ISPRS International Journal of Geo-Information

24 pages, 17080 KiB

Open AccessArticle

Spatial Nonlinear Effects of Street Vitality Constrained by Construction Intensity and Functional Diversity—A Case Study from the Streets of Shenzhen

by Jilong Li, Niuniu Kong, Shi** Lin, Jie Zeng, Yilin Ke and Jiacheng Chen

ISPRS Int. J. Geo-Inf. 2024, 13(7), 238; https://doi.org/10.3390/ijgi13070238 - 2 Jul 2024

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Abstract

As an important part of urban vitality, street vitality is an external manifestation of street economic prosperity and is affected by the built environment and the surrounding street vitality. However, existing research on the formation mechanism of street vitality focuses only on the [...] Read more.

As an important part of urban vitality, street vitality is an external manifestation of street economic prosperity and is affected by the built environment and the surrounding street vitality. However, existing research on the formation mechanism of street vitality focuses only on the built environment itself, ignoring the spatial spillover effect on street vitality. This study uses 5290 street segments in Shenzhen as examples. Utilizing geospatial and other multisource big data, this study creates spatial weight matrices at varying distances based on different living circle ranges. By combining the panel threshold model (PTM) and the spatial panel Durbin model (SPDM), this study constructs a spatial autoregressive threshold model to explore the spatial nonlinear effects of street vitality, considering various spatial weight matrices and thresholds of construction intensity and functional diversity. Our results show the following: (1) Street vitality exhibits significant spatial spillover effects, which gradually weaken as the living circle range expands (Moran indices are 0.178***, 0.160***, and 0.145*** for the 500 m, 1000 m, and 1500 m spatial weight matrices, respectively). (2) Construction intensity has a threshold, which is 0.1466 under spatial matrices of different distances. Functional diversity has two thresholds: 0.6832 and 2.2065 for the 500 m spatial weight matrix, and 0.6832 and 1.4325 for the 1000 m matrices, and 0.6832 and 1.2724 for 1500 m matrices. (3) As an international metropolis, street accessibility in Shenzhen has a significant and strong positive impact on its street vitality. This conclusion provides stakeholders with spatial patterns that influence street vitality, offering a theoretical foundation to further break down barriers to street vitality. Full article

(This article belongs to the Topic Spatial Decision Support Systems for Urban Sustainability)

24 pages, 8649 KiB

Open AccessArticle

Assessing the Impact of Land Use and Land Cover Changes on Surface Temperature Dynamics Using Google Earth Engine: A Case Study of Tlemcen Municipality, Northwestern Algeria (1989–2019)

by Imene Selka, Abderahemane Medjdoub Mokhtari, Kheira Anissa Tabet Aoul, Djamal Bengusmia, Kacemi Malika and Khadidja El-Bahdja Djebbar

ISPRS Int. J. Geo-Inf. 2024, 13(7), 237; https://doi.org/10.3390/ijgi13070237 - 2 Jul 2024

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Abstract

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and [...] Read more.

Changes in land use and land cover (LULC) have a significant impact on urban planning and environmental dynamics, especially in regions experiencing rapid urbanization. In this context, by leveraging the Google Earth Engine (GEE), this study evaluates the effects of land use and land cover modifications on surface temperature in a semi-arid zone of northwestern Algeria between 1989 and 2019. Through the analysis of Landsat images on GEE, indices such as normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and normalized difference latent heat index (NDLI) were extracted, and the random forest and split window algorithms were used for supervised classification and surface temperature estimation. The multi-index approach combining the Normalized Difference Tillage Index (NDTI), NDBI, and NDVI resulted in kappa coefficients ranging from 0.96 to 0.98. The spatial and temporal analysis of surface temperature revealed an increase of 4 to 6 degrees across the four classes (urban, barren land, vegetation, and forest). The Google Earth Engine approach facilitated detailed spatial and temporal analysis, aiding in understanding surface temperature evolution at various scales. This ability to conduct large-scale and long-term analysis is essential for understanding trends and impacts of land use changes at regional and global levels. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

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18 pages, 6121 KiB

Open AccessArticle

Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry

by Panagiotis Kalaitzis, Michael Foumelis, Antonios Mouratidis, Dimitris Kavroudakis and Nikolaos Soulakellis

ISPRS Int. J. Geo-Inf. 2024, 13(7), 236; https://doi.org/10.3390/ijgi13070236 - 2 Jul 2024

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Abstract

Persistent scatterer interferometry (PSI) has been proven to be a robust method for studying complex and dynamic phenomena such as ground displacement over time. Proper visualization of PSI measurements is both crucial and challenging from a cartographic standpoint. This study focuses on the [...] Read more.

