Environment and Planning B: Urban Analytics and City Science

Mobile phones share location records, offering the opportunity to monitor and understand emerging population dynamics in urban centers. With the aim of supporting urban planning, this study introduces a scalable methodology grounded on extracting and organizing spatiotemporal statistics from decomposed population density data. The proposed methodology serves three major purposes: (i) assess the predictability of spatiotemporal citizen density patterns; (ii) detect emerging spatiotemporal trends in population density; and (iii) uncover multi-level seasonality patterns with guarantees of actionability. Additionally, it makes available an open-access tool for deploying the proposed methodology and analyzing mobile phone network data with easy-to-use spatiotemporal visualization and navigation facilities. The results obtained from real-world, large-scale mobile data in Lisbon, Portugal, demonstrate the effectiveness and validity of the proposed methodology in extracting actionable statistics in linear time to guide both tactic and strategic urban planning.

This research proposes a design system that combines a case-based learning algorithm with a rule-based optimization algorithm to automatically generate and revise urban form prototypes based on historical cases and user requirements. The system aims to address the challenges of existing generative methods for urban forms, such as the lack of flexibility and organicity of rule-based methods and the insufficient manipulability and interpretability of the newest GAN-integrated case-based methods. It can help designers generate multiple solutions with specific indicators in the conceptual stage and has the potential to facilitate citizen participation in urban planning and design. This research demonstrates the feasibility and effectiveness of the system through a case study in Shenzhen. The research further extends the discussion about the application of the proposed system and the alternative evolution approach for the next generation of automatic design methods.

This study analyzed physical distancing in people’s daily lives and its association with travel behavior and the use of transportation modes before the COVID-19 outbreak. We used data from photographic images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s physical distancing and travel behavior and transportation choices. The multidisciplinary research approach to understand these associations accounted for a number of social, economic, and cultural factors that potentially influenced their physical isolation levels. We found that participants spend a significant amount of time physically separated from others, without anyone else within 2 meters. The use of public transportation, automobiles, active travel, and an increase in trip frequency, including trips to transportation facilities, reduced the extent of physical distancing, with public transportation having the most significant impact. Higher incomes, strong social networks, and a sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable about climate change issues increased the likelihood of physical distancing. The paper addresses a crucial gap in our understanding of how these factors intersect to create the dynamics of physical distancing in non-emergency situations and highlights their planning and operational implications while showcasing the use of unique person-based physical distancing measures derived from autonomously collected image data.

Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom traffic data to infer mixed urban functions at spatiotemporal granularities as fine as buildings and hours. A time-series decomposition method is designed to map the mixture of urban functions, which is further refined by a hierarchical agglomerative clustering method taking urban textures as an additional source of information. In a case study in Shenzhen, China, we find the function of urban buildings can be decomposed into the mixture of three basic functions, namely dwelling, work, and recreation. We further find that the introduction of urban texture information helps identify particular forms of functional combination, which indicate special-function buildings such as urban villages and roadside shops. This study implies ways to improve urban management through methodological contributions in mixed urban function identification alongside the introduction of the telecom traffic, a kind of “high-frequency” urban data, and also helps inspire a rethinking of the form/function dichotomy in the era of “High-frequent” cities.

As a significant public place, the commercial area has a potential correlation between its built environment and human activities. However, the current research primarily concentrates on the internal environment of the store and customer satisfaction, while the impact of some environmental features of the outer space of the business district on visitors is seldom systematically discussed. This study takes four commercial districts in Shenzhen as examples, and the streets were categorized into five types based on street characteristics using the cluster analysis method. The relationship between each type of street and the population distribution in the region was subsequently discussed. To this end, a holistic approach was adopted, integrating multi-source urban data such as street view panorama, points of interest (POI), and street and building vectors to describe the built environment. Furthermore, the distribution of people at different times, based on location-based services (LBS) data, was combined to establish statistical models of various streets in commercial districts and evaluate the relationship between street characteristics and human activities. The results demonstrate that the relationship between population distribution and spatial characteristics is different in the five types of streets. Different types of streets have their own advantages, and human activities in the business district are often not affected by this advantage, but by other characteristics. The impact of these factors varies significantly between weekdays and weekends. By systematically categorizing street types and assessing the impact of environmental factors on pedestrian flow, this study sheds new light on the renewal and development of urban commercial districts in the future.

