Pub Date : 2025-02-05DOI: 10.1016/j.compenvurbsys.2025.102255
Siyuan Meng , Xian Su , Guibo Sun , Maosu Li , Fan Xue
This paper presents a contrastive deep learning-based wheelability assessment method bridging street-scale smartphone point clouds and a city-scale 3D pedestrian network (3DPN). 3DPNs have been studied and mapped for walkability and smart city applications. However, the city-level scale of 3DPN in the literature was incomplete for assessing wheelchair accessibility (i.e., wheelability) due to omitted pedestrian paths, undetected stairs, and oversimplified elevated walkways; these features could be better represented if the mapping scale was at a micro-level designed for wheelchair users. In this paper, we reinforced the city-scale 3DPN using smartphone point clouds, a promising data source for supplementing fine-grain details and temporal changes due to the centimeter-level accuracy, vivid color, high density, and crowd sourcing nature. The three-step method reconstructs pedestrian paths, stairs, and slope details and enriches the city-scale 3DPN for wheelability assessment. The experimental results on pedestrian paths demonstrated accurate 3DPN centerline position (mIoU = 88.81 %), stairs detection (mIoU = 86.39 %), and wheelability assessment (MAE = 0.09). This paper contributes an automatic, accurate, and crowd sourcing wheelability assessment method that bridges ubiquitous smartphones and 3DPN for barrier-free travels in high-density and hilly urban areas.
{"title":"From 3D pedestrian networks to wheelable networks: An automatic wheelability assessment method for high-density urban areas using contrastive deep learning of smartphone point clouds","authors":"Siyuan Meng , Xian Su , Guibo Sun , Maosu Li , Fan Xue","doi":"10.1016/j.compenvurbsys.2025.102255","DOIUrl":"10.1016/j.compenvurbsys.2025.102255","url":null,"abstract":"<div><div>This paper presents a contrastive deep learning-based wheelability assessment method bridging street-scale smartphone point clouds and a city-scale 3D pedestrian network (3DPN). 3DPNs have been studied and mapped for walkability and smart city applications. However, the city-level scale of 3DPN in the literature was incomplete for assessing wheelchair accessibility (i.e., wheelability) due to omitted pedestrian paths, undetected stairs, and oversimplified elevated walkways; these features could be better represented if the mapping scale was at a micro-level designed for wheelchair users. In this paper, we reinforced the city-scale 3DPN using smartphone point clouds, a promising data source for supplementing fine-grain details and temporal changes due to the centimeter-level accuracy, vivid color, high density, and crowd sourcing nature. The three-step method reconstructs pedestrian paths, stairs, and slope details and enriches the city-scale 3DPN for wheelability assessment. The experimental results on pedestrian paths demonstrated accurate 3DPN centerline position (<em>mIoU</em> = 88.81 %), stairs detection (<em>mIoU</em> = 86.39 %), and wheelability assessment (<em>MAE</em> = 0.09). This paper contributes an automatic, accurate, and crowd sourcing wheelability assessment method that bridges ubiquitous smartphones and 3DPN for barrier-free travels in high-density and hilly urban areas.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102255"},"PeriodicalIF":7.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1016/j.compenvurbsys.2025.102249
Claire Daniel, Chris Pettit
Technological advances in big data and artificial intelligence have seen a resurgence of enthusiasm for the application of data analytics in planning. History has shown that having high expectations doesn't always mean new analytical tools will be adopted and there is a need for current empirical research to evaluate how urban analytics are produced and used in planning practice. Through combining document analysis, citation network analysis and key informant interviews, this research examined the system behind the production and use of urban analytics during a major strategic planning process in Sydney, Australia. The analysis revealed a complex political system involving over 800 referenced sources from nearly 500 organisations. Although data analytics was just one aspect of the evidence for the plans, it served several base intelligence needs. Most prominently, conceptually simple models were used to measure and forecast the need for various types of land, including residential, commercial, and industrial areas. In addition to updating empirical knowledge, the research provides a new characterization of the social and political rationales shaping digital planning practices. The influence of data analytics on decision making was found to be far from direct. Political factors influenced all aspects of the system, from the availability of data to the use of analytics to coordinate the actions of various participants. For scholars, the findings of this research assist in evaluating emerging ideas about digital planning. For practitioners, the findings contribute to more informed investment in data, tools and training that meet the specific needs of strategic planning practice.
