Environment and Planning B: Urban Analytics and City Science

Kleiner, Caspar; Jehling, Mathias

doi: 10.1177/23998083261454751pmid: N/A

Urban form is a key concern for sustainable urban development and climate change adaptation. Therefore, it has become a focus for policymakers and planners in the past decades. While many national governments implement land policies to pursue specific objectives, the actual outcomes in built urban form often remain scarcely evaluated, thus challenging the effectiveness and legitimacy of these policies in pursuit of urban sustainability objectives. In this contribution, we therefore evaluate anticipated outcomes of land policies by quantifying the emerging built urban form under these policies through a covariational approach. The French-German border-region serves as a study area due to its economic and demographic homogeneity, while fundamentally different land policies are pursued. A fine-grained geospatial data model representing the evolution of urban form is set up containing harmonised building, parcel and street network data. Based on this, building types, development blocks, ages and relative location through accessibility are delineated. The model is used to trace anticipated outcomes of key policy changes in the emerging urban form of the two countries. This assessment shows significant variance in measurable outcomes of specific land policies of the two countries, in time and by policy that can be linked to different initiatives of implementing policies in Germany and France, prompting a stronger debate of how land policies generate effects.

Cao, Qi; Tourre, Vincent; Leduc, Thomas; Servières, Myriam

doi: 10.1177/23998083251413000pmid: N/A

Urban walkability is central to sustainable urban development and, at the city scale, is increasingly viewed as shaped by both functional accessibility and pedestrian environmental comfort (PEC). However, most citywide assessments remain predominantly function-oriented, and few provide explicit behavioral validation using observed walking patterns. This study addresses this gap by proposing a spatially explicit, dual-dimensional framework that treats functional accessibility and PEC as co-equal components of walkability. Using Nantes, France, as a case study, we implement the framework on a uniform 150 m × 150 m grid and derive three composite indices—Traditional Walkability Score (TWS), a PEC index, and a PEC-enhanced Walkability Score (PEWS)—from GIS indicators standardized and combined using entropy-based weighting. Our analysis reveals a systematic spatial mismatch between functional and environmental dimensions: TWS concentrates in amenity-rich and well-connected areas, whereas PEC is highest along blue–green corridors. The integrated PEWS induces a targeted spatial re-ranking, deprioritizing functionally rich but environmentally harsh central areas while upgrading nature-rich peripheral zones. We then behaviorally validate the framework using observed pedestrian counts and gridded resident population data from INSEE, operationalized as a deviation metric comparing observed flows with population-based expectations. Blue–green features (trees, rivers, and green spaces) are associated with higher-than-expected pedestrian activity, whereas proximity to major motorized roads is associated with lower-than-expected activity. These findings support PEC as a necessary component of city-scale walkability assessment and provide actionable guidance for planning interventions.

Zhang, Mengting; Hsieh, Chun-Ming; Ni, Yezhao; Wang, Xiaotong; Shen, Yuchi

doi: 10.1177/23998083261424460pmid: N/A

Online User-Generated Content (UGC) offers valuable insights into the urban vitality of specific districts, with travelogues revealing tourists’ overall preferences and informing analyses of urban functionality. However, translating large-scale textual data into geographically anchored, preference-based information remains challenging due to the abstract and often weak spatial link in narrative descriptions. This study proposes a UGC-driven Agent-Based Modeling (ABM) workflow that integrates geocoded location data and visitation preference analysis into agents’ decision-making processes, enabling a bottom-up simulation of tourist behaviors derived from textual sources. Using the Pingjiang Historical and Cultural District as a case study, 16,053 travelogue entries (2009–2023) were analyzed, encoded, and simulated across four control groups. Results show that the UGC-driven ABM effectively reproduces movement patterns and vitality distributions, with traffic-related outputs aligning closely with Space Syntax analyses and POI-based heatmaps reflecting tourist preferences. The findings demonstrate that this approach provides a practical and scalable method for extracting and spatializing behavioral insights from textual data, offering applications in urban planning and tourism management.

López-García, David; Hernandez, Diego; Sánchez-Vargas, Armando

doi: 10.1177/23998083261428169pmid: N/A

This study examines the determinants of commuting time and distance using the mobility situations framework in the Mexico City Metropolitan Zone (MCMZ), a megacity marked by spatial mismatch, socioeconomic segregation, and fragmented transport infrastructure. Using data from the 2017 Origin-Destination Survey, we classify 192 travel districts into four mobility situations—Short Commutes, Long Commutes, Travelscarps, and Wormholes—based on average commuting time and distance. Our approach combines spatial econometrics with a machine learning LASSO algorithm to evaluate 88 potential predictors across transport infrastructure, urban spatial structure, and socioeconomic conditions. Results show that each situation is driven by distinct factors: Short Commutes align with centrality and privilege; Long Commutes with exclusion and mass transit; Travelscarps with inefficient short trips from poor infrastructure; and Wormholes with efficient long trips through multimodal strategies. The study demonstrates the value of the mobility situations framework in a Global South city and highlights machine learning’s utility for variable selection and theory-building in journey-to-work research.

