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ISPRS International Journal of Geo-Information
Journal Prestige (SJR): 0.493
Citation Impact (citeScore): 2
Number of Followers: 6  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2220-9964
Published by MDPI Homepage  [247 journals]
  • IJGI, Vol. 12, Pages 26: The Concept of a Georeferential Spatial Database
           of Topographic–Historical Objects (GSDoT-HO): A Case Study of the
           Cadastral Map of Toruń (Poland)

    • Authors: Radosław Golba, Agnieszka Pilarska, Roman Czaja
      First page: 26
      Abstract: In this study, we aimed to further the international discussion on the methodology of applying GIS technology to the editing of large-scale cadastral maps, taking the experience of editing the cadastral map of Toruń from 1910–1915 as an example. We present the concept of building a georeferential spatial database of topographic–historical objects (GSDoT-HO), which includes the stages involved in creating the database, its exemplary structure, and a proposal of good practices in this process, which were developed in the course of previous projects using a geographic information system for Historical Atlases of Polish Towns. Our works included the scanning, calibration, and rectification of a total of 178 sheets of cadastral maps (including 154 sheets of the map of Toruń and 24 sheets of the cadastral map of the then-village of Mokre) at differentiated scales of 1:250, 1:500, 1:1000, and 1:2000. Finally, in the process of vectorization, vector and attribute data were acquired, which made up the final result in the form of GSDoT-HOs. This database was created out of seven information layers with linear or polygon geometries, including the two most important layers, i.e., plots and buildings, which for the then-area of the city of Toruń, contained approximately 5800 and 10,800 vectorised polygon objects, respectively. This article shifts the focus of the discussion of standards in the use of GIS technology to edit Historic Towns Atlases from the development of interactive maps to the construction of a database that should enable comparative studies of urban spaces.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-17
      DOI: 10.3390/ijgi12020026
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 27: Free Choice Navigation in the Real World: Giving
           Back Freedom to Wayfinders

    • Authors: Bartosz Mazurkiewicz, Markus Kattenbeck, Ioannis Giannopoulos
      First page: 27
      Abstract: In recent years, there has been collected evidence suggesting that increased usage of navigation assistance systems has a harmful effect on spatial cognition, including spatial knowledge acquisition. Previously, we proposed a potential remedy called Free Choice Navigation (simulation study). This novel navigation approach aims to provide the user with more freedom while navigating, and simultaneously give fewer navigation instructions. This approach also aims at increasing engagement with the environment and fostering spatial knowledge acquisition. We conducted a human-subject study with 48 participants comparing Free Choice Navigation with the widespread Turn-by-Turn approach on the outskirts of Vienna, Austria. The study showed the viability of our navigation system in real urban environments, providing fewer navigation instructions compared to the Turn-by-Turn approach (relative to the number of traversed junctions). Fewer instructions and forced engagement with the environment, however, did not result in differences concerning spatial knowledge acquisition, but interestingly, Free Choice Navigation users (without a map) could extract spatial configuration information similarly well as Turn-by-Turn users having a map. Moreover, we provide evidence that people are interested in learning more about their environments and are willing to walk longer routes to achieve it.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-17
      DOI: 10.3390/ijgi12020027
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 28: Interactive and Immersive Digital Representation
           for Virtual Museum: VR and AR for Semantic Enrichment of Museo Nazionale
           Romano, Antiquarium di Lucrezia Romana and Antiquarium di Villa Dei
           Quintili

    • Authors: Fabrizio Banfi, Mara Pontisso, Francesca Romana Paolillo, Stefano Roascio, Clara Spallino, Chiara Stanga
      First page: 28
      Abstract: The research focuses on the generation of 3D models aimed at creating interactive virtual environments as the outcomes of scalar representations of existing realities. The purpose is to increase the narration, fruition, and dissemination of the findings that emerged from the archaeological investigations carried out in a large sector of the south-eastern suburbs of Rome. In this context, the research proposes a process oriented toward designing a virtual museum of the first group of works from the Appia Antica Archaeological Park and now exhibited at the Museo Nazionale Romano, the Antiquarium di Lucrezia Romana, and the Antiquarium di Villa Dei Quintili. Managing high historical and cultural findings through geometrical surveys, high-resolution data from 3D survey analysis, archival research, and interactive digital representation is the aim of the study. The digitisation of artefacts has made it possible to build new forms of communication that enrich virtual and on-site visits with content, both of the park and of the Museums that host the collections. In particular, it has gradually allowed a ‘virtual’ relocation of works from the Appia Park, favouring the definition of a method capable of communicating new content and laying the basis for the development of a virtual museum, a temporary exhibition, and a web platform for one of the most important historical sites of ancient Rome.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-17
      DOI: 10.3390/ijgi12020028
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 29: Acknowledgment to the Reviewers of IJGI in 2022

    • Authors: IJGI Editorial Office IJGI Editorial Office
      First page: 29
      Abstract: High-quality academic publishing is built on rigorous peer review [...]
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-18
      DOI: 10.3390/ijgi12020029
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 30: User Evaluation of Thematic Maps on Operational
           Areas of Rescue Helicopters

    • Authors: Łukasz Wielebski, Beata Medyńska-Gulij
      First page: 30
      Abstract: This article presents the results of research on users concerning six thematic maps made with various mapping techniques and related to various aspects of the activities of the Helicopter Emergency Medical Service. The aim of the survey was to determine how the respondents rank these maps in terms of the four subjective evaluation criteria, which were the graphical attractiveness of maps, the readability of maps, the usefulness and importance of information, and the complexity of information presented on the maps. The greatest discrepancies were noted for the dot map, while the flow map obtained the most consistent evaluations. To check what the respondents were guided by while building the ranking for each criterion, a catalog of factors was created, the importance of which was assessed using the Likert scale. In the case of graphical attractiveness, users attach particular importance to the arrangement of objects visible on the map. The speed of reading the information is particularly important for map readability. In the case of the usefulness and importance of the information, the map topic, important for saving health and life from the user’s point of view, was of the greatest importance, while the amount of information in the legend significantly influenced the evaluation of information complexity.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-18
      DOI: 10.3390/ijgi12020030
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 31: Multi-GPU-Parallel and Tile-Based Kernel Density
           Estimation for Large-Scale Spatial Point Pattern Analysis

    • Authors: Guiming Zhang, Jin Xu
      First page: 31
      Abstract: Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one GPU for parallel computing. Additionally, it assumes that the input data can be held in GPU memory all at once for computation, which is unrealistic when conducting KDE analysis over large geographic areas at high resolution. This study develops a multi-GPU-parallel and tile-based KDE algorithm to overcome these limitations. It exploits multiple GPUs to speedup complex KDE computation by distributing computation across GPUs, and approaches density estimation with a tile-based strategy to bypass the memory bottleneck. Experiment results show that the parallel KDE algorithm running on multiple GPUs achieves significant speedups over running on a single GPU, and higher speedups are achieved on KDE tasks of a larger problem size. The tile-based strategy renders it feasible to estimate high-resolution density surfaces over large areas even on GPUs with only limited memory. Multi-GPU parallel computing and tile-based density estimation, while incurring very little computational overhead, effectively enable conducting KDE for large-scale spatial point pattern analysis on geospatial big data.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-18
      DOI: 10.3390/ijgi12020031
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 32: Probing Regional Disparities and Their
           Characteristics in a Suburb of a Global South Megacity: The Case of Bekasi
           Regency, Jakarta Metropolitan Region

    • Authors: Adib Ahmad Kurnia, Ernan Rustiadi, Akhmad Fauzi, Andrea Emma Pravitasari, Jan Ženka
      First page: 32
      Abstract: The Jakarta metropolitan region (the Jakarta megacity), located in the fourth most populous country in the world (Indonesia), is the largest urban agglomeration in the Global South—continues to grow, especially in its outer suburbs (Bekasi Regency). The governments (Central and Local) tend to implement an urban-biased policy (UBP) to connect Bekasi Regency into global production networks and boost Bekasi Regency’s income. However, previous case studies of China and Vietnam have revealed that the UBP increases economic disparities between urban and rural areas. Therefore, this study probes urban–rural economic disparities and their characteristics at a microregional level (desa/kelurahan) in the Bekasi Regency. The methods applied in this study are geographically weighted regression (GWR), RULT index, and quantitative zoning. The results show that almost all desa/kelurahan in the high poverty (HPv) cluster are rural neighborhoods (desa/kelurahan with rural characteristics). By contrast, only 5% of desa/kelurahan with urban characteristics are HPvs, while the remainder are in the low poverty (LPv) cluster. Rural neighborhoods with HPv tend to have a high percentage of households dependent on agriculture. Thus, empirical results (with a case of a Global South megacity suburb) further support previous evidence that the UBP has caused urban–rural economic disparities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-18
      DOI: 10.3390/ijgi12020032
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 33: Automatic Production of Deep Learning Benchmark
           Dataset for Affine-Invariant Feature Matching

    • Authors: Guobiao Yao, Jin Zhang, Jianya Gong, Fengxiang Jin
      First page: 33
      Abstract: To promote the development of deep learning for feature matching, image registration, and three-dimensional reconstruction, we propose a method of constructing a deep learning benchmark dataset for affine-invariant feature matching. Existing images often have large viewpoint differences and areas with weak texture, which may cause difficulties for image matching, with respect to few matches, uneven distribution, and single matching texture. To solve this problem, we designed an algorithm for the automatic production of a benchmark dataset for affine-invariant feature matching. It combined two complementary algorithms, ASIFT (Affine-SIFT) and LoFTR (Local Feature Transformer), to significantly increase the types of matching patches and the number of matching features and generate quasi-dense matches. Optimized matches with uniform spatial distribution were obtained by the hybrid constraints of the neighborhood distance threshold and maximum information entropy. We applied this algorithm to the automatic construction of a dataset containing 20,000 images: 10,000 ground-based close-range images, 6000 satellite images, and 4000 aerial images. Each image had a resolution of 1024 × 1024 pixels and was composed of 128 pairs of corresponding patches, each with 64 × 64 pixels. Finally, we trained and tested the affine-invariant deep learning model, AffNet, separately on our dataset and the Brown dataset. The experimental results showed that the AffNet trained on our dataset had advantages, with respect to the number of matching points, match correct rate, and matching spatial distribution on stereo images with large viewpoint differences and weak texture. The results verified the effectiveness of the proposed algorithm and the superiority of our dataset. In the future, our dataset will continue to expand, and it is intended to become the most widely used benchmark dataset internationally for the deep learning of wide-baseline image matching.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-19
      DOI: 10.3390/ijgi12020033
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 34: Spatial Application of Southern U.S. Pine Water
           Yield for Prioritizing Forest Management Activities

    • Authors: Jordan Vernon, Joseph St. Peter, Christy Crandall, Olufunke E. Awowale, Paul Medley, Jason Drake, Victor Ibeanusi
      First page: 34
      Abstract: Forest management depends on forest condition data and the ability to quantify the impacts of management activities to make informed decisions. Spatially quantifying water yield (WY) from forests across large landscapes enables managers to consider potential WY changes when designing forest management plans. Current forest water yield datasets are either spatially coarse or too restricted to specific sites with in situ monitoring to support some project-level forest management decisions. In this study, we spatially apply a stand-level southern pine WY model over a forested landscape in the Florida panhandle. We informed the WY model with pine leaf area index inputs created from lidar remote sensing and field data, a spatial and temporal aridity index from PRISM and MODIS data, and a custom depth to groundwater dataset. Baseline WY conditions for the study area were created using the Esri and Python tools we developed to automate the WY workflow. Several timber thinning scenarios were then used to quantify water yield increases from forest management activities. The results of this methodology are detailed (10 m spatial resolution) forest WY raster datasets that are currently being integrated with other spatial datasets to inform forest management decisions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-19
      DOI: 10.3390/ijgi12020034
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 35: Big Data Management Algorithms, Deep
           Learning-Based Object Detection Technologies, and Geospatial Simulation
           and Sensor Fusion Tools in the Internet of Robotic Things

    • Authors: Mihai Andronie, George Lăzăroiu, Mariana Iatagan, Iulian Hurloiu, Roxana Ștefănescu, Adrian Dijmărescu, Irina Dijmărescu
      First page: 35
      Abstract: The objective of this systematic review was to analyze the recently published literature on the Internet of Robotic Things (IoRT) and integrate the insights it articulates on big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools. The research problems were whether computer vision techniques, geospatial data mining, simulation-based digital twins, and real-time monitoring technology optimize remote sensing robots. Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines were leveraged by a Shiny app to obtain the flow diagram comprising evidence-based collected and managed data (the search results and screening procedures). Throughout January and July 2022, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms comprising “Internet of Robotic Things” + “big data management algorithms”, “deep learning-based object detection technologies”, and “geospatial simulation and sensor fusion tools”. As the analyzed research was published between 2017 and 2022, only 379 sources fulfilled the eligibility standards. A total of 105, chiefly empirical, sources have been selected after removing full-text papers that were out of scope, did not have sufficient details, or had limited rigor For screening and quality evaluation so as to attain sound outcomes and correlations, we deployed AMSTAR (Assessing the Methodological Quality of Systematic Reviews), AXIS (Appraisal tool for Cross-Sectional Studies), MMAT (Mixed Methods Appraisal Tool), and ROBIS (to assess bias risk in systematic reviews). Dimensions was leveraged as regards initial bibliometric mapping (data visualization) and VOSviewer was harnessed in terms of layout algorithms.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-21
      DOI: 10.3390/ijgi12020035
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 36: A Comparison of Several UAV-Based Multispectral
           Imageries in Monitoring Rice Paddy (A Case Study in Paddy Fields in
           Tottori Prefecture, Japan)

    • Authors: Muhammad Dimyati, Supriatna Supriatna, Ryota Nagasawa, Fajar Dwi Pamungkas, Rizki Pramayuda
      First page: 36
      Abstract: In recent years, unmanned aerial vehicles (UAVs) have been actively applied in the agricultural sector. Several UAVs equipped with multispectral cameras have become available on the consumer market. Multispectral data are informative and practical for evaluating the greenness and growth status of vegetation as well as agricultural crops. The precise monitoring of rice paddy, especially in the Asian region, is crucial for optimizing profitability, sustainability, and protection of agro-ecological services. This paper reports and discusses our findings from experiments conducted to test four different commercially available multispectral cameras (Micesense RedEdge-M, Sentera Single NDVI, Mapir Survey3, and Bizworks Yubaflex), which can be mounted on a UAV in monitoring rice paddy. The survey has conducted in the typical paddy field area located in the alluvial plain in Tottori Prefecture, Japan. Six different vegetation indices (NDVI, BNDVI, GNDVI, VARI, NDRE and MCARI) captured by UAVs were also compared and evaluated monitoring contribution at three different rice cropping phases. The results showed that the spatial distribution of NDVI collected by each camera is almost similar in paddy fields, but the absolute values of NDVI differed significantly from each other. Among them, the Sentera camera showed the most reasonable NDVI values of each growing phase, indicating 0.49 in the early reproductive phase, 0.62 in the late reproductive stage, and 0.38 in the ripening phase. On the other hand, compared to the most commonly used NDVI, VARI which can be calculated from only visible RGB bands, can be used as an easy and effective index for rice paddy monitoring.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-21
      DOI: 10.3390/ijgi12020036
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 37: Landslide Susceptibility Assessment in the
           Japanese Archipelago Based on a Landslide Distribution Map

    • Authors: Masanori Kohno, Yuki Higuchi
      First page: 37
      Abstract: Though danger prediction and countermeasures for landslides are important, it is fundamentally difficult to take preventive measures in all areas susceptible to dangerous landslides. Therefore, it is necessary to perform landslide susceptibility mapping, extract slopes with high landslide hazard/risk, and prioritize locations for conducting investigations and countermeasures. In this study, landslide susceptibility mapping along the whole slope of the Japanese archipelago was performed using the analytical hierarchy process (AHP) method, and geographic information system analysis was conducted to extract the slope that had the same level of hazard/risk as areas where landslides occurred in the past, based on the ancient landslide topography in the Japanese archipelago. The evaluation factors used were elevation, slope angle, slope type, flow accumulation, geology, and vegetation. The landslide susceptibility of the slope was evaluated using the score accumulation from the AHP method for these evaluation factors. Based on the landslide susceptibility level (I to V), a landslide susceptibility map was prepared, and landslide susceptibility assessment in the Japanese archipelago was identified. The obtained landslide susceptibility map showed good correspondence with the landslide distribution, and correlated well with past landslide occurrences. This suggests that our method can be applied to the extraction of unstable slopes, and is effective for prioritizing and implementing preventative measures.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-22
      DOI: 10.3390/ijgi12020037
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 38: Usefulness of Plane-Based Augmented
           Geovisualization—Case of “The Crown of Polish Mountains
           3D”