Persistent scatterer interferometry (PSI) has been proven to be a robust method for studying complex and dynamic phenomena such as ground displacement over time. Proper visualization of PSI measurements is both crucial and challenging from a cartographic standpoint. This study focuses on the development of an interactive cartographic web map application, providing suitable visualization of PSI data, and exploring their geographic, cartographic, spatial, and temporal attributes. To this end, PSI datasets, generalized at different resolutions, are visualized in eight predefined cartographic scales. A multiscale generalization algorithm is proposed. The automation of this procedure, spurred by the development of a web application, offers users the flexibility to properly visualize PSI datasets according to the specific cartographic scale. Additionally, the web map application provides a toolset, offering state-of-the-art cartographic approaches for exploring PSI datasets. This toolset consists of exploration, measurement, filtering (based on the point’s spatial attributes), and exporting tools customized for PSI measurement. Furthermore, a graph tool, offering users the capability to interactively plot PSI time-series and investigate the evolution of ground deformation over time, has been developed and integrated into the web interface. This study reflects the need for appropriate visualization of PSI datasets at different cartographic scales. It is shown that each original PSI dataset possesses a suitable cartographic scale at which it should be visualized. Innovative cartographic approaches, such as web applications, can prove to be effective tools for users working in the domain of map** and monitoring the dynamic behavior of surface motion. Full article

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18 pages, 9371 KiB

Open AccessArticle

Performance Analysis of Random Forest Algorithm in Automatic Building Segmentation with Limited Data

by Ratri Widyastuti, Deni Suwardhi, Irwan Meilano, Andri Hernandi, Nabila S. E. Putri, Asep Yusup Saptari and Sudarman

ISPRS Int. J. Geo-Inf. 2024, 13(7), 235; https://doi.org/10.3390/ijgi13070235 - 2 Jul 2024

Viewed by 191

Abstract

Airborne laser technology produces point clouds that can be used to build 3D models of buildings. However, the work is a laborious process that could benefit from automation. Artificial intelligence (AI) has been widely used in automating building segmentation as one of the [...] Read more.

Airborne laser technology produces point clouds that can be used to build 3D models of buildings. However, the work is a laborious process that could benefit from automation. Artificial intelligence (AI) has been widely used in automating building segmentation as one of the initial stages in the 3D modeling process. The algorithms with a high success rate using point clouds for automatic semantic segmentation are random forest (RF) and PointNet++, with each algorithm having its own advantages and disadvantages. However, the training and testing data to develop and test the model usually share similar characteristics. Moreover, producing a good automation model requires a lot of training data, which may become an issue for users with a small amount of training data (limited data). The aim of this research is to test the performance of the RF and PointNet++ models in different regions with limited training and testing data. We found that the RF model developed from a small amount data, in different regions between the training and testing data, performs well compared to PointNet++, yielding an OA score of 73.01% for the RF model. Furthermore, several scenarios have been used in this research to explore the capabilities of RF in several cases. Full article

(This article belongs to the Topic Advances in Earth Observation and Geosciences)

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31 pages, 8165 KiB

Open AccessArticle

Using Knowledge Graphs to Analyze the Characteristics and Trends of Forest Carbon Storage Research at the Global Scale

by Jie Li, **liang Wang, Suling He, Chenli Liu and Lanfang Liu

ISPRS Int. J. Geo-Inf. 2024, 13(7), 234; https://doi.org/10.3390/ijgi13070234 - 1 Jul 2024

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Abstract

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the [...] Read more.

Research on forest carbon storage (FCS) is crucial for the sustainable development of human society given the context of global climate change. Previous FCS studies formed the science base of the FCS field but lacked a macrolevel knowledge summary. This study combined the scientometric map** tool VOSviewer and multiple statistical models to conduct a comprehensive knowledge graph mining and analysis of global FCS papers (covering 101 countries, 1712 institutions, 5435 authors, and 276 journals) in the Web of Science database as of 2022, focusing on revealing the macro spatiotemporal pattern, multidimensional research status, and topic evolution process of FCS research at the global scale, so as to grasp the status of global FCS research more clearly and comprehensively, thereby facilitating the future decision-making and practice of researchers. The results showed the following: (1) In the past three decades, the number of FCS papers indicated an increasing trend, with a growth rate of 4.66/yr, particularly significant after 2010. These papers were mainly from Europe, the Americas, and Asia, while there was a huge gap between Africa, Oceania, and the above regions. (2) For the research status at the national, institutional, scholar, and journal levels, the USA, with 331 FCS papers and 18,653 total citations, was the most active and influential country in global FCS research; the United States Forest Service topped the influential ranking with 4115 citations; Grant M. Domke and Jerome Chave were the most active and influential FCS researchers globally, respectively. China’s activity (237 papers) and influence (5403 citations) ranked second, and the Chinese Academy of Sciences was the most active research institution in the world. Currently, FCS research is published in a growing number of journals, among which Forest Ecology and Management ranked first in the number of papers (154 papers) and citations (6374 citations). (3) In recent years, the keyword frequency of monitoring methods, driving factors, and reasonable management for FCS has increased rapidly, and many new related keywords have emerged, which means that researchers are not only focusing on the estimation and monitoring of FCS but also increasingly concerned about its driving mechanism and sustainable development. Full article