{"title":"The use of urban analytics in strategic planning – A case study of the greater Sydney region plan","authors":"Claire Daniel, Chris Pettit","doi":"10.1016/j.compenvurbsys.2025.102249","DOIUrl":"10.1016/j.compenvurbsys.2025.102249","url":null,"abstract":"<div><div>Technological advances in big data and artificial intelligence have seen a resurgence of enthusiasm for the application of data analytics in planning. History has shown that having high expectations doesn't always mean new analytical tools will be adopted and there is a need for current empirical research to evaluate how urban analytics are produced and used in planning practice. Through combining document analysis, citation network analysis and key informant interviews, this research examined the system behind the production and use of urban analytics during a major strategic planning process in Sydney, Australia. The analysis revealed a complex political system involving over 800 referenced sources from nearly 500 organisations. Although data analytics was just one aspect of the evidence for the plans, it served several base intelligence needs. Most prominently, conceptually simple models were used to measure and forecast the need for various types of land, including residential, commercial, and industrial areas. In addition to updating empirical knowledge, the research provides a new characterization of the social and political rationales shaping digital planning practices. The influence of data analytics on decision making was found to be far from direct. Political factors influenced all aspects of the system, from the availability of data to the use of analytics to coordinate the actions of various participants. For scholars, the findings of this research assist in evaluating emerging ideas about digital planning. For practitioners, the findings contribute to more informed investment in data, tools and training that meet the specific needs of strategic planning practice.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102249"},"PeriodicalIF":7.1,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-26DOI: 10.1016/j.compenvurbsys.2025.102254
Yilong Dai , Luyu Liu , Kaiyue Wang , Meiqing Li , Xiang Yan
The assessment of bus stop amenities is important for providing fundamental data for public transit research, planning, and infrastructure enhancements. So far, public data on the amenities at bus stops have largely been unavailable. This study develops an automated, low-cost, and generalizable approach using Google Street View images and deep learning techniques to evaluate bus stop amenities. Leveraging the latest YOLOv8 model, transfer learning, and a dynamic prediction algorithm, our approach achieves efficient detection of shelters and benches with high accuracy and precision in major Florida cities. Results reveal highly heterogeneous spatial patterns for both shelters and benches within and across cities. Additionally, we conducted several tests to evaluate the transferability of the system to other urban contexts, which shows that highly accurate feature detection results can be achieved through model fine-tuning on a small sample of local data. In summary, the proposed system offers a scalable and efficient solution for large-scale real-time assessment of bus stop amenities, which can inform public transportation research and planning, especially for future transit infrastructure improvements.
{"title":"Using computer vision and street view images to assess bus stop amenities","authors":"Yilong Dai , Luyu Liu , Kaiyue Wang , Meiqing Li , Xiang Yan","doi":"10.1016/j.compenvurbsys.2025.102254","DOIUrl":"10.1016/j.compenvurbsys.2025.102254","url":null,"abstract":"<div><div>The assessment of bus stop amenities is important for providing fundamental data for public transit research, planning, and infrastructure enhancements. So far, public data on the amenities at bus stops have largely been unavailable. This study develops an automated, low-cost, and generalizable approach using Google Street View images and deep learning techniques to evaluate bus stop amenities. Leveraging the latest YOLOv8 model, transfer learning, and a dynamic prediction algorithm, our approach achieves efficient detection of shelters and benches with high accuracy and precision in major Florida cities. Results reveal highly heterogeneous spatial patterns for both shelters and benches within and across cities. Additionally, we conducted several tests to evaluate the transferability of the system to other urban contexts, which shows that highly accurate feature detection results can be achieved through model fine-tuning on a small sample of local data. In summary, the proposed system offers a scalable and efficient solution for large-scale real-time assessment of bus stop amenities, which can inform public transportation research and planning, especially for future transit infrastructure improvements.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102254"},"PeriodicalIF":7.1,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-23DOI: 10.1016/j.compenvurbsys.2025.102253