    • Authors: Łukasz Halik, Łukasz Wielebski
      First page: 38
      Abstract: In this article, we suggest the introduction of a new method of generating AR content, which we propose to call plane-based augmented geovisualizations (PAGs). This method concerns cases in which AR geovisualizations are embedded directly on any plane detected by the AR device, as in the case of the investigated “Crown of Polish Mountains 3D” application. The study on the usefulness of the AR solution against a classic solution was conducted as part of an online survey of people from various age and social groups. The application in the monitor version showing 3D models of mountain peaks (without AR mode) was tested by the respondents themselves. The use of the application in the AR mode, which requires a smartphone with the appropriate module, was tested by the respondents based on a prepared video demonstrating its operation. The results of the research on three age groups show that the AR mode was preferred among users against all compared criteria, but some differences between age groups were clearly visible. In the case of the criterion of ease of use of the AR mode, the result was not so unambiguous, which is why further research is necessary. The research results show the potential of the AR mode in presenting 3D terrain models.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-22
      DOI: 10.3390/ijgi12020038
      Issue No: Vol. 12, No. 2 (2023)
       
  • IJGI, Vol. 12, Pages 13: Spatial–Temporal Data Imputation Model of
           Traffic Passenger Flow Based on Grid Division

    • Authors: Li Cai, Cong Sha, Jing He, Shaowen Yao
      First page: 13
      Abstract: Traffic flows (e.g., the traffic of vehicles, passengers, and bikes) aim to reveal traffic flow phenomena generated by traffic participants in traffic activities. Various studies of traffic flows rely heavily on high-quality traffic data. The taxi GPS trajectory data are location data that include latitude, longitude, and time. These data are critical for traffic flow analysis, planning, infrastructure layout, and recommendations for urban residents. A city map can be divided into multiple grids according to the latitude and longitude coordinates, and traffic passenger flows data derived from taxi trajectory data can be extracted. However, random missing data occur due to weather and equipment failure. Therefore, the effective imputation of missing traffic flow data is a hot topic. This study proposes the spatio-temporal generative adversarial imputation net (ST-GAIN) model to solve the traffic passenger flows imputation. An adversarial game with multiple generators and one discriminator is established. The generator observes some components of the time-domain and regional traffic data vector extracted from the grid. It effectively imputes the missing values of the spatio-temporal traffic passenger flow data. The experimental data are accurate Kunming taxi trajectory data, and experimental results show that the proposed method outperforms five baseline methods regarding the imputation accuracy. It is significant and suggests the possibility of effectively applying the model to predict the passenger flows in some areas where traffic data cannot be collected for some reason or traffic data are randomly missing.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-04
      DOI: 10.3390/ijgi12010013
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 14: Deep Learning Semantic Segmentation for Land Use
           and Land Cover Types Using Landsat 8 Imagery

    • Authors: Wuttichai Boonpook, Yumin Tan, Attawut Nardkulpat, Kritanai Torsri, Peerapong Torteeka, Patcharin Kamsing, Utane Sawangwit, Jose Pena, Montri Jainaen
      First page: 14
      Abstract: Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This is because of the deep convolution layer and multiple levels of deep steps of the baseline network, which can cause a degradation problem in small land use features. In this paper, a deep learning semantic segmentation algorithm which comprises an adjustment network architecture (LoopNet) and land use dataset is proposed for automatic land use classification using Landsat 8 imagery. The experimental results illustrate that deep learning semantic segmentation using the baseline network (SegNet, U-Net) outperforms pixel-based machine learning algorithms (MLE, SVM, RF) for land use classification. Furthermore, the LoopNet network, which comprises a convolutional loop and convolutional block, is superior to other baseline networks (SegNet, U-Net, PSPnet) and improvement networks (ResU-Net, DeeplabV3+, U-Net++), with 89.84% overall accuracy and good segmentation results. The evaluation of multispectral bands in the land use dataset demonstrates that Band 5 has good performance in terms of extraction accuracy, with 83.91% overall accuracy. Furthermore, the combination of different spectral bands (Band 1–Band 7) achieved the highest accuracy result (89.84%) compared to individual bands. These results indicate the effectiveness of LoopNet and multispectral bands for land use classification using Landsat 8 imagery.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-07
      DOI: 10.3390/ijgi12010014
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 15: Diagnosis and Planning Strategies for Quality of
           Urban Street Space Based on Street View Images

    • Authors: Jiwu Wang, Yali Hu, Wuxihong Duolihong
      First page: 15
      Abstract: Under the background of stock planning, improving the quality of urban public space has become an important work of urban planning, design, and construction management. An accurate diagnosis of the spatial quality of streets and the effective implementation of street renewal planning play important roles in the high-quality development of urban spatial environments. However, traditional planning design and study methods, typically based on questionnaires, interviews, and on-site research, are inefficient and make it difficult to objectively and comprehensively grasp the overall construction characteristics and problems of urban street space in a large area, thus making it challenging to meet the needs of practical planning. Therefore, based on street view images, this study combined machine learning with an artificial audit to put forward a methodological framework for diagnosing the quality issues of street space. The Gongshu District of Hangzhou, China, was selected as a case study, and the diagnosis of quality problems for streets at different grades was achieved. The diagnosis results showed the current situation and problems of the selected area. Simultaneously, a series of targeted strategies for street spatial update planning was proposed to solve these problems. This diagnostic method, based on a combination of subjective and objective approaches, can be conducive to the precise and comprehensive identification of urban public spatial problems, which is expected to become an effective tool to assist in urban renewal and other planning decisions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-07
      DOI: 10.3390/ijgi12010015
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 16: Explanatory Factors of Daily Mobility Patterns in
           Suburban Areas: Applications and Taxonomy of Two Metropolitan Corridors in
           Madrid Region

    • Authors: Andrea Alonso, Andrés Monzón, Iago Aguiar, Alba Ramírez-Saiz
      First page: 16
      Abstract: Understanding the characteristics that shape mobility could help to achieve more sustainable transport systems. A considerable body of scientific studies tries to determine these characteristics at the urban level. However, there is a lack of studies analyzing those factors for the heterogeneous zones existing in the suburbs of big cities. The study presented in this paper intends to fill this gap, in the context of two metropolitan corridors in the Madrid Region. Correlation analyses are used to examine how mobility patterns are affected by socioeconomic and urban form variables. Then, a cluster analysis is carried out to classify the types of zones we may find in the suburbs. Results show that the main characteristics leading towards higher car use are low urban density, few local activities, a high percentage of children, and a low percentage of seniors. As for the variable distance to the city center, it does not explain car use. Moreover, some remote areas have many walking trips. This is well understood in the cluster analysis; there are zones far away from the city center but that are dense and well provided for, which work as self-sufficient urban centers. Results reinforce the theories underlying polycentrism as a solution to the urban sprawl challenge.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-09
      DOI: 10.3390/ijgi12010016
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 17: Evaluation of Geological Hazard Susceptibility
           Based on the Regional Division Information Value Method

    • Authors: Jingru Ma, Xiaodong Wang, Guangxiang Yuan
      First page: 17
      Abstract: The traditional susceptibility evaluation of geological hazards usually comprises a global susceptibility evaluation of the entire study area but ignores the differences between the local areas caused by spatial non-stationarity. In view of this, the geographically weighted regression model (GWR) was used to divide the study area at regional scale. Seven local areas were obtained with low spatial auto-correlation of each evaluation factor. Additionally, 11 evaluation factors, including the aspect, elevation, curvature, ground roughness, relief amplitude, slope, lithology, distance from the fault, height of the cut slope, multiyear average rainfall and the normalized difference vegetation index (NDVI) were selected to establish the evaluation index system of the geological hazard susceptibility. The Pearson coefficient was used to remove the evaluation factors with high correlation. The global and seven local areas were evaluated for susceptibility using the information value model and the global and regional division susceptibility evaluation results were obtained. The results show that the regional division information value model had better prediction performance (AUC = 0.893) and better accuracy. This model adequately considers the influence of the geological hazard impact factors in the different local areas on geological hazard susceptibility and weakens the influence of some factors that have higher influence in the global model but lower influence in local areas on the evaluation results. Therefore, the use of the regional division information value model for susceptibility evaluation is more consistent with the actual situation in the study area and is more suitable for guiding risk management and hazard prevention and mitigation.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-10
      DOI: 10.3390/ijgi12010017
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 18: Measuring Metro Accessibility: An Exploratory
           Study of Wuhan Based on Multi-Source Urban Data

    • Authors: Tao Wu, Mingjing Li, Ye Zhou
      First page: 18
      Abstract: Metro accessibility has attracted interest in sustainable transport analyses. Hence, the accuracy of metro-accessibility measures have become increasingly vital. Various spatiotemporal factors, including by-metro accessibility, land-use accessibility and to-metro accessibility, affect metro accessibility; however, measuring metro accessibility while considering all these components simultaneously is challenging. By integrating these factors into a unified analysis framework, this study aims to strengthen the method for metro-accessibility assessment. Specifically, we proposed the “By metro–Land use–To metro” model to conduct a metro-accessibility index and develop an accessibility-based station typology. The results show that Wuhan metro system accessibility presented a “high-medium-low” spatial disparity from the urban center to the periphery. Meanwhile, the variety of metro-accessibility characteristics and typologies in Wuhan will equip urban planners and policymakers with a useful tool for better organising by-metro accessibility, land-use accessibility and to-metro accessibility.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-10
      DOI: 10.3390/ijgi12010018
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 19: Automatic Clustering of Indoor Area Features in
           Shopping Malls

    • Authors: Ziren Gao, Yi Shen, Jingsong Ma, Jie Shen, Jing Zheng
      First page: 19
      Abstract: The comprehensive expression of indoor maps directly affects the visualization effect of the map and the user’s map reading experience. Currently, only the points, lines, and polygons of outdoor maps are used as objects of cartographic generalization. Therefore, this study considers indoor map area features as generalization objects and deems the automatic clustering of the indoor area features of shopping malls as the research goal. The approach is used to construct an encoder-decoder clustering model, where the encoder consists of a graph convolutional network and its variant models. The results show that the proposed model framework effectively extracts the area features suitable for the indoor space clustering of shopping malls and improves clustering efficacy. Specifically, the model with the Relational Graph Convolutional Network as the encoder demonstrated the best performance, time complexity, and accuracy of clustering results, with accuracy up to 95%. This study extends the research object of cartographic generalization to indoor maps, enabling the automatic clustering of indoor area features, and proposes a clustering model for the important indoor scene of shopping malls. This is valuable for scholars interested in the cartographic generalization of indoor maps.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-10
      DOI: 10.3390/ijgi12010019
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 20: Modeling Land Administration Data Dissemination
           Processes: A Case Study in Croatia

    • Authors: Josip Križanović, Miodrag Roić
      First page: 20
      Abstract: Establishing land administration systems is enough of a challenge as it is, and the task of keeping the system up to date with developments in society is even more challenging. They have to serve society on a long-term basis and normally have a long-term return on investment; therefore, both the static and dynamic components of the system must be considered when designing land administration systems. The processes within land administration systems are registration and dissemination. In this study, the authors formalized and analyzed the two most common use cases of land administration data dissemination processes. The first use case depicts the dissemination of land use constraints imposed by spatial planning, whereas the second case depicts the dissemination of available utilities. The aim of this study was to examine how the land administration data dissemination processes could be optimized and improved in a standardized formal manner. From the formalized processes, certain elements, such as actors, activities, input and output data, and the timeframe, were identified and matched with existing LADM classes. The importance of institutional agreements and the need for more time-efficient and user-friendly access to the disseminated data are also discussed in the current paper.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-12
      DOI: 10.3390/ijgi12010020
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 21: Visual Attention and Recognition Differences
           Based on Expertise in a Map Reading and Memorability Study

    • Authors: Merve Keskin, Vassilios Krassanakis, Arzu Çöltekin
      First page: 21
      Abstract: This study investigates how expert and novice map users’ attention is influenced by the map design characteristics of 2D web maps by building and sharing a framework to analyze large volumes of eye tracking data. Our goal is to respond to the following research questions: (i) which map landmarks are easily remembered' (memorability), (ii) how are task difficulty and recognition performance associated' (task difficulty), and (iii) how do experts and novices differ in terms of recognition performance' (expertise). In this context, we developed an automated area-of-interest (AOI) analysis framework to evaluate participants’ fixation durations, and to assess the influence of linear and polygonal map features on spatial memory. Our results demonstrate task-relevant attention patterns by all participants, and better selective attention allocation by experts. However, overall, we observe that task type and map feature type mattered more than expertise when remembering the map content. Predominantly polygonal map features such as hydrographic areas and road junctions serve as attentive features in terms of map reading and memorability. We make our dataset entitled CartoGAZE publicly available.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-12
      DOI: 10.3390/ijgi12010021
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 22: Modeling the Impact of Investment and National
           Planning Policies on Future Land Use Development: A Case Study for Myanmar
           

    • Authors: Yuan Jin, Ainong Li, Jinhu Bian, Xi Nan, Guangbin Lei
      First page: 22
      Abstract: Land use change (LUC) can be affected by investment growth and planning policies under the context of regional economic cooperation and development. Previous studies on land use simulation mostly emphasized the effects of local socioeconomic factors and planning constraint areas that prevent land conversions. However, investment and national planning policies that trigger regional LUC were often ignored. This study aims to couple the economic theory-based Computable General Equilibrium of Land Use Change (CGELUC) model and the cellular automata-based Future Land Use Simulation (FLUS) model to incorporate macroscopic impacts of investment into land use simulation, while proposing an updated mechanism that integrates into the FLUS model to consider the local impacts of planning policies. Taking Myanmar as a case, the method was applied to project the land use patterns (LUPs) during 2017–2050 under three scenarios: baseline, fast, and harmonious development. Specifically, the simulated land use structure (LUS) in 2018 acquired by the CGELUC model was verified by the existing data, and the future LUSs under different scenarios were projected later. Simultaneously, the consistencies between the results simulated by the FLUS model and land use maps in 2013, 2015, and 2017 were represented by the kappa coefficient. The updated mechanism was applied to update the Probability-of-Occurrence (PoO) surfaces based on the planning railway networks and special economic zone. Lastly, the LUPs under different scenarios were projected based on the future LUSs and updated PoO surfaces. Results reveal that the validation accuracy reaches 96.87% for the simulated LUS, and satisfactory accuracies of the simulated LUPs are obtained (kappa coefficients > 0.83). The updated mechanism increases the mean PoO values of built-up land in areas affected by planning policies (increasing by 0.01 to 0.21), indicating the importance of the planning policies in simulation. The cultivated land and built-up land increase with investment increasing under all three scenarios. The harmonious development scenario, showing the least forest encroachment and the highest diversity of LUP, is the optimal approach to achieve land sustainability. This study highlights the impacts of investment and planning policies on future LUCs of Myanmar, and a dynamic simulation process is expected to minimize the uncertainties of the input data and model in the future work.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-13
      DOI: 10.3390/ijgi12010022
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 23: Geospatial Network Analysis and
           Origin-Destination Clustering of Bike-Sharing Activities during the
           COVID-19 Pandemic

    • Authors: Rui Xin, Linfang Ding, Bo Ai, Min Yang, Ruoxin Zhu, Bin Cao, Liqiu Meng
      First page: 23
      Abstract: Bike-sharing data are an important data source to study urban mobility in the context of the coronavirus disease 2019 (COVID-19). However, studies that focus on different bike-sharing activities including both riding and rebalancing are sparse. This limits the comprehensiveness of the analysis of the impact of the pandemic on bike-sharing. In this study, we combine geospatial network analysis and origin-destination (OD) clustering methods to explore the spatiotemporal change patterns hidden in the bike-sharing data during the pandemic. Different from previous research that mostly focuses on the analysis of riding behaviors, we also extract and analyze the rebalancing data of a bike-sharing system. In this study, we propose a framework including three components: (1) a geospatial network analysis component for a statistical and spatiotemporal description of the overall riding flows and behaviors, (2) an origin-destination clustering component that compensates the network analysis by identifying large flow groups in which individual edges start from and end at nearby stations, and (3) a rebalancing data analysis component for the understanding of the rebalancing patterns during the pandemic. We test our framework using bike-sharing data collected in New York City. The results show that the spatial distribution of the main riding flows changed significantly in the pandemic compared to pre-pandemic time. For example, many riding trips seemed to expand the purposes of riding for work–home commuting to more leisure activities. Furthermore, we found that the changes in the riding flow patterns led to changes in the spatiotemporal distributions of bike rebalancing, such as the shifting of the rebalancing peak time and the increased ratio between the number of rebalancing and the total number of rides. Policy implications are also discussed based on our findings.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-13
      DOI: 10.3390/ijgi12010023
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 24: Multi-Scale Massive Points Fast Clustering Based
           on Hierarchical Density Spanning Tree

    • Authors: Song Chen, Fuhao Zhang, Zhiran Zhang, Siyi Yu, Agen Qiu, Shangqin Liu, Xizhi Zhao
      First page: 24
      Abstract: Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-14
      DOI: 10.3390/ijgi12010024
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 25: Forecasting Short-Term Passenger Flow of Subway
           Stations Based on the Temporal Pattern Attention Mechanism and the Long
           Short-Term Memory Network

