Yufan Deng, Zhongan Tang, Baoju Liu, Yan Shi, Min Deng, Enbo Liu
Urban land use multi-objective optimization aims to achieve greater economic, social, and environmental benefits by the rational allocation and planning of urban land resources in space. However, not only land use reconstruction, but renovation, which has been neglected in most studies, is the main optimization direction of urban land use. Meanwhile, urban land use optimization is subject to cost constraints, so as to obtain a more practical optimization scheme. Thus, this paper evaluated the renovation and reconstruction costs of urban land use and proposed a cost-heuristic genetic algorithm (CHGA). The algorithm determined the selection probability of candidate optimization cells by considering the renovation and reconstruction costs of urban land and integrated the renovation and reconstruction costs to determine the direction of optimization so that the optimization model can more practically simulate the actual situation of urban planning. The reliability of this model was validated through its application in Shenzhen, China, demonstrating that it can reduce the cost consumption of the optimization process by 35.86% at the expense of sacrificing a small amount of economic benefits (1.18%). The balance of benefits and costs enhances the applicability of the proposed land use optimization method in mature, developed areas where it is difficult to demolish buildings that are constrained by costs.
{"title":"Renovation and Reconstruction of Urban Land Use by a Cost-Heuristic Genetic Algorithm: A Case in Shenzhen","authors":"Yufan Deng, Zhongan Tang, Baoju Liu, Yan Shi, Min Deng, Enbo Liu","doi":"10.3390/ijgi13070250","DOIUrl":"https://doi.org/10.3390/ijgi13070250","url":null,"abstract":"Urban land use multi-objective optimization aims to achieve greater economic, social, and environmental benefits by the rational allocation and planning of urban land resources in space. However, not only land use reconstruction, but renovation, which has been neglected in most studies, is the main optimization direction of urban land use. Meanwhile, urban land use optimization is subject to cost constraints, so as to obtain a more practical optimization scheme. Thus, this paper evaluated the renovation and reconstruction costs of urban land use and proposed a cost-heuristic genetic algorithm (CHGA). The algorithm determined the selection probability of candidate optimization cells by considering the renovation and reconstruction costs of urban land and integrated the renovation and reconstruction costs to determine the direction of optimization so that the optimization model can more practically simulate the actual situation of urban planning. The reliability of this model was validated through its application in Shenzhen, China, demonstrating that it can reduce the cost consumption of the optimization process by 35.86% at the expense of sacrificing a small amount of economic benefits (1.18%). The balance of benefits and costs enhances the applicability of the proposed land use optimization method in mature, developed areas where it is difficult to demolish buildings that are constrained by costs.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"9 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments.
{"title":"A Pathfinding Algorithm for Large-Scale Complex Terrain Environments in the Field","authors":"Luchao Kui, Xianwen Yu","doi":"10.3390/ijgi13070251","DOIUrl":"https://doi.org/10.3390/ijgi13070251","url":null,"abstract":"Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"49 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141611158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Topographic Chart of the Kingdom of Poland (pol. Topograficzna Karta Królestwa Polskiego, commonly referred to as ‘the Quartermaster’s Map’, hereinafter: TKKP) is the first Polish modern topographic map of Poland (1:126,000, 1843). Cartographic historians acclaim its conception by the General Quartermaster of the Polish Army, noting its editorial principles and technical execution as exemplars of the early 19th-century cartographic standards. Today, it stands as a national heritage relic, furnishing invaluable insights into the former Polish Kingdom’s topography. Although extensively utilised in geographical and historical inquiries, the TKKP has yet to undergo a comprehensive geomatic investigation and publication as spatial data services. Primarily, this delay stems from the challenges of mosaicking and georeferencing its 60 constituent sheets, owing to the uncertain mathematical framework and irregular sheet cuts. In 2023, the authors embarked on rectifying this by creating a unified TKKP mosaic and georeferencing the map to contemporary reference data benchmarks. This endeavour involved scrutinising the map’s mathematical accuracy and verifying prior findings. The resultant product is accessible via the ‘Maps with the Past’ platform, developed by the Institute of History of the Polish Academy of Sciences The dissemination of raster data services adhering to OGC standards such as WMTS (Web Map Tile Service), ECW (Enhanced Compression Wavelet), and COG (Cloud Optimized GeoTIFF) facilitates the swift and seamless integration of the generated data into web and GIS tools. The digital edition of the TKKP emerges as a pivotal resource for investigations spanning natural and anthropogenic environmental transformations, sustainable development, and cultural heritage studies.
