Environment and Planning B: Urban Analytics and City Science

We present gridmappr, an R package that automates the process of generating gridmaps – small multiple data graphics of regular size, laid out with an approximate geographic arrangement. Given a set of real geographic point locations, gridmappr allocates points to a regularly sized grid of stated row–column dimensions. This allocation is constrained such that the distance between points in real and grid space is minimised and with a parameter that affects how compactly points are allocated to the regular grid. For geographies with features such as large bodies of water, fixed spacers can be introduced – reserved cells that cannot be allocated points. Layout examples are presented using different parameterisations, and code for generating a family of information-rich glyphmap and origin-destination maps is demonstrated using standard ggplot2.

This paper describes a straightforward method for calculating an open-source Walkable Accessibility Score (WAS) that measures walkability at the block group scale based on walking distance to business establishments, schools, and parks. Exploratory analysis of the WAS reveals high concentrations of walkable accessibility in the centres of the densest and/or largest cities. Our optimised specification (decay = 0.008, upper = 800, k = 30) performs very well, achieving a Spearman rank correlation of 0.912 with proprietary Walk Score® values (for 2011). We provided pre-calculated data for each year from 1997 to 2019 and Python code for calculating the WAS at the project’s GitHub repository. The method is particularly useful in that it uses simple Euclidean distance calculations, and thus can be run at scale on a laptop or personal computer.

The QGIS ‘Polygon Divider’ plugin solves the problem of partitioning an arbitrarily complex polygon into an irregular grid of equal area rectangles, which has a range of applications for city science and GIS more broadly. This is achieved by the iterative partition of the polygon using cutlines that are located using Brent’s method, which is an efficient optimisation algorithm. At the time of release, this was the only tool with such functionality in a major GIS platform, though the functionality has since been replicated.

Evaluating accessibility based on multiple notions of justice allows for a multi-perspective analysis of the trade-offs between the benefits and burdens associated with the provision of infrastructure. This presents a challenge due to a lack of metrics which operationalise multiple notions of justice for comparative purposes. It is further complicated by the reliance on General Transit Feed Specification (GTFS) data to do many kinds of accessibility analyses, which is often not freely available and accessible, especially in data scarce regions. This paper presents the MAP open-source software package that allows for the incorporation of multiple notions of justice in accessibility analysis. Firstly, MAP supports the development of an Urban Network Model based on open-access data. Secondly, using this model it enables the calculation of Neighbourhood Reach Centrality, a cumulative accessibility metric. Finally, it allows for the evaluation of accessibility based on three comparative metrics of spatial justice visualised through maps. For illustrative purposes, data sets from the City of Cape Town in South Africa are provided as a ready-to-use data-product. This software package offers an efficient method for incorporating spatial justice considerations into accessibility analysis offering the potential to be used as a boundary object within interdisciplinary teams of researchers, policy-analysts, transport engineers, and other stakeholders.

Route network datasets are fundamental to transport models, serving as both inputs for analysis and outputs for visualization and decision-making. The increasing complexity of route network data from sources like OpenStreetMap allows for more detailed modelling of sustainable transport modes such as walking and cycling. However, this level of detail can introduce challenges for the clear visualization and interpretation of model results. A common problem is the representation of single transport corridors by multiple parallel lines, which can create visual clutter and obscure important patterns in transport flows. The purpose of the work presented in this paper is to provide a basis for computationally efficient analysis and visualization of route networks for strategic transport planning, where intricate geometries, such as parallel or ‘braided’ linestrings, are unhelpful. We present and evaluate two distinct methods for simplifying complex route networks that are intended to be used as a ‘pre-processing’ step to speed up and improve the results of strategic transport network analysis, modelling, and visualization workflows. First, we present skeletonization, an approach that uses ‘thinning’ of rasterized network data to extract a simplified representation of the network. Second, we present a Voronoi-based approach using Voronoi diagrams to identify centrelines. We demonstrate the practical application of these methods using the ‘Simplified network’ layer in the Transport for Scotland-funded Network Planning Tool, a publicly accessible resource at https://www.npt.scot. To support reproducible research, we implement the methods in the open-source parenx Python package, enabling their use alongside other open source tools for transport planning, research, and educational applications.

