among software, hardware, data, and users in the GIS field. This study collected publications related to OGC specifications from the Web of Science (WoS database) between 1994 to 2020 and conducted a literature analysis using Derwent Data Analyzer and VosViewer, finding that OGC specifications have been widely applied in academic fields. The most productive organizations were Wuhan University and George Mason University; the most common keywords were interoperability, data, and web service. Since 2018, the emerging keywords that have attracted much attention from researchers were 3D city models, 3D modeling, and smart cities. To make geospatial data FAIR, the OGC specifications SWE and WMS served more for “Findable”, SWE contributed more to “Accessible”, WPS and WCS served more for “Interoperable”, and WPS, XML schemas, WFS, and WMS served more for “Reusable”. The OGC specification also serves data and web services for large-scale infrastructure such as the Digital Earth Platform of the Chinese Academy of Sciences.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-11
      DOI: 10.3390/ijgi11040251
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 252: Fine Crop Classification Based on UAV
           Hyperspectral Images and Random Forest

    • Authors: Zhihua Wang, Zhan Zhao, Chenglong Yin
      First page: 252
      Abstract: The classification of unmanned aerial vehicle hyperspectral images is of great significance in agricultural monitoring. This paper studied a fine classification method for crops based on feature transform combined with random forest (RF). Aiming at the problem of a large number of spectra and a large amount of calculation, three feature transform methods for dimensionality reduction, minimum noise fraction (MNF), independent component analysis (ICA), and principal component analysis (PCA), were studied. Then, RF was used to finely classify a variety of crops in hyperspectral images. The results showed: (1) The MNF–RF combination was the best ideal classification combination in this study. The best classification accuracies of the MNF–RF random sample set in the Longkou and Honghu areas were 97.18% and 80.43%, respectively; compared with the original image, the RF classification accuracy was improved by 6.43% and 8.81%, respectively. (2) For this study, the overall classification accuracy of RF in the two regions was positively correlated with the number of random sample points. (3) The image after feature transform was less affected by the number of sample points than the original image. The MNF transform curve of the overall RF classification accuracy in the two regions varied with the number of random sample points but was the smoothest and least affected by the number of sample points, followed by the PCA transform and ICA transform curves. The overall classification accuracies of MNF–RF in the Longkou and Honghu areas did not exceed 0.50% and 3.25%, respectively, with the fluctuation of the number of sample points. This research can provide reference for the fine classification of crops based on UAV-borne hyperspectral images.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-12
      DOI: 10.3390/ijgi11040252
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 253: Automatic Positioning of Street Objects Based on
           Self-Adaptive Constrained Line of Bearing from Street-View Images

    • Authors: Guannan Li, Xiu Lu, Bingxian Lin, Liangchen Zhou, Guonian Lv
      First page: 253
      Abstract: In order to realize the management of various street objects in smart cities and smart transportation, it is very important to determine their geolocation. Current positioning methods of street-view images based on mobile mapping systems (MMSs) mainly rely on depth data or image feature matching. However, auxiliary data increase the cost of data acquisition, and image features are difficult to apply to MMS data with low overlap. A positioning method based on threshold-constrained line of bearing (LOB) overcomes the above problems, but threshold selection depends on specific data and scenes and is not universal. In this paper, we propose the idea of divide–conquer based on the positioning method of LOB. The area to be calculated is adaptively divided by the driving trajectory of the MMS, which constrains the effective range of LOB and reduces the unnecessary calculation cost. This method achieves reasonable screening of the positioning results within range without introducing other auxiliary data, which improves the computing efficiency and the geographic positioning accuracy. Yincun town, Changzhou City, China, was used as the experimental area, and pole-like objects were used as research objects to test the proposed method. The results show that the 6104 pole-like objects obtained through object detection realized by deep learning are mapped as LOBs, and high-precision geographic positioning of pole-like objects is realized through region division and self-adaptive constraints (recall rate, 93%; accuracy rate, 96%). Compared with the existing positioning methods based on LOB, the positioning accuracy of the proposed method is higher, and the threshold value is self-adaptive to various road scenes.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-12
      DOI: 10.3390/ijgi11040253
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 254: Geospatial Web Services Discovery through
           Semantic Annotation of WPS