Zicheng Fan , Chen-Chieh Feng , Filip Biljecki
Street View Imagery (SVI) has emerged as a valuable data form in urban studies, enabling new ways to map and sense urban environments. However, fundamental concerns regarding the representativeness, quality, and reliability of SVI remain underexplored, e.g. to what extent can cities be captured by such data and do data gaps result in bias. This research, positioned at the intersection of spatial data quality and urban analytics, addresses these concerns by proposing a novel and effective method to estimate SVI's element-level coverage in the urban environment. The method integrates the positional relationships between SVI and target elements, as well as the impact of physical obstructions. Expanding the domain of data quality to SVI, we introduce an indicator system that evaluates the extent of coverage, focusing on the completeness and frequency dimensions. Taking London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban environmental elements, using building facades as an example. It is found that despite their high availability along urban road networks, Google Street View covers only 62.4 % of buildings in the case study area. The average facade coverage per building is 12.4 %. SVI tends to over-represent non-residential buildings, thus possibly resulting in biased analyses, and its coverage of environmental elements is position-dependent. The research also highlights the variability of SVI coverage under different data acquisition practices and proposes an optimal sampling interval range of 50–60 m for SVI collection. The findings suggest that while SVI offers valuable insights, it is no panacea – its application in urban research requires careful consideration of data coverage and element-level representativeness to ensure reliable results.
{"title":"Coverage and bias of street view imagery in mapping the urban environment","authors":"Zicheng Fan , Chen-Chieh Feng , Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102253","DOIUrl":"10.1016/j.compenvurbsys.2025.102253","url":null,"abstract":"<div><div>Street View Imagery (SVI) has emerged as a valuable data form in urban studies, enabling new ways to map and sense urban environments. However, fundamental concerns regarding the representativeness, quality, and reliability of SVI remain underexplored, e.g. to what extent can cities be captured by such data and do data gaps result in bias. This research, positioned at the intersection of spatial data quality and urban analytics, addresses these concerns by proposing a novel and effective method to estimate SVI's element-level coverage in the urban environment. The method integrates the positional relationships between SVI and target elements, as well as the impact of physical obstructions. Expanding the domain of data quality to SVI, we introduce an indicator system that evaluates the extent of coverage, focusing on the completeness and frequency dimensions. Taking London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban environmental elements, using building facades as an example. It is found that despite their high availability along urban road networks, Google Street View covers only 62.4 % of buildings in the case study area. The average facade coverage per building is 12.4 %. SVI tends to over-represent non-residential buildings, thus possibly resulting in biased analyses, and its coverage of environmental elements is position-dependent. The research also highlights the variability of SVI coverage under different data acquisition practices and proposes an optimal sampling interval range of 50–60 m for SVI collection. The findings suggest that while SVI offers valuable insights, it is no panacea – its application in urban research requires careful consideration of data coverage and element-level representativeness to ensure reliable results.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102253"},"PeriodicalIF":7.1,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-21DOI: 10.1016/j.compenvurbsys.2025.102250
Yuan Liao , Jorge Gil , Sonia Yeh , Rafael H.M. Pereira , Laura Alessandretti
Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation.