    • Authors: Lingxiang Wei, Dongjun Guo, Zhilong Chen, Jincheng Yang, Tianliu Feng
      First page: 25
      Abstract: Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway stations. However, complex and nonlinear interdependencies between time steps and time series complicate such predictions. This study considered temporal patterns across multiple time steps and selected relevant information on short-term passenger flow for prediction. A hybrid model based on the temporal pattern attention (TPA) mechanism and the long short-term memory (LSTM) network was developed (i.e., TPA-LSTM) for predicting the future number of passengers in subway stations. The TPA mechanism focuses on the hidden layer output values of different time steps in history and of the current time as well as correlates these output values to improve the accuracy of the model. The card swiping data from the Hangzhou Metro automatic fare collection system in China were used for verification and analysis. This model was compared with a convolutional neural network (CNN), LSTM, and CNN-LSTM. The results showed that the TPA-LSTM outperformed the other models with good applicability and accuracy. This study provides a theoretical basis for the pre-allocation of subway resources to avoid subway station crowding and stampede accidents.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2023-01-16
      DOI: 10.3390/ijgi12010025
      Issue No: Vol. 12, No. 1 (2023)
       
  • IJGI, Vol. 12, Pages 1: Directional and Weighted Urban Network Analysis in
           the Chengdu-Chongqing Economic Circle from the Perspective of New Media
           Information Flow

    • Authors: Changwei Xiao, Chunxia Liu, Yuechen Li
      First page: 1
      Abstract: The study of the two-way information flow between cities is of great significance to promote regional coordinated development, but the current mainstream non-directional network analysis method cannot analyze it effectively. In this paper, the quantities of relevant media articles in WeChat and Weibo between cities are taken as the traffic indices to construct a directional and weighted urban network of the Chengdu-Chongqing Economic Circle in China. Based on this network construction method, which adds direction thinking, we analyze the characteristics of information interconnection between cities. According to the analysis, we find that the provincial boundary hinders information interconnection, and the imbalance of external information interconnection is more serious in Chongqing's central urban area, Liangping, Ya 'an and Mianyang. In addition, we analyze the centrality status of different cities in the outward and inward perspective and further explore the factors that cause these differences in centrality. The results show that the centrality of the information network is not sensitive to the basic strength of the city, and it is the accessibility, including high-speed rail transportation access and telecommunication access, which controls the centrality of the city network.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-20
      DOI: 10.3390/ijgi12010001
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 2: Toward 3D Property Valuation—A Review of
           Urban 3D Modelling Methods for Digital Twin Creation

    • Authors: Yue Ying, Mila Koeva, Monika Kuffer, Jaap Zevenbergen
      First page: 2
      Abstract: Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies employ 2D geoinformation in hedonic price models, while the benefits of 3D modelling potentially brought for property valuation and the general context of digital twin (DT) creation are not sufficiently explored. Therefore, this review aims to identify appropriate urban 3D modelling method(s) for city DT, which can be used for 3D property valuation (3DPV) in the future (both short-term and long-term). We focused on 3D modelling studies investigating buildings and urban elements directly linked with residential properties. In total, 180 peer-reviewed journal papers were selected between 2016 and 2020 with a narrative review approach. Analytical criteria for 3D modelling methods were explicitly defined and covered four aspects: metadata, technical characteristics, users’ requirements, and ethical considerations. From this, we derived short-term and long-term prospects for 3DPV. The results provide references for integrating 3D modelling and DT in property valuation and call for interdisciplinary collaboration including researchers and stakeholders in the real estate sector, such as real estate companies, house buyers and local governments.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-22
      DOI: 10.3390/ijgi12010002
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 3: High-Speed Railway Access Pattern and Spatial
           Overlap Characteristics of the Yellow River Basin Urban Agglomeration

    • Authors: Yajun Xiong, Hui Tang, Tao Xu
      First page: 3
      Abstract: With the rapid development of high-speed railway (HSR) transportation in China, its impact on regional spatial patterns and shaping has become increasingly significant. This study took seven urban agglomerations in the Yellow River Basin as the research object, using the 2 h HSR access time in the Yellow River Basin to comparatively analyze the differences in HSR access in the urban agglomeration in the Yellow River Basin, and using the 3 h HSR access to central cities as the background to conduct regional division and overlapping space identification through cross-regional economic links, before finally selecting the overlapping city of Changzhi for long-term space development strategic planning. The main conclusions were as follows: First, the low-value area of HSR travel time in the Yellow River Basin urban agglomerations was biased toward the center of the urban agglomerations, while the peripheral areas were relatively high-value travel traffic circles, and the HSR travel time showed a circular spatial pattern characteristic of continuous expansion from the center to the peripheral areas. Four urban agglomerations in the upper reaches of the city achieved a 2 h access pattern within the urban agglomeration, whereas three urban agglomerations in the middle and lower reaches of the city only reached the 2 h access level in the center. Second, the Yellow River Basin was divided into six community spaces using the SLPA model based on the economic linkage between the central city and other cities, which were filtered by the 3 h access time from the central city to each city for HSR travel. Three of the six communities produced overlapping spaces, i.e., Community 3 and Community 4 produced overlapping spaces containing Linfen, Community 3 and Community 5 produced overlapping spaces containing Changzhi, Handan, and Xingtai, and Community 4 and Community 5 produced overlapping spaces containing Yuncheng and Sanmenxia. Third, the overlapping space of Changzhi City was selected as a case study for a visionary strategic planning outlook. Combining the geographic location characteristics and future development opportunities of Changzhi, we can try to transform a pass-through node like Changzhi into a hub node in the future, strengthening the gateway status and expanding the hinterland. According to the results of the research and analysis, policymakers can try to implement the expansion and renovation of HSR trunk lines, break the transportation bottlenecks in less developed areas, improve the coverage of the HSR network, and establish a “cross-urban agglomeration” cooperation and coordination mechanism.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-22
      DOI: 10.3390/ijgi12010003
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 4: Recognition of Intersection Traffic Regulations
           from Crowdsourced Data

    • Authors: Stefania Zourlidou, Monika Sester, Shaohan Hu
      First page: 4
      Abstract: In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules for regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which, in turn, affects vehicle idling time at intersections, fuel consumption, CO2 emissions, and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable, and inexpensive way to identify the type of intersection control (e.g., traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicle crossing intersections is proposed. A modification of a well-known clustering algorithm is used to detect stopping and deceleration episodes. These episodes are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per intersection arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the intersection arms according to their traffic control type (dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single intersection arm are used (one-arm model) and another where features also from neighboring intersection arms of the same intersection are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 95% to 97%.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-23
      DOI: 10.3390/ijgi12010004
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 5: Characterizing Intercity Mobility Patterns for the
           Greater Bay Area in China

    • Authors: Yanzhong Yin, Qunyong Wu, Mengmeng Li
      First page: 5
      Abstract: Understanding intercity mobility patterns is important for future urban planning, in which the intensity of intercity mobility indicates the degree of urban integration development. This study investigates the intercity mobility patterns of the Greater Bay Area (GBA) in China. The proposed workflow starts by analyzing intercity mobility characteristics, proceeds to model the spatial-temporal heterogeneity of intercity mobility structures, and then identifies the intercity mobility patterns. We first conduct a complex network analysis, based on weighted degrees and the PageRank algorithm, to measure intercity mobility characteristics. Next, we calculate the Normalized Levenshtein Distance for Population Mobility Structure (NLPMS) to quantify the differences in intercity mobility structures, and we use the Non-negative Matrix Factorization (NMF) to identify intercity mobility patterns. Our results showed an evident ‘Core-Periphery’ differentiation characterized by intercity mobility, with Guangzhou and Shenzhen as the two core cities. An obvious daily intercity commuting pattern was found between Guangzhou and Foshan, and between Shenzhen and Dongguan cities at working time. This pattern, however, changes during the holidays. This is because people move from the core cities to peripheral cities at the beginning of holidays and return at the end of holidays. This study concludes that Guangzhou and Foshan have formed a relatively stable intercity mobility pattern, and the Shenzhen–Dongguan–Huizhou metropolitan area has been gradually formed.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-26
      DOI: 10.3390/ijgi12010005
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 6: Canopy Assessment of Cycling Routes: Comparison of
           Videos from a Bicycle-Mounted Camera and GPS and Satellite Imagery

    • Authors: Albert Bourassa, Philippe Apparicio, Jérémy Gelb, Geneviève Boisjoly
      First page: 6
      Abstract: Many studies have proven that urban greenness is an important factor when cyclists choose a route. Thus, detecting trees along a cycling route is a major key to assessing the quality of cycling routes and providing further arguments to improve ridership and the better design of cycling routes. The rise in the use of video recordings in data collection provides access to a new point of view of a city, with data recorded at eye level. This method may be superior to the commonly used normalized difference vegetation index (NDVI) from satellite imagery because satellite images are costly to obtain and cloud cover sometimes obscures the view. This study has two objectives: (1) to assess the number of trees along a cycling route using software object detection on videos, particularly the Detectron2 library, and (2) to compare the detected canopy on the videos to other canopy data to determine if they are comparable. Using bicycles installed with cameras and GPS, four participants cycled on 141 predefined routes in Montréal over 87 h for a total of 1199 km. More than 300,000 images were extracted and analyzed using Detectron2. The results show that the detection of trees using the software is accurate. Moreover, the comparison reveals a strong correlation (>0.75) between the two datasets. This means that the canopy data could be replaced by video-detected trees, which is particularly relevant in cities where open GIS data on street vegetation are not available.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-27
      DOI: 10.3390/ijgi12010006
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 7: Correlation of Road Network Structure and Urban
           Mobility Intensity: An Exploratory Study Using Geo-Tagged Tweets

    • Authors: Li Geng, Ke Zhang
      First page: 7
      Abstract: Urban planners have been long interested in understanding how urban structure and activities are mutually influenced. Human mobility and economic activities naturally drive the formation of road network structure and the accessibility of the latter shapes the patterns of movement flow across urban space. In this paper, we perform an exploratory study on the relationship between the street network structure and the intensity of human movement in urban areas. We focus on two cities and we utilize a dataset of geo-tagged tweets that can form a proxy to urban mobility and the corresponding street networks as obtained from OpenStreetMap. We apply three network centrality measures, including closeness, betweenness and straightness centrality, calculated at a global or local scale, as well as under mixed or individual transportation mode (e.g., driving, biking and walking) with its directional accessibility, to uncover the structural properties of urban street networks. We further design an urban area transition network and apply PageRank to capture the intensity of human mobility. Our correlation analysis indicates different centrality metrics have different levels of correlation with the intensity of human movement. The closeness centrality consistently shows the highest correlation (with a coefficient around 0.6) with human movement intensity when calculated at a global scale, while straightness centrality often shows no correlation at the global scale or weaker correlation ρ≈0.4 at the local scale. The correlation levels further depend on the type of directional accessibility and of various types of transportation modes. Hence, the directionality and transportation mode, largely ignored in the analysis of road networks, are crucial. Furthermore, the strength of the correlation varies in the two cities examined, indicating potential differences in urban spatial structure and human mobility patterns.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-28
      DOI: 10.3390/ijgi12010007
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 8: Dynamic Analysis of School Mobility Using
           Geolocation Web Technologies

    • Authors: David Fernández-Arango, Francisco-Alberto Varela-García, Jorge López-Fernández
      First page: 8
      Abstract: Pedestrian travel represents one of the most complex forms of mobility owing to the numerous parameters that influence its analysis and the difficulty of acquiring accurate travel information. In addition, the vulnerability of its protagonists, especially in urban environments, in coexistence with other types of transport, makes its study interesting. This paper proposes a web tool for use in geolocated surveys that allows the acquisition of georeferenced thematic information of interest for mobility studies. The analysis of different school routes from students’ homes to their respective schools has been proposed as a case study. This work covered a sample of 1883 students from 26 schools in Galicia (Spain), where population dispersion generates a particular type of mobility. We obtained relevant mobility data, such as the routes most traveled by students in their daily commute to school, the most efficient routes, the most used means of transport, or the exact location of various elements that hinder and dangerously affect students traveling these routes, such as sidewalks or crosswalks in poor condition, among others.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-29
      DOI: 10.3390/ijgi12010008
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 9: Billion Tree Tsunami Forests Classification Using
           Image Fusion Technique and Random Forest Classifier Applied to Sentinel-2
           and Landsat-8 Images: A Case Study of Garhi Chandan Pakistan

    • Authors: Shabnam Mateen, Narissara Nuthammachot, Kuaanan Techato, Nasim Ullah
      First page: 9
      Abstract: In order to address the challenges of global warming, the Billion Tree plantation drive was initiated by the government of Khyber Pakhtunkhwa, Pakistan, in 2014. The land cover changes as a result of Billion Tree Tsunami project are relatively unexplored. In particular, the utilization of remote sensing techniques and satellite image classification has not yet been done. Recently, the Sentinel-2 (S2) satellite has found much utilization in remote sensing and land cover classification. Sentinel-2 (S2) sensors provide freely available images with a spatial resolution of 10, 20 and 60 m. The higher classification accuracy is directly dependent on the higher spatial resolution of the images. This research aims to classify the land cover changes as a result of the Billion Tree plantation drive in the areas of our interest using Random Forest Classifier (RFA) and image fusion techniques applied to Sentinel-2 and Landsat-8 satellite images. A state-of-the-art, model-based image-sharpening technique was used to sharpen the lower resolution Sentinel-2 bands to 10 m. Then the RFA classifier was used to classify the sharpened images and an accuracy assessment was performed for the classified images of the years 2016, 2018, 2020 and 2022. Finally, ground data samples were collected using an unmanned aerial vehicle (UAV) drone and the classified image samples were compared with the real data collected for the year 2022. The real data ground samples were matched by more than 90% with the classified image samples. The overall classification accuracies [%] for the classified images were recorded as 92.87%, 90.79%, 90.27% and 93.02% for the sample data of the years 2016, 2018, 2020 and 2022, respectively. Similarly, an overall Kappa hat classification was calculated as 0.87, 0.86, 0.83 and 0.84 for the sample data of the years 2016, 2018, 2020 and 2022, respectively.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-29
      DOI: 10.3390/ijgi12010009
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 10: Artificial Intelligence for Multisource
           Geospatial Information

    • Authors: Gloria Bordogna, Cristiano Fugazza
      First page: 10
      Abstract: The term Geospatial Artificial Intelligence (GeoAI) is quite cumbersome, and it has no single, shared definition [...]
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-30
      DOI: 10.3390/ijgi12010010
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 11: A Sensor Placement Strategy for Comprehensive
           Urban Heat Island Monitoring

    • Authors: Prasad Pathak, Pranav Pandya, Sharvari Shukla, Aamod Sane, Raja Sengupta
      First page: 11
      Abstract: Urban heat islands (UHIs) increase the energy consumption of cities and impact the health of its residents. In light of the correlation between energy consumption and health and UHI variations observed at a local level within the canopy layer, satellite-derived land surface temperatures (LSTs) may be insufficient to provide comprehensive information about these deleterious effects. For both LST and air temperatures to be collected in a spatially representative and continuous manner, and for the process to be affordable, on-ground temperature and humidity sensors must be strategically placed. This study proposes a strategy for placing on-ground sensors that utilizes the spatial variation of measurable factors linked to UHI (i.e., seasonal variation in LSTs, wind speed, wind direction, bareness, and local climate zones), allowing for the continuous measurement of UHI within the canopy layer. As a representative city, Pune, India, was used to demonstrate how to distribute sensors based on the spatial variability of UHI-related variables. The proposed method may be helpful for any city requiring local-level observations of UHI, regardless of the climate zone. Further, we evaluate the placement of low-cost technology sensors that use LoRaWAN technology for this purpose, in order to overcome the problem of high costs associated with traditional in-situ weather stations.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-30
      DOI: 10.3390/ijgi12010011
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 12, Pages 12: Spatial Decision Support Systems with Automated
           Machine Learning: A Review

    • Authors: Richard Wen, Songnian Li
      First page: 12
      Abstract: Many spatial decision support systems suffer from user adoption issues in practice due to lack of trust, technical expertise, and resources. Automated machine learning has recently allowed non-experts to explore and apply machine-learning models in the industry without requiring abundant expert knowledge and resources. This paper reviews recent literature from 136 papers, and proposes a general framework for integrating spatial decision support systems with automated machine learning as an opportunity to lower major user adoption barriers. Challenges of data quality, model interpretability, and practical usefulness are discussed as general considerations for system implementation. Research opportunities related to spatially explicit models in AutoML, and resource-aware, collaborative/connected, and human-centered systems are also discussed to address these challenges. This paper argues that integrating automated machine learning into spatial decision support systems can not only potentially encourage user adoption, but also mutually benefit research in both fields—bridging human-related and technical advancements for fostering future developments in spatial decision support systems and automated machine learning.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-30
      DOI: 10.3390/ijgi12010012
      Issue No: Vol. 12, No. 1 (2022)
       
  • IJGI, Vol. 11, Pages 580: Classification of Floods in Europe and North
           America with Focus on Compound Events