{"title":"Methodology of Mosaicking and Georeferencing for Multi-Sheet Early Maps with Irregular Cuts Using the Example of the Topographic Chart of the Kingdom of Poland","authors":"Jakub Kuna, Tomasz Panecki, Mateusz Zawadzki","doi":"10.3390/ijgi13070249","DOIUrl":"https://doi.org/10.3390/ijgi13070249","url":null,"abstract":"The Topographic Chart of the Kingdom of Poland (pol. Topograficzna Karta Królestwa Polskiego, commonly referred to as ‘the Quartermaster’s Map’, hereinafter: TKKP) is the first Polish modern topographic map of Poland (1:126,000, 1843). Cartographic historians acclaim its conception by the General Quartermaster of the Polish Army, noting its editorial principles and technical execution as exemplars of the early 19th-century cartographic standards. Today, it stands as a national heritage relic, furnishing invaluable insights into the former Polish Kingdom’s topography. Although extensively utilised in geographical and historical inquiries, the TKKP has yet to undergo a comprehensive geomatic investigation and publication as spatial data services. Primarily, this delay stems from the challenges of mosaicking and georeferencing its 60 constituent sheets, owing to the uncertain mathematical framework and irregular sheet cuts. In 2023, the authors embarked on rectifying this by creating a unified TKKP mosaic and georeferencing the map to contemporary reference data benchmarks. This endeavour involved scrutinising the map’s mathematical accuracy and verifying prior findings. The resultant product is accessible via the ‘Maps with the Past’ platform, developed by the Institute of History of the Polish Academy of Sciences The dissemination of raster data services adhering to OGC standards such as WMTS (Web Map Tile Service), ECW (Enhanced Compression Wavelet), and COG (Cloud Optimized GeoTIFF) facilitates the swift and seamless integration of the generated data into web and GIS tools. The digital edition of the TKKP emerges as a pivotal resource for investigations spanning natural and anthropogenic environmental transformations, sustainable development, and cultural heritage studies.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"33 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Huimin Liu, Qiu Yang, Xuexi Yang, Jianbo Tang, Min Deng, Rong Gui
Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.
{"title":"Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis","authors":"Huimin Liu, Qiu Yang, Xuexi Yang, Jianbo Tang, Min Deng, Rong Gui","doi":"10.3390/ijgi13070248","DOIUrl":"https://doi.org/10.3390/ijgi13070248","url":null,"abstract":"Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"16 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141586749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis.
{"title":"Spatiotemporal Analysis of Nighttime Crimes in Vienna, Austria","authors":"Jiyoung Lee, Michael Leitner, Gernot Paulus","doi":"10.3390/ijgi13070247","DOIUrl":"https://doi.org/10.3390/ijgi13070247","url":null,"abstract":"Studying the spatiotemporal dynamics of crime is crucial for accurate crime geography research. While studies have examined crime patterns related to weekdays, seasons, and specific events, there is a noticeable gap in research on nighttime crimes. This study focuses on crimes occurring during the nighttime, investigating the temporal definition of nighttime crime and the correlation between nighttime lights and criminal activities. The study concentrates on four types of nighttime crimes, assault, theft, burglary, and robbery, conducting univariate and multivariate analyses. In the univariate analysis, correlations between nighttime crimes and nighttime light (NTL) values detected in satellite images and between streetlight density and nighttime crimes are explored. The results highlight that nighttime burglary strongly relates to NTL and streetlight density. The multivariate analysis delves into the relationships between each nighttime crime type and socioeconomic and urban infrastructure variables. Once again, nighttime burglary exhibits the highest correlation. For both univariate and multivariate regression models the geographically weighted regression (GWR) outperforms ordinary least squares (OLS) regression in explaining the relationships. This study underscores the importance of considering the location and offense time in crime geography research and emphasizes the potential of using NTL in nighttime crime analysis.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"89 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141588449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study’s objectives. While existing platforms like Esri’s software and Google Earth VR show promise, they lack complete integration for immersive GIS visualization. This gap has led to the need for a dedicated workflow to integrate selected GIS data into a game engine for visualization purposes. This study primarily utilizes QGIS for data preparation and Unreal Engine for immersive visualization. QGIS handles data management, while Unreal Engine offers advanced rendering and interactivity for immersive experiences. To tackle the challenge of handling extensive GIS datasets, this study proposes a workflow involving tiling, digital elevation model generation, and transforming GeoTIFF data into 3D objects. Leveraging QGIS and Three.js streamlines the conversion process for integration into Unreal Engine. The resultant virtual reality application features distinct stations, enabling users to navigate, visualize, compare, and animate GIS data effectively. Each station caters to specific functionalities, ensuring a seamless and informative experience within the VR environment. This study also delves into augmented reality applications, adapting methodologies to address hardware limitations for smoother user experiences. By optimizing textures and implementing augmented reality functionalities through modules Swift, RealityKit, and ARKit, this study extends the immersive GIS experience to iOS devices. In conclusion, this research demonstrates the potential of integrating virtual reality, augmented reality, and GIS, pushing data visualization into new realms. The innovative workflows and applications developed serve as a testament to the evolving landscape of spatial data interpretation and engagement.