(This article belongs to the Topic Geospatial Digital Innovations for Smart Agriculture and Forestry)

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20 pages, 3553 KiB

Open AccessArticle

Optimizing Station Placement for Free-Floating Electric Vehicle Sharing Systems: Leveraging Predicted User Spatial Distribution from Points of Interest

by Qi Cao, Shunchao Wang, Bingtong Wang and **gfeng Ma

ISPRS Int. J. Geo-Inf. 2024, 13(7), 233; https://doi.org/10.3390/ijgi13070233 - 1 Jul 2024

Viewed by 300

Abstract

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed [...] Read more.

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Bei**g FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Bei**g, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies. Full article

20 pages, 8876 KiB

Open AccessArticle

A Comprehensive Survey on High-Definition Map Generation and Maintenance

by Kaleab Taye Asrat and Hyung-Ju Cho

ISPRS Int. J. Geo-Inf. 2024, 13(7), 232; https://doi.org/10.3390/ijgi13070232 - 1 Jul 2024

Viewed by 249

Abstract

The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully [...] Read more.

The automotive industry has experienced remarkable growth in recent decades, with a significant focus on advancements in autonomous driving technology. While still in its early stages, the field of autonomous driving has generated substantial research interest, fueled by the promise of achieving fully automated vehicles in the foreseeable future. High-definition (HD) maps are central to this endeavor, offering centimeter-level accuracy in map** the environment and enabling precise localization. Unlike conventional maps, these highly detailed HD maps are critical for autonomous vehicle decision-making, ensuring safe and accurate navigation. Compiled before testing and regularly updated, HD maps meticulously capture environmental data through various methods. This study explores the vital role of HD maps in autonomous driving, delving into their creation, updating processes, and the challenges and future directions in this rapidly evolving field. Full article

(This article belongs to the Special Issue Geographic Information Systems and Cartography for a Sustainable World)

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25 pages, 29087 KiB

Open AccessArticle

HBIM for Conservation of Built Heritage

by Yahya Alshawabkeh, Ahmad Baik and Yehia Miky

ISPRS Int. J. Geo-Inf. 2024, 13(7), 231; https://doi.org/10.3390/ijgi13070231 - 1 Jul 2024

Viewed by 245

Abstract

Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM [...] Read more.

Building information modeling (BIM) has recently become more popular in historical buildings as a method to rebuild their geometry and collect relevant information. Heritage BIM (HBIM), which combines high-level data about surface conditions, is a valuable tool for conservation decision-making. However, implementing BIM in heritage has its challenges because BIM libraries are designed for new constructions and are incapable of accommodating the morphological irregularities found in historical structures. This article discusses an architecture survey workflow that uses TLS, imagery, and deep learning algorithms to optimize HBIM for the conservation of the Nabatean built heritage. In addition to creating new resourceful Nabatean libraries with high details, the proposed approach enhanced HBIM by including two data outputs. The first dataset contained the TLS 3D dense mesh model, which was enhanced with high-quality textures extracted from independent imagery captured at the optimal time and location for accurate depictions of surface features. These images were also used to create true orthophotos using accurate and reliable 2.5D DSM derived from TLS, which eliminated all image distortion. The true orthophoto was then used in HBIM texturing to create a realistic decay map and combined with a deep learning algorithm to automatically detect and draw the outline of surface features and cracks in the BIM model, along with their statistical parameters. The use of deep learning on a structured 2D true orthophoto produced segmentation results in the metric units required for damage quantifications and helped overcome the limitations of using deep learning for 2D non-metric imagery, which typically uses pixels to measure crack widths and areas. The results show that the scanner and imagery integration allows for the efficient collection of data for informative HBIM models and provide stakeholders with an efficient tool for investigating and analyzing buildings to ensure proper conservation. Full article

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17 pages, 10644 KiB

Open AccessArticle

Monitoring and Cause Analysis of Land Subsidence along the Yangtze River Utilizing Time-Series InSAR

by Yuanyuan Chen, Lin Guo, Jia Xu, Qiang Yang, Hao Wang and Chenwei Zhu

ISPRS Int. J. Geo-Inf. 2024, 13(7), 230; https://doi.org/10.3390/ijgi13070230 - 1 Jul 2024

Viewed by 231

Abstract

Time-series monitoring of the land subsidence in the Yangtze River coastal area is crucial for maintaining river stability and early warning of disasters. This study employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nan**g, using a [...] Read more.

