Computers Environment and Urban Systems最新文献

{"title":"Locating and orienting facilities with anisotropic coverage","authors":"Enbo Zhou ,&nbsp;Alan T. Murray ,&nbsp;Jiwon Baik ,&nbsp;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}

{"title":"From points to patterns: An explorative POI network study on urban functional distribution","authors":"Xuhui Lin ,&nbsp;Tao Yang ,&nbsp;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}

{"title":"Capturing the spatial arrangement of POIs in crime modeling","authors":"Lin Liu ,&nbsp;Xin Gu ,&nbsp;Minxuan Lan ,&nbsp;Hanlin Zhou ,&nbsp;Debao Chen ,&nbsp;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}

{"title":"Urban attractiveness according to ChatGPT: Contrasting AI and human insights","authors":"Milad Malekzadeh,&nbsp;Elias Willberg,&nbsp;Jussi Torkko,&nbsp;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}

{"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}

{"title":"Activity-based simulations for neighbourhood planning towards social-spatial equity","authors":"S. Somanath ,&nbsp;L. Thuvander,&nbsp;J. Gil,&nbsp;A. Hollberg","doi":"10.1016/j.compenvurbsys.2024.102242","DOIUrl":"10.1016/j.compenvurbsys.2024.102242","url":null,"abstract":"<div><div>Urban planners use static analysis techniques like network and proximity analysis to evaluate a neighbourhood's accessibility. However, these techniques do not adequately capture the distributional effects of accessibility on individuals. This paper introduces an activity-based model that simulates residents' daily activities to assess the distributional effects of the built environment (BE) on their accessibility. The model consists of a pipeline to generate a synthetic population covering 96 neighbourhoods in Gothenburg, Sweden, performs origin and destination assignment, and supports four travel modes and different activity types. The synthetic population and the travel demand model are validated across demographic and travel survey data. Additionally, we introduce Trip Completion Rate (TCR), an indicator of distributional accessibility and apply our model to a proposed redevelopment plan for a neighbourhood in Gothenburg to demonstrate its utility.</div><div>The results show that techniques used in transportation research can be effectively applied to neighbourhood planning, providing planners with insights into residents' ability to fulfil their daily needs. An advantage of our model is its ability to generate synthetic residents for a neighbourhood and then simulate how changes in the BE affect the resident's ability to achieve their daily needs, thus switching the focus of the analysis from the neighbourhood BE to including the residents that live in it. This paper extends the application of techniques used in transportation planning to neighbourhood planning, thereby empowering urban planners to create more equitable neighbourhoods.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102242"},"PeriodicalIF":7.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141408","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}

{"title":"Homogeneity and heterogeneity of diurnal and nocturnal hotspots and the implications for synergetic mitigation in heat-resilient urban planning","authors":"Huimin Liu ,&nbsp;Miao Li ,&nbsp;Qingming Zhan ,&nbsp;Zhengyue Ma ,&nbsp;Bao-Jie He","doi":"10.1016/j.compenvurbsys.2024.102241","DOIUrl":"10.1016/j.compenvurbsys.2024.102241","url":null,"abstract":"<div><div>Many cities are under intense heat challenges with severe environmental, social, and economic consequences, sparking great concern on heat-resilient urban planning, yet normally with biased focus on limited (e.g., diurnal) mitigation needs. Particularly, the recognition of urban thermal hotspots is crucial for adding effective cooling interventions for mitigation and avoiding overheating in newly built areas. However, the hotspots and associated drivers vary across time and space, bringing challenges to urban planners to make win-win decisions to synchronously address diurnal and nocturnal heat stresses through an integrated set of cooling strategies. This study aims to recognize the homogeneity and heterogeneity of diurnal and nocturnal hotspots and interpret principal and synergetic drivers behind them by developing a robust methodological scheme in addressing uncertainties associated with temperature data and analytical models. It explicitly 1) identified summer diurnal and nocturnal hotspots using rigorously screened satellite data; 2) recognized the typical typologies of hotspot-prone urban landscape according to urban composition, morphology, and function; 3) explored the day-night similarities and disparities in major urban factors and their robust effective ranges for synergetic mitigation through multi-model non-linear analysis with diverse machine learning techniques covering random forest, gradient boosting machines, and boosted regression trees. Results revealed that the specific locations and typical urban landscape features varied between diurnal and nocturnal hotspots. Among the six typologies recognized, industrial-dominated ones were more inclined to emerge as diurnal hotspots, while mid- to high-rise and mid-density blocks, with diversified land uses (mostly residential-dominated), tended to become diurnal, and more likely, nocturnal hotspots. All three models reached robust conclusion that urban morphology exhibited significant influence on both diurnal and nocturnal hotspot formation. Although trade-offs remained unavoidable in many cases, synergetic mitigation could be achieved through optimizing area averaged building height below 15 m or above 25 m, and building volume density under 2 % for Wuhan, China. Overall, this study responds to the emerging multidimensional urban science and praxis and extends the conventional one-dimensional planning against urban heat to win-win decisions over both diurnal and nocturnal hotspots. The empirical findings can benefit the development of complete, unbiased, and implementable actions for enhanced climate-resilience.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102241"},"PeriodicalIF":7.1,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141411","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}

