Computers Environment and Urban Systems最新文献_第2页

How well do street view images predict crime rates in London? A comparison with social and macro-level environmental features 街景图像预测伦敦犯罪率的效果如何？社会与宏观层面环境特征的比较

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-12-19 DOI: 10.1016/j.compenvurbsys.2025.102390

Sitong Guo, Richard Harris, Rui Zhu

In research on the causes of crime, geographic context is considered important in relation to how neighbourhood features influence crime. These features include the social and physical environmental features. Historically, measuring the impact of the physical environment – especially its micro-level characteristics – on crime has been challenging due to the lack of fine-grained quantitative data. Recent advances in computer imagery have enabled researchers to extract structured data from street view imagery, creating new opportunities to quantify features of the physical environment at this scale – particularly those visible from the streetscape perspective. However, the predictive power of these features, and particularly how they compare to more traditional sources of neighbourhood data, remain underexplored. Conducting the analysis across a large urban area also presents a significant challenge. To address these gaps, this study uses a stratified random sampling technique (stratified by classes of socio-economic deprivation/affluence) to select and extract data on micro-level environmental features from Google Street View (GSV) images. These are studied alongside other social and macro-level environmental data for 1000 Lower Super Output Areas (LSOAs) in London, using Random Forest as the core predictive model, with Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) serving as supplementary tools to predict and analyse crime rates at an additional 500 randomly sampled LSOAs. While ‘social and macro-level environmental features’ – specifically renter occupancy rate, the number of POIs, and transport accessibility scores – were generally the most important predictors of crime, for certain crimes, such as criminal damage and arson, incorporating micro-level environmental features improved model accuracy. Overall, models incorporating spatial information in the relationships between environmental and social features and crime rates, outperformed other models. This underscores the importance of considering spatial heterogeneity when analysing features influencing crime.

在对犯罪原因的研究中，地理环境被认为是与社区特征如何影响犯罪有关的重要因素。这些特征包括社会特征和物理环境特征。从历史上看，由于缺乏细粒度的定量数据，测量物理环境——尤其是其微观层面特征——对犯罪的影响一直是一项挑战。计算机图像的最新进展使研究人员能够从街景图像中提取结构化数据，创造了新的机会来量化这种规模的物理环境特征，特别是那些从街景角度可见的特征。然而，这些特征的预测能力，特别是它们与更传统的社区数据来源的比较，仍然没有得到充分的探索。在大型城市地区进行分析也面临重大挑战。为了解决这些差距，本研究使用分层随机抽样技术（按社会经济剥夺/富裕等级分层）从谷歌街景（GSV）图像中选择和提取微观环境特征数据。这些数据与伦敦1000个低超级产出区（lsoa）的其他社会和宏观环境数据一起进行了研究，使用随机森林作为核心预测模型，使用普通最小二乘法（OLS）和地理加权回归（GWR）作为补充工具来预测和分析另外500个随机抽样lsoa的犯罪率。虽然“社会和宏观层面的环境特征”——特别是租户入住率、poi数量和交通可达性得分——通常是最重要的犯罪预测指标，但对于某些犯罪，如刑事破坏和纵火，结合微观层面的环境特征可以提高模型的准确性。总体而言，在环境和社会特征与犯罪率之间的关系中包含空间信息的模型优于其他模型。这强调了在分析影响犯罪的特征时考虑空间异质性的重要性。

{"title":"How well do street view images predict crime rates in London? A comparison with social and macro-level environmental features","authors":"Sitong Guo, Richard Harris, Rui Zhu","doi":"10.1016/j.compenvurbsys.2025.102390","DOIUrl":"10.1016/j.compenvurbsys.2025.102390","url":null,"abstract":"<div><div>In research on the causes of crime, geographic context is considered important in relation to how neighbourhood features influence crime. These features include the social and physical environmental features. Historically, measuring the impact of the physical environment – especially its micro-level characteristics – on crime has been challenging due to the lack of fine-grained quantitative data. Recent advances in computer imagery have enabled researchers to extract structured data from street view imagery, creating new opportunities to quantify features of the physical environment at this scale – particularly those visible from the streetscape perspective. However, the predictive power of these features, and particularly how they compare to more traditional sources of neighbourhood data, remain underexplored. Conducting the analysis across a large urban area also presents a significant challenge. To address these gaps, this study uses a stratified random sampling technique (stratified by classes of socio-economic deprivation/affluence) to select and extract data on micro-level environmental features from Google Street View (GSV) images. These are studied alongside other social and macro-level environmental data for 1000 Lower Super Output Areas (LSOAs) in London, using Random Forest as the core predictive model, with Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) serving as supplementary tools to predict and analyse crime rates at an additional 500 randomly sampled LSOAs. While ‘social and macro-level environmental features’ – specifically renter occupancy rate, the number of POIs, and transport accessibility scores – were generally the most important predictors of crime, for certain crimes, such as criminal damage and arson, incorporating micro-level environmental features improved model accuracy. Overall, models incorporating spatial information in the relationships between environmental and social features and crime rates, outperformed other models. This underscores the importance of considering spatial heterogeneity when analysing features influencing crime.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"125 ","pages":"Article 102390"},"PeriodicalIF":8.3,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791514","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}

引用次数: 0

Estimating road speed classes: Integrating OpenStreetMap and Street View imagery for missing data imputation 估计道路速度等级：整合OpenStreetMap和街景图像用于缺失数据的输入

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-12-17 DOI: 10.1016/j.compenvurbsys.2025.102392

Shiyu Tang , Sukanya Randhawa , Jin Rui , Christina Ludwig , Steffen Knoblauch , Charles Hatfield , Alexander Zipf