Time-series monitoring of the land subsidence in the Yangtze River coastal area is crucial for maintaining river stability and early warning of disasters. This study employed PS-InSAR and SBAS-InSAR techniques to monitor the land subsidence along the Yangtze River in Nan**g, using a total of 42 Sentinel-1A images obtained between April 2015 and November 2021. The accuracy of both methods was compared and validated, while a comprehensive analysis was conducted to ascertain the spatial distribution characteristics and underlying causes of land subsidence. The maximum deviation between the two methods and six leveling point data did not exceed ±5 mm. Within the 5 km buffer zone on either side of the Yangtze River in Nan**g, four subsidence funnels were identified. Analysis of the factors contributing to land subsidence in this area indicates that underground engineering construction and operation, increasing ground building area, and geological condition all have certain correlations to the land subsidence. The results obtained through PS-InSAR and SBAS-InSAR technologies revealed a high degree of consistency in monitoring outcomes, and the latter method exhibited superior monitoring accuracy than the former one in this area. This study holds significant implications for guiding the scientific management of urban geohazards along the Yangtze River. Full article

(This article belongs to the Topic Geotechnics for Hazard Mitigation)

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18 pages, 4952 KiB

Open AccessArticle

Graph Representation Learning for Street-Level Crime Prediction

by Haishuo Gu, **guang Sui and Peng Chen

ISPRS Int. J. Geo-Inf. 2024, 13(7), 229; https://doi.org/10.3390/ijgi13070229 - 1 Jul 2024

Viewed by 215

Abstract

In contemporary research, the street network emerges as a prominent and recurring theme in crime prediction studies. Meanwhile, graph representation learning shows considerable success, which motivates us to apply the methodology to crime prediction research. In this article, a graph representation learning approach [...] Read more.

In contemporary research, the street network emerges as a prominent and recurring theme in crime prediction studies. Meanwhile, graph representation learning shows considerable success, which motivates us to apply the methodology to crime prediction research. In this article, a graph representation learning approach is utilized to derive topological structure embeddings within the street network. Subsequently, a heterogeneous information network that incorporates both the street network and urban facilities is constructed, and embeddings through link prediction tasks are obtained. Finally, the two types of high-order embeddings, along with other spatio-temporal features, are fed into a deep neural network for street-level crime prediction. The proposed framework is tested using data from Bei**g, and the outcomes demonstrate that both types of embeddings have a positive impact on crime prediction, with the second embedding showing a more significant contribution. Comparative experiments indicate that the proposed deep neural network offers superior efficiency in crime prediction. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)

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27 pages, 8838 KiB

Open AccessArticle

Exploring Summer Variations of Driving Factors Affecting Land Use Zoning Based on the Surface Urban Heat Island in Chiang Mai, Thailand

by Damrongsak Rinchumphu, Manat Srivanit, Niti Iamchuen and Chuchoke Aryupong

ISPRS Int. J. Geo-Inf. 2024, 13(7), 228; https://doi.org/10.3390/ijgi13070228 - 30 Jun 2024

Viewed by 398

Abstract

Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) [...] Read more.

Numerous studies have examined land surface temperature (LST) changes in Thailand using remote sensing, but there has been little research on LST variations within urban land use zones. This study addressed this gap by analyzing summer LST changes in land use zoning (LUZ) blocks in the 2012 Chiang Mai Comprehensive Plan and their relationship with surface biophysical parameters (NDVI, NDBI, MNDWI). The approach integrated detailed zoning data with remote sensing for granular LST analysis. Correlation and stepwise regression analyses (SRA) revealed that NDBI significantly impacted LST in most block types, while NDVI and MNDWI also influenced LST, particularly in 2023. The findings demonstrated the complexity of LST dynamics across various LUZs in Chiang Mai, with SRA results explaining 45.7% to 53.2% of summer LST variations over three years. To enhance the urban environment, adaptive planning strategies for different block categories were developed and will be considered in the upcoming revision of the Chiang Mai Comprehensive Plan. This research offers a new method to monitor the urban heat island phenomenon at the block level, providing valuable insights for adaptive urban planning. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