{"title":"Socio-spatial segregation and human mobility: A review of empirical evidence","authors":"Yuan Liao , Jorge Gil , Sonia Yeh , Rafael H.M. Pereira , Laura Alessandretti","doi":"10.1016/j.compenvurbsys.2025.102250","DOIUrl":"10.1016/j.compenvurbsys.2025.102250","url":null,"abstract":"<div><div>Socio-spatial segregation is the physical separation of different social, economic, or demographic groups within a geographic space, often resulting in unequal access to resources, services, and opportunities. The literature has traditionally focused on residential segregation, examining how individuals' residential locations are distributed differently across neighborhoods based on various social attributes, e.g., race, ethnicity, and income. However, this approach overlooks the complexity of spatial segregation in people's daily activities, which often extend far beyond residential areas. Since the 2010s, emerging mobility data sources have enabled a new understanding of socio-spatial segregation by considering daily activities such as work, school, shopping, and leisure visits. From traditional surveys to GPS trajectories, diverse data sources reveal that daily mobility can result in spatial segregation levels that differ from those observed in residential segregation. This literature review focuses on three critical questions: (a) What are the strengths and limitations of segregation research incorporating extensive mobility data? (b) How do human mobility patterns relate to individuals' residential vs. experienced segregation levels? and (c) What key factors explain the relationship between one's mobility patterns and experienced segregation? Our literature review enhances the understanding of socio-spatial segregation at the individual level and clarifies core concepts and methodological challenges in the field. Our review explores studies of key themes: segregation, activity space, co-presence, and the built environment. By synthesizing their findings, we aim to offer actionable insights for reducing segregation.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102250"},"PeriodicalIF":7.1,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.compenvurbsys.2025.102248
Enbo Zhou , Alan T. Murray , Jiwon Baik , Jing Xu
Siting facilities strategically is critical to ensure system design efficiency, enhance social equity and reduce operational costs. Coverage models look to optimize facility configuration, often to minimize the number of necessary facilities or maximize demand served within established proximity standards. Existing location models, such as maximal covering, often assume facility standards to be isotropic, resulting in a perceived circular service area. However, many facility proximity contexts, such as travel time on a transportation network, sound propagation, surveillance cameras and non-vertical lights, have irregular or noncircular service areas, potentially complicating existing coverage modeling approaches. Additionally, anisotropic coverage of facilities raises the issue of how to orient them when sited. The goal of this paper is to extend existing approaches to account for anisotropic coverage, simultaneously locating and orienting facilities. A location model is formulated to address anisotropic service coverage of facilities. A finite dominating set is derived, enabling reformulation as an integer programming problem that can be solved via branch and bound. Applications involving emergence response and surveillance camera placement in both 2-D and 3-D spaces demonstrate the effectiveness of this modeling extension. The resultant anisotropic coverage model addresses a critical aspect of system performance, highlighting that the omission of such considerations greatly overestimates what may be achieved in operation.
{"title":"Locating and orienting facilities with anisotropic coverage","authors":"Enbo Zhou , Alan T. Murray , Jiwon Baik , Jing Xu","doi":"10.1016/j.compenvurbsys.2025.102248","DOIUrl":"10.1016/j.compenvurbsys.2025.102248","url":null,"abstract":"<div><div>Siting facilities strategically is critical to ensure system design efficiency, enhance social equity and reduce operational costs. Coverage models look to optimize facility configuration, often to minimize the number of necessary facilities or maximize demand served within established proximity standards. Existing location models, such as maximal covering, often assume facility standards to be isotropic, resulting in a perceived circular service area. However, many facility proximity contexts, such as travel time on a transportation network, sound propagation, surveillance cameras and non-vertical lights, have irregular or noncircular service areas, potentially complicating existing coverage modeling approaches. Additionally, anisotropic coverage of facilities raises the issue of how to orient them when sited. The goal of this paper is to extend existing approaches to account for anisotropic coverage, simultaneously locating and orienting facilities. A location model is formulated to address anisotropic service coverage of facilities. A finite dominating set is derived, enabling reformulation as an integer programming problem that can be solved via branch and bound. Applications involving emergence response and surveillance camera placement in both 2-D and 3-D spaces demonstrate the effectiveness of this modeling extension. The resultant anisotropic coverage model addresses a critical aspect of system performance, highlighting that the omission of such considerations greatly overestimates what may be achieved in operation.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102248"},"PeriodicalIF":7.1,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-03DOI: 10.1016/j.compenvurbsys.2024.102246
Xuhui Lin , Tao Yang , Stephen Law
In the context of rapid urbanization, urban spaces not only accommodate a growing population but also produces complex socio-economic activities and cultural exchanges. Cities are complex systems, and conventional Points of Interest (POI) analysis methods, which usually assess the density and diversity of POIs in various neighbourhoods, often fails to capture this complexity. To address these limitations, this study introduces a novel approach by transforming POI sequences into words along streets and applying Latent Dirichlet Allocation (LDA) model to identify urban functional regions. Unlike traditional approaches that rely on subjective delineation of administrative boundaries, Voronoi cells or regular grids, our approach identifies street level functional areas that align more closely with human experience. Based on these functional topics, a multi-layered Poi-Topic network is then constructed to help better understand the roles specific POI plays within urban functional regions. This approach effectively distills the spatial distributional patterns of urban functions and provides a micro-level foundations for analysing the contextual interrelationships between POIs, thereby offering a more nuanced understanding of urban spaces. The effectiveness of the approach is demonstrated through the London case study. The results show that the proposed approach can effectively identify and delineate urban functional areas based on the co-occurrence patterns and network structure of POI vocabularies. The network centrality analysis further reveals the structural properties and interaction patterns, providing valuable insights into the roles and positions of different POI types in the functional organization of urban space. This method of using POI sequences and network analysis offers a new tool for urban planners, geospatial scientists, and policymakers, enabling them to understand and plan urban spaces with greater precision.