Traffic speed is a significant indicator for evaluating road network performance and supporting intelligent transportation systems, as it informs congestion management, routing, and operational decisions. Although traffic information is available from commercial platforms and sensor-based monitoring systems, such data are often costly, proprietary, or spatially limited, which restricts their broader usability. To overcome these limitations, we designed a spatial prediction model based on the Graph Sample and Aggregation (GraphSAGE) to infer traffic speeds in unobserved areas. Instead of predicting continuous speed values, we classified traffic into speed classes, which enhanced model robustness in the absence of historical observations and better reflected long-term typical traffic patterns relevant to downstream applications such as routing, emission assessment, and traffic management. Taking Berlin as a case study, the model incorporated multi-source features, including topological features, OpenStreetMap-based road features, and semantic Street View imagery indicators. Uber Movement average speed data were used as supervised learning labels. Results showed that the multi-source feature fusion improved the prediction performance, with the F1 score increasing from 0.6228 to 0.6917. Feature analysis revealed that OSM contextual features contributed the most under limited label coverage, while Street View imagery added complementary information to facilitate model discrimination. Despite only 28 % of road segments being covered by Uber observations, similar feature patterns between labeled and unlabeled areas enabled the model to generalize and infer missing speed data citywide. The framework makes scalable and low-cost speed class inference available for urban traffic monitoring and modeling.

交通速度是评估道路网络性能和支持智能交通系统的重要指标，因为它为拥堵管理、路线和运营决策提供了信息。虽然交通信息可以从商业平台和基于传感器的监测系统中获得，但这些数据通常价格昂贵、专有或空间有限，这限制了它们的广泛可用性。为了克服这些限制，我们设计了一个基于图样本和聚合（GraphSAGE）的空间预测模型来推断未观测区域的交通速度。我们没有预测连续的速度值，而是将交通划分为速度等级，这增强了模型在缺乏历史观测的情况下的鲁棒性，并更好地反映了与下游应用（如路由、排放评估和交通管理）相关的长期典型交通模式。以柏林为例，该模型结合了多源特征，包括拓扑特征、基于openstreetmap的道路特征和语义街景图像指标。优步运动平均速度数据作为监督学习标签。结果表明，多源特征融合提高了预测性能，F1分数从0.6228提高到0.6917。特征分析表明，在有限的标签覆盖范围下，OSM上下文特征贡献最大，而街景图像添加了补充信息，以促进模型识别。尽管Uber的观测数据只覆盖了28%的路段，但标记区域和未标记区域之间的相似特征模式使该模型能够在全市范围内推广和推断缺失的速度数据。该框架为城市交通监控和建模提供了可扩展和低成本的速度类推断。

{"title":"Estimating road speed classes: Integrating OpenStreetMap and Street View imagery for missing data imputation","authors":"Shiyu Tang , Sukanya Randhawa , Jin Rui , Christina Ludwig , Steffen Knoblauch , Charles Hatfield , Alexander Zipf","doi":"10.1016/j.compenvurbsys.2025.102392","DOIUrl":"10.1016/j.compenvurbsys.2025.102392","url":null,"abstract":"<div><div>Traffic speed is a significant indicator for evaluating road network performance and supporting intelligent transportation systems, as it informs congestion management, routing, and operational decisions. Although traffic information is available from commercial platforms and sensor-based monitoring systems, such data are often costly, proprietary, or spatially limited, which restricts their broader usability. To overcome these limitations, we designed a spatial prediction model based on the Graph Sample and Aggregation (GraphSAGE) to infer traffic speeds in unobserved areas. Instead of predicting continuous speed values, we classified traffic into speed classes, which enhanced model robustness in the absence of historical observations and better reflected long-term typical traffic patterns relevant to downstream applications such as routing, emission assessment, and traffic management. Taking Berlin as a case study, the model incorporated multi-source features, including topological features, OpenStreetMap-based road features, and semantic Street View imagery indicators. Uber Movement average speed data were used as supervised learning labels. Results showed that the multi-source feature fusion improved the prediction performance, with the F1 score increasing from 0.6228 to 0.6917. Feature analysis revealed that OSM contextual features contributed the most under limited label coverage, while Street View imagery added complementary information to facilitate model discrimination. Despite only 28 % of road segments being covered by Uber observations, similar feature patterns between labeled and unlabeled areas enabled the model to generalize and infer missing speed data citywide. The framework makes scalable and low-cost speed class inference available for urban traffic monitoring and modeling.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"125 ","pages":"Article 102392"},"PeriodicalIF":8.3,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791515","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}

引用次数: 0

Realized spatial accessibility vs. potential spatial accessibility in the United States: A case study based on geospatial big data 美国已实现空间可达性与潜在空间可达性：基于地理空间大数据的案例研究

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-12-10 DOI: 10.1016/j.compenvurbsys.2025.102382

Yaxiong Shao , Wei Luo

The COVID-19 pandemic drew significant attention to disparities in spatial access to healthcare. While potential spatial accessibility has been extensively researched, realized spatial accessibility remains relatively underexplored. This study employs geospatial big data (SafeGraph Monthly Pattern) to explore the differences between these two types of spatial accessibility using the Two-step Floating Catchment Area (2SFCA) model for the entire population at the Census Tract Level across the contiguous United States. By integrating methods such as point of interest (POI) Placekey matching, Partial Placekey, and fuzzy matching, we successfully matched SafeGraph foot traffic patterns with the American Hospital Association (AHA) survey dataset. Employing total beds as a representation of healthcare facility supply and adjusted SafeGraph visit counts as a representation of the actual healthcare service utilization, the 2SFCA model was applied to compute realized spatial accessibility. A distance decay function, derived from SafeGraph foot traffic patterns, and the same supply data along with potential demand populations were incorporated to calculate potential spatial accessibility. Results show significant differences between potential and realized spatial accessibility across the contiguous US. Compared to realized accessibility measure, the potential spatial accessibility measure significantly underestimates the spatial access to healthcare. Our approach suggests that the realized accessibility based on SafeGraph data can not only help policymakers in making more informed decisions but also serve as a catalyst in improving health access equity.