{"title":"Using Virtual and Augmented Reality with GIS Data","authors":"Karel Pavelka, Martin Landa","doi":"10.3390/ijgi13070241","DOIUrl":"https://doi.org/10.3390/ijgi13070241","url":null,"abstract":"This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study’s objectives. While existing platforms like Esri’s software and Google Earth VR show promise, they lack complete integration for immersive GIS visualization. This gap has led to the need for a dedicated workflow to integrate selected GIS data into a game engine for visualization purposes. This study primarily utilizes QGIS for data preparation and Unreal Engine for immersive visualization. QGIS handles data management, while Unreal Engine offers advanced rendering and interactivity for immersive experiences. To tackle the challenge of handling extensive GIS datasets, this study proposes a workflow involving tiling, digital elevation model generation, and transforming GeoTIFF data into 3D objects. Leveraging QGIS and Three.js streamlines the conversion process for integration into Unreal Engine. The resultant virtual reality application features distinct stations, enabling users to navigate, visualize, compare, and animate GIS data effectively. Each station caters to specific functionalities, ensuring a seamless and informative experience within the VR environment. This study also delves into augmented reality applications, adapting methodologies to address hardware limitations for smoother user experiences. By optimizing textures and implementing augmented reality functionalities through modules Swift, RealityKit, and ARKit, this study extends the immersive GIS experience to iOS devices. In conclusion, this research demonstrates the potential of integrating virtual reality, augmented reality, and GIS, pushing data visualization into new realms. The innovative workflows and applications developed serve as a testament to the evolving landscape of spatial data interpretation and engagement.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"9 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haijun Li, Jie Zhou, Qiang Niu, Mingxiang Feng, Dongming Zhou
With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction of logistics facilities. However, due to spatiotemporal trajectory data (such as truck GPS data) being used less often in planning, the method of quantitative analysis for freight spatiotemporal activity is limited. Thus, the spatial layout of logistics and warehousing land does not match the current demand very well. In addition, it is necessary to consider the interactive relationship with the urban built environment in the process of optimizing layout, in order to comprehensively balance the spatial coupling with the functions of housing, transportation, industry, and so on. Therefore, the layout of logistics and warehouse land could be treated as a multi-objective optimization problem. This study aims to establish a model for logistics and warehouse land layout optimization to achieve a supply–demand matching. The proposed model comprehensively considers economic benefits, time benefits, cost benefits, environmental benefits, and other factors with freight GPS data, land-use data, transportation network data, and other multi-source data. A genetic algorithm is built to solve the model. Finally, this study takes the Wuhan urban development area as an example to practice the proposed method in three scenarios in order to verify its effectiveness. The results show that the optimization model solves the problem of mismatch between the supply and demand of logistics spaces to a certain extent, demonstrating the efficiency and scientificity of the optimization solutions. Based on the results of the three scenarios, it is proven that freight activities could effectively enhance the scientific validity of the optimization solution and the proposed model could optimize layouts under different scenario requirements. In summary, this study provides a practical and effective tool for logistics- and warehouse-land layout evaluation and optimization for urban planners and administrators.