    • Authors: Steven Brazda, Mojca Šraj, Nejc Bezak
      First page: 580
      Abstract: Compound events occur when multiple drivers or hazards occur in the same region or on the same time scale, hence amplifying their impacts. Compound events can cause large economic damage or endanger human lives. Thus, a better understanding of the characteristics of these events is needed in order to protect human lives. This study investigates the drivers and characteristics of floods in Europe and North America from the compound event perspective. More than 100 catchments across Europe and North America were selected as case study examples in order to investigate characteristics of floods during a 1979–2019 period. Air temperature, precipitation, snow thickness, snow liquid water equivalent, wind speed, vapour pressure, and soil moisture content were used as potential drivers. Annual maximum floods were classified into several flood types. Predefined flood types were snowmelt floods, rain-on-snow floods, short precipitation floods and long precipitation floods that were further classified into two sub-categories (i.e., wet and dry initial conditions). The results of this study show that snowmelt floods were often the dominant flood type in the selected catchments, especially at higher latitudes. Moreover, snow-related floods were slightly less frequent for high altitude catchments compared to low- and medium-elevation catchments. These high-altitude areas often experience intense summer rainstorms that generate the highest annual discharges. On the other hand, snowmelt-driven floods were the predominant flood type for the lower elevation catchments. Moreover, wet initial conditions were more frequent than the dry initial conditions, indicating the importance of the soil moisture for flood generation. Hence, these findings can be used for flood risk management and modelling.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-22
      DOI: 10.3390/ijgi11120580
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 581: Different Ways Ambient and Immobile Population
           Distributions Influence Urban Crime Patterns

    • Authors: Natalia Sypion-Dutkowska, Minxuan Lan, Marek Dutkowski, Victoria Williams
      First page: 581
      Abstract: The article aims to propose a new way of estimating the ambient and immobile urban population using geotagged tweets and age structure, and to test how they are related to urban crime patterns. Using geotagged tweets and age structure data in 37 neighborhoods of Szczecin, Poland, we analyzed the following crime types that occurred during 2015–2017: burglary in commercial buildings, drug crime, fight and battery, property damage, and theft. Using negative binomial regression models, we found a positive correlation between the size of the ambient population and all investigated crime types. Additionally, neighborhoods with more immobile populations (younger than 16 or older than 65) tend to experience more commercial burglaries, but not other crime types. This may be related to the urban structure of Szczecin, Poland. Neighborhoods with higher rates of poverty and unemployment tend to experience more commercial burglaries, drug problems, property damage, and thefts. Additionally, the count of liquor stores is positively related to drug crime, fight-battery, and theft. This article suggests that the age structure of the population has an influence on the distribution of crime, thus it is necessary to tailor crime prevention strategies for different areas of the city.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-22
      DOI: 10.3390/ijgi11120581
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 582: Georeferencing Accuracy Assessment of Historical
           Aerial Photos Using a Custom-Built Online Georeferencing Tool

    • Authors: Su Zhang, Hays A. Barrett, Shirley V. Baros, Paul R. H. Neville, Sandeep Talasila, Lisa L. Sinclair
      First page: 582
      Abstract: As one of the earliest forms of remote sensing, aerial photography has been regarded as an important part of the mapmaking process. Aerial photos, especially historical aerial photos, provide significant amount of valuable information for many applications and fields. However, due to limited funding support, most historical aerial photos have not been digitized and georeferenced yet, which substantially limits their utility for today’s computer-based image processing and analysis. Traditionally, historical aerial photos are georeferenced with desktop GIS software applications. However, this method is expensive, time-consuming, and labor-intensive. To address these limitations, this research developed a custom-built online georeferencing tool to enable georeferencing digitized historical aerial photos in a web environment, which is able to georeference historical aerial photos in a rapid and cost-effective manner. To evaluate the georeferencing performance, a set of 50 historical aerial photos were georeferenced with not only the developed online georeferencing tool but also two commercial desktop software programs. Research results revealed the custom-built online georeferencing tool provided the highest degree of accuracy while maximizing its accessibility.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-22
      DOI: 10.3390/ijgi11120582
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 583: Robust Watermarking Scheme for Vector Geographic
           Data Based on the Ratio Invariance of DWT–CSVD Coefficients

    • Authors: Chengyi Qu, Xu Xi, Jinglong Du, Tong Wu
      First page: 583
      Abstract: Traditional frequency-domain watermarking algorithms for vector geographic data suffer from disadvantages such as the random watermark embedding position, unpredictable embedding strength, and difficulty in resisting multiple attacks at the same time. To address these problems, we propose a novel watermarking algorithm based on the geometric invariance of the ratios of discrete wavelet transform (DWT) and complex singular value decomposition (CSVD) coefficients, which embeds the watermark information in a new embedding domain. The proposed scheme first extracts feature points from the original vector geographic data using the Douglas–Peucker algorithm, and then constructs a complex sequence based on the feature points set. The two-level DWT is then performed on the complex sequence to obtain the low frequency coefficients (L2) and high frequency coefficients (H2). On this premise, the CSVD algorithm is utilized to calculate the singular values of L2 and H2, and the ratio of the singular values of L2 and H2 is acquired as the watermark embedding domain. During the watermark embedding process, a new watermark sequence is created by the fusion of the original watermark index value and bits value to improve the recognition of the watermark information, and the decimal part at different positions of the ratio is altered by the new watermark sequence to control the watermark embedding strength. The experimental results show that the proposed watermarking algorithm is not only robust to common attacks such as geometric, cropping, simplification, and coordinate point editing, but also can extract watermark images with a high probability under random multiple attacks.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-22
      DOI: 10.3390/ijgi11120583
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 584: Ship Target Recognition Based on
           Context-Enhanced Trajectory

    • Authors: Zhan Kong, Yaqi Cui, Wei Xiong, Zhenyu Xiong, Pingliang Xu
      First page: 584
      Abstract: Ship target recognition based on trajectories has great potential in the field of target recognition. In the existing research, the context information is ignored, which limits the improvement of ship target recognition ability. In addition, the process of trajectory feature extraction is complex, and recognition accuracy needs to be further improved. In this paper, a ship target recognition method based on a context-enhanced trajectory is proposed. The maritime context knowledge base is constructed to enhance the trajectory information and to improve the separability of different types of target trajectories. A deep learning model is used to extract trajectory features and context features automatically. Offline training and online recognition are adopted to complete the target recognition task. Experimental analysis and verification are carried out using the automatic identification system (AIS) dataset. The recognition accuracy increases by 7.91% after context enhancement, which shows that the context enhancement is efficient. The proposed method also has a strong anti-noise ability. In the noisy environment set in this paper, the recognition accuracy of the proposed method is still maintained at 86.13%.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-23
      DOI: 10.3390/ijgi11120584
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 585: Effects of Atmospheric Correction and Image
           Enhancement on Effective Plastic Greenhouse Segments Based on a
           Semi-Automatic Extraction Method

    • Authors: Yao Yao, Shixin Wang
      First page: 585
      Abstract: To improve the multi-resolution segmentation (MRS) quality of plastic greenhouses (PGs) in GaoFen-2 (GF-2) images, the effects of atmospheric correction and image enhancement on effective PG segments (EPGSs) were evaluated. A new semi-automatic method was also proposed to extract EPGSs in an accurate and efficient way. Firstly, GF-2 images were preprocessed via atmospheric correction, orthographical correction, registration, fusion, linear compression, or spatial filtering, and, then, boundary-removed point samples with adjustable density were made based on reference polygons by taking advantage of the characteristics of chessboard segmentation. Subsequently, the point samples were used to quickly and accurately extract segments containing 70% or greater of PG pixels in each MRS result. Finally, the extracted EPGSs were compared and analyzed via intersection over union (IoU), over-segmentation index (OSI), under-segmentation index (USI), error index of total area (ETA), and composite error index (CEI). The experimental results show that, along with the change in control variables, the optimal scale parameter, time of segmentation, IoU, OSI, USI, and CEI all showed strong changing trends, with the values of ETA all close to 0. Furthermore, compared with the control group, all the CEIs of the EPGSs extracted from those corrected and enhanced images resulted in lower values, and an optimal CEI involved linearly compressing the DN value of the atmospheric-corrected fusion image to 0–255, and then using Fast Fourier Transform and a circular low-pass filter with a radius of 800 pixels to filter from the spatial frequency domain; in this case, the CEI had a minimum value of 0.159. The results of this study indicate that the 70% design in the experiment is a reasonable pixel ratio to determine the EPGSs, and the OSI-USI-ETA-CEI pattern can be more effective than IoU when it is needed to evaluate the quality of EPGSs. Moreover, taking into consideration heterogeneity and target characteristics, atmospheric correction and image enhancement prior to MRS can improve the quality of EPGSs.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-23
      DOI: 10.3390/ijgi11120585
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 586: Regional Small Towns Classification Assessment
           and Spatial Pattern Integration: A Case Study of the Yunnan Section of the
           China–Laos Economic Corridor

    • Authors: Jing Han, Yue Wang, Xingping Wang
      First page: 586
      Abstract: The role of small towns in regional development is being emphasized, especially in developing countries, where small towns are driving regional spatial integration and optimization from the ‘bottom up’. In the context of further refinement of regional governance, it is important to identify the characteristics of regional small towns and explore the spatial pattern and structure of their development to achieve regional strategic goals. Taking the Yunnan section of the China–Laos Economic Corridor as an example, this study integrated small towns and regional high-quality development needs, constructed a regional small-town classification and evaluation index system, used various quantitative analysis methods to explore the spatial differentiation of regional small towns’ development levels, and constructed a spatial pattern of regional small towns. Our results reveal that: (1) Small towns in the Yunnan section of the China–Laos Economic Corridor showed large variations in the scores of the four indicator types, which were spatially distributed as ‘core-edge’ and ‘peripheral core’. (2) There was spatial autocorrelation in the classification assessment results of small towns, where small towns with similar levels of development were spatially adjacent and dominated by hot spot agglomerations, but with different agglomeration patterns and distribution locations. (3) The spatial pattern of regional small towns was composed of various elements such as points, lines, axes, rings, and clusters, which can meet the diversified development needs of the region. (4) Our study found that the horizontal transportation links of the Yunnan section require strengthening and suggested the construction of a ‘1 + 3’ regional transportation network.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-23
      DOI: 10.3390/ijgi11120586
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 587: Mitigating Imbalance of Land Cover Change Data
           for Deep Learning Models with Temporal and Spatiotemporal Sample Weighting
           Schemes

    • Authors: Alysha van Duynhoven, Suzana Dragićević
      First page: 587
      Abstract: An open problem impeding the use of deep learning (DL) models for forecasting land cover (LC) changes is their bias toward persistent cells. By providing sample weights for model training, LC changes can be allocated greater influence in adjustments to model internal parameters. The main goal of this research study was to implement and evaluate temporal and spatiotemporal sample weighting schemes that manage the influence of persistent and formerly changed areas. The proposed sample weighting schemes allocate higher weights to more recently changed areas based on the inverse temporal and spatiotemporal distance from previous changes occurring at a location or within the location’s neighborhood. Four spatiotemporal DL models (CNN-LSTM, CNN-GRU, CNN-TCN, and ConvLSTM) were used to compare the sample weighting schemes to forecast the LC changes of the Columbia-Shuswap Regional District in British Columbia, Canada, using data obtained from the MODIS annual LC dataset and other auxiliary spatial variables. The results indicate that the presented weighting schemes facilitated improvement over no sample weighting and the common inverse frequency weighting scheme for multi-year LC change forecasts, lowering errors due to quantity while reducing overall allocation error severity. This research study contributes to strategies for addressing the characteristic imbalances of multitemporal LC change datasets for DL modeling endeavors.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-23
      DOI: 10.3390/ijgi11120587
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 588: Land Use Changes and Ecosystem Services: The
           Case Study of the Abruzzo Region Coastal Strip

    • Authors: Francesco Zullo, Cristina Montaldi, Gianni Di Pietro, Chiara Cattani
      First page: 588
      Abstract: Consistent and optimized territorial planning, imply the use of numerous variables aimed at improving life quality and reduction of environmental impacts. The resilience of the territory to climate change threats is strongly linked to its progressive transformation. This fact is extremely evident in coastal systems, which are intrinsically fragile systems due to their high environmental value and strong anthropogenic pressure. The existing tools and techniques provide to outline future transformation effects through the scenarios analysis. This work has the objective to evaluate the effects of land use changes in the territory of the Abruzzo coast. The conversion from natural soils to artificial uses has a significant impact on several ecosystem services. The regulation services considered in this work are flood regulation, carbon storage and sequestration, and habitat for biodiversity. The first is directly connected to soil sealing which determines a reduction of water infiltration with the consequent overloading of the existing sewerage systems. The quantitative evaluation is made using the concept of surface runoff coefficient. Instead, the estimation of the last two ecosystem services has been made using InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) models, specifically the Carbon Storage and Sequestration model and the Habitat Quality model. The results show that Land Use Changes (2012–2018) caused a potential increase of 10% in runoff and an annual Carbon Sequestration loss estimated at about €820,000.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-23
      DOI: 10.3390/ijgi11120588
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 589: Correlation between Land Use Pattern and Urban
           Rail Ridership Based on Bicycle-Sharing Trajectory

    • Authors: Xiangyu Li, Gobi Krishna Sinniah, Ruiwei Li, Xiaoqing Li
      First page: 589
      Abstract: As a form of rapid mass transportation, urban rail systems have always been widely used to alleviate urban traffic congestion and reconstruct urban structures. Land use characteristics are indispensable to this system and correlate with urban ridership. Dock-less bicycle-sharing expands the station service coverage range because it integrates public transportation with an urban rail system to create a convenient travel model. Consequently, the land use pattern with dock-less bicycle-sharing is associated with urban rail ridership. This paper measures the correlation between land use and urban rail ridership based on the trajectory of dock-less bicycle-sharing, which precisely reflects the travel behavior of passengers along the trip chain. The specific relationship has been determined using the random forest model. This paper found that the land use pattern could better explain the egress ridership during morning peak hours. In particular, it could explain 48.46% of the urban rail ridership in terms of egress, but the explicability for the ingress ridership slightly decreased to 36.88%. This suggests that the land use pattern is related to urban rail ridership. However, the impact situation varies, so we should understand this relationship with greater care.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-24
      DOI: 10.3390/ijgi11120589
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 590: Interday Stability of Taxi Travel Flow in Urban
           Areas

    • Authors: Ping Tu, Wei Yao, Zhiyuan Zhao, Pengzhou Wang, Sheng Wu, Zhixiang Fang
      First page: 590
      Abstract: Taxi travel flow patterns and their interday stability play an important role in the planning of urban transportation and public service facilities. Existing studies pay little attention to the stability of the travel flow patterns between days, and it is difficult to consider the impact of dynamic changes in daily travel demand analysis when supporting related decision making. Taxi trajectory data have been widely used in urban taxi travel-pattern analysis. This paper uses the taxi datasets of Shenzhen and New York to analyze and compare the interday stability of the taxi travel spatial structure and the flow volume based on the improved Levenshtein algorithm and geographic flow theory. The results show that (1) interday differences in taxi travel flow are obvious in both spatial structure and flow volume, high-frequency origin–destination (OD) trips are relatively stable; (2) the ODs between the central urban area and surrounding areas exhibit high traffic volume and high interday stability, and the ODs starting or ending at an airport exhibit high traffic stability; (3) one week’s data can describe 86% of the overall travel structure and 84% of travel flow in Shenzhen, and one week’s New York data can describe 73% of travel structure and 76% of travel flow. There are differences in the travel patterns of people in different cities, and the representativeness of datasets in different cities will be different. These findings can help to better understand the outcomes of taxi travel patterns derived from a relatively short period of data to avoid potential misuse in related decision making.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-24
      DOI: 10.3390/ijgi11120590
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 591: Point Cloud Convolution Network Based on Spatial
           Location Correspondence

    • Authors: Jiabin Xv, Fei Deng, Haibing Liu
      First page: 591
      Abstract: The study of convolutional neural networks for 3D point clouds is becoming increasingly popular, and the difficulty lies mainly in the disorder and irregularity of point clouds. At present, it is straightforward to propose a convolution operation and perform experimental validation. Although good results are achieved, the principles behind them are not explained—i.e., why this can solve the disorder and irregularity of point clouds—and it is difficult for the researchers to design a point cloud convolution network suitable for their needs. For this phenomenon, we propose a point convolution network framework based on spatial location correspondence. Following the correspondence principle can guide us in designing convolution networks adapted to our needs. We analyzed the intrinsic mathematical nature of the convolution operation, and we argue that the convolution operation remains the same when the spatial location correspondence between the convolution kernel points and the convolution range elements remains unchanged. Guided by this principle, we formulated a general point convolution framework based on spatial location correspondence, which explains how to handle a disordered point cloud. Moreover, we discuss different kinds of correspondence based on spatial location, including M-to-M, M-to-N, and M-to-1 relationships, etc., which explain how to handle the irregularity of point clouds. Finally, we give the example of a point convolution network whose convolution kernel points are generated based on the sample’s covariance matrix distribution according to our framework. Our convolution operation can be applied to various point cloud processing networks. We demonstrated the effectiveness of our framework for point cloud classification and semantic segmentation tasks, achieving competitive results with state-of-the-art networks.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-25
      DOI: 10.3390/ijgi11120591
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 592: The Limits of GIS Implementation in Education: A
           Systematic Review