30 pages, 4786 KiB

Open AccessSystematic Review

The Application of Space Syntax to Enhance Sociability in Public Urban Spaces: A Systematic Review

by Reza Askarizad, Patxi José Lamíquiz Daudén and Chiara Garau

ISPRS Int. J. Geo-Inf. 2024, 13(7), 227; https://doi.org/10.3390/ijgi13070227 - 28 Jun 2024

Viewed by 382

Abstract

Public urban spaces are vital settings for fostering social interaction among people. However, understanding how spatial layouts can promote positive social behaviors remains a critical and debated challenge for urban designers and planners aiming to create socially sustainable environments. Space syntax, a well-established [...] Read more.

Public urban spaces are vital settings for fostering social interaction among people. However, understanding how spatial layouts can promote positive social behaviors remains a critical and debated challenge for urban designers and planners aiming to create socially sustainable environments. Space syntax, a well-established theory and research method, explores the influence of spatial configurations on social aspects. Despite its significant contributions, there is a lack of comprehensive systematic reviews evaluating its effectiveness in enhancing social interaction within urban public spaces. This study aims to identify the existing scientific gaps in the domain of space syntax studies, with a primary focus on sociability in public urban spaces. Following the PRISMA framework, a thorough literature search was conducted in the Scopus database, yielding 1107 relevant articles. After applying screening and eligibility criteria, 26 articles were selected for in-depth review. This review adopted a novel approach to synthesizing and analyzing the findings for identifying underexplored scientific gaps. The findings suggested a wide variety of research gaps to address, encompassing evidence, knowledge, practical, methodological, empirical, theoretical, and target populations to provide a thorough overview of the current state of knowledge in this field. In conclusion, by exploring the interplay between space syntax and design elements such as the urban infrastructure, landsca**, and microclimate in these areas, future research can bridge this gap, particularly when considering a cross-cultural lens. This study underscores the importance of space syntax in promoting social interaction in urban public spaces, offering a robust foundation for future research and practical applications to create more socially engaging environments. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

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25 pages, 2889 KiB

Open AccessArticle

Automated Geospatial Approach for Assessing SDG Indicator 11.3.1: A Multi-Level Evaluation of Urban Land Use Expansion across Africa

by Orion S. E. Cardenas-Ritzert, Jody C. Vogeler, Shahriar Shah Heydari, Patrick A. Fekety, Melinda Laituri and Melissa McHale

ISPRS Int. J. Geo-Inf. 2024, 13(7), 226; https://doi.org/10.3390/ijgi13070226 - 28 Jun 2024

Viewed by 398

Abstract

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations’ (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land [...] Read more.

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations’ (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia’s urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries. Full article

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15 pages, 994 KiB

Open AccessArticle

Research on the Spatial Distribution Characteristics and Influencing Factors of Educational Facilities Based on POI Data: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area

by Bowen Chen, Hongfeng Zhang, Cora Un In Wong, **aolong Chen, Fanbo Li, **aoyu Wei and Junxian Shen

ISPRS Int. J. Geo-Inf. 2024, 13(7), 225; https://doi.org/10.3390/ijgi13070225 - 27 Jun 2024

Viewed by 301

Abstract

This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, [...] Read more.

This study aims to provide a precise assessment of the distribution of educational facilities within the Guangdong–Hong Kong–Macao Greater Bay Area, serving as a crucial foundation for managing educational resource allocation and enhancing the quality of educational services. Utilizing a kernel density analysis, global autocorrelation analysis, and geographic detectors, this research systematically analyzes the spatial distribution characteristics and influencing factors of educational facilities in the area. The findings reveal significant geographical disparities in facility distribution with dense clusters in urban centers such as Guangzhou and Shenzhen, and less dense distributions in peripheral areas like Zhongshan and Macau. These facilities exhibit a multi-center cluster pattern with strong spatial autocorrelation, mainly influenced by the population size and economic and urban development levels. The results provide actionable insights for refining educational planning and resource allocation, contributing to the enhancement of educational quality across diverse urban landscapes. Full article

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32 pages, 4601 KiB

Open AccessArticle

Develo** a Bi-Level Optimization Model for the Coupled Street Network and Land Subdivision Design Problem with Various Lot Areas in Irregular Blocks

by Alireza Sahebgharani and Szymon Wiśniewski

ISPRS Int. J. Geo-Inf. 2024, 13(7), 224; https://doi.org/10.3390/ijgi13070224 - 27 Jun 2024

Viewed by 226

Abstract

Street design and land subdivision are significant tasks in the development and redevelopment planning process. Optimizing street and land subdivision layouts within a unified framework to achieve solutions that meet a set of objectives and constraints (e.g., minimizing parcel area deviation from standard [...] Read more.