    • Authors: Meriem Sabrine Halilali, Eric Gouardères, Mauro Gaio, Florent Devin
      First page: 254
      Abstract: This paper presents an approach to GWS (GeospatialWeb Service) discovery through the semantic annotation of WPS (Web Processing Service) service descriptions. The rationale behind this work is that search engines that use appropriate semantic-based similarity measures in the matching process are more accurate in terms of precision and recall than those based on syntactic matching alone. The lack of semantics in the description of services using a standard such as WPS prevents the use of such a matching process and is considered a limitation of GWS discovery. The GWS discovery approach presented is based on the consideration of semantics in the service description method and in the matching process. The description of services is based on a semantic lightweight meta-model instantiated in the WPS 2.0 standard, extending the description of the service through metadata tags. The matching process is performed in three steps (functionality matching step, I/O (Input/Output) matching step and non-functional matching step). Its core is a semantic similarity measure that combines logical and non-logical matching methods. Finally, the paper presents the results of an experiment applying the proposed discovery approach on a GWS corpus, showing promising results and the added value of the three-step matching process.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-12
      DOI: 10.3390/ijgi11040254
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 255: HDRLM3D: A Deep Reinforcement Learning-Based
           Model with Human-like Perceptron and Policy for Crowd Evacuation in 3D
           Environments

    • Authors: Dong Zhang, Wenhang Li, Jianhua Gong, Lin Huang, Guoyong Zhang, Shen Shen, Jiantao Liu, Haonan Ma
      First page: 255
      Abstract: At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned global (or local) perceptron (GOLP) to form a human-like perception model. We propose a double-branch feature extraction and decision network (DBFED-Net) as the policy, which can extract features and make behavioral decisions. Moreover, we validate our method’s ability to reproduce typical phenomena and behaviors through experiments in two different scenarios. In scenario I, we reproduce the bottleneck effect of crowds and verify the effectiveness and advantages of HDRLM3D by comparing it with real crowd experiments and classical methods in terms of density maps, fundamental diagrams, and evacuation times. In scenario II, we reproduce agents’ navigation and obstacle avoidance behaviors and demonstrate the advantages of HDRLM3D for crowd simulation in unknown 3D environments by comparing it with other deep reinforcement learning-based models in terms of trajectories and numbers of collisions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-13
      DOI: 10.3390/ijgi11040255
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 256: An Adaptive Embedding Network with Spatial
           Constraints for the Use of Few-Shot Learning in Endangered-Animal
           Detection

    • Authors: Jiangfan Feng, Juncai Li
      First page: 256
      Abstract: Image recording is now ubiquitous in the fields of endangered-animal conservation and GIS. However, endangered animals are rarely seen, and, thus, only a few samples of images of them are available. In particular, the study of endangered-animal detection has a vital spatial component. We propose an adaptive, few-shot learning approach to endangered-animal detection through data augmentation by applying constraints on the mixture of foreground and background images based on species distributions. First, the pre-trained, salient network U2-Net segments the foregrounds and backgrounds of images of endangered animals. Then, the pre-trained image completion network CR-Fill is used to repair the incomplete environment. Furthermore, our approach identifies a foreground–background mixture of different images to produce multiple new image examples, using the relation network to permit a more realistic mixture of foreground and background images. It does not require further supervision, and it is easy to embed into existing networks, which learn to compensate for the uncertainties and nonstationarities of few-shot learning. Our experimental results are in excellent agreement with theoretical predictions by different evaluation metrics, and they unveil the future potential of video surveillance to address endangered-animal detection in studies of their behavior and conservation.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-14
      DOI: 10.3390/ijgi11040256
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 257: Assessment of Groundwater Potential Zones Using
           GIS and Fuzzy AHP Techniques—A Case Study of the Titel Municipality
           (Northern Serbia)