    • Authors: Veronika Bernhäuserová, Lenka Havelková, Kateřina Hátlová, Martin Hanus
      First page: 592
      Abstract: Despite the extensive discussion on the educational potential of GIS and the changes made in the curricula in many countries, the implementation of GIS in classrooms has still been relatively slow. This is because of variables limiting the process of GIS implementation in lessons. Although research into the limits of GIS implementation has been carried out quite extensively, there is a need for knowledge systematisation in the field. Therefore, the presented systematic review of 34 empirical studies addresses this need and pays attention to the methodological approaches used to research the limits, the identified limits of GIS implementation, their categorisation, and any temporal trends in their occurrence. Altogether, the analysed studies identified 68 limits of GIS implementation in education using mainly quantitative methodology (especially the questionnaire), with utmost attention paid to teachers as participants. These limits then formed complex categorisation that distinguishes elementarily between the limits related to humans and resources. The most frequent and variable category of limits was teachers followed by technology, while both kept their positions in all periods. The systematisation of the research enables the formulation of implications for educational and geoinformatics practice and recommendations for future research.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-26
      DOI: 10.3390/ijgi11120592
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 593: A Study on the Emergency Shelter Spatial
           Accessibility Based on the Adaptive Catchment Size 2SFCA Method

    • Authors: Zilin Ding, Hongjun Dong, Liang Yang, Na Xue, Lanping He, Xinqiang Yao
      First page: 593
      Abstract: In order to access the spatial accessibility of emergency shelters, the relationship between the supply and demand of emergency shelters in the two dimensions of space and non-space must be comprehensively considered. Meanwhile, it is vital to understand the competitive relationship among emergency shelters. However, there are disadvantages when using the two-step floating catchment area (2SFCA) method and the improved 2SFCA method when addressing these issues. This study proposes the adaptive catchment size 2SFCA (A-2SFCA) method to calculate spatial accessibility values, which can work alongside the two relationships mentioned above. The analysis procedure of the A-2SFCA method has two stages. Firstly, this method adjusts the catchment size of the shelters by observing how crowded they are and repeatedly using this statistic in a service subset. At the end of this stage, every catchment area is determined. Secondly, the catchment areas are used to calculate the spatial accessibility values. The method was used to study a region in the Tianjin urban area in China. The proposed A-2SFCA and fixed-coverage-based two-step floating catchment area (FC2SFCA) methods are employed to measure and compare the spatial accessibility values. The result shows that the spatial accessibility in Tianjin urban area is unstable. The spatial accessibility result obtained from the A-2SFCA method is more reasonable than the FC2SFCA method when analyzing the reasonable catchment areas of emergency shelters. The A-2SFCA method provides a method for determining the catchment size of public service providers, which can be used for the accessibility analysis of various other public facilities.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-26
      DOI: 10.3390/ijgi11120593
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 594: Landscape Visual Impact Evaluation for Onshore
           Wind Farm: A Case Study

    • Authors: Jinjin Guan
      First page: 594
      Abstract: Wind energy is an effective solution for achieving the carbon-neutrality target and mitigating climate change. The expansion of onshore wind energy evokes extensive attention to environmental impact in the locality. The landscape visual impact has become the critical reason for the local protest. This paper proposed a landscape visual impact evaluation (LVIE) model that combines the theoretical framework and practical solutions and optimizes the onshore wind farm planning procedures. Based on the theoretical research on landscape connotation, the evaluation principles, criteria, and a quantitative indicator set is constructed for LVIE model with three dimensions: landscape sensitivity, the visual impact of WTs, and viewer exposure. The practicality of this evaluation model is conducted through multi-criteria GIS analysis by the case study of Friedrich-Wilhelm Raiffeisen Wind Farm in Germany. The evaluation results illustrate detailed, visualized outcomes of landscape visual impact that are deeply combined with planning procedures. The innovation of this paper is to refine the form of evaluation results, optimize the procedures of wind farm planning, and enable cooperation between different planning departments and stakeholders with definite, visible, user-friendly evaluation results. This research provides precise comparison opportunities for different projects or the same project at different periods to obtain quantitative conclusions and feedback information. This paper enhances the accurate processing of multiple information and standardization process in wind energy visual impact evaluation.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-26
      DOI: 10.3390/ijgi11120594
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 595: A GtoG Direct Coding Mapping Method for
           Multi-Type Global Discrete Grids Based on Space Filling Curves

    • Authors: Yalu Li, Xuesheng Zhao, Wenbin Sun, Guangsong Wang, Fuli Luo, Zheng Wang, Yuanzheng Duan
      First page: 595
      Abstract: DGGS (Discrete Global Grid System) has many subdivision models and coding methods. Due to the lack of underlying consistency of different DGGS codes, most of them are converted through longitude–latitude, which greatly reduces the interoperability efficiency of different DGGS data and has become one of the bottlenecks in efficient integration of multi-source DGGS data. Therefore, a direct mapping method from one grid code to another (Grid to Grid, GtoG) for multi-type DGGSs is proposed based on three classical DGGSs (triangular, diamond and hexagonal grids) and two commonly used filling curves (Hilbert curve and Z-curve). The mutual conversion rules of different grids expressing spatial point, line and surface data are constructed. Then, the above method is extended to the spherical icosahedral grid framework, and three different region coding mapping rule tables of the basic inside cells, boundary cells and vertex cells are designed. Finally, the experimental results show that, compared with the longitude–latitude conversion method, the average conversion efficiency of spatial point, line and surface data is increased by 2–4 orders of magnitude. This new method greatly improves the interoperability efficiency and provides a feasible solution for the efficient integration of multi-source DGGS data.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-27
      DOI: 10.3390/ijgi11120595
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 596: Geocomputational Approach to Simulate and
           Understand the Spatial Dynamics of COVID-19 Spread in the City of
           Montreal, QC, Canada

    • Authors: Navid Mahdizadeh Mahdizadeh Gharakhanlou, Liliana Perez
      First page: 596
      Abstract: Throughout history, pandemics have forced societies to think beyond typical management and control protocols. The main goals of this study were to simulate and understand the spatial dynamics of COVID-19 spread and assess the efficacy of two policy measures in Montreal, Canada, to mitigate the COVID-19 outbreak. We simulated the COVID-19 outbreak using a Geographical Information System (GIS)-based agent-based model (ABM) and two management scenarios as follows: (1) human mobility reduction; and (2) observation of self-isolation. The ABM description followed the ODD (Overview, Design concepts, Details) protocol. Our simulation experiments indicated that the mainstream of COVID-19 transmissions (i.e., approximately 90.34%) occurred in public places. Besides, the results indicated that the rules aiming to reduce population mobility, led to a reduction of about 63 infected people each week, on average. Furthermore, our scenarios revealed that if instead of 42% (i.e., the adjusted value in the calibration), 10%, 20%, and 30% of infectious people had followed the self-isolation measure, the number of infected people would have risen by approximately 259, 207, and 83 more each week, on average, respectively. The map of critical locations of COVID-19 spreading resulted from our modeling and the evaluated effectiveness of two control measures on the COVID-19 outbreak could assist health policymakers to navigate through the pandemic.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-27
      DOI: 10.3390/ijgi11120596
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 597: Low-Cost System for Automatic Recognition of
           Driving Pattern in Assessing Interurban Mobility using Geo-Information

    • Authors: Oscar Romero, Aika Silveira Miura, Lorena Parra, Jaime Lloret
      First page: 597
      Abstract: Mobility in urban and interurban areas, mainly by cars, is a day-to-day activity of many people. However, some of its main drawbacks are traffic jams and accidents. Newly made vehicles have pre-installed driving evaluation systems, which can prevent accidents. However, most cars on our roads do not have driver assessment systems. In this paper, we propose an approach for recognising driving styles and enabling drivers to reach safer and more efficient driving. The system consists of two physical sensors connected to a device node with a display and a speaker. An artificial neural network (ANN) is included in the node, which analyses the data from the sensors, and then recognises the driving style. When an abnormal driving pattern is detected, the speaker will play a warning message. The prototype was assembled and tested using an interurban road, in particular on a conventional road with three driving styles. The gathered data were used to train and validate the ANN. Results, in terms of accuracy, indicate that better accuracy is obtained when the velocity, position (latitude and longitude), time, and turning speed for the 3-axis are used, offering an average accuracy of 83%. If the classification is performed considering just two driving styles, normal and aggressive, then the accuracy reaches 92%. When the geo-information and time data are included, the main novelty of this paper, the classification accuracy is improved by 13%.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-27
      DOI: 10.3390/ijgi11120597
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 598: Geographic Named Entity Recognition by Employing
           Natural Language Processing and an Improved BERT Model

    • Authors: Liufeng Tao, Zhong Xie, Dexin Xu, Kai Ma, Qinjun Qiu, Shengyong Pan, Bo Huang
      First page: 598
      Abstract: Toponym recognition, or the challenge of detecting place names that have a similar referent, is involved in a number of activities connected to geographical information retrieval and geographical information sciences. This research focuses on recognizing Chinese toponyms from social media communications. While broad named entity recognition methods are frequently used to locate places, their accuracy is hampered by the many linguistic abnormalities seen in social media posts, such as informal sentence constructions, name abbreviations, and misspellings. In this study, we describe a Chinese toponym identification model based on a hybrid neural network that was created with these linguistic inconsistencies in mind. Our method adds a number of improvements to a standard bidirectional recurrent neural network model to help with location detection in social media messages. We demonstrate the results of a wide-ranging evaluation of the performance of different supervised machine learning methods, which have the natural advantage of avoiding human design features. A set of controlled experiments with four test datasets (one constructed and three public datasets) demonstrates the performance of supervised machine learning that can achieve good results on the task, significantly outperforming seven baseline models.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-28
      DOI: 10.3390/ijgi11120598
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 599: An Automatic Generalization Method for a Dense
           Road Network Area Considering Spatial Structural Features as Constraints

    • Authors: Pengda Wu, Yong Yin, Chuangqi Wu, Xiaofei Bai, Chunxiao Zhang, Zhaoxin Dai
      First page: 599
      Abstract: Road networks are the skeletal elements of topographic maps at different scales, and road selection is a prerequisite for implementing continuous multiscale spatial representations of road networks. The mesh-based approach is a common, advanced and powerful method for road selection in dense road areas in which small meshes are removed and road segments with the least importance in each mesh are eliminated. However, small meshes in a map can be classified into two types: aggregated small meshes and isolated small meshes. The number of the former is small, and that of the latter is large. Existing methods are generally applicable for the latter, and some or even most spatial characteristics will be lost when they are applied to the former; as a result, the road selection quality will be affected. Therefore, as a supplement to the mesh-based selection method, this paper proposed an automatic generalization method of dense road network areas (areas formed by aggregated small meshes) considering spatial structural features as constraints. First, the aggregated areas of small meshes were identified based on the number of adjoining small meshes, and the boundaries of aggregated areas are extracted and used as hard constraints during mesh elimination. Second, the starting meshes were redefined by simultaneously considering the edge features and mesh density of small meshes, and an ordinal elimination algorithm was proposed to eliminate the meshes in the stroke connection direction. Third, road selection was implemented by identifying the starting meshes and sequentially processing the related mesh pairs. This iterative process continued until all mesh densities of the newly formed meshes are beyond the threshold or the problem becomes a simple elimination problem involving two adjoining small meshes or one isolated small mesh. Finally, a 1:10,000 standard topographic road map for Jiangsu Province, China, was used for validation. The experimental results showed that in the aggregated areas with two small meshes, 31% of the areas obtained the same selection results by using the mesh-based method and the proposed method, and the remaining 69% obtained a more compact result with the proposed method. Moreover, for all aggregated areas with more than two small meshes, the spatial distribution structure of small meshes was preserved better by the proposed method.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-28
      DOI: 10.3390/ijgi11120599
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 600: Spatiotemporal Changes and Driving Factors of
           Ecosystem Health in the Qinling-Daba Mountains

    • Authors: Ting Xiang, Xiaoliang Meng, Xinshuang Wang, Jing Xiong, Zelin Xu
      First page: 600
      Abstract: Rapid industrialization and urbanization have accelerated land-use changes in mountainous areas, with dramatic impacts on ecosystem health. In particular, the Qinling-Daba Mountains, as China’s central water tower, ecological green lung, and biological gene bank, have rich resource endowments and extremely high ecological value and are an important protective wall to China’s ecological security. Therefore, understanding the level of ecosystem health and its drivers in the research area contributes to the conservation and restoration of the mountain ecosystem. Based on remote sensing image data and land-use data from 2000 to 2020, we explored the spatial characteristics of ecosystem health, and supplemented with socio-economic data to explore its driving factors. The results show that (1) the ecosystem health in the study area has been continuously improved during the study period, and the regional differences in ecological organization are the most prominent; (2) the level of ecosystem health in the Qinling-Daba Mountains has been spatially improved from the peripheral areas to the central area, showing significant spatial autocorrelation and local spatial aggregation; (3) the ecosystem health is influenced by a combination of natural and anthropogenic factors, among which the negative effect of GRDP is mainly concentrated in the eastern region, the negative effect of the proportion of built-up land gradually spreads to the western region, and the positive effect of the proportion of forest land has a large scale. This study contributes to a better understanding of ecosystem health in mountainous counties in China and provides useful information for policymakers to formulate ecological and environmental management policies.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-29
      DOI: 10.3390/ijgi11120600
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 601: Characteristics of False-Positive Active Fires
           for Biomass Burning Monitoring in Indonesia from VIIRS Data and Local
           Geo-Features

    • Authors: Parwati Sofan, Fajar Yulianto, Anjar Dimara Sakti
      First page: 601
      Abstract: In this study, we explored the characteristics of thermal anomalies other than biomass burning to establish a zone map of false-positive active fires to support efficient ground validation for firefighters. We used the ASCII file of VIIRS active fire data (VNP14IMGML), which provides attributes of thermal anomalies every month from 2012 to 2020 in Indonesia. The characteristics of thermal anomalies other than biomass burning were explored using fire radiative power (FRP) values, confidence levels of active fire, fire pixel areas, and their allocations to permanent geographical features (i.e., volcano, river, lake, coastal line, road, and industrial/settlement areas). The Tukey test showed that there was a significant difference between the mean FRP values of the other thermal anomalies, type-1 (active volcano), type-2 (other static land sources), and type-3 (detection over water/offshore), at a confidence level of 95%. Most thermal anomalies other than biomass burning were in the nominal confidence level with a fire pixel area of 0.21 km2. High spatial images validated these thermal anomaly types as false positives of biomass burning. A zone map of potential false-positive active fire for biomass burning was established in this study by referring to the allocation of thermal anomalies from permanent geographical features. Implementing the zone map removed approximately 13% of the VIIRS active fires as the false positive of biomass burning. Insights gleaned through this study will support efficient ground validation of actual forest/land fires.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-01
      DOI: 10.3390/ijgi11120601
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 602: A Comparison Study of Landslide Susceptibility
           Spatial Modeling Using Machine Learning

    • Authors: Nurwatik, Ummah, Cahyono, Darminto, Hong
      First page: 602
      Abstract: One hundred seventeen landslides occurred in Malang Regency throughout 2021, triggering the need for practical hazard assessments to strengthen the disaster mitigation process. In terms of providing a solution for investigating the location of landslides more precisely, this research aims to compare machine learning algorithms to produce an accurate landslide susceptibility model. This research applies three machine learning algorithms composed of RF (random forest), NB (naïve Bayes), and KNN (k-nearest neighbor) and 12 conditioning factors. The conditioning factors consist of slope, elevation, aspect, NDVI, geological type, soil type, distance from the fault, distance from the river, river density, TWI, land cover, and annual rainfall. This research performs seven models over three ratios between the training and testing dataset encompassing 50:50, 60:40, and 70:30 for KNN and NB algorithms and 70:30 for the RF algorithm. This research measures the performance of each model using eight parameters (ROC, AUC, ACC, SN, SP, BA, GM, CK, and MCC). The results indicate that RF 70:30 generates the best performance, witnessed by the evaluation parameters ACC (0.884), SN (0.765), GM (0.863), BA (0.857), CK (0.749), MCC (0.876), and AUC (0.943). Overall, seven models have reasonably good accuracy, ranging between 0.806 and 0.884. Furthermore, based on the best model, the study area is dominated by high susceptibility with an area coverage of 51%, which occurs in the areas with high slopes. This research is expected to improve the quality of landslide susceptibility maps in the study area as a foundation for mitigation planning. Furthermore, it can provide recommendations for further research in splitting ratio scenarios between training and testing data.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-02
      DOI: 10.3390/ijgi11120602
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 603: Groundwater Potential Zone Mapping: Integration
           of Multi-Criteria Decision Analysis (MCDA) and GIS Techniques for the
           Al-Qalamoun Region in Syria

    • Authors: Imad Alrawi, Jianping Chen, Arsalan Ahmed Othman
      First page: 603
      Abstract: One of the most critical processes for the long-term management of groundwater resources is Groundwater Potential Zonation (GWPZ). Despite their importance, traditional groundwater studies are costly, difficult, complex, and time-consuming. This study aims to investigate GWPZ mapping for the Al-Qalamoun region, in the Western part of Syria. We combined the Multi-Influence Factor (MIF) and Analytic Hierarchy Process (AHP) methods with the Geographic Information Systems (GIS) to estimate the GWPZ. The weight and score factors of eight factors were used to develop the GWPZ including drainage density, lithology, slope, lineament density, geomorphology, land use/land cover, rainfall, and soil. According to the findings, about 46% and 50.6% of the total area of the Al-Qalamoun region was classified as suitable for groundwater recharge by the AHP and MIF methods, respectively. However, 54% and 49.4% of the area was classified as having poor suitability for groundwater recharge by the AHP and MIF methods, respectively. These areas with poor suitability can be utilized for gathering surface water. The validation of the results showed that the AHP and MIF methods have similar accuracy for the GWPZ; however, the accuracy and results depend on influencing factors and their weights assigned by experts.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-02
      DOI: 10.3390/ijgi11120603
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 604: Identification and Mapping of High Nature Value
           Farmland in the Yellow River Delta Using Landsat-8 Multispectral Data