Street design and land subdivision are significant tasks in the development and redevelopment planning process. Optimizing street and land subdivision layouts within a unified framework to achieve solutions that meet a set of objectives and constraints (e.g., minimizing parcel area deviation from standard values, minimizing land consumption for street construction, etc.) is a critical concern for planners, particularly in complex contexts such as blocks with irregular shapes and parcels of varying sizes and requirements. To address this challenge, a mathematical formulation is presented for the bi-level street network and land subdivision optimization problem. Subsequently, the solution procedure is outlined, which utilizes a genetic-based algorithm for street design and a memetic–genetic-based algorithm for land subdivision. Finally, two cases are presented, solved, and discussed to analyze and verify the proposed mathematical model and solution procedures. The results suggest that the formulated problem is suitable for addressing the coupled street network and land subdivision design problem, and it can be adapted and extended to other case studies. Additionally, the introduced ideas and algorithms satisfactorily solved the stated problem. Full article

13 pages, 1832 KiB

Open AccessArticle

Characterizing Spatio-Temporal Patterns of Child Sexual Abuse in Mexico City Before, During, and After the COVID-19 Pandemic

by Francisco Carrillo-Brenes and Luis M. Vilches-Blázquez

ISPRS Int. J. Geo-Inf. 2024, 13(7), 223; https://doi.org/10.3390/ijgi13070223 - 27 Jun 2024

Viewed by 298

Abstract

This study conducts a spatio-temporal analysis to identify trends and clusters of child sexual abuse in Mexico City before, during, and after the COVID-19 pandemic. Sexual abuses of children were analyzed considering various crime theories. Trends and patterns were identified using time series [...] Read more.

This study conducts a spatio-temporal analysis to identify trends and clusters of child sexual abuse in Mexico City before, during, and after the COVID-19 pandemic. Sexual abuses of children were analyzed considering various crime theories. Trends and patterns were identified using time series decomposition and spatial autocorrelation techniques. Time series considered three relevant periods. Anselin’s Local Moran’s I identified the spatial distribution of significant clusters. The child sexual abuse rate presented similar values following school closures. The resumption of classes entailed a decrease of −1.5% (children under 15) and an increase of 29% (children over 15). Particular locations in Mexico City experienced significant clusters among those over 15. There were eight noteworthy clusters displaying recidivism patterns with lower poverty rates and a high level of education. Efforts to combat child sexual abuse should prioritize specific areas in Mexico City where female children over 15 are at high risk of becoming victims of sexual abuse. Full article

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27 pages, 10814 KiB

Open AccessArticle

UPGAN: An Unsupervised Generative Adversarial Network Based on U-Shaped Structure for Pansharpening

by **n **, Yuting Feng, Qian Jiang, Shengfa Miao, **ng Chu, Huangqimei Zheng and Qianqian Wang

ISPRS Int. J. Geo-Inf. 2024, 13(7), 222; https://doi.org/10.3390/ijgi13070222 - 26 Jun 2024

Viewed by 431

Abstract

Pansharpening is the fusion of panchromatic images and multispectral images to obtain images with high spatial resolution and high spectral resolution, which have a wide range of applications. At present, methods based on deep learning can fit the nonlinear features of images and [...] Read more.

Pansharpening is the fusion of panchromatic images and multispectral images to obtain images with high spatial resolution and high spectral resolution, which have a wide range of applications. At present, methods based on deep learning can fit the nonlinear features of images and achieve excellent image quality; however, the images generated with supervised learning approaches lack real-world applicability. Therefore, in this study, we propose an unsupervised pansharpening method based on a generative adversarial network. Considering the fine tubular structures in remote sensing images, a dense connection attention module is designed based on dynamic snake convolution to recover the details of spatial information. In the stage of image fusion, the fusion of features in groups is applied through the cross-scale attention fusion module. Moreover, skip layers are implemented at different scales to integrate significant information, thus improving the objective index values and visual appearance. The loss function contains four constraints, allowing the model to be effectively trained without reference images. The experimental results demonstrate that the proposed method outperforms other widely accepted state-of-the-art methods on the QuickBird and WorldView2 data sets. Full article

(This article belongs to the Special Issue Advances in AI-Driven Geospatial Analysis and Data Generation)

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22 pages, 4992 KiB

Open AccessArticle

Spatiotemporal Distribution of Inscription Sites in Henan Province

by Yuhang Zhang, Jiaji Gao and **aotong Ni

ISPRS Int. J. Geo-Inf. 2024, 13(7), 221; https://doi.org/10.3390/ijgi13070221 - 25 Jun 2024

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Abstract

This paper takes 1929 inscription sites in Henan Province as the study object and uses methods such as kernel density and nearest neighbor index to analyze their spatiotemporal distribution patterns and influencing factors. It also studies those patterns of various levels of inscription [...] Read more.