Carbon & Place (https://www.carbon.place) is an ongoing research project to produce a free family of web tools intended to explain the spatial variation in per-capita carbon footprints across Great Britain and how they can be reduced. The tools present results via interactive maps using GIS data, small area statistics, surveys, and models to aid planners, policymakers, and communities in understanding their climate impact. Local people can benefit from disaggregated analysis as it can be more personally relevant and account for local circumstances and needs. This paper provides an overview of the project, its open-source website, and analysis pipeline, as well as reporting on its progress to date and future work.

Urban development model is transitioning from disorderly sprawl to compact growth. In this process, urban growth boundary (UGB) is important for preventing excessive spatial expansion and optimizing land use structure. However, few existing studies have focused on delineation strategies that integrate both rigid and elastic UGBs. Taking Zhengzhou as a case study, we developed a framework for delineating rigid and elastic UGBs involving identification of basic farmland and ecological protection zone, evaluation of land suitability, and multi-scenario simulation of urban development. The results showed that urban space increased significantly by 210.56 km² from 2000 to 2020, which posed risks of imbalanced land use structure. Identified basic farmland and ecological protection zone covered 41.99 km² and 57.68 km², respectively. Their scope was prohibited for urban construction and was used as guidance to delineate rigid UGB, which covered 712.21 km². Sustainable development scenario was considered as dominant paradigm for urban development. Therefore, it was used as guidance to delineate elastic UGB, which covered 595.55 km². These findings confirm the effectiveness of a delineation strategy that combines rigid and elastic UGBs in maintaining ecological security and constraining spatial sprawl. Additionally, technical references for delineating UGB are provided for cities facing compact growth demands.

Arrivals and departures lie at the intersection of travel and building occupancy behaviours which dominate the landscape of energy demand in urban areas. Although transport and building systems are clearly linked, existing studies rarely consider the interactions between these systems in their modelling frameworks, thus restricting the policy-relevant scenarios that can be tested. This paper contributes to the field of data-driven energy modelling by proposing a flexible framework to integrate the modelling of travel and building occupancy behaviours, in which a travel simulator is coupled with a building occupancy model through a proposed mesoscopic link. The framework is operationalised in the context of the South Kensington Campus, Imperial College London, using the UK Time Use Survey data and Wi-Fi traceable logs. Implementing the framework for a hypothetical transport incident (i.e. sudden closure of the nearest underground station) generates people’s occupancy and circulation patterns across buildings, thus providing actionable insights for district-level smart grid planning and management. From a district planning perspective, occupancy schedules and dynamics in closed buildings are sensitive to incidents, whereas open and shared buildings are relatively stable. This finding indicates the need for flexible energy controls and smart grids with energy storage. From a building management perspective, occupancy durations generally reduce when affected by incidents, suggesting shortening the schedules of heating, ventilation and air-conditioning systems. From a facility management perspective, big changes in occupancy of closed buildings indicate unstable demands for the surrounding equipment (e.g. e-scooters, chargers), and efficiencies may be gained by allocating spaces/schedules to meet the dynamic demand.

Urban fabric preservation is a crucial objective in urban conservation. Identifying fabric types is essential for protecting and maintaining urban fabric. However, precise methods for identifying fabric types are lacking. This study expands the concept of fabric from the similarity of individual buildings to the similarity of buildings and the external spaces between them, as well as from a two-dimensional relationship to a three-dimensional perspective. A K-means clustering method, which uses building footprint area, building height, and exterior building space area as primary indicators, is proposed for urban fabric identification. The manifestation patterns of urban fabric in various areas of Shanghai are examined as a case study. Results shows: first, the identification method proposed in this study has a good identification effect, with an identification rate of 77%. Second, four types of urban fabrics constitute the main urban fabric of Shanghai. Third, the implementation of policies such as the delineation of historic and culture areas has played a certain role in the protection of historical fabric, but the historical fabric of unprotected areas is disappearing rapidly, better conservation policies are urgently needed. This study develops fabric identification method and analyses the fabric of Shanghai’s urban area, providing valuable insights for research in urban morphology theories and methods, as well as urban preservation and revitalisation practices.