    • Authors: Cailin Li, Fan Lin, Aziguli Aizezi, Zeao Zhang, Yingqiang Song, Na Sun
      First page: 604
      Abstract: The development of high nature value farmland (HNVf) can effectively improve the problems of biodiversity reduction, non-point source pollution and carbon loss in intensive farmland. To this end, we developed a set of general indicators based on Landsat 8 OLI imagery, including land cover (LC), normalized difference vegetation index (NDVI), Shannon diversity (SH) and Simpson’s index (SI). Combined with a Kohonen neural network (KNN), we assigned weights and developed the first potential HNVf map of the Yellow River Delta in China. The results showed that the four indicators were very effective for the expression of HNVf characteristics in the study area, and that SH and SI, in particular, could reflect the potential characteristics of HNVf at the edge of intensive farmland. LC, NDVI, SH and SI were weighted as 0.45, 0.25, 0.15 and 0.15, respectively. It was found that the potential HNVf type 2 (i.e., low-intensity agriculture, and natural and structural elements such as shrubs, woodlands and small rivers) in the study area was concentrated at the edges of intensive farmland, the transition zones from farmland to rivers and the estuary wetland areas of northern and eastern rivers. LC played a leading role in identifying HNVf. Based on six randomly selected real-world verification data from Map World, it was found that the accuracy of the validation set for HNVf type 2 was 83.33%, which exhibited the good development potential of HNVf in the study area. This is the first potential HNVf type 2 map of the Yellow River Delta in China and could provide a great deal of potential guidance for the development and protection of farmland biodiversity and regional carbon sequestration.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-04
      DOI: 10.3390/ijgi11120604
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 605: A Spatio-Temporal Cognitive Framework for
           Individual Route Choice in Outdoor Evacuation Scenarios

    • Authors: Fei Gao, Zhiqiang Du, Chenyu Fang, Lin Zhou, Martin Werner
      First page: 605
      Abstract: Route choice is a complex issue in simulating individual behaviors and reproducing collective phenomena during evacuations. A growing concern has been given to the individual cognitive mechanism to investigate how routing decisions are made in specific situations. However, the essential role of multiple spatio-temporal scales has not been completely considered in the current cognitive frameworks, which leads to the inaccuracy of cognition representation in evacuation decisions. This study proposes a novel spatio-temporal cognitive framework integrated with multiple spatio-temporal scales for individual route choice. First, a complete spatio-temporal cognitive mechanism is constructed to depict the individual evacuation cognition process. Second, a spatio-temporal route choice strategy that emerges from agent-based simulation and extends into the spatio-temporal potential field is designed to represent the overall time-varying cost along routes in individual subjective estimation. Finally, a spatio-temporal A* algorithm is developed for individual optimal route planning in complex outdoor evacuation scenarios. The experimental results show that the proposed framework outperformed the conventional potential field model in evacuation performance, in both objective crowd evacuation evaluation metrics and individual subjectively estimated evacuation cost in cognition, and may provide more insights on crowd evacuation management and guidance.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-04
      DOI: 10.3390/ijgi11120605
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 606: A Novel Approach Based on Machine Learning and
           Public Engagement to Predict Water-Scarcity Risk in Urban Areas

    • Authors: Sadeq Khaleefah Hanoon, Ahmad Fikri Abdullah, Helmi Z. M. Shafri, Aimrun Wayayok
      First page: 606
      Abstract: Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world’s population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by engaging crowdsourcing. This study aims to develop a novel approach based on public participation (PP) with a geographic information system coupled with machine learning (ML) in the urban water domain. The approach was used to detect (WSR) in two ways, namely, prediction using ML models directly and using the weighted linear combination (WLC) function in GIS. Five types of ML algorithm, namely, support vector machine (SVM), multilayer perceptron, K-nearest neighbour, random forest and naïve Bayes, were incorporated for this purpose. The Shapley additive explanation model was added to analyse the results. The Water Evolution and Planning system was also used to predict unmet water demand as a relevant criterion, which was aggregated with other criteria. The five algorithms that were used in this work indicated that diagnosing WSR using PP achieved good-to-perfect accuracy. In addition, the findings of the prediction process achieved high accuracy in the two proposed techniques. However, the weights of relevant criteria that were extracted by SVM achieved higher accuracy than the weights of the other four models. Furthermore, the average weights of the five models that were applied in the WLC technique increased the prediction accuracy of WSR. Although the uncertainty ratio was associated with the results, the novel approach interpreted the results clearly, supporting decision makers in the proactive exploration processes of urban WSR, to choose the appropriate alternatives at the right time.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-04
      DOI: 10.3390/ijgi11120606
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 607: DP-CSM: Efficient Differentially Private
           Synthesis for Human Mobility Trajectory with Coresets and Staircase
           Mechanism

    • Authors: Xin Yao, Juan Yu, Jianmin Han, Jianfeng Lu, Hao Peng, Yijia Wu, Xiaoqian Cao
      First page: 607
      Abstract: Generating differentially private synthetic human mobility trajectories from real trajectories is a commonly used approach for privacy-preserving trajectory publishing. However, existing synthetic trajectory generation methods suffer from the drawbacks of poor scalability and suboptimal privacy–utility trade-off, due to continuous spatial space, high dimentionality of trajectory data and the suboptimal noise addition mechanism. To overcome the drawbacks, we propose DP-CSM, a novel differentially private trajectory generation method using coreset clustering and the staircase mechanism, to generate differentially private synthetic trajectories in two main steps. Firstly, it generates generalized locations for each timestamp, and utilizes coreset-based clustering to improve scalability. Secondly, it reconstructs synthetic trajectories with the generalized locations, and uses the staircase mechanism to avoid the over-perturbation of noises and maintain utility of synthetic trajectories. We choose three state-of-the-art clustering-based generation methods as the comparative baselines, and conduct comprehensive experiments on three real-world datasets to evaluate the performance of DP-CSM. Experimental results show that DP-CSM achieves better privacy–utility trade-off than the three baselines, and significantly outperforms the three baselines in terms of efficiency.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-05
      DOI: 10.3390/ijgi11120607
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 608: A Map Tile Data Access Model Based on the Jump
           Consistent Hash Algorithm

    • Authors: Wei Wang, Xiaojing Yao, Jing Chen
      First page: 608
      Abstract: Tiled maps are one of the key GIS technologies used in the development and construction of WebGIS in the era of big data; there is an urgent need for high-performance tile map services hosted on big data GIS platforms. To address the current inefficiency of massive tile map data management and access, this paper proposes a massive tile map data access model that utilizes the jump consistent hash algorithm. Via the uniformity and consistency of a certain seed of a pseudo-random function, the algorithm can generate a storage slot for each tile data efficiently. By recording the slot information in the head of a row key, a uniform distribution of the tiles on the physical cluster nodes is achieved. This effectively solves the problem of hotspotting caused by the monotonicity of tile row keys in the data access process, thereby maximizing the random-access performance of a big data platform and greatly improving concurrent database access. Experiments show that this model can significantly improve the efficiency of tile map data access by more than 39% compared to a direct storage method, thereby confirming the model’s advantages in accessing massive tile map data on a big data GIS platform.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-06
      DOI: 10.3390/ijgi11120608
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 609: Progressive Collapse of Dual-Line Rivers Based
           on River Segmentation Considering Cartographic Generalization Rules

    • Authors: Fubing Zhang, Qun Sun, Jingzhen Ma, Zheng Lyu, Bowei Wen
      First page: 609
      Abstract: Collapse is a common cartographic generalization operation in multi-scale representation and cascade updating of vector spatial data. During transformation from large- to small-scale, the dual-line river shows progressive collapse from narrow river segment to line. The demand for vector spatial data with various scales is increasing; however, research on the progressive collapse of dual-line rivers is lacking. Therefore, we proposed a progressive collapse method based on vector spatial data. First, based on the skeleton graph of the dual-line river, the narrow and normal river segments are preliminarily segmented by calculating the width of the river. Second, combined with the rules of cartographic generalization, the collapse and exaggeration priority strategies are formulated to determine the handling mode of the river segment. Finally, based on the two strategies, progressive collapse of dual-line rivers is realized by collapse and exaggeration of the river segment. Experimental results demonstrated that the progressive collapse results of the proposed method were scale-driven, and the collapse part had no burr and topology problems, whereas the remaining part was clearly visible. The proposed method can be better applied to progressive collapse of the dual-line river through qualitative and quantitative evaluation with another progressive collapse method.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-06
      DOI: 10.3390/ijgi11120609
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 610: Interpretation of Spatial-Temporal Patterns of
           Community Green Spaces Based on Service Efficiency and Distribution
           Characteristics: A Case Study of the Main Urban Area of Beijing, China

    • Authors: Xiaoyi Zu, Zhixian Li, Chen Gao, Yi Wang
      First page: 610
      Abstract: Urban-scale green spaces have been a central topic as of late, but community-scale green spaces are overlooked in urban studies. This paper takes community green spaces in the main urban area of Beijing as the case to quantitatively interpret the spatial-temporal patterns of their service efficiency and distribution characteristics. The measurement section of the paper includes two parts: the first part compares the applicability of two major green space service efficiency measurement methods on the community scale and determines that the Shortest Time Distance method performs better in describing the spatial-temporal patterns of service efficiency. The second part applies the Time Distance Entropy method to initially identify the locational relationship between community green spaces and neighboring residential buildings, then proposes the Green Space Distribution Coefficient method based on this relationship to analyze the ‘courtyard’, ‘mixed’, and ‘centralized’ distribution types alongside the transition relationships between them, and the spatial-temporal patterns of distribution characteristics are measured. The results of service efficiency reveal that the community paradigms transform from ‘humanistic-oriented’ to ‘benefit-oriented’ as the Shortest Time Distance measurement values show an ascending trend with the passage of years and the outward expansion of the ring roads. The results of distribution characteristics reveal that the community residential culture transforms from ‘closeness’ to ‘detachment’ as Green Space Distribution Coefficient measurement values show a descending trend under the same conditions. Based on the measurements, this paper further provides several optimizing strategies for community green spaces in the central urban area of Beijing.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-06
      DOI: 10.3390/ijgi11120610
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 611: VGI and Satellite Imagery Integration for Crisis
           Mapping of Flood Events

    • Authors: Alberto Vavassori, Daniela Carrion, Benito Zaragozi, Federica Migliaccio
      First page: 611
      Abstract: Timely mapping of flooded areas is critical to several emergency management tasks including response and recovery activities. In fact, flood crisis maps embed key information for an effective response to the natural disaster by delineating its spatial extent and impact. Crisis mapping is usually carried out by leveraging data provided by satellite or airborne optical and radar sensors. However, the processing of these kinds of data demands experienced visual interpretation in order to achieve reliable results. Furthermore, the availability of in situ observations is crucial for the production and validation of crisis maps. In this context, a frontier challenge consists in the use of Volunteered Geographic Information (VGI) as a complementary in situ data source. This paper proposes a procedure for flood mapping that integrates VGI and optical satellite imagery while requiring limited user intervention. The procedure relies on the classification of multispectral images by exploiting VGI for the semi-automatic selection of training samples. The workflow has been tested with photographs and videos shared on social media (Twitter, Flickr, and YouTube) during two flood events and classification consistency with reference products shows promising results (with Overall Accuracy ranging from 87% to 93%). Considering the limitations of social media-sourced photos, the use of QField is proposed as a dedicated application to collect metadata needed for the image classification. The research results show that the integration of high-quality VGI data and semi-automatic data processing can be beneficial for crisis map production and validation, supporting crisis management with up-to-date maps.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-06
      DOI: 10.3390/ijgi11120611
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 612: Evaluation Method of Equalization of Basic
           Medical Services from the Spatial Perspective: The Case of Xinjiang, China
           

    • Authors: Liang Zhan, Nana Li, Chune Li, Xuejia Sang, Jun Ma
      First page: 612
      Abstract: Protecting residents’ health and improving equality are important goals of the United Nations Sustainable Development Goals. The recent outbreak of COVID-19 has placed a heavy burden on the medical systems of many countries and been disastrous for the low-income population of the world, which has further increased economic, health, and lifelong inequality in society. One way to improve the population’s health is to equalize basic medical services. A scientific evaluation of the status quo or the equalization of basic medical services (EBMS) is the basic prerequisite and an important basis for realizing the equitable allocation of medical resources. Traditional evaluation methods ignore the spatial characteristics of medical services, mostly using the indicator of equal weight evaluation, which restricts the objectivity of the evaluation results. Given this, this research proposes a set of EBMS evaluation methods from a spatial perspective and takes the Xinjiang Uygur Autonomous Region of China (Xinjiang) as an example for studying the status quo of EBMS. This study puts forward a set of EBMS evaluation methods from a geospatial perspective and makes full use of spatial analysis and information theory techniques to construct a two-level evaluation indicator that takes into account the spatial characteristics of EBMS. The entropy weight method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method have been used to reveal the current status quo of EBMS in Xinjiang to objectively reflect the differences in EBMS. When using the entropy and TOPSIS methods, the evaluation is always based on the data so that the results can more objectively reveal the medical resources available to the residents. Therefore, the government can realize a reasonable allocation of medical resources.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-07
      DOI: 10.3390/ijgi11120612
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 613: Correction: Yang et al. A Study on the
           Spatio-Temporal Land-Use Changes and Ecological Response of the Dongting
           Lake Catchment. ISPRS Int. J. Geo-Inf. 2021, 10, 716

    • Authors: Nan Yang, Wenbo Mo, Maohuang Li, Xian Zhang, Min Chen, Feng Li, Wanchao Gao
      First page: 613
      Abstract: The authors of the published paper [...]
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-08
      DOI: 10.3390/ijgi11120613
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 614: Job Accessibility as a Lens for Understanding
           the Urban Structure of Colonial Cities: A Digital Humanities Study of the
           Colonial Seoul in the 1930s Using GIS

    • Authors: Youngjoon Kim, Junghwan Kim, Hui Jeong Ha, Naoto Nakajima, Jinhyung Lee
      First page: 614
      Abstract: This study examined the urban structure of colonial Seoul in the 1930s, the capital city of Korea under the rule of the Japanese empire, by adopting quantitative geographical methods. We utilized a job accessibility index to operationalize the urban structure. We also used geographic information science (GIScience) analysis tools to digitize neighborhood-level sociodemographic and parcel-level business location information from historical materials. The results illustrated several findings that were not revealed by previous studies based on qualitative approaches. First, transit-based job accessibility (13.392) is significantly higher (p < 0.001) than walk-based job accessibility (10.575). Second, there is a Γ-shaped area with higher job accessibility, including the central part of colonial Seoul. Third, Japanese-dominant neighborhoods had significantly (p < 0.001) higher transit-based (27.156) job accessibility than Korean-dominant neighborhoods (9.319). Fourth, transit-based job accessibility is not significantly correlated with the unemployment rate overall. Although colonial Seoul was the seventh-largest city of the Japanese empire, few practical planning actions were taken to resolve urban issues, unlike the other large cities in mainland Japan.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-08
      DOI: 10.3390/ijgi11120614
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 615: Influence of Varied Ambient Population
           Distribution on Spatial Pattern of Theft from the Person: The Perspective
           from Activity Space

    • Authors: Guangwen Song, Chunxia Zhang, Luzi Xiao, Zhuoting Wang, Jianguo Chen, Xu Zhang
      First page: 615
      Abstract: The ambient population has been regarded as an important indicator for analyzing or predicting thefts. However, the literature has taken it as a homogenous group and seldom explored the varied impacts of different kinds of ambient populations on thefts. To fill this gap, supported by mobile phone trajectory data, this research investigated the relationship between ambient populations of different social groups and theft in a major city in China. With the control variables of motivated offenders and guardianship, spatial-lag negative binominal models were built to explore the effects of the ambient populations of different social groups on the distribution of theft. The results found that the influences of ambient populations of different social groups on the spatial distribution of theft are different. Accounting for the difference in the “risk–benefit” characteristics among different activity groups to the offenders, individuals from the migrant population are the most likely to be potential victims, followed by suburban and middle-income groups, while college, affluent, and affordable housing populations are the least likely. The local elderly population had no significant impact. This research has further enriched the studies of time geography and deepened routine activity theory. It suggests that the focus of crime prevention and control strategies developed by police departments should shift from the residential space to the activity space.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-08
      DOI: 10.3390/ijgi11120615
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 616: Spatial Distribution of Urban Parks’
           Effect on Air Pollution-Related Health and the Associated Factors in
           Beijing City