    • Authors: Mirjana Radulović, Sanja Brdar, Minučer Mesaroš, Tin Lukić, Stevan Savić, Biljana Basarin, Vladimir Crnojević, Dragoslav Pavić
      First page: 257
      Abstract: Groundwater is one of the most important natural resources for reliable and sustainable water supplies in the world. To understand the use of water resources, the fundamental characteristics of groundwater need to be analyzed, but in many cases, in situ data measurements are not available or are incomplete. In this study, we used GIS and fuzzy analytic hierarchy process (FAHP) techniques for delineation of the groundwater potential zones (GWPZ) in the Titel Municipality (northern Serbia) based on quantitative assessment scores by experts (hydrologists, hydrogeologists, environmental and geoscientists, and agriculture experts). Six thematic layers, such as geology, geomorphology, slope, soil, land use/land cover, and drainage density were prepared and integrated into GIS software for generating the final map. The area falls into five classes: very good (25.68%), good (12.10%), moderate (15.18%), poor (41.34%), and very poor (5.70%). The GWPZ map will serve to improve the management of these natural resources to ensure future water protection and development of the agricultural sector, and the implemented method can be used in other similar natural conditions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-15
      DOI: 10.3390/ijgi11040257
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 258: Editorial on Geomatic Applications to Coastal
           Research: Challenges and New Developments

    • Authors: Cristina Ponte Lira, Rita González-Villanueva
      First page: 258
      Abstract: This editorial introduces the Special Issue entitled “Geomatic Applications to Coastal Research: Challenges and New Developments” and succinctly evaluates future trends of the use of geomatics in the field of coastal research. This Special Issue was created to emphasize the importance of using different methodologies to study the very complex and dynamic environment of the coast. The field of geomatics offers various tools and methods that are capable of capturing and understanding coastal systems at different scales (i.e., time and space). This Special Issue therefore features nine articles in which different methodologies and study cases are presented, highlighting what the field of geomatics has to offer to the field of coastal research. The featured articles use a range of methodologies, from GIS to remote sensing, as well as statistical and spatial analysis techniques, to advance the knowledge of coastal areas and improve management and future knowledge of these areas.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-15
      DOI: 10.3390/ijgi11040258
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 259: Analyzing Air Pollutant Reduction Possibilities
           in the City of Zagreb

    • Authors: Nikola Kranjčić, Dragana Dogančić, Bojan Đurin, Anita Ptiček Siročić
      First page: 259
      Abstract: This paper aims to present possible areas to plant different vegetation types near traffic jams to reduce air pollution in the capital of Croatia, the city of Zagreb. Based on main traffic road and random forest machine learning using WorldView-2 European cities data, potential areas are established. It is seen that, based on a 10 m buffer, there is a possible planting area of more than 220,000 square meters, and based on 15 m buffer, there is a possible planting area of more than 410,000 square meters. The proposed plants are Viburnum lucidum, Photinia x fraseri, Euonymus japonicus, Tilia cordata, Aesculus hippocastanum, Pinus sp., Taxus baccata, Populus alba, Quercus robur, Betula pendula, which are characteristic for urban areas in Croatia. The planting of proposed trees may result in an increase of 3–5% in the total trees in the city of Zagreb. Although similar research has been published, this paper presents novelty findings from combined machine learning methods for defining green urban areas. Additionally, this paper presents original results for this region.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-15
      DOI: 10.3390/ijgi11040259
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 260: Spatiotemporal Analysis of Traffic Accidents
           Hotspots Based on Geospatial Techniques