This paper takes 1929 inscription sites in Henan Province as the study object and uses methods such as kernel density and nearest neighbor index to analyze their spatiotemporal distribution patterns and influencing factors. It also studies those patterns of various levels of inscription cultural relics’ protection units and content. All of these will help our understanding of the development process and characteristics of Central Plains art and provide reference for the protection and development of inscriptions in Henan in the future. The study indicates the following: (1) The spatial distribution of inscription sites is relatively uneven and the clustering is obvious, being mainly concentrated in the northern and northwestern regions of Henan, showing the characteristics of “one belt and four clusters” as a whole. The density is high in the north and low in the south, gradually decreasing from north to south. (2) In terms of time, the number of these inscription sites shows a fluctuating trend of first a slight increase and then a decrease with a significant increase and then a decrease. The center of the sites migrates from southwest to northeast over time. (3) These inscriptions can be divided into five primary themes and further subdivided into 16 secondary themes in terms of content. The main type is chronicle. (4) Inscriptions in Henan are mainly influenced by five major factors: topography, climate, economy and transportation, politics and society, culture and religion. Full article

22 pages, 5396 KiB

Open AccessArticle

A Spatial Semantic Feature Extraction Method for Urban Functional Zones Based on POIs

by **n Yang and **’ang Ma

ISPRS Int. J. Geo-Inf. 2024, 13(7), 220; https://doi.org/10.3390/ijgi13070220 - 25 Jun 2024

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Abstract

Accurately extracting semantic features of urban functional zones is crucial for understanding urban functional zone types and urban functional spatial structures. Points of interest provide comprehensive information for extracting the semantic features of urban functional zones. Many researchers have used topic models of [...] Read more.

Accurately extracting semantic features of urban functional zones is crucial for understanding urban functional zone types and urban functional spatial structures. Points of interest provide comprehensive information for extracting the semantic features of urban functional zones. Many researchers have used topic models of natural language processing to extract the semantic features of urban functional zones from points of interest, but topic models cannot consider the spatial features of points of interest, which leads to the extracted semantic features of urban functional zones being incomplete. To consider the spatial features of points of interest when extracting semantic features of urban functional zones, this paper improves the Latent Dirichlet Allocation topic model and proposes a spatial semantic feature extraction method for urban functional zones based on points of interest. In the proposed method, an assumption (that points of interest belonging to the same semantic feature are spatially correlated) is introduced into the generation process of urban functional zones, and then, Gibbs sampling is combined to carry out the parameter inference process. We apply the proposed method to a simulated dataset and the point of interest dataset for Chaoyang District, Bei**g, and compare the semantic features extracted by the proposed method with those extracted by the Latent Dirichlet Allocation. The results show that the proposed method sufficiently considers the spatial features of points of interest and has a higher capability of extracting the semantic features of urban functional zones than the Latent Dirichlet Allocation. Full article

(This article belongs to the Special Issue Application of Geographical Information System in Urban Design, Management or Evaluation)

19 pages, 9772 KiB

Open AccessArticle

Study on Multiscale Virtual Environment Construction and Spatial Navigation Based on Hierarchical Structure

by Chao Chen, Chaoyang Li, Kai Lu, Hao Chen, **n **ao and Chaoyang Fang

ISPRS Int. J. Geo-Inf. 2024, 13(7), 219; https://doi.org/10.3390/ijgi13070219 - 24 Jun 2024

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Abstract

Multiscale virtual environments (MSVEs) allow the integration of elements and environments at different scale levels into a unified space, which facilitates researchers’ perception, understanding, and experimental research of complex geospatial spaces. Although there have been several methods for achieving multiscale effects in virtual [...] Read more.