    • Authors: Huimin Ji, Juan Wang, Yanrong Zhu, Changsheng Shi, Shaohua Wang, Guoqing Zhi, Bin Meng
      First page: 616
      Abstract: Urban parks play an essential role in mitigating the effects of air pollution on human health in a healthy city construction process. However, due to the data limitations, little is known about the spatial distribution of real-time expressed air pollution-related health (APRH) across different urban parks and the contribution of the associated factors. To fill this research gap, this research was conducted based on social media Weibo data (Chinese Twitter) and other geographical data using semantic analyses and the Geo-Detector method by taking 169 urban parks in Beijing as the study area. The results showed that there were more Weibo items relating to APRH clustered within the third ring road and decreasing outward along the ring road. A total of 16 factors in three categories were introduced to analyze the driving forces of this spatial distribution. Accessibility was outstanding with a q-value of the number of subway stations (X14) as high as 0.79, followed by built environment and finally park attributes. Distinguished from those reports based on the traditional statistical data, this research demonstrated that although the urban parks improved the APRH, the exposure to air pollution also increased the health risks when visiting the urban park. It also provides a geographical understanding of the urban parks’ effect on APRH and theoretical guidance for urban park planning and construction.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-08
      DOI: 10.3390/ijgi11120616
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 617: A GIS-Based Spatiotemporal Analysis of the
           Relationship between the Outbreak of COVID-19, Delta Variant and
           Construction in Sydney and Melbourne

    • Authors: Kai Ilie Smith, Sara Shirowzhan
      First page: 617
      Abstract: The outbreak of the Delta Variant of COVID-19 presents a natural experiment without modern precedent. As authorities scrambled to control the spread of the disease in Australia’s largest cities, construction workers were allowed to keep working on site without the benefit of mandatory vaccination, unlike their peers in healthcare, defense, education or aviation. Using publicly available COVID-19 surveillance data, we analyzed the geographic spread of the Delta Variant and its relationship with construction in both cities. The period of this study covers the identification of the first case of community transmission to the achievement of 90% full vaccination in the eligible population. We show how the risk profile of construction workers varies according to socio-economic status such that Machinery Operators and Drivers were most at risk, followed by Laborers, owing to where they tend to live in each city. Moreover, these highly mobile workers may unknowingly serve as vectors for the spread of infectious disease to the most vulnerable communities in an urban setting. Remarkably, we also found that the risk profile of construction businesses can also be described similarly in terms of annual income. Sole traders and small businesses were mostly located in vulnerable areas, which presents threats to business continuity that public policy must address. We observed that the first eight weeks of an outbreak are critical; after this time, vulnerable workers and most construction businesses will see steep rises in their exposure to the risk of infection until the disease is brought under control. Accordingly, we recommend short, sharp pauses of all construction works on site to control the spread of future pandemic outbreaks once cases of community transmission are detected. Fiscal policy must support workers and small business owners, so they are not forced to choose between their health and earning a living during these periods. The government and trade unions must commit to mandatory vaccination for construction workers to safeguard their communities. Health authorities must continuously engage with particularly vulnerable workers as immunity wanes and vaccine boosters become necessary. Digital disinformation must be tirelessly countered by consistent expert medical advice at all levels of the industry.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-11
      DOI: 10.3390/ijgi11120617
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 618: Parametric Modeling Method for 3D Symbols of
           Fold Structures

    • Authors: Li, Chen, Du, Sun, Liu
      First page: 618
      Abstract: Most fabrication methods for three-dimensional (3D) geological symbols are limited to two types: directly increasing the dimensionality of a 2D geological symbol or performing appropriate modeling for an actual 3D geological situation. The former can express limited vertical information and only applies to the three-dimensional symbol-making of point mineral symbols, while the latter weakens the difference between 3D symbols and 3D geological models and has several disadvantages, such as high dependence on measured data, redundant 3D symbol information, and low efficiency when displayed in a 3D scene. Generating a 3D geological symbol is represented by the process of constructing a 3D geological model. This study proposes a parametric modeling method for 3D fold symbols according to the complexity and diversity of the fold structures. The method involves: (1) obtaining the location of each cross-section in the symbol model, based on the location parameters; (2) constructing the middle cross-section, based on morphological parameters and the Bezier curve; (3) performing affine transformation according to the morphology of the hinge zone and the middle section to generate the sections at both ends of the fold; (4) generating transition sections of the 3D symbol model, based on morphing interpolation; and (5) connecting the point sets of each transition section and stitching them to obtain a 3D fold-symbol model. Case studies for different typical fold structures show that this method can eliminate excessive dependence on geological survey data in the modeling process and realize efficient, intuitive, and abstract 3D symbol modeling of fold structures based on only a few parameters. This method also applies to the 3D geological symbol modeling of faults, joints, intrusions, and other geological structures and 3D geological modeling of typical geological structures with a relatively simple spatial morphology.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-13
      DOI: 10.3390/ijgi11120618
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 619: Attention-based Multiscale Spatiotemporal
           Network for Traffic Forecast with Fusion of External Factors

    • Authors: Jeba Nadarajan, Rathi Sivanraj
      First page: 619
      Abstract: Periodic traffic prediction and analysis is essential for urbanisation and intelligent transportation systems (ITS). However, traffic prediction is challenging due to the nonlinear flow of traffic and its interdependencies on spatiotemporal features. Traffic flow has a long-term dependence on temporal features and a short-term dependence on local and global spatial features. It is strongly influenced by external factors such as weather and points of interest. Existing models consider long-term and short-term predictions in Euclidean space. In this paper, we design an attention-based encoder–decoder with stacked layers of LSTM to analyse multiscale spatiotemporal dependencies in non-Euclidean space to forecast traffic. The attention weights are obtained adaptively and external factors are fused with the output of the decoder to evaluate region-wide traffic predictions. Extensive experiments are conducted to evaluate the performance of the proposed attention-based non-Euclidean spatiotemporal network (ANST) on real-world datasets. The proposed model has improved prediction accuracy over previous methods. The insights obtained from traffic prediction would be beneficial for daily commutation and logistics.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-13
      DOI: 10.3390/ijgi11120619
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 620: Spatial and Attribute Neural Network Weighted
           Regression for the Accurate Estimation of Spatial Non-Stationarity

    • Authors: Ni, Wang, Wang, Wang, Li, Wang
      First page: 620
      Abstract: Geographically neural network weighted regression is an improved model of GWR combined with a neural network. It has a stronger ability to fit nonlinear functions, and complex geographical processes can be modeled more fully. GNNWR uses the distance metric of Euclidean space to express the relationship between sample points. However, except for spatial location features, geographic entities also have many diverse attribute features. Incorporating attribute features into the modeling process can make the model more suitable for the real geographical process. Therefore, we proposed a spatial-attribute proximities deep neural network to aggregate data from the spatial feature and attribute feature, so that one unified distance metric can be used to express the spatial and attribute relationships between sample points at the same time. Based on GNNWR, we designed a spatial and attribute neural network weighted regression (SANNWR) model to adapt to this new unified distance metric. We developed one case study to examine the effectiveness of SANNWR. We used PM2.5 concentration data in China as the research object and compared the prediction accuracy between GWR, GNNWR and SANNWR. The results showed that the “spatial-attribute” unified distance metric is useful, and that the SANNWR model showed the best performance.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-13
      DOI: 10.3390/ijgi11120620
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 621: A Trajectory Big Data Storage Model
           Incorporating Partitioning and Spatio-Temporal Multidimensional
           Hierarchical Organization

    • Authors: Zhixin Yao, Jianqin Zhang, Taizeng Li, Ying Ding
      First page: 621
      Abstract: Trajectory big data is suitable for distributed storage retrieval due to its fast update speed and huge data volume, but currently there are problems such as hot data writing, storage skew, high I/O overhead and slow retrieval speed. In order to solve the above problems, this paper proposes a trajectory big data model that incorporates data partitioning and spatio-temporal multi-perspective hierarchical organization. At the spatial level, the model partitions the trajectory data based on the Hilbert curve and combines the pre-partitioning mechanism to solve the problems of hot writing and storage skewing of the distributed database HBase; at the temporal level, the model takes days as the organizational unit, finely encodes them into a minute system and then fuses the data partitioning to build spatio-temporal hybrid encoding to hierarchically organize the trajectory data and solve the problems of efficient storage and retrieval of trajectory data. The experimental results show that the model can effectively improve the storage and retrieval speed of trajectory big data under different orders of magnitude, while ensuring relatively stable writing and query speed, which can provide an efficient data model for trajectory big data mining and analysis.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-13
      DOI: 10.3390/ijgi11120621
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 622: Research on Gridding of Urban Spatial Form Based
           on Fractal Theory

    • Authors: Qindong Fan, Xuejian Mei, Chenming Zhang, Xiaoyu Yang
      First page: 622
      Abstract: Urban spatial form is a significant reference to getting to know cities and running the cities. The fractal theory is an effective means to quantify urban spatial form. Taking the buildings in the outer ring of Zhengzhou City as the research object, the basic architectural models are built by extracting their forms. The research site is subdivided into 199 regions. The distribution of architectural forms in Zhengzhou is analyzed by fractal theory and spatial autocorrelation from the perspective of two-dimensional(2D) and three-dimensional(3D). The results indicate that the architectural layout of Zhengzhou has distinct fractal characteristics; Both global spatial autocorrelation and local spatial autocorrelation show significant positive correlations; There are obvious spatial differences in architectural space forms in different regions. The refined grid analysis strengthens the understanding of the urban spatial structure and development rules in more detail. The study promotes the refinement and visualization of fractal theory effectively and improves the depth of urban spatial form cognition.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-13
      DOI: 10.3390/ijgi11120622
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 623: Interactive Impacts of Built Environment Factors
           on Metro Ridership Using GeoDetector: From the Perspective of TOD

    • Authors: Xingdong Deng, Ji Zhang, Shunyi Liao, Chujie Zhong, Feng Gao, Li Teng
      First page: 623
      Abstract: TOD (transit-oriented development) is a planning concept that uses public transportation stations as the center of development, and it aims to integrate land use efficiency and transportation planning linkages to encourage the use of public transportation. The impact of metro TOD projects on urban transportation is multifaceted and complex, and the promotion of metro TOD ridership is an important topic in academic circles. However, the theoretical analysis framework of the impact mechanism of metro TOD ridership is still not perfect. Most studies ignore the TOD characteristics of the stations and the interaction between the station area’s land use and the station area functional linkage. Moreover, a few studies have focused on the mechanisms of the impact of TOD built environment factors on the spatial differentiation of station ridership, and the interactive effects of built environment factors. In this paper, the factors of a metro TOD station built environment were selected based on the node–place–linkage model expanded by the 5D principle of TOD, and a solution is provided for the computable transformation of the 5D principle. The GeoDetector method was used to detect the individual and interactive effects of the TOD built environment factors. The results show that the spatial distribution of the metro TOD station area ridership shows a core–peripheral structure and spatial heterogeneity, both on weekdays and weekends. Moreover, the individual effects of each factor can explain up to 49% and 35% of the traffic distribution on weekdays and weekends, respectively. In addition, the two-factor interactive effect has a stronger influence on metro ridership. The interactive effect can explain up to 72% and 77% of the traffic distribution on weekdays and weekends, respectively. Furthermore, the individual effects of each factor exhibited spatial heterogeneity in the local spaces, showing spatial facilitation and inhibition, respectively. Finally, the main policy recommendations are as follows: One of the important ways to guide the development of cities toward polycentric structure is to promote a TOD model in the peripheral areas of the cities. Building more public open spaces in TOD station areas and improving the collection and distribution capacity of the bus transport systems can effectively stimulate the ridership of metro stations.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-15
      DOI: 10.3390/ijgi11120623
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 624: Analysis of Urban Vitality in Nanjing Based on a
           Plot Boundary-Based Neural Network Weighted Regression Model

    • Authors: Yi Yang, Hong Wang, Shuhong Qin, Xiuneng Li, Yunfeng Zhu, Yicong Wang
      First page: 624
      Abstract: As a representative indicator for the level and sustainability of urban development, urban vitality has been widely used to assess the quality of urban development. However, urban vitality is too blurry to be accurately quantified and is often limited to a particular type of expression of vitality. Current regression models often fail to accurately express the spatial heterogeneity of vibrancy and drivers. Therefore, this paper took Nanjing as the study area and quantified the social, cultural, and economic vitality indicators based on mobile phone data, POI data, and night-light remote sensing data. We also mapped the spatial distribution of comprehensive urban vitality using an improved entropy method and analyzed the spatial heterogeneity of urban vitality and its influencing factors using a plot boundary-based neural network weighted regression (PBNNWR). The results show: (1) The comprehensive vitality in Nanjing is distributed in a “three-center” pattern with one large and two small centers; (2) PBNNWR can be used to investigate the local regression relationships among the driving factors and urban vitality, and the fitting accuracy (95.6%) of comprehensive vitality in weekdays is higher than that of ordinary least squares regression (OLS) (65.9%), geographically weighted regression (GWR) (89.9%), and geographic neural network weighted regression (GNNWR) (89.5%) models; (3) House price, functional diversity, building density, metro station accessibility, and residential facility density are factors that significantly affect urban vitality. The study’s findings can provide theoretical guidance for optimizing the urban spatial layout, resource allocation, and targeted planning strategies for areas with different vitality values.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-15
      DOI: 10.3390/ijgi11120624
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 625: A Knowledge Graph Convolutional Networks Method
           for Countryside Ecological Patterns Recommendation by Mining Geographical
           Features

    • Authors: Xuhui Zeng, Shu Wang, Yunqiang Zhu, Mengfei Xu, Zhiqiang Zou
      First page: 625
      Abstract: The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the ‘cold start’ problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-15
      DOI: 10.3390/ijgi11120625
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 626: Testing Small-Scale Vitality Measurement Based
           on 5D Model Assessment with Multi-Source Data: A Resettlement Community
           Case in Suzhou

    • Authors: Jinliu Chen, Wenkang Tian, Kexin Xu, Paola Pellegrini
      First page: 626
      Abstract: In China’s fourteenth five-year plan, urban regeneration has become one of the most crucial strategies for activating the existing cities. Since creating vibrant urban spaces is a critical component of urban regeneration, understanding the patterns of community vitality helps formulate reactive regeneration policies and design interventions. However, the lack of local-scale measurement criteria and data collection methods has posed significant constraints to assessing and rejuvenating community vitality. Taking Suzhou Nanhuan New Village as a study area, our research involved a comparative study approach to investigate the fundamental driving mechanism of urban vitality with the support of a theoretical model (5D theory), multi-source data input, real-time photography technologies, and statistical analysis tools (Analytic Hierarchy Process). The result shows at the community level, the original ‘3d’ dimensions (‘Density’, ‘Diversity’, ‘Design’) remain key elements for forming vibrant spatial quality and functionality, and density factors matter significantly. This study intends to provide a new paradigm for small-scale community vitality assessment, verification, and regeneration by combining urban morphology with people-oriented and environmental-oriented perspectives. This research could support quantitative research on creating vibrant high-density communities in the urban regeneration process and bring insights to academics and design practitioners.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-15
      DOI: 10.3390/ijgi11120626
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 627: Multi-Scale Flood Mapping under Climate Change
           Scenarios in Hexagonal Discrete Global Grids

    • Authors: Mingke Li, Heather McGrath, Emmanuel Stefanakis
      First page: 627
      Abstract: Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-17
      DOI: 10.3390/ijgi11120627
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 628: Information in Streetscapes—Research on
           Visual Perception Information Quantity of Street Space Based on
           Information Entropy and Machine Learning

    • Authors: Ziyi Liu, Xinyao Ma, Lihui Hu, Shan Lu, Xiaomin Ye, Shuhang You, Zhe Tan, Xin Li
      First page: 628
      Abstract: Urban street space is a critical reflection of a city’s vitality and image and a critical component of urban planning. While visual perceptual information about an urban street space can reflect the composition of place elements and spatial relationships, it lacks a unified and comprehensive quantification system. It is frequently presented in the form of element proportions without accounting for realistic factors, such as occlusion, light and shadow, and materials, making it difficult for the data to accurately describe the complex information found in real scenes. The conclusions of related studies are insufficiently focused to serve as a guide for designing solutions, remaining merely theoretical paradigms. As such, this study employed semantic segmentation and information entropy models to generate four visual perceptual information quantity (VPIQ) measures of street space: (1) form; (2) line; (3) texture; and (4) color. Then, at the macro level, the streetscape coefficient of variation (SCV) and K-means cluster entropy (HCK) were proposed to quantify the street’s spatial variation characteristics based on VPIQ. Additionally, we used geographically weighted regression (GWR) to investigate the relationship between VPIQ and street elements at the meso level as well as its practical application. This method can accurately and objectively describe and detect the current state of street spaces, assisting urban planners and decision-makers in making decisions about planning policies, urban regeneration schemes, and how to manage the street environment.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-17
      DOI: 10.3390/ijgi11120628
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 629: Domain Constraints-Driven Automatic Service
           Composition for Online Land Cover Geoprocessing