    • Authors: Hazaymeh, Almagbile, Alomari
      First page: 260
      Abstract: This paper aims to explore the spatiotemporal pattern of traffic accidents using five years of data between 2015 and 2019 for the Irbid Governorate, Jordan. The spatial pattern of traffic-accident hotspots and their temporal evolution were identified along the internal and arterial roads network in the study area using spatial autocorrelation (Global Moran I index) and local hotspot analysis (Getis–Ord Gi*) techniques within the GIS environment. The study showed a gradual increase in the reported traffic accidents of approximately 38% at the year level. The analysis of traffic accidents at the severity level showed a distinguished spatial distribution of hotspot locations. The less severe traffic accidents (~95%) occurred on the internal road network in the Irbid Governorate’s towns where the highest traffic volume exist. The spatial autocorrelation analysis and the Getis–Ord Gi* statistics with 99% of significance level showed clustering patterns of traffic accidents along the internal and the arterial road network segments. Between 2015 and 2019, a notable evolution of the traffic-accident hotspots clusters was pronounced. The results can be used to guide traffic managers and decision makers to take appropriate actions for enhancing the hotspot locations and improving their traffic safety status.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-15
      DOI: 10.3390/ijgi11040260
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 261: Toward User-Generated Content as a Mechanism of
           Digital Placemaking—Place Experience Dimensions in Spatial Media

    • Authors: Maciej Główczyński
      First page: 261
      Abstract: Spatial media bring out new forms of interaction with places, leading to the emergence of new ways of embodying the experience. The perception of place and its dynamics of change has been multiplied by the emergence of digital platforms, which create many and varied representations of place in spatial media. These representations are dependent on the digital platforms’ ecosystem, formed by platform-specific mechanisms of digital placemaking. The study applied text mining techniques and statistical methods to explore the role of user-generated content as a digital placemaking practice in shaping place experience. The online reviews were collected from Google Maps for 23 places from Poznań, Poland. The analysis showed that place experience is described by three dimensions: attributes, practices and atmosphere, or place practices that most closely reflect the specificity of a place. The place attributes blurred the boundaries between their digital images, whereas the atmosphere dimension reduces the diversity and uniqueness of the place. In conclusion, user-generated content (UGC) as an element of the process of digital placemaking increases place awareness and democratizes human participation in its creation, yet it affects its reduction to homogeneous information processed through mechanisms operating within a given digital platform.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-16
      DOI: 10.3390/ijgi11040261
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 262: Optimization of Shelter Location Based on a
           Combined Static/Dynamic Two-Stage Optimization Methodology: A Case Study
           in the Central Urban Area of Xinyi City, China

    • Authors: Guangchun Zhong, Guofang Zhai, Wei Chen
      First page: 262
      Abstract: Determining how to reasonably allocate shelters in the central area of the city and improve evacuation efficiency are important issues in the field of urban disaster prevention. This paper introduces the methodology and mathematical model from the field of crowd emergency evacuation to shelter location optimization. Moreover, a shelter location optimization method based on the combination of static network analysis and dynamic evacuation simulation is proposed. The construction costs and evacuation times are taken as the objective functions. In the first stage, based on the static network analysis, a circular evacuation allocation rule based on the gravity model is proposed, and the genetic algorithm is then designed to solve the feasible schemes with the lowest shelter construction costs. In the second stage, the evacuation time is taken as the optimization objective. The age differences of refugees, the selection of evacuation routes, and the behavior of adults helping children and the elderly are simulated in a dynamic evacuation simulation model. The traditional social force model is improved to conduct a regional evacuation simulation and determine the optimal scheme with the shortest evacuation time. Finally, the central urban area of Xinyi City, Jiangsu Province, China, is taken as an empirical case.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-16
      DOI: 10.3390/ijgi11040262
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 263: Hybrid-TransCD: A Hybrid Transformer Remote
           Sensing Image Change Detection Network via Token Aggregation