Multiscale virtual environments (MSVEs) allow the integration of elements and environments at different scale levels into a unified space, which facilitates researchers’ perception, understanding, and experimental research of complex geospatial spaces. Although there have been several methods for achieving multiscale effects in virtual environments (VEs), they cannot assist users in constructing more complete spatial cognitive maps and presenting multiscale information efficiently. This study proposes a hierarchical-structure-based MSVE construction method, which can effectively integrate multiscale information and ensure that the richness of details of information is gradually enhanced with the progression of the hierarchical structure. In addition, a spatial navigation study is conducted, considering the relationship between users’ perspective changes and spatial cognition, and the effects of users’ perspective changes on their spatial cognition in an MSVE are explored. A multiscale virtual wetland environment covering four levels is constructed to conduct a case study of a virtual environment of a wetland of Poyang Lake. The research results show that the proposed method is feasible. Moreover, the spatial navigation based on the change in the hierarchical perspective is in line with the spatial cognitive habits of users, which can satisfy the cognitive needs of users from the macro-region to specific wetland landscapes. Full article

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20 pages, 6968 KiB

Open AccessArticle

The Influence of Origin Attributes on the Destination Choice of Discretionary Home-Based Walk Trips

by Salman Aghidi Kheyrabadi and Amir Reza Mamdoohi

ISPRS Int. J. Geo-Inf. 2024, 13(7), 218; https://doi.org/10.3390/ijgi13070218 - 24 Jun 2024

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Abstract

Walking has been recognized as an important mode of transportation in recent years, and recent research has improved travel demand models for walk trips. One important added stage is the distribution of walk trips, which can be evaluated using destination choice models. Previous [...] Read more.

Walking has been recognized as an important mode of transportation in recent years, and recent research has improved travel demand models for walk trips. One important added stage is the distribution of walk trips, which can be evaluated using destination choice models. Previous studies have overlooked the importance of origin trip attributes in the destination choice of walk trips. With the aim of improving destination choice models for discretionary home-based walk trips, a questionnaire based on the previous day’s walk trips was used, and 422 trips were collected from individuals. A discrete choice logit model is used for discretionary trips by utilizing policy-related variables, such as origin-sensitive variables, land-use-related variables, and socio-economic conditions of individuals. Additionally, a solution is proposed to address the issue of data scarcity in considering the choice set. The results demonstrate that origin land-use (LU) variables, such as LU diversity index and access to green spaces, as well as socio-economic variables, like age and homeownership status, are statistically significant in the destination choice of discretionary home-based walk trips. One prominent result is that reducing the diversity of unattractive LU compared to increasing the diversity of attractive LU has a greater impact on the destination choice of such trips. Specifically, a 1% increase in the diversity of attractive LU in the origin area leads to a 0.031% increase in the probability of choosing a destination within that area, while a 1% decrease in the diversity of unattractive LU results in a 0.124% increase in the probability of choosing a destination within the area. The findings can be utilized in urban LU distribution and assessing their impact on destination choice for walk trips, ultimately informing future urban planning efforts in the context of pedestrian mobility. Full article

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19 pages, 12973 KiB

Open AccessArticle

A Novel Flexible Geographically Weighted Neural Network for High-Precision PM2.5 Map** across the Contiguous United States

by Dongchao Wang, Jianfei Cao, Baolei Zhang, Ye Zhang and Lei **e

ISPRS Int. J. Geo-Inf. 2024, 13(7), 217; https://doi.org/10.3390/ijgi13070217 - 22 Jun 2024

Viewed by 331

Abstract

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this [...] Read more.

Air quality degradation has triggered a large-scale public health crisis globally. Existing machine learning techniques have been used to attempt the remote sensing estimates of PM2.5. However, many machine learning models ignore the spatial non-stationarity of predictive variables. To address this issue, this study introduces a Flexible Geographically Weighted Neural Network (FGWNN) to estimate PM2.5 based on multi-source remote sensing data. FGWNN incorporates the Flexible Geographical Neuron (FGN) and Geographical Activation Function (GWAF) within the framework of Artificial Neural Network (ANN) to capture the intricate spatial non-stationary relationships among predictive variables. A robust air quality remote sensing estimation model was constructed using remote sensing data of Aerosol Optical Depth (AOD), Normalized Difference Vegetation Index (NDVI), Temperature (TMP), Specific Humidity (SPFH), Wind Speed (WIND), and Terrain Elevation (HGT) as inputs, and Ground-Based PM2.5 as the observation. The results indicated that FGWNN successfully generates PM2.5 remote sensing data with a 2.5 km spatial resolution for the contiguous United States (CONUS) in 2022. It exhibits higher regression accuracy compared to traditional ANN and Geographically Weighted Regression (GWR) models. FGWNN holds the potential for applications in high-precision and high-resolution remote sensing scenarios. Full article

(This article belongs to the Special Issue HealthScape: Intersections of Health, Environment, and GIS&T)

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ISPRS Int. J. Geo-Inf., Volume 13, Issue 7 (July 2024) – 22 articles

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