    • Authors: Huaqiao Xing, Chang Liu, Rui Li, Haihang Wang, Jinhua Zhang, Huayi Wu
      First page: 629
      Abstract: With the rapid development of web service technology, automatic land cover web service composition has become one of the key challenges in solving complex geoprocessing tasks of land cover. Service composition requires the creation of service chains based on semantic information about the services and all the constraints that should be respected. Artificial intelligence (AI) planning algorithms have recently significantly progressed in solving web service composition problems. However, the current approaches lack effective constraints to guarantee the accuracy of automatic land cover service composition. To address this challenge, the paper proposes a domain constraints-driven automatic service composition approach for online land cover geoprocessing. First, a land cover service ontology was built to semantically describe land cover tasks, data, and services, which assist in constructing domain constraints. Then, a constraint-aware GraphPlan algorithm was proposed, which constructs a service planning graph and searches services based on the domain constraints for generating optimal web service composition solutions. In this paper, the above method was integrated into a web prototype system and a case study for the online change detection automatic geoprocessing was implemented to test the accuracy of the method. The experimental results show that with this method, a land cover service chain can generate automatically by user desire objective and domain constraints, and the service chain execution result is more accurate.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-18
      DOI: 10.3390/ijgi11120629
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 630: The Effects of Colour Content and Cumulative
           Area of Outdoor Advertisement Billboards on the Visual Quality of Urban
           Streets

    • Authors: Mastura Adam, Ammar Al-Sharaa, Norafida Ab Ghafar, Riyadh Mundher, Shamsul Abu Bakar, Ameer Alhasan
      First page: 630
      Abstract: Visual comfort has a critical effect that significantly influences public appreciation of urban environments. Although colour is an integral part of billboard design, little empirical evidence exists to support some of the popularly held ideas about the effects of colour on task performance and human psychological wellbeing. Thus, attempting to set a threshold level of allowed undesirable visual stimuli in each urban setting is considered to be essential in achieving a satisfactory level of visual quality. Therefore, this research investigates the effects of colour content of outdoor advertisement billboards on the appreciation of urban scenes by the public. This research utilises pictorial survey, R.G.B bivariate histogram technique, and an areal cumulative analysis of a group of collected pictures within one of Kuala Lumpur’s high streets. Results of the pictorial survey are cross analysed against the results of the pictorial RGB content analysis and pictorial outdoor advertisement (OA) cumulative areal analysis to indicated a strong correlation between environmental colour content, OAs’ cumulative area, and visual comfort. The study suggests that the lack of guidelines and regulations of the color content of outdoor billboard advertisement design could potentially be detrimental for the public’s appreciation of urban environments. Future research initiatives are encouraged to develop a visual quality assessment framework that contributes to the image and identity of the city of Kuala Lumpur.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-18
      DOI: 10.3390/ijgi11120630
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 631: Integrating Remote Sensing and Street View
           Imagery for Mapping Slums

    • Authors: Abbas Najmi, Caroline M. Gevaert, Divyani Kohli, Monika Kuffer, Jati Pratomo
      First page: 631
      Abstract: Mapping slums is vital for monitoring the Sustainable Development Goal (SDG) indicators. In the absence of reliable data, Remote Sensing (RS)-based approaches, particularly the Deep Learning (DL) methods, have gained recognition and high accuracies for slum mapping. However, using RS alone has its limitation in complex urban environments. Previous studies showed the added value of combining ground-level information with RS. Therefore, this research aims to integrate Remote Sensing Imagery (RSI) and Street View Images (SVI) for slum mapping. Jakarta city is the study area representing the challenge of distinguishing between slum and non-slum kampungs, and these kampungs accommodate approximately 60% of the population of Jakarta. This research compares the mapping results obtained by four DL networks: FCN-DK6 used only RSI, a VGG16 used only SVI, and two networks combined RSI and SVI (FCN-DK6-i and Modified FCN-DK6). Further, the Modified FCN-DK6 network was explored by integrating SVI at each convolutional layer, i.e., Modified FCN-DK6_1, Modified FCN-DK6_2, Modified FCN-DK6_3, Modified FCN-DK6_4, and Modified FCN-DK6_5. Experimental results demonstrate that combining RSI and SVI improves the accuracy, depending on how and at what level in the FCN network they are integrated. The Modified FCN-DK6_2 outperforms the rest in Modified FCN-DK6 experiments and FCN-DK6-i.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-12-19
      DOI: 10.3390/ijgi11120631
      Issue No: Vol. 11, No. 12 (2022)
       
  • IJGI, Vol. 11, Pages 570: A Machine Learning Approach for Detecting Rescue
           Requests from Social Media

    • Authors: Zheye Wang, Nina S. N. Lam, Mingxuan Sun, Xiao Huang, Jin Shang, Lei Zou, Yue Wu, Volodymyr V. Mihunov
      First page: 570
      Abstract: Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different machine learning models. We investigate how the outcomes of our ZIP code filtering differ from those of a recent, comparable study in terms of generating training data for machine learning models. Following this, experiments are conducted to test how the existence of ZIP codes would affect the performance of machine learning models by simulating different percentages of ZIP-code-tagged positive samples. The findings show that (1) all machine learning classifiers except K-nearest neighbors and Naïve Bayes achieve state-of-the-art performance in detecting rescue requests from social media; (2) using ZIP code filtering could increase the effectiveness of gathering rescue requests for training machine learning models; (3) machine learning models are better able to identify rescue requests that are associated with ZIP codes. We thereby encourage every rescue-seeking victim to include ZIP codes when posting messages on social media. This study is a useful addition to the literature and can be helpful for first responders to rescue disaster victims more efficiently.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-16
      DOI: 10.3390/ijgi11110570
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 571: Site Selection of Natural Gas Emergency Response
           Team Centers in Istanbul Metropolitan Area Based on GIS and FAHP

    • Authors: Mehmet Şerif Sarıkaya, Mustafa Yanalak, Himmet Karaman
      First page: 571
      Abstract: The location of natural gas emergency response team centers (NGERTCs) is critical in terms of addressing natural gas notifications that require a timely emergency response. The selection of NGERTCs in Istanbul has an important place in terms of providing better service, due to the necessity of responding to emergency natural gas notifications within 15 min, in addition to the over 200,000 natural gas notifications per year and heavy traffic conditions. Therefore, this study proposes a solution based on GIS and FAHP to determine suitable NGERTC locations in Istanbul Metropolitan Area. In the first stage of the study, the required 15-min coverage areas for emergency calls for 36 existing NGERTCs in Istanbul were extracted and the adequacy of their locations was analyzed. In the second stage of the study, the weights of seven criteria determined for new NGERTC site selection were calculated by the FAHP method. With spatial analysis made, 12 new NGERTC locations were proposed. Finally, re-coverage analysis was performed for proposed and existing NGERTCs, and changes in coverage area within a 15 min response time were analyzed. Natural gas network coverage increased from 70.04% to 83.86%, and natural gas subscriber coverage increased from 91.03% to 96.27%. The results show that GIS and FAHP are worth using in selecting suitable NGERTC locations.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-16
      DOI: 10.3390/ijgi11110571
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 572: Building Block Extraction from Historical Maps
           Using Deep Object Attention Networks

    • Authors: Yao Zhao, Guangxia Wang, Jian Yang, Lantian Zhang, Xiaofei Qi
      First page: 572
      Abstract: The geographical feature extraction of historical maps is an important foundation for realizing the transition from human map reading to machine map reading. The current methods for building block extraction from historical maps have many problems, such as low accuracy and poor scalability. Moreover, the high cost of annotating historical maps further limits its applications. In this study, a method for extracting building blocks from historical maps is proposed based on the deep object attention network. Based on the OCRNet framework, multiple attention mechanisms were used to improve the ability of the network to extract the contextual information of the target. Moreover, through the optimization of the feature extraction network structure, the impact of the down-sampling process on local information and boundary contours was reduced, in order to improve the network’s ability to capture boundary information. Subsequently, the transfer learning method was used to jointly train the network model on both remote sensing datasets and few-shot historical map datasets to further improve the feature learning ability of the network, which overcomes the constraints of small sample sizes. The experimental results show that the proposed method can effectively improve the extraction accuracy of building blocks from historical maps.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-16
      DOI: 10.3390/ijgi11110572
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 573: Evaluating BFASTMonitor Algorithm in Monitoring
           Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT
           Time Series: A Case Study on Marmara Region, Turkey

    • Authors: Mashhadi, Alganci
      First page: 573
      Abstract: Time series analysis combined with remote sensing data allows for the study of abrupt changes in the environment due to significant and severe disturbances such as deforestation, agricultural activities, fires, and urban expansion, as well as gradual changes such as climate variability and forest degradation in the ecosystem. The precision of any change detection analysis is highly dependent upon its ability to separate actual changes and fluctuations on a seasonal scale. One of the efficient methods in this context is using the Breaks for Additive Seasonal and Trend (BFAST) set of algorithms. This study aims to perform a comprehensive and comparative evaluation of different Vis’ performance in forest degradation with the Landsat 8 images and BFASTMonitor approach. Through evaluation, the study also considers the potential effects of different forest types and deforestation scales in the Marmara region of Turkey. For this purpose, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Normalized Burn Ratio (NBR) vegetation indices (VI) were selected for a comparative evaluation. The overall accuracy of VIs in deciduous forests was around 85% for NDVI, NDMI, and NBR, and 78.80% for EVI, while in coniferous forests, the overall accuracy demonstrated higher values of about 88% for NDVI, NDMI, and EVI, and 87.28% for NBR. Consequently, water-sensitive VIs that utilize shortwave infrared bands proved to be slightly more sensitive in detecting forest disturbances while chlorophyll-sensitive VIs represented lower accuracy for both forest types. Overall, all VIs faced an underestimation error in deforested area detection that was observable through negative BIAS. The results illuminate that BFASTMonitor can be considered as a tool in monitoring forest environments due to its acceptable deforestation determination capability in deciduous and coniferous forests, with slightly higher performance for small-scale deforestation patterned regions.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-16
      DOI: 10.3390/ijgi11110573
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 574: Field Cognitive Styles on Visual Cognition in
           the Event Structure Design of Bivariate Interactive Dorling
           Cartogram—The Similarities and Differences of Field-Independent and
           Field-Dependent Users

    • Authors: Yanfei Zhu, Jie Gu, Yun Lin, Mo Chen, Qi Guo, Xiaoxi Du, Chengqi Xue
      First page: 574
      Abstract: As a simple, discontinuous, surface deformation statistical map, Dorling cartograms are effective means with which to characterize the geographic distribution of event data attributes. According to existing research, behavioral differences exist in the visual cognition of individuals with different cognitive field styles in the spatial task of switching layers in a two-dimensional electronic map. However, there are few studies that compare the visual cognitive ability of individuals with different cognitive field styles in the cross-layer structure design of Dorling cartogram event information. This paper uses the visual behavior measurement method to analyze the similarities and differences in the visual cognitive ability of two types of individuals, namely, field-independent and field-dependent individuals, in the cross-layer event structure design of Dorling cartograms. We recruited 40 subjects to perform visualization tasks on Dorling cartograms designed with two event structures, and we recorded the visual cognition data for the two types of subjects in both tasks. The results show that the subjects with the field-independent style perform better in the cognition of the Dorling cartogram event structure than the subjects with the field-dependent style, and the “S-T” event structure design is generally more user-friendly than the “T-S” event structure design. Our findings help to provide some references for the event structure design of human-centered Dorling cartograms.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-17
      DOI: 10.3390/ijgi11110574
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 575: Urban Human-Land Spatial Mismatch Analysis from
           a Source-Sink Perspective with ICT Support

    • Authors: Tong Li, Chunliang Xiu, Huisheng Yu
      First page: 575
      Abstract: The development management of the city constantly pursues sustainable development of human-land matching. Under the new research framework, this study discusses the urban human-land relationship from the perspective of the source-sink of daily population mobility, making up for the lack of a static research perspective in the past. The spatial relationship between population source-sink and land use intensity was studied by bivariate Moran’s I and multivariate correspondence analysis. The results show that there is a significant spatial correlation between urban population source-sink and land use intensity, which is obviously affected by urban circles and land use types, and these laws are cyclical day after day. The urban fringe becomes the main place where spatial mismatch occurs. Currently, the spatial mismatch of cities in northeast China, represented by Shenyang, is dominated by the high intensity of land use and low flow of the population. The key to solving the problem is to curb the high-density urban sprawl. The research results improve the integrity and accuracy of urban human-land spatial mismatch analysis and provide support for formulating more specific urban land use policies.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-17
      DOI: 10.3390/ijgi11110575
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 576: Spatial Pattern and Influencing Factors of Basic
           Education Resources in Rural Areas around Metropolises—A Case Study
           of Wuhan City’s New Urban Districts

    • Authors: Liang Jiang, Jie Chen, Ye Tian, Jing Luo
      First page: 576
      Abstract: Basic education resources are basic urban and rural social public security resources, and their spatial distribution is an important issue related to people’s livelihoods and social justice. Taking Wuhan as a case study, this paper analyzed the spatial distribution characteristics of rural basic education resources based on the methods of the average nearest neighbor index, imbalance index, kernel density analysis and two-step floating catchment area and then used geographic detector analysis to detect its influencing factors. The following findings were obtained: (1) Rural kindergartens and elementary schools in Wuhan City’s new urban districts showed a clustered distribution pattern, while secondary schools showed a uniform distribution trend. The spatial distribution of rural basic education resources is poorly balanced, with a tendency to cluster in Huangpi District, Xinzhou District and Caidian District; the overall spatial distribution density of rural basic education resources showed the distribution characteristics of “block-like clustering and multicenter development”. (2) The spatial accessibility of kindergartens showed a spatial pattern of “large dispersion and small clustering”, with multiple high-value clustering areas; and the accessibility of elementary and secondary schools showed a spatial pattern of high in the south and low in the north. (3) The population, economy and education development level are the main factors affecting the spatial distribution of rural basic education resources, while the influence of infrastructure construction is weak. The core influencing factors of the spatial distribution of each type of basic education resource are both consistent and different. According to the interaction factor detection, the spatial distribution of rural basic education resources in Wuhan City’s new urban districts is the result of the combined effect of multiple factors.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-19
      DOI: 10.3390/ijgi11110576
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 577: Map Design and Usability of a Simplified
           Topographic 2D Map on the Smartphone in Landscape and Portrait
           Orientations

    • Authors: Beata Medyńska-Gulij, Jacek Gulij, Paweł Cybulski, Krzysztof Zagata, Jakub Zawadzki, Tymoteusz Horbiński
      First page: 577
      Abstract: Map design and usability issues are crucial when considering different device orientations. It is visible, especially in exploring the topographical space in landscape or portrait orientation on the mobile phone. In this study, we aim to reveal the main differences and similarities among participants’ performance in a map-based task. The study presents an original research scheme, including establishing conceptual assumptions, developing map applications with gaming elements, user testing, and visualizing results. It appears that the different phone orientation triggers different visual strategy. This transfers into decision-making about the path selection. It turned out that in landscape orientation, participants preferred paths oriented east–west. On the other hand, portrait orientation supported north–south path selection. However, considering the given task accomplishment, both mobile phones’ orientations are adequate for the exploration of topographical space.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-20
      DOI: 10.3390/ijgi11110577
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 578: Does Culture Shape Our Spatial Ability' An
           Investigation Based on Eye Tracking

    • Authors: Yuyang Tian, Tianyu Yang, Weihua Dong
      First page: 578
      Abstract: Culture affects people’s spatial memory, mental representations, and spatial reference frameworks. People with different cultural backgrounds show different degrees of spatial ability. However, the current research does not reveal the shaping of spatial ability by culture from the perspective of visual cognition. In this study, we used eye tracking and designed mental rotation, spatial visualization, spatial orientation, and spatial correlation tasks to compare the spatial ability of Chinese and Malaysian Chinese people. The results showed that there were some minimal differences between them. Chinese participants had higher accuracy in the mental rotation task, showed more fixation to landmarks in spatial orientation, showed more fixation to the main map, and switched more frequently between the two thematic maps when judging spatial relationships. As “cultural citizens” of China, Malaysian Chinese people’s spatial ability is not only shaped by their own ethnic culture in terms of language but also influenced by foreign races in terms of education, wayfinding tendency, and cognitive style. This study can contribute to the understanding of the influence of culture on spatial ability and its possible causes.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-21
      DOI: 10.3390/ijgi11110578
      Issue No: Vol. 11, No. 11 (2022)
       
  • IJGI, Vol. 11, Pages 579: Multi-Mode Huff-Based 2SFCA: Examining
           Geographical Accessibility to Food Outlets in Austin, Texas

    • Authors: He Jin, Yongmei Lu
      First page: 579
      Abstract: The retail food environment draws much attention from scholars because it can shape individuals’ eating behaviors and health outcomes. Although much progress has been made, current retail food environment assessments mainly use simple food accessibility measures while overlooking the role of multiple transportation modes. This research proposed a multiple-mode Huff-based Two-step Floating Catchment Area (2SFCA) method to measure geographical access to food outlets in Austin, Texas. The spatial accessibility score was calculated with low to high impedance coefficients. Our analyses revealed an urban core-and-peripheral disparity in spatial accessibility to food outlets. We also compared the proposed multiple-mode Huff-based 2SFCA with its single-mode counterpart using t-test and relative difference methods. The comparison illustrates that the difference between the two methods of calculating healthy and unhealthy food accessibility is significant when the impedance coefficient is set to be 1.4 and 1.5, respectively. Our proposed multi-mode Huff-based 2SFCA method accounts for the various transport means and the spatial heterogeneity in population demand for food services; this could support developing intervention strategies to target under-served healthy food areas and over-served unhealthy food areas.
      Citation: ISPRS International Journal of Geo-Information
      PubDate: 2022-11-21
      DOI: 10.3390/ijgi11110579
      Issue No: Vol. 11, No. 11 (2022)
       
 
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