2019冠状病毒病大流行引起了人们对医疗保健空间可及性差异的高度关注。潜在的空间可达性已经得到了广泛的研究，但对已实现的空间可达性的探索相对较少。本研究采用地理空间大数据（SafeGraph Monthly Pattern），采用两步浮动集水区（two -step Floating Catchment Area, 2SFCA）模型，对美国各地人口普查区的整体人口进行了空间可达性分析。通过整合兴趣点（POI） Placekey匹配、部分Placekey匹配和模糊匹配等方法，我们成功地将SafeGraph人流量模式与美国医院协会（AHA）调查数据集进行了匹配。采用总床位作为医疗设施供应的表示，调整后的SafeGraph访问量作为实际医疗服务利用的表示，应用2SFCA模型计算实现的空间可达性。从SafeGraph步行交通模式中导出的距离衰减函数，以及相同的供应数据和潜在需求人口，结合计算潜在的空间可达性。结果表明，美国相邻地区潜在可达性与实现可达性存在显著差异。与已实现可达性测度相比，潜在空间可达性测度显著低估了医疗服务的空间可达性。我们的方法表明，基于SafeGraph数据实现的可及性不仅可以帮助决策者做出更明智的决策，还可以促进卫生可及性的公平性。

{"title":"Realized spatial accessibility vs. potential spatial accessibility in the United States: A case study based on geospatial big data","authors":"Yaxiong Shao , Wei Luo","doi":"10.1016/j.compenvurbsys.2025.102382","DOIUrl":"10.1016/j.compenvurbsys.2025.102382","url":null,"abstract":"<div><div>The COVID-19 pandemic drew significant attention to disparities in spatial access to healthcare. While potential spatial accessibility has been extensively researched, realized spatial accessibility remains relatively underexplored. This study employs geospatial big data (SafeGraph Monthly Pattern) to explore the differences between these two types of spatial accessibility using the Two-step Floating Catchment Area (2SFCA) model for the entire population at the Census Tract Level across the contiguous United States. By integrating methods such as point of interest (POI) Placekey matching, Partial Placekey, and fuzzy matching, we successfully matched SafeGraph foot traffic patterns with the American Hospital Association (AHA) survey dataset. Employing total beds as a representation of healthcare facility supply and adjusted SafeGraph visit counts as a representation of the actual healthcare service utilization, the 2SFCA model was applied to compute realized spatial accessibility. A distance decay function, derived from SafeGraph foot traffic patterns, and the same supply data along with potential demand populations were incorporated to calculate potential spatial accessibility. Results show significant differences between potential and realized spatial accessibility across the contiguous US. Compared to realized accessibility measure, the potential spatial accessibility measure significantly underestimates the spatial access to healthcare. Our approach suggests that the realized accessibility based on SafeGraph data can not only help policymakers in making more informed decisions but also serve as a catalyst in improving health access equity.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"125 ","pages":"Article 102382"},"PeriodicalIF":8.3,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145705680","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}

引用次数: 0

Impacts of spatial resolution on agent-based transportation simulations with shared autonomous vehicles 空间分辨率对基于智能体的共享自动驾驶交通模拟的影响

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-27 DOI: 10.1016/j.compenvurbsys.2025.102371

Kentaro Mori , Fatemeh Fakhrmoosavi , Krishna Murthy Gurumurthy , Pedro Camargo , Kara M. Kockelman

Agent-based transportation models have been used to simulate shared autonomous vehicle (SAV) fleet operations, enabling a growing understanding of SAVs' operations, impacts, and opportunities. This paper investigates the issue of spatial resolution, since most studies have been conducted on coarsened networks, with many missing links and with aggregated addresses for trip origins and destinations. This work presents simulation results for dynamic traffic assignment with SAV fleet operations in Austin, Texas, comparing outcomes across two networks and two sets of addresses for trip ends in the region's six counties. The comparison involves the Capital Area Metropolitan Planning Organization's (CAMPO's) planning network with addresses highly aggregated (census block centroids supplemented with business establishment information), versus OpenStreetMap's (OSM's) real network with actual addresses sourced from OpenAddresses. CAMPO's network contains 40.6 % of the OSM lane-miles, while the aggregated address points are highly concentrated in the urban core and represent 23 actual addresses on average. Agent-based simulation results using the POLARIS model suggest that omitting a large share of collector and residential links significantly affects network flows, increasing VMT and VHT along non-expressway arterials by 18.9 % and 10.4 %, respectively, for the case of Austin. By contrast, address aggregation (at least at the level implemented in this study) has little impact on traffic. SAVs benefit from increased network connectivity and alternative routes in the complete network to reduce passenger pickup distances and ridepooling detours, lowering VMT by 10 % per SAV—nearly five times the reduction seen in network-wide VMT—and empty VMT (%eVMT) by 2.5 to 3.5 percentage points.

基于智能体的交通模型已被用于模拟共享自动驾驶汽车（SAV）车队的运营，从而使人们对共享自动驾驶汽车的运营、影响和机遇有了更深入的了解。本文探讨了空间分辨率问题，因为大多数研究都是在粗糙的网络上进行的，这些网络有许多缺失的链接，并且旅行起点和目的地的地址都是聚合的。本研究展示了德克萨斯州奥斯汀市SAV车队运行的动态交通分配模拟结果，比较了该地区六个县的两个网络和两组行程终点地址的结果。比较包括首都地区都市规划组织（CAMPO）的规划网络，其地址高度汇总（人口普查块中心点补充商业机构信息），与OpenStreetMap （OSM）的真实网络，其实际地址来自OpenAddresses。CAMPO的网络包含40.6%的OSM车道里程，而聚合地址点高度集中在城市核心，平均代表23个实际地址。使用POLARIS模型的基于agent的仿真结果表明，省去大量集线器和住宅链路会显著影响网络流量，以奥斯汀为例，非高速公路主干道上的VMT和VHT分别增加了18.9%和10.4%。相比之下，地址聚合（至少在本研究中实现的级别上）对流量的影响很小。sav受益于网络连接的增加和整个网络中的替代路线，以减少乘客接送距离和搭车绕路，每个sav降低10%的行驶里程，几乎是整个网络行驶里程减少的五倍，空车行驶里程（%eVMT）降低2.5至3.5个百分点。