{"title":"From points to patterns: An explorative POI network study on urban functional distribution","authors":"Xuhui Lin , Tao Yang , Stephen Law","doi":"10.1016/j.compenvurbsys.2024.102246","DOIUrl":"10.1016/j.compenvurbsys.2024.102246","url":null,"abstract":"<div><div>In the context of rapid urbanization, urban spaces not only accommodate a growing population but also produces complex socio-economic activities and cultural exchanges. Cities are complex systems, and conventional Points of Interest (POI) analysis methods, which usually assess the density and diversity of POIs in various neighbourhoods, often fails to capture this complexity. To address these limitations, this study introduces a novel approach by transforming POI sequences into words along streets and applying Latent Dirichlet Allocation (LDA) model to identify urban functional regions. Unlike traditional approaches that rely on subjective delineation of administrative boundaries, Voronoi cells or regular grids, our approach identifies street level functional areas that align more closely with human experience. Based on these functional topics, a multi-layered Poi-Topic network is then constructed to help better understand the roles specific POI plays within urban functional regions. This approach effectively distills the spatial distributional patterns of urban functions and provides a micro-level foundations for analysing the contextual interrelationships between POIs, thereby offering a more nuanced understanding of urban spaces. The effectiveness of the approach is demonstrated through the London case study. The results show that the proposed approach can effectively identify and delineate urban functional areas based on the co-occurrence patterns and network structure of POI vocabularies. The network centrality analysis further reveals the structural properties and interaction patterns, providing valuable insights into the roles and positions of different POI types in the functional organization of urban space. This method of using POI sequences and network analysis offers a new tool for urban planners, geospatial scientists, and policymakers, enabling them to understand and plan urban spaces with greater precision.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102246"},"PeriodicalIF":7.1,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-26DOI: 10.1016/j.compenvurbsys.2024.102245
Lin Liu , Xin Gu , Minxuan Lan , Hanlin Zhou , Debao Chen , Zihan Su
Many scholars have established that facilities represented by Points-of-Interests (POIs) may function as crime generators and attractors, influencing criminal activities. While existing measurements of POIs primarily rely on quantitative counts, this count-based approach overlooks the spatial arrangement of POIs within an area, which can also contribute to crime. This paper introduces two methods to capture the spatial arrangement characteristics of POIs. One is called the normalized Shannon Voronoi Diagram-based Entropy (n_SVDE). A Voronoi diagram is constructed based on the spatial distributions of POIs in an area, resulting in polygons, each corresponding one POI. The area proportions of these polygons are then used to calculate Shannon Entropy. A low entropy value indicates a clustering pattern, while a high value reflects a dispersed distribution. The other is the average nearest neighbor distance ratio (ANN_ratio). It is a ratio of the average of the nearest distances of POIs in an area over the expected average. The effectiveness of these two methods is tested by using negative binominal models to explain street robberies in Cincinnati. Our findings show that the n_SVDE significantly explains street robbery, while the ANN_ratio shows no statistical significance. Specifically, a less clustered spatial distribution of POIs is positively associated with an increased likelihood of crime events, while a highly clustered distribution corresponds to a lower likelihood of crime. This study represents one of the pioneering implementations in explicitly examining the spatial configuration of POIs, contributing new insights into environmental criminology and providing valuable empirical evidence for enhancing place management and optimizing police patrols.