Understanding the impact of building morphology on building energy consumption is crucial for policymakers and urban planners to develop effective strategies for energy efficiency and sustainable development. This study develops an analysis framework based on geospatial data and spatial regression models to analyze the impact of building morphology on urban building energy consumption. To ascertain the efficacy of the proposed framework, a case study was conducted in the city of Beijing. The results reveal spatial variations in building energy consumption at the high-resolution level, with higher levels observed in the central urban areas that gradually decrease towards the outskirts. The six indicators of building morphology highlight notable variations in urban form characteristics across regions. In addition, the spatial regression analysis indicates that footprint area, envelope area, and floor area ratio show a substantial influence on the energy consumption of buildings. Finally, the policy recommendations are presented for the mitigation of building energy consumption.

Large cities have benefits offset by costs. Since the inter-urban Rosen–Roback model, economists interpret the attractiveness of a city in terms of spatial equilibrium: a cost-benefit balance across locations. High incomes are counterbalanced by high prices or disamenities. A cross-sectional regression analysis on the ∼ 8000 Italian municipalities whose population sizes range from around 30 to 3 million, found the following association: by doubling the population of a settlement, holding constant its per capita income and touristic presences, its house price rises by 9%. House price increases are not offset by an increase in income. Bigger cities are more expensive than smaller. This opens reasoning upon indirect economic valuations of the value - or cost if you see it differently - (opportunities, services, vibrancy…) that urban size has for people who choose - or must choose - bigger settlements. At the best of my knowledge, this is the first work, in this form, finding a scaling coefficient for house price. It resulted around 0.13, namely, by passing from a town of 10˙000 inhabitants to a city of 1 million inhabitants (even if holding constant tourism and income), the average house price would almost double (+82%), almost tripled (+183%) from a village of 1˙000 inhabitants to a 3 million inhabitants’ city, and almost quadrupled (+282%) from a hamlet of 100 inhabitants to a 3 million inhabitants city.

Environmental characteristics affect how individuals perceive an environment, and the emotions created by environmental characteristics originate from subjective feelings. Despite cities being crucial human living spaces, few studies have used geospatial technology to determine the relationship between urban environments and emotions. Therefore, this study explored the effects of urban environmental characteristics on emotions by surveying 50 sampling areas in Taipei City. Deep learning was performed with the DeepLab V3 architecture in combination with the LaDeco tool to identify environmental characteristics in over 200,000 Google Street View (GSV) images. These characteristics were divided into five major types, namely, vegetationscapes, waterscapes, streetscapes, landformscapes, and archiscapes, then further classified into 53 categories. To identify the emotions related to urban environments, 2090 participants who were asked to view GSV videos and report their emotions. Subsequent multiple regression analyses revealed that in vegetationscapes and waterscapes, grass and fountains induced positive emotions, whereas trees reduced negative emotions. Meanwhile, dense, old, and disorganized buildings, such as hovels, reduced positive emotions. The results of this study may serve as a reference to help designers create an urban environment that fosters positive emotions.

As big data becomes increasingly integral to urban studies, the evolving profile of its applications, including the trends in data, methodologies, and contributions to urban theories, remains unclear. This study systematically reviewed literature from 57 representative journals in the fields of Urban Studies, Geography, and Environmental Studies (JCR Q1) over the past decade (2013–2023), with a focus on quantitative research involving urban spatial big data. A total of 1,425 articles were identified through automated keyword filtering and manual screening, of which 82 were selected for further analysis due to their theoretical contributions. The results revealed key thematic areas, including urban transportation, spatial quality, vitality, and structure, with a predominant focus on developed countries and large cities. The applications of big data and novel methodologies in urban studies have increased from 2013 to 2023, with increasingly higher levels of spatiotemporal resolution. However, studies linking big data to urban theories remained limited, with most quantitative research centered on applying or proposing new methods or uncovering new phenomena. Theoretical contributions primarily explored the mechanisms underlying urban environment formation and their impacts on human activities and behaviors. This study provides a comprehensive understanding of big data applications and advances in theories in urban spatial studies and highlights key directions for future research.

