Environment and Planning B: Urban Analytics and City Science

The rapid digitization and publication of local government records presents researchers with an unprecedented chance to study governance processes. In tandem, advances in computer science and statistics—alongside significant increases in computational power—have led to the development of “text-as-data” methods and their application to social science and policy research. This paper evaluates the potential utility of digitized public meeting minutes and video recordings for studying decision-making about technology adoption by local public agencies, using survey data on the same topic as a benchmark. Focusing on transit agencies in California, we evaluate surveys and digitized meeting records with respect to overall data availability, bias in data availability, and the types of information about technology adoption contained. We find that meeting minutes and video recordings are available for more than twice as many agencies than for a state transit agency-sponsored survey, and that the availability of digitized records is not skewed toward larger agencies, as is the case for survey data. Meanwhile, we find important complementarities with respect to the type of information available about technology adoption in these three data sources.

Income inequality, which refers to the uneven distribution of income in a population, has been linked to many societal problems, including crime. Although environmental criminology theories, such as rational choice theory, suggest a positive association between income inequality and crime, previous empirical studies have reported divergent results based on different crime types, statistical models, and spatial units of analysis. This study employs non-spatial and spatial regression models using frequentist and Bayesian modelling frameworks to explore the impacts of within-area and across-area income inequality on five types of major crimes in the City of Toronto at the census tract and dissemination area scales. The use of spatial regression models improves the model fit in both frequentist and Bayesian frameworks. The Bayesian shared component model, which accounts for the interactions between different types of crimes, further enhances model performance. Results obtained from the best-fitting frequentist and Bayesian models are inconsistent but do not conflict in terms of the relationship between crime and income inequality, where within-area income inequality generally increases major crime rates, while across-area income inequality has varying effects dependent on crime type and spatial scale. The discrepancies between spatial scales are a manifestation of the modifiable areal unit problem (MAUP).

Studying urban amenities is crucial for understanding their impact on quality of life, social equity, and sustainable city development. However, the valuation of urban amenities on a holistic level is understudied. This paper builds upon the framework of isobenefit lines, where residents supposedly receive the same level of combined benefits from all urban amenities. Specifically, our proposed approach takes into account the spatial heterogeneity of amenities and the proxy data of property transaction prices in Singapore to estimate their underlying monetary values. These monetary values were quantified using geographically weighted regression (GWR) that can adequately address spatial autocorrelation issues. Our results show that GWR outperforms the global linear regression model by approximately 150% increase in the R-square value, demonstrating a much better goodness of fit when dealing with spatial data sets. Additionally, the signs of the average coefficients of GWR are largely consistent with those of the global model, while the signs and magnitude of the GWR coefficients vary spatially. The spatial variations of modeling performance tend to intensify from older to younger towns. The results reveal that the central regions of Singapore are among the top spots that receive the highest levels of composite benefits. It is also observed that a spatial structure with multiple centers with high benefit scores emerges in the younger towns located in the peripheral rings of the city. This observation demonstrates the efforts of local authorities to promote a city with several regional centers of diverse functions. As the first study that applies the concept of isobenefit lines in a real urban setting, we demonstrate that the developed framework can be a useful addition to the existing toolbox of urban and infrastructure planners.