    • Authors: Qingtian Ke, Peng Zhang
      First page: 263
      Abstract: Existing optical remote sensing image change detection (CD) methods aim to learn an appropriate discriminate decision by analyzing the feature information of bitemporal images obtained at the same place. However, the complex scenes in high-resolution (HR) remote images cause unsatisfied results, especially for some irregular and occluded objects. Although recent self-attention-driven change detection models with CNN achieve promising effects, the computational and consumed parameters costs emerge as an impassable gap for HR images. In this paper, we utilize a transformer structure replacing self-attention to learn stronger feature representations per image. In addition, concurrent vision transformer models only consider tokenizing single-dimensional image tokens, thus failing to build multi-scale long-range interactions among features. Here, we propose a hybrid multi-scale transformer module for HR remote images change detection, which fully models representation attentions at hybrid scales of each image via a fine-grained self-attention mechanism. The key idea of the hybrid transformer structure is to establish heterogeneous semantic tokens containing multiple receptive fields, thus simultaneously preserving large object and fine-grained features. For building relationships between features without embedding with token sequences from the Siamese tokenizer, we also introduced a hybrid difference transformer decoder (HDTD) layer to further strengthen multi-scale global dependencies of high-level features. Compared to capturing single-stream tokens, our HDTD layer directly focuses representing differential features without increasing exponential computational cost. Finally, we propose a cascade feature decoder (CFD) for aggregating different-dimensional upsampling features by establishing difference skip-connections. To evaluate the effectiveness of the proposed method, experiments on two HR remote sensing CD datasets are conducted. Compared to state-of-the-art methods, our Hybrid-TransCD achieved superior performance on both datasets (i.e., LEVIR-CD, SYSU-CD) with improvements of 0.75% and 1.98%, respectively.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-17
      DOI: 10.3390/ijgi11040263
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 264: A Distributed Hybrid Indexing for Continuous KNN
           Query Processing over Moving Objects

    • Authors: Imene Bareche, Ying Xia
      First page: 264
      Abstract: The magnitude of highly dynamic spatial data is expanding rapidly due to the instantaneous evolution of mobile technology, resulting in challenges for continuous queries. We propose a novel indexing approach model, namely, the Velocity SpatioTemporal indexing approach (VeST), for continuous queries, mainly Continuous K-nearest Neighbor (CKNN) and continuous range queries using Apache Spark. The proposed structure is based on a selective velocity partitioning method, i.e., since different objects have varying speeds, we divide the objects into two sets according to the actual mean speed we calculate before building the index and accessing data. Then the adopted indexing structure base unit comprises a nonoverlapping R-tree and a two dimension grid. The tree divides the space into nonoverlapping minimum bounding regions that point to the grids. Then, the uniform grid stores the object data of leaf nodes. This access method reduces the update cost and improves response time and query precision. In order to enhance performances for large-scale processing, we design a compact multilayer index structure on a distributed setting and propose a CKNN search algorithm for accurate results using a candidate cell identification process. We provide a comprehensive vision of our indexing model and the adopted query technique. The simulation results show that for query intervals of 100, the proposed approach is 13.59 times faster than the traditional approach, and the average time of the VeST approach is less than 0.005 for all query intervals. This proposed method improves response time and query precision. The precision of the VeST algorithm is almost equal to 100% regardless of the length of the query interval.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-17
      DOI: 10.3390/ijgi11040264
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 265: An Information Fusion Model between GeoSOT Grid
           and Global Hexagonal Equal Area Grid

    • Authors: Qingmei Li, Xin Chen, Xiaochong Tong, Xuantong Zhang, Chengqi Cheng
      First page: 265
      Abstract: In order to cope with the rapid growth of spatiotemporal big data, data organization models based on discrete global grid systems have developed rapidly in recent years. Due to the differences in model construction methods, grid level subdivision and coding rules, it is difficult for discrete global grid systems to integrate, share and exchange data between different models. Aiming at the problem of information fusion between a GeoSOT grid and global hexagonal equal area grid system, this paper proposes the GeoSOT equivalent aggregation model (the GEA model). We establish a spatial correlation index method between GeoSOT grids and global hexagonal equal area grids, and based on the spatial correlation index, we propose an interoperable transformation method for grid attributes information. We select the POI (points of interest) data of Beijing bus and subway stations and carry out the transformation experiment of hexagonal grid to GeoSOT grid information so as to verify the effectiveness of the GEA model. The experimental results show that when the 17th-level GeoSOT grid is selected as the particle grid to fit the hexagonal grid, the accuracy and efficiency can be well balanced. The fitting accuracy is 95.51%, and the time consumption is 30.9 ms. We establish the associated index of the GeoSOT grid and the hexagonal grid and finally realized the exchange of information.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-17
      DOI: 10.3390/ijgi11040265
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 266: Increasing Access to Cultural Heritage Objects
           from Multiple Museums through Semantically-Aware Maps