{"title":"Impacts of spatial resolution on agent-based transportation simulations with shared autonomous vehicles","authors":"Kentaro Mori , Fatemeh Fakhrmoosavi , Krishna Murthy Gurumurthy , Pedro Camargo , Kara M. Kockelman","doi":"10.1016/j.compenvurbsys.2025.102371","DOIUrl":"10.1016/j.compenvurbsys.2025.102371","url":null,"abstract":"<div><div>Agent-based transportation models have been used to simulate shared autonomous vehicle (SAV) fleet operations, enabling a growing understanding of SAVs' operations, impacts, and opportunities. This paper investigates the issue of spatial resolution, since most studies have been conducted on coarsened networks, with many missing links and with aggregated addresses for trip origins and destinations. This work presents simulation results for dynamic traffic assignment with SAV fleet operations in Austin, Texas, comparing outcomes across two networks and two sets of addresses for trip ends in the region's six counties. The comparison involves the Capital Area Metropolitan Planning Organization's (CAMPO's) planning network with addresses highly aggregated (census block centroids supplemented with business establishment information), versus OpenStreetMap's (OSM's) real network with actual addresses sourced from OpenAddresses. CAMPO's network contains 40.6 % of the OSM lane-miles, while the aggregated address points are highly concentrated in the urban core and represent 23 actual addresses on average. Agent-based simulation results using the POLARIS model suggest that omitting a large share of collector and residential links significantly affects network flows, increasing VMT and VHT along non-expressway arterials by 18.9 % and 10.4 %, respectively, for the case of Austin. By contrast, address aggregation (at least at the level implemented in this study) has little impact on traffic. SAVs benefit from increased network connectivity and alternative routes in the complete network to reduce passenger pickup distances and ridepooling detours, lowering VMT by 10 % per SAV—nearly five times the reduction seen in network-wide VMT—and empty VMT (%eVMT) by 2.5 to 3.5 percentage points.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"124 ","pages":"Article 102371"},"PeriodicalIF":8.3,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624519","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}

引用次数: 0

Generative AI for spatial regeneration planning: Integrating urban planning theories and ethics 生成式人工智能在空间再生规划中的应用：城市规划理论与伦理的融合

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-24 DOI: 10.1016/j.compenvurbsys.2025.102380

Yan Wang , Yanjie Fu , Ward Lyles

Generative Artificial Intelligence (GenAI) is rapidly emerging as a promising tool for urban planning, particularly spatial regeneration planning (SRP) aimed at ongoing redevelopment in urban areas. Driven by increasing availability of urban spatial data and a strong interest in technological innovation, GenAI models can generate diverse, context-sensitive planning scenarios beyond the limits of conventional approaches. However, the application of GenAI to SRP also raises pressing concerns: Are planning problems appropriately defined and rigorously modeled? How closely do model design and data processing align with established planning theories and ethics? What strategies can mitigate the risk of perpetuating spatial disadvantages embedded in historical data? How do we meaningfully evaluate GenAI's real-world impact? And how can GenAI be governed to ensure equity, transparency, and meaningful collaboration among all planning stakeholders? This Review critically assesses the intersection of SRP theories and GenAI, identifying key vulnerabilities along the modeling process, from problem formulation and data selection to model training, evaluation, and governance. We propose key technical pathways for developing GenAI-based SRP that are grounded in planning theories and ethics, aiming to advance both the rigor and societal relevance of spatial planning research for sustainable, smart, and resilient cities.

生成式人工智能（GenAI）正迅速成为城市规划的一个有前途的工具，特别是针对城市地区正在进行的重建的空间再生规划（SRP）。在城市空间数据可用性不断提高和对技术创新的强烈兴趣的推动下，GenAI模型可以产生超越传统方法限制的多样化、环境敏感的规划情景。然而，GenAI在SRP中的应用也引起了迫切的关注：规划问题是否得到了适当的定义和严格的建模？模型设计和数据处理与已建立的规划理论和伦理的一致性有多紧密？哪些策略可以减轻历史数据中存在的空间劣势的风险？我们如何有意义地评估GenAI对现实世界的影响？如何治理GenAI以确保所有规划利益相关者之间的公平、透明和有意义的合作？本综述批判性地评估了SRP理论和GenAI的交集，确定了建模过程中的关键漏洞，从问题制定和数据选择到模型训练、评估和治理。我们提出了发展基于genai的SRP的关键技术途径，这些路径以规划理论和伦理为基础，旨在提高可持续、智能和弹性城市空间规划研究的严谨性和社会相关性。

{"title":"Generative AI for spatial regeneration planning: Integrating urban planning theories and ethics","authors":"Yan Wang , Yanjie Fu , Ward Lyles","doi":"10.1016/j.compenvurbsys.2025.102380","DOIUrl":"10.1016/j.compenvurbsys.2025.102380","url":null,"abstract":"<div><div>Generative Artificial Intelligence (GenAI) is rapidly emerging as a promising tool for urban planning, particularly spatial regeneration planning (SRP) aimed at ongoing redevelopment in urban areas. Driven by increasing availability of urban spatial data and a strong interest in technological innovation, GenAI models can generate diverse, context-sensitive planning scenarios beyond the limits of conventional approaches. However, the application of GenAI to SRP also raises pressing concerns: Are planning problems appropriately defined and rigorously modeled? How closely do model design and data processing align with established planning theories and ethics? What strategies can mitigate the risk of perpetuating spatial disadvantages embedded in historical data? How do we meaningfully evaluate GenAI's real-world impact? And how can GenAI be governed to ensure equity, transparency, and meaningful collaboration among all planning stakeholders? This Review critically assesses the intersection of SRP theories and GenAI, identifying key vulnerabilities along the modeling process, from problem formulation and data selection to model training, evaluation, and governance. We propose key technical pathways for developing GenAI-based SRP that are grounded in planning theories and ethics, aiming to advance both the rigor and societal relevance of spatial planning research for sustainable, smart, and resilient cities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"124 ","pages":"Article 102380"},"PeriodicalIF":8.3,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624521","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}