{"title":"Capturing the spatial arrangement of POIs in crime modeling","authors":"Lin Liu , Xin Gu , Minxuan Lan , Hanlin Zhou , Debao Chen , Zihan Su","doi":"10.1016/j.compenvurbsys.2024.102245","DOIUrl":"10.1016/j.compenvurbsys.2024.102245","url":null,"abstract":"<div><div>Many scholars have established that facilities represented by Points-of-Interests (POIs) may function as crime generators and attractors, influencing criminal activities. While existing measurements of POIs primarily rely on quantitative counts, this count-based approach overlooks the spatial arrangement of POIs within an area, which can also contribute to crime. This paper introduces two methods to capture the spatial arrangement characteristics of POIs. One is called the normalized Shannon Voronoi Diagram-based Entropy (<em>n_SVDE</em>). A Voronoi diagram is constructed based on the spatial distributions of POIs in an area, resulting in polygons, each corresponding one POI. The area proportions of these polygons are then used to calculate Shannon Entropy. A low entropy value indicates a clustering pattern, while a high value reflects a dispersed distribution. The other is the average nearest neighbor distance ratio (<em>ANN_ratio</em>). It is a ratio of the average of the nearest distances of POIs in an area over the expected average. The effectiveness of these two methods is tested by using negative binominal models to explain street robberies in Cincinnati. Our findings show that the <em>n_SVDE</em> significantly explains street robbery, while the <em>ANN_ratio</em> shows no statistical significance. Specifically, a less clustered spatial distribution of POIs is positively associated with an increased likelihood of crime events, while a highly clustered distribution corresponds to a lower likelihood of crime. This study represents one of the pioneering implementations in explicitly examining the spatial configuration of POIs, contributing new insights into environmental criminology and providing valuable empirical evidence for enhancing place management and optimizing police patrols.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102245"},"PeriodicalIF":7.1,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1016/j.compenvurbsys.2024.102243
Milad Malekzadeh, Elias Willberg, Jussi Torkko, Tuuli Toivonen
The attractiveness of urban environments significantly impacts residents' satisfaction with their living spaces and their overall mood, which in turn, affects their health and well-being. Given the resource-intensive nature of gathering evaluations on urban attractiveness through surveys or inquiries from residents, there is a constant quest for automated solutions to streamline this process and support spatial planning. In this study, we applied an off-the-shelf AI model to automate the analysis of urban attractiveness, using over 1800 Google Street View images of Helsinki, Finland. By incorporating the GPT-4 model, we assessed these images through three criteria-based prompts. Simultaneously, 24 participants, categorised into residents and non-residents, were asked to rate the images. To gain insights into the non-transparent decision-making processes of GPT-4, we employed semantic segmentation to explore how the model uses different image features. Our results demonstrated a strong alignment between GPT-4 and participant ratings, although geographic disparities were noted. Specifically, GPT-4 showed a preference for suburban areas with significant greenery, contrasting with participants who found these areas less attractive. Conversely, in the city centre and densely populated urban regions of Helsinki, GPT-4 assigned lower attractiveness scores than participant ratings. The semantic segmentation analysis revealed that GPT-4's ratings were primarily influenced by physical features like vegetation, buildings, and sidewalk. While there was general agreement between AI and human assessments across various locations, GPT-4 struggled to incorporate contextual nuances into its ratings, unlike participants, who considered both context and features of the urban environment. The study suggests that leveraging AI models like GPT-4 allows spatial planners to gather insights into the attractiveness of different areas efficiently. However, caution is necessary, while we used an off-the-shelf model, it is crucial to develop models specifically trained to understand the local context. Although AI models provide valuable insights, human perspectives are essential for a comprehensive understanding of urban attractiveness.