    • Authors: Cristina Portalés, Pablo Casanova-Salas, Javier Sevilla, Jorge Sebastián, Arabella León, Jose Javier Samper
      First page: 266
      Abstract: Geographical information is gaining new momentum as an analysis and visualization tool for collections of cultural objects. It provides all kinds of users with new opportunities to contextualize and understand these objects in ways that resemble our ordinary spatially-located experience and to do so better than textual narratives. The SeMap project has built an online resource that shows more than 200,000 cultural objects through spatiotemporal maps, thus enabling new experiences and perspectives around these objects. Data come from the CER.ES repository and were created by a network of more than 100 Spanish museums. This article explains the refinement of the data provided by the repository, mostly by adding a semantic structure thanks to the CIDOC-CRM ontology, and by simplifying the exceedingly complex terminologies employed in the original records. Particular attention is paid to the methods for geolocating the information, as well as adding temporal filters (among others) to user queries. The functionalities, interface, and technical requirements are also explored at length.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-18
      DOI: 10.3390/ijgi11040266
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 267: Structural Differences of PM2.5 Spatial
           Correlation Networks in Ten Metropolitan Areas of China

    • Authors: Shuaiqian Zhang, Fei Tao, Qi Wu, Qile Han, Yu Wang, Tong Zhou
      First page: 267
      Abstract: The cross-impact of environmental pollution among cities has been reported in more research works recently. To implement the coordinated control of environmental pollution, it is necessary to explore the structural characteristics and influencing factors of the PM2.5 spatial correlation network from the perspective of the metropolitan area. This paper utilized the gravity model to construct the PM2.5 spatial correlation network of ten metropolitan areas in China from 2019 to 2020. After analyzing the overall characteristics and node characteristics of each spatial correlation network based on the social network analysis (SNA) method, the quadratic assignment procedure (QAP) regression analysis method was used to explore the influence mechanism of each driving factor. Patent granted differences, as a new indicator, were also considered during the above. The results showed that: (1) In the overall network characteristics, the network density of Chengdu and the other three metropolitan areas displayed a downward trend in two years, and the network density of Wuhan and Chengdu was the lowest. The network density and network grade of Hangzhou and the other four metropolitan areas were high and stable, and the network structure of each metropolitan area was unstable. (2) From the perspective of the node characteristics, the PM2.5 spatial correlation network all performed trends of centralization and marginalization. Beijing-Tianjin-Hebei and South Central Liaoning were “multi-core” metropolitan areas, and the other eight were “single-core” metropolitan areas. (3) The analysis results of QAP regression illustrated that the top three influencing factors of the six metropolitan areas were geographical locational relationship, the secondary industrial proportion differences, respectively, and patent granted differences, and the other metropolitan areas had no dominant influencing factors.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-17
      DOI: 10.3390/ijgi11040267
      Issue No: Vol. 11, No. 4 (2022)
       
  • IJGI, Vol. 11, Pages 268: Quantitative Relations between Topological
           Similarity Degree and Map Scale Change of Contour Clusters in Multi-Scale
           Map Spaces

    • Authors: Rong Wang, Haowen Yan, Xiaomin Lu
      First page: 268
      Abstract: Quantitative relations between topological similarity degree and map scale change of multi-scale contour clusters are vital to the automation of map generalization. However, no method has been proposed to calculate the relations. This paper aims at filling the gap by proposing a new approach. It firstly constructed a directed contour tree by pre-processing of unclosed contours, and then developed a quantitative expression of topological relations of contour cluster based on directed contour tree. After this, it employed 108 groups of multi-scale contour clusters with different geomorphological types to explore the changing regularity of topological indices with map scale. Last, it used 416 points to calculate the quantitative relations between topological similarity degree and map scale change by curve fitting method. The results show that the quantitative expression of multi-scale topological indexes is closely related to the contour interval change, and power function is the best fit among the candidate functions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-04-18
      DOI: 10.3390/ijgi11040268
      Issue No: Vol. 11, No. 4 (2022)
       
 
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