{"title":"Comparative analysis of pedestrian volume models: Agent-based models, machine learning methods and multiple regression analysis","authors":"Lior Wolpert,&nbsp;Itzhak Omer","doi":"10.1016/j.compenvurbsys.2024.102238","DOIUrl":"10.1016/j.compenvurbsys.2024.102238","url":null,"abstract":"<div><div>Pedestrian flow distributions can inform planning for walkability and improve understanding of factors that influence pedestrian activity. However, detailed data is rarely available so pedestrian volume models, commonly relying on the Space Syntax framework, are often utilized to predict pedestrian volumes. This study compares the performance and dominant variables of three modelling families – multiple regression analyses, machine learning models, and agent-based models – in Tel Aviv-Yafo, Israel. Using 247 flow observations, optimal models from each family were fitted and validated for 3 separate areas that differ in their urban growth and morphological characteristics, as well for the whole city. Results showed that ensemble-based machine learning models were best for city-wide predictions while agent-based models had an advantage at the local scale of neighborhoods – especially in neighborhoods that did not develop in a self-organized process. Regression analyses fell short for all areas, even when using principal component analysis to reduce multicollinearity and overfitting. These differences are attributed to the relative influence of cognitive-behavioral and structural factors on pedestrian flows: agent-based models outperform statistical models in individual areas, where behavior is captured more accurately using a small set of cognitive-behavioral parameters. Statistical models are dominant in the city-wide context, where structural variables can predict aggregate patterns. This is crucially important when evaluating the distribution of pedestrians in a planned urban environment. Overall, our results indicate that stepwise regression are not sufficient for pedestrian volume modelling, that agent-based models better capture complex interactions between independent variables, and that machine learning models have a strong potential for city-wide pedestrian volume modelling.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102238"},"PeriodicalIF":7.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141410","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}

{"title":"Reproducible methods for modeling combined public transport and cycling trips and associated benefits: Evidence from the biclaR tool","authors":"Rosa Félix ,&nbsp;Filipe Moura ,&nbsp;Robin Lovelace","doi":"10.1016/j.compenvurbsys.2024.102230","DOIUrl":"10.1016/j.compenvurbsys.2024.102230","url":null,"abstract":"<div><div>A high proportion of car trips can be replaced by a combination of public transit and cycling for the first-and-last mile. This paper estimates the potential for cycling combined with public transit as a substitute for car trips in the Lisbon metropolitan area and assesses its socio-environmental impacts using open data and open source tools. A decision support tool that facilitates the design and development of a metropolitan cycling network was developed (<em>biclaR</em>). The social and environmental impacts were assessed using Health World Organization tools. The impacts of shifting car trips to public transport were also estimated and monetized. The results show that 10 % of all trips could be made by cycling in combination with public transport. Shifting to cycling for the shorter first and last mile stages can reduce annual CO₂eq emissions from 3000 to 7500 tons/year, while for the public transport leg, the transfer from car avoids of up to 20,500 tons of CO₂eq emissions per year. The estimated socio-environmental benefits are of €125 million to €325 million over 10 years. This evidence can support policymakers to prioritize interventions that reduce the reliance on private motor vehicles.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102230"},"PeriodicalIF":7.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141423","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}

{"title":"GeoAI-enhanced community detection on spatial networks with graph deep learning","authors":"Yunlei Liang ,&nbsp;Jiawei Zhu ,&nbsp;Wen Ye ,&nbsp;Song Gao","doi":"10.1016/j.compenvurbsys.2024.102228","DOIUrl":"10.1016/j.compenvurbsys.2024.102228","url":null,"abstract":"<div><div>Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from the network and reveal the hidden relationships between nodes, but they usually do not involve the attribute information. To consider edge-based interactions and node attributes together, this study proposed a family of GeoAI-enhanced unsupervised community detection methods called <em>region2vec</em> based on Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN). The <em>region2vec</em> methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering. The proposed GeoAI-based methods are compared with multiple baselines and perform the best when one wants to maximize node attribute similarity and spatial interaction intensity simultaneously within the spatial network communities. It is further applied in the shortage area delineation problem in public health and demonstrates its promise in regionalization problems.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102228"},"PeriodicalIF":7.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141422","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}