A growing body of research has linked social and environmental factors to health outcomes and health equity, with most studies focusing on residential neighborhoods. While activity space—the places people regularly visit—has been increasingly recognized as critical to health, there remains a lack of scalable, quantitative methods to assess its impact on health equity. This study addresses this gap by developing a big-data-driven approach that integrates 5.48 million origin–destination (O–D) pairs from SafeGraph travel data with demographic and environmental datasets to examine health equity in Southeast Michigan. The study assessed the difference between home ranges of residents of Black-/Hispanic-/White-dominant census tracts and measured three health-related aspects of residential and activity spaces with the Mann–Whitney U test (U) and Rank-Biserial Correlation (rrb). Through a systematic variable selection process, 13 out of 31 variables were selected to construct six indices that present: (1) socioeconomic disadvantages of residents (NDI [Neighborhood Disadvantage Index]/ANDI [Activity Space Neighborhood Disadvantage Index]), (2) access to health-essential resources (HOI [Health Opportunity Index]/AHOI [Activity Space Health Opportunity Index]), and (3) exposure to health disamenities (UEI [Unhealthy Exposure Index]/AUEI [Activity Space Unhealthy Exposure Index]). Results indicate that 91.2% of trips extend beyond residents’ home census tracts, and 73.6% reach beyond immediate neighboring tracts. White-dominant tract residents exhibit significantly larger home range areas (median = 782.3 km²), which is 2.43 and 1.35 times that of Hispanic- and Black-dominant tract residents, respectively, indicating better mobility and affordance to diverse destinations. Furthermore, residents of Hispanic- and Black-dominant tracts experienced significantly higher levels of socioeconomic disadvantage and health risks both at home and in their activity spaces, reinforcing persistent disparities. As evidenced by increased rrb of pairwise U tests between three groups, social and environmental disparities were also intensified in activity spaces (e.g., rrb-NDI _White-Black = 0.82 and rrb-ANDI _White-Black = 0.92). By comparing the activity space patterns and health indices of different racial groups, this research provides a big-data-empowered and straightforward method to assess social and environmental disparities that contribute to health gaps. These findings provide actionable insights for urban planners to develop health-equity-focused, activity-space-informed policies aimed at mitigating structural inequalities in mobility and environmental exposure.

People living in the Global North spend most of their time indoors in the built environment, especially their homes. Indoor air pollution has therefore become a major health concern, particularly in urban environments. Both exposure to poor-quality indoor environments, as well as vulnerability to adverse effects on health and well-being, have a unique geography, varying socially, spatially and temporally. Yet to date, the measurement of indoor air quality is relatively technical in focus, failing to account for the ways in which indoor environments are complex and varied, shaped by the physical environment, housing stock, policies, household dynamics, incomes and cultural norms. This paper aims to better understand the complex social and spatial drivers of vulnerability to poor indoor air quality. This is done by establishing a conceptual framing and building a new classification of vulnerability to indoor air pollution at the neighbourhood scale across England and Wales. First, the paper builds three separate indices utilising a spectrum of open-source data: environmental, structural and human-related. Second, it uses cluster analysis to generate indoor air quality profiles for neighbourhoods, identifying key patterns and drivers of vulnerability that typify different geographies. The findings allow for national, regional and local comparisons of vulnerability, which can be useful to a diverse range of stakeholders to assess potential exposure and guide intervention. Moreover, it highlights the dramatically different relationship between structural, environmental and human dimensions of vulnerability, encouraging indoor air quality exposure to be understood from multiple perspectives, not solely focused on low incomes.