Existing studies have highlighted that green space is associated with non-communicable diseases. However, scant attention has been paid to the association between green space quantity and quality with communicable diseases. Here, we explore the relationships between green space and influenza cases in Guangzhou, China, using street-view green (SVG) space quantity and SVG-quality indicators, which offer a better assessment of urban green space than traditional remote sensing metrics. Influenza cases were collected from hospitalization records, while street-level green space was measured by street-view data and deep neural networks. The neighbourhood deprivation index (NDI) was also used as a proxy for neighbourhood-level socio-economic status. We employed the Random Effects-Eigenvector Spatial Filtering (RE-ESF) regression model because of its usefulness in handling spatial dependence. Findings showed that higher levels of SVG-quantity and quality are associated with a lower number of influenza cases, implying a negative relationship. Specifically, the marginal effects for SVG indicate that influenza may decrease by 145 cases for every unit increase in SVG-quantity, and by 11 cases for every unit increase in SVG-quality. In terms of planning, this could mean that though green quality is essential for the aesthetic part of urban life, quantity is much more critical concerning the containment of influenza. In addition, SVG-quantity and quality moderated the positive association between NDI and influenza cases. In other words, people in more deprived neighbourhoods were more influenced by SVG-quantity and quality compared to people living in less deprived areas. This means that more green space should be added to such neighbourhoods. We also observed that the association between SVG-quality and influenza cases was weaker for females, people aged between 18 and 45, and employed people. Because influenza is the most common pandemic worldwide, green space at the street level should be considered when promoting equitable public health and this study provides quantifiable evidence for the negative effect of green space quantity and quality over influenza cases.

Recognition for the importance of the night-time economy (NTE) in cities is mounting in both academia and policy. Yet, much of this discourse is centred on the consumption side of the NTE. Analytical and policy insights into the role of those who work to keep our cities ticking 24/7 and the NTE flourishing is still severely limited. Just how many people work at night? And how can we count them? Current assessments, where at all present, often diverge drastically, whilst cities and countries step up more and more policy efforts to grow the NTE. We present here a case study, centred on the task of assessing the Australian night-time economy’s workforce, to underscore continuing challenges in accounting for night shift workers. We underline how counting night shift workers provides for a more effective evidence base for urban policy. We demonstrate both definitional difficulties and data limitations, arguing for the pressing need for more precise urban science of the night, and of the NTE specifically, as a precondition to stepping up our engagement with night shift workers, in order to account for them in policymaking.

Civil or government organizations base human settlement transformation decisions on limited and sparse data. However, broader and denser information is necessary. Camera and LiDAR data processing is a more effective, automatic, and affordable method to fully characterize the morphological structure of human settlements. This work presents a system for estimating metrics about relevant morphological characteristics of human settlements using LiDAR data. We provide a quantitative analysis of these metrics obtained in the city of Cali, Colombia. Additionally, we enable the automatic calculation of urban metrics such as the street canyon ratio, which relates building height to street width, a metric highly correlated with air quality. Moreover, we extrapolate findings from existing literature to compare our results and understand how indirectly measured variables, such as thermal sensation and perceived beauty of the environment, might behave Our system can potentially be used by civil and government organizations to develop informed and precise urban planning and transformation strategies, including land use zoning, infrastructure development, and addressing issues related to housing, transportation, and environmental sustainability.

This study explores the integration of text-to-image generative AI, particularly Stable Diffusion, in conjunction with ControlNet and LoRA models in conceptual landscape design. Traditional methods in landscape design are often time-consuming and limited by the designer’s individual creativity, also often lacking efficiency in the exploration of diverse design solutions. By leveraging AI tools, we demonstrate a workflow that efficiently generates detailed and visually coherent landscape designs, including natural parks, city plazas, and courtyard gardens. Through both qualitative and quantitative evaluations, our results indicate that fine-tuned models produce superior designs compared to non-fine-tuned models, maintaining spatial consistency, control over scale, and relevant landscape elements. This research advances the efficiency of conceptual design processes and underscores the potential of AI in enhancing creativity and innovation in landscape architecture.