{"title":"Urban attractiveness according to ChatGPT: Contrasting AI and human insights","authors":"Milad Malekzadeh, Elias Willberg, Jussi Torkko, Tuuli Toivonen","doi":"10.1016/j.compenvurbsys.2024.102243","DOIUrl":"10.1016/j.compenvurbsys.2024.102243","url":null,"abstract":"<div><div>The attractiveness of urban environments significantly impacts residents' satisfaction with their living spaces and their overall mood, which in turn, affects their health and well-being. Given the resource-intensive nature of gathering evaluations on urban attractiveness through surveys or inquiries from residents, there is a constant quest for automated solutions to streamline this process and support spatial planning. In this study, we applied an off-the-shelf AI model to automate the analysis of urban attractiveness, using over 1800 Google Street View images of Helsinki, Finland. By incorporating the GPT-4 model, we assessed these images through three criteria-based prompts. Simultaneously, 24 participants, categorised into residents and non-residents, were asked to rate the images. To gain insights into the non-transparent decision-making processes of GPT-4, we employed semantic segmentation to explore how the model uses different image features. Our results demonstrated a strong alignment between GPT-4 and participant ratings, although geographic disparities were noted. Specifically, GPT-4 showed a preference for suburban areas with significant greenery, contrasting with participants who found these areas less attractive. Conversely, in the city centre and densely populated urban regions of Helsinki, GPT-4 assigned lower attractiveness scores than participant ratings. The semantic segmentation analysis revealed that GPT-4's ratings were primarily influenced by physical features like vegetation, buildings, and sidewalk. While there was general agreement between AI and human assessments across various locations, GPT-4 struggled to incorporate contextual nuances into its ratings, unlike participants, who considered both context and features of the urban environment. The study suggests that leveraging AI models like GPT-4 allows spatial planners to gather insights into the attractiveness of different areas efficiently. However, caution is necessary, while we used an off-the-shelf model, it is crucial to develop models specifically trained to understand the local context. Although AI models provide valuable insights, human perspectives are essential for a comprehensive understanding of urban attractiveness.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102243"},"PeriodicalIF":7.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-24DOI: 10.1016/j.compenvurbsys.2024.102244
Hanchen Yu
This paper proposes Generalized Geographically and Temporally Weighted Regression (GGTWR) to address the limitations of Geographically and Temporally Weighted Regression (GTWR). The proposed GGTWR framework encompasses various generalized linear models, e.g. Poisson regression, negative binomial regression, and other models of the exponential distribution family. The paper also shows the classic GTWR bandwidth search algorithm is not suitable for GGTWR and proposes a new bandwidth search algorithm for GGTWR. Several simulation experiments are used to prove that GGTWR can effectively capture spatiotemporal non-stationary. The GGTWR framework enables the estimation of varying regression coefficients that capture spatial and temporal heterogeneity for generalized linear relationships, providing a comprehensive understanding of how predictor variables influence the response variable across different locations and time periods. An application to interprovincial population migration in China using 2005–2020 census data demonstrates the interpretability of the GGTWR framework. GGTWR provides a flexible modeling approach that more accurately explains real-world phenomena.
{"title":"Generalized geographically and temporally weighted regression","authors":"Hanchen Yu","doi":"10.1016/j.compenvurbsys.2024.102244","DOIUrl":"10.1016/j.compenvurbsys.2024.102244","url":null,"abstract":"<div><div>This paper proposes Generalized Geographically and Temporally Weighted Regression (GGTWR) to address the limitations of Geographically and Temporally Weighted Regression (GTWR). The proposed GGTWR framework encompasses various generalized linear models, e.g. Poisson regression, negative binomial regression, and other models of the exponential distribution family. The paper also shows the classic GTWR bandwidth search algorithm is not suitable for GGTWR and proposes a new bandwidth search algorithm for GGTWR. Several simulation experiments are used to prove that GGTWR can effectively capture spatiotemporal non-stationary. The GGTWR framework enables the estimation of varying regression coefficients that capture spatial and temporal heterogeneity for generalized linear relationships, providing a comprehensive understanding of how predictor variables influence the response variable across different locations and time periods. An application to interprovincial population migration in China using 2005–2020 census data demonstrates the interpretability of the GGTWR framework. GGTWR provides a flexible modeling approach that more accurately explains real-world phenomena.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102244"},"PeriodicalIF":7.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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