引用次数: 0

Leveraging sidewalk robots for walkability-related analyses 利用人行道机器人进行与步行相关的分析

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-24 DOI: 10.1016/j.compenvurbsys.2025.102381

Xing Tong , Michele D. Simoni , Kaj Munhoz Arfvidsson , Jonas Mårtensson

Walkability is a key component of sustainable urban development. In walkability studies, collecting detailed pedestrian infrastructure data remains challenging due to the high costs and limited scalability of traditional methods. Sidewalk delivery robots, increasingly deployed in urban environments, offer a promising solution to these limitations. This paper explores how these robots can serve as mobile data collection platforms, capturing sidewalk-level features related to walkability in a scalable, automated, and real-time manner. A sensor-equipped robot was deployed on a sidewalk network at KTH in Stockholm, completing 101 trips covering 900 segment records. From the collected data, different typologies of features are derived, including robot trip characteristics (e.g., speed, duration), sidewalk conditions (e.g., width, surface unevenness), and sidewalk utilization (e.g., pedestrian density). Their walkability-related implications were investigated with a series of analyses. The results demonstrate that pedestrian movement patterns are strongly influenced by sidewalk characteristics, with higher density, reduced width, and surface irregularity associated with slower and more variable trajectories. Notably, robot speed closely mirrors pedestrian behavior, highlighting its potential as a proxy for assessing pedestrian dynamics. The proposed framework enables continuous monitoring of sidewalk conditions and pedestrian behavior, contributing to the development of more walkable, inclusive, and responsive urban environments.

可步行性是可持续城市发展的关键组成部分。在可步行性研究中，由于传统方法的高成本和有限的可扩展性，收集详细的行人基础设施数据仍然具有挑战性。人行道送货机器人越来越多地部署在城市环境中，为这些限制提供了一个有希望的解决方案。本文探讨了这些机器人如何作为移动数据收集平台，以可扩展、自动化和实时的方式捕获与步行性相关的人行道级特征。配备传感器的机器人被部署在斯德哥尔摩KTH的人行道网络上，完成了101次行程，覆盖了900个路段记录。从收集到的数据中，可以得出不同类型的特征，包括机器人的行程特征（如速度、持续时间）、人行道条件（如宽度、表面不平度）和人行道利用率（如行人密度）。通过一系列分析调查了它们与步行相关的影响。结果表明，行人的运动模式受到人行道特征的强烈影响，人行道的密度增大、宽度减小、表面不平整与更慢、更可变的轨迹相关。值得注意的是，机器人的速度密切反映了行人的行为，突出了它作为评估行人动态的代理的潜力。拟议的框架能够持续监测人行道状况和行人行为，有助于发展更适合步行、包容和响应的城市环境。

{"title":"Leveraging sidewalk robots for walkability-related analyses","authors":"Xing Tong , Michele D. Simoni , Kaj Munhoz Arfvidsson , Jonas Mårtensson","doi":"10.1016/j.compenvurbsys.2025.102381","DOIUrl":"10.1016/j.compenvurbsys.2025.102381","url":null,"abstract":"<div><div>Walkability is a key component of sustainable urban development. In walkability studies, collecting detailed pedestrian infrastructure data remains challenging due to the high costs and limited scalability of traditional methods. Sidewalk delivery robots, increasingly deployed in urban environments, offer a promising solution to these limitations. This paper explores how these robots can serve as mobile data collection platforms, capturing sidewalk-level features related to walkability in a scalable, automated, and real-time manner. A sensor-equipped robot was deployed on a sidewalk network at KTH in Stockholm, completing 101 trips covering 900 segment records. From the collected data, different typologies of features are derived, including robot trip characteristics (e.g., speed, duration), sidewalk conditions (e.g., width, surface unevenness), and sidewalk utilization (e.g., pedestrian density). Their walkability-related implications were investigated with a series of analyses. The results demonstrate that pedestrian movement patterns are strongly influenced by sidewalk characteristics, with higher density, reduced width, and surface irregularity associated with slower and more variable trajectories. Notably, robot speed closely mirrors pedestrian behavior, highlighting its potential as a proxy for assessing pedestrian dynamics. The proposed framework enables continuous monitoring of sidewalk conditions and pedestrian behavior, contributing to the development of more walkable, inclusive, and responsive urban environments.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"124 ","pages":"Article 102381"},"PeriodicalIF":8.3,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624520","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}

引用次数: 0

Detecting uneven impacts of rental housing financialisation and platformisation on neighbourhoods in China: A multi-source data mining approach 中国租赁住房金融化和平台化对社区的不均衡影响：一种多源数据挖掘方法

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-15 DOI: 10.1016/j.compenvurbsys.2025.102369

Zixin Luo , Mengzhu Zhang , Dongwei Liu , Juan Li , Anthony G.O. Yeh

The rapid financialisation and platformisation of rental housing (FPR) have been examined as a major factor for neighbourhood changes worldwide after the 2010s. Studies have revealed the appreciation and disamenity effects with which the financialisation and platformisation reshape neighbourhoods, whereas the uneven impacts of the variegated FPR on neighbourhoods remain insufficiently understood. Therefore, this study fills this gap by the evidence from 6645 neighbourhoods in five Chinese cities to detect the uneven impacts of FPR on neighbourhoods and excavate those vulnerable communities. Leveraging geographically weighted regression, computer vision analysis on street view images and SOFM-based clustering analysis, this study reveals that (1) the appreciation effects widely spread across the cities, particularly in the new urban areas in Suzhou, the suburban areas in Shenzhen and the central city in Shanghai. Conversely, the disamenity effects are more severe in suburban areas in Shenzhen and Beijing than other cities due to the large number of urban villages and dilapidated buildings there; (2) type-C neighbourhoods featuring old low-rise buildings with local elderly populations is least affected by FPR (both appreciation and disamenity). By contrast, type-D neighbourhoods encompassing a large number of migrant workers in the suburbs are most susceptible to FPR. This study introduces the housing market segmentation theory to understand FPR'S effect on neighbourhood segregation and provides policy implications for niche and means to mitigate the negative externalities of FPR