Recognizing the critical role of cities in mitigating greenhouse gas emissions, many cities are adopting carbon neutrality goals as part of their climate action strategies. The efficacy of these initiatives, however, has been undermined by complexity of systemic problems, ineffectiveness in planning implementation, and lack of stakeholder engagement. Urban and community-level carbon reduction should transcend urban design and systems optimization to incorporate multi-faceted dimensions in urban contexts. To address these challenges, this paper proposes a framework of urban digital twins that includes digital representation, performance modeling, design interventions and interactive platform for decisions over temporal processes. The CANVAS, or Carbon Neutrality Architecting New Visions for Architectural Systems, is a systems architecting approach to modeling the process of urban revitalization for achieving carbon neutrality by 2050. The developed workflow integrates multidisciplinary approaches for carbon mapping, gap identification, alternative generation, Urban Building Energy Modeling (UBEM) simulation, evaluation, and decision-making to demonstrates applicability of the proposed framework through a case study of the Nihonbashi district in Tokyo. The approach revealed that Energy Use Intensity (EUI) can be decreased by 99 kWh/m²/y through reconstruction and operational improvements. Emerging photovoltaic technologies can further cut EUI by an average of 42.5 kWh/m²/y, although results vary significantly in respect to building characteristics, particularly geometry and floor area. The incremental, cyclical systems architecting approach revealed that a 97% reduction in carbon emissions could be achieved by the seventh cycle through stakeholder-centric system interventions. This paper contributes to the development of urban digital twin methodologies by integrating systems architecting concepts with UBEM as transformative tools for carbon neutral urban design and development.

In this paper, we study urban road infrastructure in densely populated cities. As the subject of our study, we choose road networks from 35 populous cities worldwide, including China, India, Pakistan, Colombia, Brazil, Bangladesh, and Cote d’Ivoire. We abstract road networks as complex systems, represented by graphs consisting of nodes and links, and employ tools from network science to study their topological properties. Our multi-scale analysis includes macro-, meso-, and micro-scale perspectives, deriving insights into both common and unexpected patterns in these networks. At the macro-scale, we examine the global properties of these networks, summarizing the results in radar diagrams. This analysis reveals significant correlations among key metrics, indicating that more robust networks tend to be more efficient, while diameter and average path length show negative correlations with other properties. At the meso-scale, we explore the existence of sub-structures embedded within the road networks using two main concepts, namely, community and core-periphery structures. We find that while these densely populated city road networks show particularly strong community structures (high modularity values, close to 1.0) that are not typical to other networks, they exhibit a low level of presence of core-periphery structures, with an average coreness of 6.3%. This points to the cities being polycentric. At the micro-scale, we find nodal-level properties of the network. Specifically, we compute the various centrality measures and examine their distributions to capture the prevalent characteristics of these networks. We observe that the centrality measures present different distribution patterns. While the degree distribution demonstrates a limited range of degree values, the betweenness centrality distribution follows a power law, and the closeness centrality exhibits a binomial distribution—yet these patterns remain consistent across the studied cities. Overall, our multi-scale analysis provides valuable insights into the topological properties of urban road networks, informing city planning, traffic management, and infrastructure development in similar urban environments.

The urban heat island (UHI) phenomenon is recognized as a main urban sustainability problem in the face of a changing climate, affecting human health, energy consumption, and other socio-economic considerations. The UHI can be mitigated by urban greenery, but it needs further investigation of detailed impacts across the urban landscape. The aim was to study UHI and model the relation to greenery in combination with urban grey structures, at a high spatiotemporal resolution across the urban landscape, in Stockholm. Temperature data was collected through opportunistic drive-by sensors on electric three-wheeled taxis. Data on greenview and skyview factors were used to inform on greenery and building density along the roads. During night and morning hours, the surface temperature was in general higher than air temperature, indicating that some densely built-up environments stored heat overnight. Hot zones were unevenly distributed throughout the city, while greenery had a cooling effect, especially when combined with skyview as an inverse measure of building density. Our results provide information on the spatiotemporal distribution of heat that can be used to inform efforts to use greenery for mitigating impacts of UHI on urban residents.