The utilization of deep learning for form analysis facilitates the classification of an extensive number of forms based on their morphological features. A critical consideration for implementing such analysis methods in architectural or urban forms is whether building orientation should be embedded within the data. Orientation functions as a form variable significantly influenced by environmental, social, and cultural contexts within a city. In contrast to other domains where forms are extrapolated in relation to their context, in the city, domain orientation uniquely characterizes building form. In this paper, we introduce a pipeline for constructing an extensive building form dataset and scrutinizing the morphological identity of building forms, with a particular focus on the implications of building orientation as a manifestation of urban locality. Through a case study situated in Montreal, we engage in a comparative analysis employing two distinct datasets—those with orientation-embedded forms and those with orientation-normalized forms. Our research aims to investigate the typo-morphological characteristics of the building forms of the city and to examine how building orientation contributes to the identification of these traits and mirrors urban locality.

Cities’ transportation systems have substantial impacts on urban vitality. Given the increasing availability of data on residents’ activities, cities’ tangible/intangible vitality can be analyzed more accurately. This study examined the associations of tangible and intangible vitality with transportation system features, specifically exploring various transportation modes’ accessibility, features related to block forms, and border vacuums at a block scale across different urban areas. Nanjing, China, was analyzed as a case study. Our findings reveal a declining gradient of urban vitality from the Old Town to the Main City and the New Area. Consequently, we suggest prioritizing efforts to enhance urban vitality in the New Area, particularly in its low-vitality blocks. Strategies for improvement include increasing public transportation accessibility and road density, which can positively influence the overall vitality of the entire city. Improving active travel accessibility has a positive impact on tangible vitality, while enhancing automobile accessibility potentially contributes to intangible vitality. Negative border effects of large transportation projects on tangible vitality should be mitigated. Interestingly, we found that intersection density has opposite effects on tangible and intangible vitality. These insights offer valuable guidance for urban planners aiming to enhance vitality levels across an entire city or within specific areas.

This paper investigates the geography of Facebook use at an urban-regional scale, focussing on place-named groups, meaning various interest groups with names relating to places such as towns, neighbourhoods, or points of interest. Conceptualising Facebook as a digital infrastructure – that is, the platform’s urban footprint, in the form of its place-named groups, rather than what individuals share and create using the service – we explore the location, theme, and scale of 3016 groups relating to places in Greater London. Firstly, we address the quantitative and qualitative methodological challenges that we faced to identify the groups and ground them geographically. Secondly, we analyse the scale of the toponyms in the group names, which are predominantly linked to London’s suburbs. Thirdly, we study the spatial distribution of groups, both overall and by specific types, in relation to the socio-demographic characteristics of residents at the borough level. Through correlation and robust regression analyses, the presence and activity of groups are linked to a relatively older, non-deprived, and non-immigrant population living in less dense areas, with high variability across different group types. These results portray place-named Facebook groups as communication infrastructure skewed towards more banal interactions and places in Greater London’s outlying boroughs. This research is among the first to explore and visualise the urban geographies of Facebook groups at a metropolitan scale, showing the extent, nature, and locational tendencies of large-scale social media use as increasingly ordinary aspects of how people come to know, experience, live, and work in cities.

Agent-based models are computational methods for simulating the actions and reactions of autonomous entities with the ability to capture their effects on a system through interaction rules. This study develops an agent-based simulation model (RANGE) to replicate the growth of Sydney Trains network by given exogenous historical evolution in land use. A set of locational rules has been defined to find a sequence of optimal stations from an initial seed. The model framework is an iterative process that includes five consecutive components including environment loading, measuring access, locating stations, connecting stations, and evaluating connections. In each iteration, following the locating/connecting process in each line of railways network, the accessibility will be calculated, and land use will be updated. Based on the compilation of network topology and properties, each iteration will be a year-on-year time step analysis. The network evolves based on a set of locational rules in regards to changes in the historic land use. Also, two coverage indices are defined to evaluate the fitness of the simulated lines in comparison to the Sydney tram and train network.

Cities across the United States and around the globe are embracing urban greening as a strategy for mitigating the effects of rising temperatures on human health and quality-of-life. Better understanding how the spatial configuration of tree canopy influences land surface temperature should help to increase the positive impacts of urban greening. This study applies a machine learning approach for modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature (LST) using data from nine cities located in nine different climate zones of the United States. We collected summer LST data from the U.S. Geological Survey (USGS) Analysis Ready Data series and processed them to derive mean, minimum, and maximum LST in degrees Fahrenheit for each Census block group within the cities considered. We also calculated the percentage of each block group comprised by the land cover designations in the 2016 or 2019 National Land Cover Database (NLCD) maintained by the USGS, depending on the vintage of the available LST data. High resolution tree canopy data were purchased for all the study cities and the spatial configuration of tree canopy was measured at the block group level using established landscape metrics. Landscape metrics of the waterbodies were also calculated to incorporate the cooling effects of waterbodies. We used a Generalized Boosted Regression Model (GBM) algorithm to predict LST from the collected data. Our results show that tree canopy exerts a consistent and significant influence on predicted land surface temperatures across all study cities, but that the configuration of tree canopy and water patches matters more in some locations than in others. The findings underscore the importance of considering the local climate and existing landscape features when planning for urban greening.