租赁住房（FPR）的快速金融化和平台化已被视为2010年代后全球社区变化的主要因素。研究揭示了金融化和平台化重塑社区的升值和破坏效应，而多样化的FPR对社区的不平衡影响仍未得到充分理解。因此，本研究通过中国5个城市6645个社区的证据来填补这一空白，发现FPR对社区的不均衡影响，挖掘弱势社区。利用地理加权回归、街景图像的计算机视觉分析和基于sofm的聚类分析，研究发现(1)升值效应在城市间广泛分布，特别是在苏州新城区、深圳近郊和上海中心城区。相反，深圳和北京的城郊由于城中村和残破建筑较多，其残废效应较其他城市更为严重；(2)以老旧低层建筑为主的c类社区受FPR的影响最小（无论是欣赏还是厌恶）。相比之下，包含大量郊区外来务工人员的d型社区最容易发生FPR。本研究引入住房市场分割理论来理解住房市场分割对邻里隔离的影响，并提供政策启示和减轻住房市场分割的负外部性的方法

{"title":"Detecting uneven impacts of rental housing financialisation and platformisation on neighbourhoods in China: A multi-source data mining approach","authors":"Zixin Luo , Mengzhu Zhang , Dongwei Liu , Juan Li , Anthony G.O. Yeh","doi":"10.1016/j.compenvurbsys.2025.102369","DOIUrl":"10.1016/j.compenvurbsys.2025.102369","url":null,"abstract":"<div><div>The rapid financialisation and platformisation of rental housing (FPR) have been examined as a major factor for neighbourhood changes worldwide after the 2010s. Studies have revealed the appreciation and disamenity effects with which the financialisation and platformisation reshape neighbourhoods, whereas the uneven impacts of the variegated FPR on neighbourhoods remain insufficiently understood. Therefore, this study fills this gap by the evidence from 6645 neighbourhoods in five Chinese cities to detect the uneven impacts of FPR on neighbourhoods and excavate those vulnerable communities. Leveraging geographically weighted regression, computer vision analysis on street view images and SOFM-based clustering analysis, this study reveals that (1) the appreciation effects widely spread across the cities, particularly in the new urban areas in Suzhou, the suburban areas in Shenzhen and the central city in Shanghai. Conversely, the disamenity effects are more severe in suburban areas in Shenzhen and Beijing than other cities due to the large number of urban villages and dilapidated buildings there; (2) type-C neighbourhoods featuring old low-rise buildings with local elderly populations is least affected by FPR (both appreciation and disamenity). By contrast, type-D neighbourhoods encompassing a large number of migrant workers in the suburbs are most susceptible to FPR. This study introduces the housing market segmentation theory to understand FPR'S effect on neighbourhood segregation and provides policy implications for niche and means to mitigate the negative externalities of FPR</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"124 ","pages":"Article 102369"},"PeriodicalIF":8.3,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528120","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}

引用次数: 0

City identity recognition: how representation bias influences model predictability and replicability? 城市身份识别：表征偏差如何影响模型的可预测性和可复制性？

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-11 DOI: 10.1016/j.compenvurbsys.2025.102370

Xiang Zhang , Fan Yang , Zongze He , Weijia Li , Min Yang

Street-view images (SVIs) and social media photos have been widely used with deep learning in urban perception (e.g., predicting socioeconomic variables, recognizing city identities, and geolocalization). However, different data sources and sampling schemes are arbitrarily used in existing studies, and the induced representation bias that can drastically alter model predictions has long been overlooked. To bridge this gap, we systematically evaluate how different biases influence model predictability and replicability using city identity recognition (CIR), a task to understand the unique characters of cities and to recognize their identities from photos. The task was implemented by firstly extracting features with a model pretrained on a general scene recognition task, and secondly training city classifiers with the extracted features. We answer the research question by carefully designed experiments, where factors like intrinsic similarity between cities, data sources, camera perspectives, and spatial sampling are examined. In general, we show that recognizing cities from photos was not equally replicable across the world: intra-country CIR was more challenging than inter-country CIR. Contrast to common wisdom, the claimed advantages of social media photos in indoor and social scenarios were not effective on CIR and were largely surpassed by SVIs. For SVIs, perspectives along streets surprisingly better capture the uniqueness of a city than looking at building facades; biased spatial sampling can even overturn model predictions (e.g., rankings of city uniqueness). Finally, we explore the data diversity by projecting the learned representations into a low-dimensional semantic space, and critically discuss our results and implications.

街景图像（SVIs）和社交媒体照片已被广泛用于城市感知的深度学习（例如，预测社会经济变量，识别城市身份和地理定位）。然而，在现有的研究中，不同的数据来源和抽样方案被任意使用，而诱导的表征偏差可能会极大地改变模型预测，长期以来一直被忽视。为了弥补这一差距，我们使用城市身份识别（CIR）系统地评估了不同的偏见如何影响模型的可预测性和可复制性，CIR是一项了解城市独特特征并从照片中识别城市身份的任务。该任务首先通过在一般场景识别任务上预训练的模型提取特征，然后利用提取的特征训练城市分类器来实现。我们通过精心设计的实验来回答研究问题，其中包括城市之间的内在相似性、数据源、相机视角和空间采样等因素。总的来说，我们发现从照片中识别城市在世界范围内的可复制性并不相同：国家内部的CIR比国家之间的CIR更具挑战性。与普遍观点相反，社交媒体照片在室内和社交场景中的优势在CIR上并不有效，并且在很大程度上被svi所超越。对于svi来说，沿着街道的视角比看建筑立面更能捕捉城市的独特性；有偏差的空间抽样甚至可以推翻模型预测（例如，城市独特性排名）。最后，我们通过将学习到的表示投射到低维语义空间来探索数据多样性，并批判性地讨论我们的结果和意义。