Quantifying, understanding and predicting the number of pedestrians that are likely be present in a particular place and time (‘footfall’) is critical for many academic, business and policy questions. However, limited data availability and complexities in the behaviour of the underlying pedestrian ‘system’ make it extremely difficult to accurately model footfall. This paper presents a machine learning model that is trained on a combination of hourly footfall count data from sensors across a city as well as important contextual factors that are associated with pedestrian movements such as the structure of the built environment and local weather conditions. The aims are to better understand the relationship between various contextual factors and footfall and to predict footfall volumes across a spatially heterogeneous city. The case study area is the city of Melbourne, Australia, where abundant pedestrian count data exist. Time-related variables, particularly time-of-day and day-of-week, emerged as the most significant predictors. While some built environment factors such as the presence of certain landmarks and weather conditions were influential, they were less so than temporal cycles. Interestingly the model over-estimates footfall in the years following the COVID-19 pandemic, suggesting that urban dynamics have yet to return to pre-pandemic levels (and may never do). The paper also demonstrates how the model can be used to assess the impacts that large events have had on footfall, which has implications for policy makers as they try to encourage foot traffic back into city centres.

Short-term rental markets are constantly evolving in response to dynamic market conditions. Leveraging the large-scale external market shocks resulting from the COVID-19 pandemic, this paper aims to better understand how short-term rental submarkets are formulated in response to changing market conditions. Using sequence analysis on monthly booking records between mid-2019 and the end of 2022 in Australia, this paper identifies six distinct short-term rental types underlying their varying longevities on the market. Temporal, geographic, operational and hedonic characteristics are considered to differentiate thriving from diving market trends and derive a taxonomy that narrates six submarkets. The results reveal a shifting STR market landscape from central cities and longstanding tourist destinations to peri-urban regional towns and a shift from properties run by small-scale amateur hosts toward professionally operated rentals with substantially more space and amenities. This suggests that short-term rentals have emerged as a distinct accommodation class to hotels, albeit in ways that differ based on spatial context.

This paper describes {ActiveCA}, an open data product with Canadian time use data. {ActiveCA} is an R data package that contains analysis-ready data related to active travel spanning almost 40 years, extracted from Cycles 2 (1986), 7 (1992), 12 (1998), 19 (2005), 24 (2010), 29 (2015), and 34 (2022) of the Time Use Survey (TUS) from the General Social Survey (GSS). Active travel episodes are characterized by mode, with walking being part of every cycle and bicycling starting in 1992. The attributes of active trips are the types of locations of origins and destinations, the duration of trips, and episode weights for expanding the trips to population-wide estimates. Based on the year of the survey, a variety of locations are coded. In earlier cycles, these include home, work or school, and other’s home, whereas in later cycles these are augmented with locations such as grocery stores, restaurants, outdoor destinations, and others. The geographical resolution includes the province and whether the episode was in an urban or rural setting.

Following the reform of the spatial planning system, China’s national territorial spatial planning (NTSP) has designated constraints on land use and strategic positioning for all provinces and cities across the nation. It has drawn up a consistent blueprint for sustainable future development. This study presents a systematic analysis of China’s NTSP through the text analysis of 42 State Council approval documents. We employed Nightingale rose diagrams to visualise the quantitative characteristics of land use regulation and used symbols to differentiate cities’ strategic positioning and functional roles. The results reveal that urban development boundaries occupy far more land space in the eastern coastal areas, while the northeast region places greater emphasis on the protection of agricultural zones, and the western regions prioritise ecological conservation. This study also visualises the stratification of central cities and their diverse functional roles, revealing a balance between adapting measures to local conditions and seeking regional coordination.

This study explores global queer emotional geographies using the digital counter-mapping platform Queering the Map (QTM), which archives LGBTQ2IA+ experiences tied to physical locations. Addressing limitations of previous qualitative research, we applied quantitative sentiment analysis to user-generated content. We adopted a BERT-based neural network to assign sentiment scores to 123,209 data points collected through web scraping, and visualized the results in a Global Queer Sentiment Map. The map reveals marked regional disparities: Anglo-Saxon regions exhibit denser emotional expressions than Third World areas, while remote uninhabited locations such as the Arctic, Antarctic, and oceans still exhibit sparse emotional expressions. The integration of sentiment analysis with queer cartography offers novel insights into the global dynamics of queer emotional geographies amid digitalization and globalization.