{"title":"Layout Optimization of Logistics and Warehouse Land Based on a Multi-Objective Genetic Algorithm—Taking Wuhan City as an Example","authors":"Haijun Li, Jie Zhou, Qiang Niu, Mingxiang Feng, Dongming Zhou","doi":"10.3390/ijgi13070240","DOIUrl":"https://doi.org/10.3390/ijgi13070240","url":null,"abstract":"With the rapid development of the logistics industry, the demand for logistics activities is increasing significantly. Concurrently, growing urbanization is causing the space for logistics and warehousing to become limited. Thus, more and more attention is being paid to the planning and construction of logistics facilities. However, due to spatiotemporal trajectory data (such as truck GPS data) being used less often in planning, the method of quantitative analysis for freight spatiotemporal activity is limited. Thus, the spatial layout of logistics and warehousing land does not match the current demand very well. In addition, it is necessary to consider the interactive relationship with the urban built environment in the process of optimizing layout, in order to comprehensively balance the spatial coupling with the functions of housing, transportation, industry, and so on. Therefore, the layout of logistics and warehouse land could be treated as a multi-objective optimization problem. This study aims to establish a model for logistics and warehouse land layout optimization to achieve a supply–demand matching. The proposed model comprehensively considers economic benefits, time benefits, cost benefits, environmental benefits, and other factors with freight GPS data, land-use data, transportation network data, and other multi-source data. A genetic algorithm is built to solve the model. Finally, this study takes the Wuhan urban development area as an example to practice the proposed method in three scenarios in order to verify its effectiveness. The results show that the optimization model solves the problem of mismatch between the supply and demand of logistics spaces to a certain extent, demonstrating the efficiency and scientificity of the optimization solutions. Based on the results of the three scenarios, it is proven that freight activities could effectively enhance the scientific validity of the optimization solution and the proposed model could optimize layouts under different scenario requirements. In summary, this study provides a practical and effective tool for logistics- and warehouse-land layout evaluation and optimization for urban planners and administrators.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"32 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Muhammad Farooq Hussain, Xiaoliang Meng, Syed Fahim Shah, Muhammad Asif Hussain
Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations.
{"title":"Integrating Spatiotemporal Analysis of Land Transformation and Urban Growth in Peshawar Valley and Its Implications on Temperature in Response to Climate Change","authors":"Muhammad Farooq Hussain, Xiaoliang Meng, Syed Fahim Shah, Muhammad Asif Hussain","doi":"10.3390/ijgi13070239","DOIUrl":"https://doi.org/10.3390/ijgi13070239","url":null,"abstract":"Examining the interconnected dynamics of urbanization and climate change is crucial due to their implications for environmental, social, and public health systems. This study provides a comprehensive analysis of these dynamics in the Peshawar Valley, a rapidly urbanizing region in Khyber Pakhtunkhwa, Pakistan, over a 30-year period (1990–2020). A novel methodological framework integrating remote sensing, GIS techniques, and Google Earth Engine (GEE) was developed to analyze land use/land cover (LULC) changes, particularly the expansion of the built-up environment, along with the land surface temperature (LST) and heat index (HI). This framework intricately links these elements, providing a unique perspective on the environmental transformations occurring in the Peshawar Valley. Unlike previous studies that focused on individual aspects, this research offers a holistic understanding of the complex interplay between urbanization, land use changes, temperature dynamics, and heat index variations. Over three decades, urbanization expanded significantly, with built-up areas increasing from 6.35% to 14.13%. The population surged from 5.3 million to 12.6 million, coupled with significant increases in registered vehicles (from 0.171 million to 1.364 million) and operational industries (from 327 to 1155). These transitions influenced air quality and temperature dynamics, as evidenced by a highest mean LST of 30.30 °C and a maximum HI of 55.48 °C, marking a notable increase from 50.54 °C. These changes show strong positive correlations with built-up areas, population size, registered vehicles, and industrial activity. The findings highlight the urgent need for adaptive strategies, public health interventions, and sustainable practices to mitigate the environmental impacts of urbanization and climate change in the Peshawar Valley. Sustainable urban development strategies and climate change mitigation measures are crucial for ensuring a livable and resilient future for the region. This long-term analysis provides a robust foundation for future projections and policy recommendations.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"21 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Panagiotis Kalaitzis, Michael Foumelis, Antonios Mouratidis, Dimitris Kavroudakis, Nikolaos Soulakellis
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 mapping and monitoring the dynamic behavior of surface motion.
{"title":"Multiscale Visualization of Surface Motion Point Measurements Associated with Persistent Scatterer Interferometry","authors":"Panagiotis Kalaitzis, Michael Foumelis, Antonios Mouratidis, Dimitris Kavroudakis, Nikolaos Soulakellis","doi":"10.3390/ijgi13070236","DOIUrl":"https://doi.org/10.3390/ijgi13070236","url":null,"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 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 mapping and monitoring the dynamic behavior of surface motion.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"63 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141531780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"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)","authors":"Imene Selka, Abderahemane Medjdoub Mokhtari, Kheira Anissa Tabet Aoul, Djamal Bengusmia, Kacemi Malika, Khadidja El-Bahdja Djebbar","doi":"10.3390/ijgi13070237","DOIUrl":"https://doi.org/10.3390/ijgi13070237","url":null,"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 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.","PeriodicalId":48738,"journal":{"name":"ISPRS International Journal of Geo-Information","volume":"2012 1","pages":""},"PeriodicalIF":3.4,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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