{"title":"City identity recognition: how representation bias influences model predictability and replicability?","authors":"Xiang Zhang , Fan Yang , Zongze He , Weijia Li , Min Yang","doi":"10.1016/j.compenvurbsys.2025.102370","DOIUrl":"10.1016/j.compenvurbsys.2025.102370","url":null,"abstract":"<div><div>Street-view images (SVIs) and social media photos have been widely used with deep learning in urban perception (e.g., predicting socioeconomic variables, recognizing city identities, and geolocalization). However, different data sources and sampling schemes are arbitrarily used in existing studies, and the induced representation bias that can drastically alter model predictions has long been overlooked. To bridge this gap, we systematically evaluate how different biases influence model predictability and replicability using city identity recognition (CIR), a task to understand the unique characters of cities and to recognize their identities from photos. The task was implemented by firstly extracting features with a model pretrained on a general scene recognition task, and secondly training city classifiers with the extracted features. We answer the research question by carefully designed experiments, where factors like intrinsic similarity between cities, data sources, camera perspectives, and spatial sampling are examined. In general, we show that recognizing cities from photos was not equally replicable across the world: intra-country CIR was more challenging than inter-country CIR. Contrast to common wisdom, the claimed advantages of social media photos in indoor and social scenarios were not effective on CIR and were largely surpassed by SVIs. For SVIs, perspectives along streets surprisingly better capture the uniqueness of a city than looking at building facades; biased spatial sampling can even overturn model predictions (e.g., rankings of city uniqueness). Finally, we explore the data diversity by projecting the learned representations into a low-dimensional semantic space, and critically discuss our results and implications.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"123 ","pages":"Article 102370"},"PeriodicalIF":8.3,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520364","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}

引用次数: 0

Exploring climate-induced migration impacts on urban growth 探讨气候导致的移民对城市增长的影响

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-07 DOI: 10.1016/j.compenvurbsys.2025.102368

Thomas Estabrook , Derek Van Berkel , Jenna Jorns , Maria Carmen Lemos

As climate change intensifies, cities may experience stressors like extreme heat and flooding, as well as possibly human in-migration from highly exposed regions facing more extreme climate stressors. This study explores the potential impacts of climate-induced migration on urban development in Grand Rapids, Michigan, and Colorado Springs, Colorado. Using the FUTURES land-change model, we project urban expansion from 2016 to 2045, comparing different policy and planning scenarios to the status quo. These scenarios include urban infill, avoidance of location-specific climate stressors (e.g., fire- and flood-prone areas), and climate migration projections. Our results reveal that while promoting infill development can reduce the loss of green spaces and agricultural land, it has diverging effects on exposure to these climate risks. In Grand Rapids, infill policies led to greater urban expansion in flood-prone areas, while in Colorado Springs, similar policies mitigated development in wildfire-prone zones. Scenarios of climate migration due to sea-level rise suggest minimal urban expansion from migration influxes, largely due to current policies favoring densification in these cities. Uncertainty surrounding climate migration suggests a need to improve population projections by incorporating a broader range of climate stressors (e.g., flooding, wildfires, drought) that are likely to drive resettlement across the United States. Our findings suggest that receiving cities require information on potential impacts to balance the opportunities of climate migration while mitigating related environmental vulnerabilities. Decision support tools like land-use models can be useful for preparing cities to address future demographic and environmental challenges.

随着气候变化的加剧，城市可能会经历极端高温和洪水等压力因素，以及人类从面临更极端气候压力的高度暴露地区迁入。本研究探讨了气候引起的移民对密歇根州大急流城和科罗拉多州科罗拉多斯普林斯城市发展的潜在影响。利用未来土地变化模型，我们预测了2016年至2045年的城市扩张，并将不同的政策和规划情景与现状进行了比较。这些情景包括城市填充、避免特定地点的气候压力源（例如，火灾和洪水易发地区）以及气候迁移预测。研究结果表明，虽然促进填充物开发可以减少绿地和农业用地的损失，但它对这些气候风险的暴露程度存在差异。在大急流城，填埋政策导致洪水易发地区的城市扩张，而在科罗拉多斯普林斯，类似的政策缓解了野火易发地区的发展。海平面上升导致的气候移民情景表明，移民流入导致的城市扩张最小，这主要是由于当前的政策有利于这些城市的密集化。围绕气候移民的不确定性表明，有必要通过纳入更广泛的气候压力因素（例如，洪水、野火、干旱）来改进人口预测，这些因素可能会推动美国各地的重新安置。我们的研究结果表明，接收城市需要有关潜在影响的信息，以平衡气候移民的机会，同时减轻相关的环境脆弱性。土地利用模型等决策支持工具可以帮助城市做好应对未来人口和环境挑战的准备。

{"title":"Exploring climate-induced migration impacts on urban growth","authors":"Thomas Estabrook , Derek Van Berkel , Jenna Jorns , Maria Carmen Lemos","doi":"10.1016/j.compenvurbsys.2025.102368","DOIUrl":"10.1016/j.compenvurbsys.2025.102368","url":null,"abstract":"<div><div>As climate change intensifies, cities may experience stressors like extreme heat and flooding, as well as possibly human in-migration from highly exposed regions facing more extreme climate stressors. This study explores the potential impacts of climate-induced migration on urban development in Grand Rapids, Michigan, and Colorado Springs, Colorado. Using the FUTURES land-change model, we project urban expansion from 2016 to 2045, comparing different policy and planning scenarios to the status quo. These scenarios include urban infill, avoidance of location-specific climate stressors (e.g., fire- and flood-prone areas), and climate migration projections. Our results reveal that while promoting infill development can reduce the loss of green spaces and agricultural land, it has diverging effects on exposure to these climate risks. In Grand Rapids, infill policies led to greater urban expansion in flood-prone areas, while in Colorado Springs, similar policies mitigated development in wildfire-prone zones. Scenarios of climate migration due to sea-level rise suggest minimal urban expansion from migration influxes, largely due to current policies favoring densification in these cities. Uncertainty surrounding climate migration suggests a need to improve population projections by incorporating a broader range of climate stressors (e.g., flooding, wildfires, drought) that are likely to drive resettlement across the United States. Our findings suggest that receiving cities require information on potential impacts to balance the opportunities of climate migration while mitigating related environmental vulnerabilities. Decision support tools like land-use models can be useful for preparing cities to address future demographic and environmental challenges.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"123 ","pages":"Article 102368"},"PeriodicalIF":8.3,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466734","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}