Kumar, Vaibhav; Lohani, Bharat; Lal, Pyare; Bais, Aakash Singh; Aditya, Aditya

doi: 10.1177/23998083261430812pmid: N/A

Labeled Mobile Laser Scanning (MLS) data are increasingly in demand for applications such as urban planning and autonomous driving. However, there is a severe shortage of manually labeled, high-fidelity MLS datasets, especially from developing countries like India. Deep learning models trained on less complex environments often fail to generalize to Indian scenarios, hindering the development of robust applications. To address this gap, we introduce Ke-MLS, the largest publicly available, expert-labeled MLS LiDAR dataset from Kerala, India. The dataset comprises 48 classes and supports the training of a variety of deep learning models for multiple applications. We evaluate the performance of state-of-the-art 3D deep learning algorithms on Ke-MLS, achieving mean Intersection-Over-Union (mIoU) scores in the range of 70–80%. The dataset is publicly available at https://lidaverse.com/.

Arribas-Bel, Daniel; Franklin, Rachel

doi: 10.1177/23998083261456945pmid: N/A

Li, Ling; Yu, Shujin

doi: 10.1177/23998083261453981pmid: N/A

Displacement constitutes the primary channel through which urban renewal influences both individuals and the city at large, reshaping the lived experiences of affected residents while extending renewal impacts far beyond project boundaries. Yet, our understanding of displacement remains limited by the scarcity of spatially disaggregated migration data that can reveal its micro-level dynamics. To address this gap, this study draws on longitudinal, anonymized mobile phone data that capture residents’ continuous residential movements. Using this dataset, we examine renewal-induced displacement across multiple urban renewal projects in Shenzhen, China, with a particular focus on its spatial configurations and characteristics of receiving neighborhoods. We find that Shenzhen’s urban renewal produces a spatially bounded and directionally patterned displacement process. Most displaced residents relocate to areas near renewal sites, clustered at the intersections of multiple project zones. They tend to move to more affordable neighborhoods with better access to essential public services, while inward migrations do not lead to higher housing costs in the receiving neighborhoods. These displacement patterns are especially evident in projects involving urban villages that house many low-income tenants. Based on the micro-geographies of displacement, urban renewal may reproduce localized social inequalities by spatially re-concentrating vulnerable groups around renewal sites.

Garau, Chiara; Frankhauser, Pierre

doi: 10.1177/23998083261460673pmid: N/A

Metropolitan regions frequently exhibit hierarchical imbalances between central cores and peripheral subcentres, resulting in uneven accessibility, and service concentration. This paper presents Fractalopolis as a multi-scale scenario-modelling framework that encodes metropolitan hierarchy through the integration of fractal geometry, central-place logic, demographic allocation, and accessibility-based satisfaction indicators within a GIS environment. Applied to the Metropolitan City of Cagliari (Italy), a coastal-insular system characterised by strong edge effects and statutory environmental constraints, the model reproduces existing hierarchical gradients and evaluates alternative development scenarios through controlled amenity redistribution and residential additions. Results show that service-led reinforcement of peripheral nodes produces disproportionate improvements in suitability, whereas densification without proportional service upgrading yields limited gains. By embedding regulatory and ecological masking within hierarchical modelling, the study advances Fractalopolis toward a regulation-aware, transferable decision-support methodology for environmentally constrained metropolitan contexts.

Mato, Monique; Trudelle, Catherine; Claramunt, Christophe; Libner, Eliott

doi: 10.1177/23998083261422089pmid: N/A

Urban housing conflicts are increasingly shaping the social and spatial dynamics of cities, yet they remain difficult to analyze systematically due to their multi-actor complexity and fragmented representation across textual and spatial data. Existing studies tend to focus on either narrative or spatial aspects, rarely capturing the structural, temporal, and geographic dimensions of these conflicts in an integrated way. This paper addresses this gap by proposing a reproducible methodological framework that combines Large Language Models (LLMs), graph databases, and Geographic Information Systems (GIS) to analyze housing conflicts in Montréal between 2001 and 2024. The study aims to demonstrate how urban housing conflicts can be systematically extracted, classified, and analyzed across time and space using AI-based methods, and how their structural patterns reflect underlying socio-political dynamics. The resulting framework offers new insights into the evolution of conflicts linked to gentrification, economic vulnerability, and shifting governance, while contributing a replicable, scalable methodology for studying complex urban phenomena at the intersection of AI, spatial analysis, and social science.

Bai, Zhaocheng; Ji, Rui; Gao, Feng; Mao, Yuheng; Liu, Song

doi: 10.1177/23998083261420306pmid: N/A

Street greenery, as critical green infrastructure, provides extensive ecosystem services, and accurately evaluating its value benefits urban greening strategies and funding decisions. Previous studies relying on cross-sectional data and hedonic regression models are limited by confounding factors, thus failing to accurately measure the net effects of street greenery characteristics on housing prices. To address this limitation, hedonic regression, propensity score matching and difference-in-differences methodologies were employed based on housing price and built environment data from 2017 to 2022 to analyze how street greenery quantity and vegetation structure affect housing prices. Results indicated that street greenery impacts differ significantly between the central area and the expanded central area, revealing a negative price effect defined as the “Green Burden Effect” in expanded central area. Additionally, greenery quantity changes exerted stronger and more stable price impacts compared to vegetation structure. The DID analysis further clarified temporal changes in these relationships. Employing multiple econometric models revealed the complex spatial-temporal relationships between street greenery and housing prices, emphasizing the importance of spatially targeted greening strategies to optimize residential values and promote equitable urban development.