引用次数: 0

VoxCity: A seamless framework for open geospatial data integration, grid-based semantic 3D city model generation, and urban environment simulation VoxCity：一个用于开放地理空间数据集成、基于网格的语义三维城市模型生成和城市环境模拟的无缝框架

IF 8.3 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-11-06 DOI: 10.1016/j.compenvurbsys.2025.102366

Kunihiko Fujiwara , Ryuta Tsurumi , Tomoki Kiyono , Zicheng Fan , Xiucheng Liang , Binyu Lei , Winston Yap , Koichi Ito , Filip Biljecki

Three-dimensional urban environment simulation is a powerful tool for informed urban planning. However, the intensive manual effort required to prepare input 3D city models has hindered its widespread adoption. To address this challenge, we present VoxCity, an open-source Python package that provides a one-stop solution for grid-based 3D city model generation and urban environment simulation for cities worldwide. VoxCity’s ‘generator’ subpackage automatically downloads building heights, tree canopy heights, land cover, and terrain elevation within a specified target area, and voxelizes buildings, trees, land cover, and terrain to generate an integrated voxel city model. The ‘simulator’ subpackage enables users to conduct environmental simulations, including solar radiation and view index analyses. Users can export the generated models using several file formats compatible with external software, such as ENVI-met (INX), Blender, and Rhino (OBJ). We generated 3D city models for eight global cities, and demonstrated the calculation of solar irradiance, sky view index, and green view index. We also showcased microclimate simulation and 3D rendering visualization through ENVI-met and Rhino, respectively, through the file export function. Additionally, we reviewed openly available geospatial data to create guidelines to help users choose appropriate data sources depending on their target areas and purposes. VoxCity can significantly reduce the effort and time required for 3D city model preparation and promote the utilization of urban environment simulations. This contributes to more informed urban and architectural design that considers environmental impacts, and in turn, fosters sustainable and livable cities. VoxCity is released openly at https://github.com/kunifujiwara/VoxCity.

三维城市环境模拟是明智的城市规划的有力工具。然而，准备输入3D城市模型所需的大量手工工作阻碍了其广泛采用。为了应对这一挑战，我们提出了VoxCity，这是一个开源的Python包，为全球城市提供基于网格的3D城市模型生成和城市环境模拟的一站式解决方案。VoxCity的“生成器”子包自动下载指定目标区域内的建筑高度、树冠高度、土地覆盖和地形高度，并将建筑物、树木、土地覆盖和地形体素化，生成一个集成的体素城市模型。“模拟器”子包使用户能够进行环境模拟，包括太阳辐射和视图索引分析。用户可以使用几种与外部软件兼容的文件格式导出生成的模型，例如ENVI-met (INX)， Blender和Rhino （OBJ）。我们生成了全球8个城市的三维城市模型，并演示了太阳辐照度、天空视图指数和绿色视图指数的计算。我们还通过文件导出功能分别展示了通过ENVI-met和Rhino实现的小气候模拟和3D渲染可视化。此外，我们审查了公开可用的地理空间数据，以创建指导方针，帮助用户根据其目标区域和目的选择适当的数据源。VoxCity可以显著减少3D城市模型准备的工作量和时间，促进城市环境模拟的利用。这有助于更明智地考虑环境影响的城市和建筑设计，从而促进可持续和宜居的城市。VoxCity在https://github.com/kunifujiwara/VoxCity公开发布。

{"title":"VoxCity: A seamless framework for open geospatial data integration, grid-based semantic 3D city model generation, and urban environment simulation","authors":"Kunihiko Fujiwara , Ryuta Tsurumi , Tomoki Kiyono , Zicheng Fan , Xiucheng Liang , Binyu Lei , Winston Yap , Koichi Ito , Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102366","DOIUrl":"10.1016/j.compenvurbsys.2025.102366","url":null,"abstract":"<div><div>Three-dimensional urban environment simulation is a powerful tool for informed urban planning. However, the intensive manual effort required to prepare input 3D city models has hindered its widespread adoption. To address this challenge, we present VoxCity, an open-source Python package that provides a one-stop solution for grid-based 3D city model generation and urban environment simulation for cities worldwide. VoxCity’s ‘generator’ subpackage automatically downloads building heights, tree canopy heights, land cover, and terrain elevation within a specified target area, and voxelizes buildings, trees, land cover, and terrain to generate an integrated voxel city model. The ‘simulator’ subpackage enables users to conduct environmental simulations, including solar radiation and view index analyses. Users can export the generated models using several file formats compatible with external software, such as ENVI-met (INX), Blender, and Rhino (OBJ). We generated 3D city models for eight global cities, and demonstrated the calculation of solar irradiance, sky view index, and green view index. We also showcased microclimate simulation and 3D rendering visualization through ENVI-met and Rhino, respectively, through the file export function. Additionally, we reviewed openly available geospatial data to create guidelines to help users choose appropriate data sources depending on their target areas and purposes. VoxCity can significantly reduce the effort and time required for 3D city model preparation and promote the utilization of urban environment simulations. This contributes to more informed urban and architectural design that considers environmental impacts, and in turn, fosters sustainable and livable cities. VoxCity is released openly at <span><span>https://github.com/kunifujiwara/VoxCity</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"123 ","pages":"Article 102366"},"PeriodicalIF":8.3,"publicationDate":"2025-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466752","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}

引用次数: 0