Local land-use plans help guide future development, but it is often difficult to compare content across jurisdictions, making regional coordination and plan evaluation challenging. This research reviews federal, state, and local data infrastructure guidance for land-use plans and compares such guidance to compliance with a California use-case. Findings indicate a number of obstacles to fostering data sharing and comparative analysis of plans: there is currently no central repository of land-use plans; plans are not uniform in format and are often out of date; many plans are not machine-readable thereby inhibiting text extraction, and planning language varies so greatly that there are numerous synonyms for terms of interest. Nonetheless, we demonstrate that the creation of digital platforms for archiving and searching across plans is currently feasible and enables large-scale quantitative analysis. Based on currently available metadata in existing land-use plans, we designed and piloted a structured database to enable users to search for terms and phrases across over 500 land-use plans. To center issues of social equity, the open access platform was developed in collaboration with state agencies and community organizations focused on environmental justice. Based on the pilot, we conclude with a framework for both developing plan data infrastructure given current constraints in standardized plan metadata and availability as well as guidance for plan formatting using FAIR standards (Findable, Accessible, Interoperable, and Reusable).

Cultural scenes are essential units and value collections within consumer spaces, and metro scenes in large cities are a new perspective for cultural scene research. Based on scene theory, we isolated distinct urban metro scenes through the perspective of slow travelling, through which we identified the dimensions of Shanghai’s metro cultural scenes. Furthermore, we identified five patterns of metro cultural scenes through factor analysis and cluster analysis of scene dimensions, namely, mechanically modern, charming and expressive, local and down-to-earth, public welfare and rationality, and ordinary scenes. We found that the names of metro stations could influence scene formation by influencing the category of amenities around the station, while the convenience of the metro stations could significantly promote the formation of some scene dimensions. In addition, urban planning and crowd distribution also have an impact on the metro culture scene. Our study reveals the characteristics and patterns of metro scenes in Shanghai and proposes a pathway by which metro scenes are formed, providing a new direction for urban scene research.

This study examines the efficacy of urban spatial patterns at alleviating the urban heat island (UHI) effect in Germany’s city regions (Großstadtregionen) using multivariate and non-parametric regression methods. Urban spatial patterns are quantified using five landscape metrics that capture the spatial arrangement of urban footprints and greenspaces, along with a polycentricity index that measures the distribution of human activities. The results indicate that certain features of urban fabric, including fragmentation, mixed land use, and regular-shaped urban patches, have the potential to mitigate the UHI effect. Moreover, dispersing multiple smaller greenspaces throughout the urban area demonstrates a greater cooling effect compared to having a single large and more aggregated park. In addition, our analysis reveals that a doubling (100%) of the polycentricity degree corresponds to a significant decrease in both day- and night-time UHI effects, with reductions of 10.4% and 24.6%, respectively. This study confirms that polycentric development yields greater benefits in reducing urban heat for large-sized city regions compared to medium- and small-sized ones; and its effectiveness is mostly pronounced near urban center(s). These findings suggest that polycentric development represents an efficient and feasible strategy for urban thermal planning of large-sized city regions, surpassing other commonly discussed urban configurations, such as compact or dispersed urban development.

Warehouse CITY is an open data product used to visualize and quantify the cumulative impact of warehouses within Southern California. Community groups, researchers, planners, and local agencies apply this open data product in project approval processes, research, lawsuits, and education. Warehouse CITY estimates the cumulative impacts of warehouse counts, acreage, building footprint, heavy-duty truck trips, diesel particulate matter emissions, oxides of nitrogen emissions, carbon dioxide emissions, and jobs. The Warehouse CITY open data product and dashboard is available as a website and at a GitHub repository.

Point of Interest data that is globally available, open access and of good quality is sparse, despite being important inputs for research in a number of application areas. New data from the Overture Maps Foundation offers significant potential in this arena, but accessing the data relies on computational resources beyond the skillset and capacity of the average researcher. In this article, we provide a processed version of the Overture places (POI) dataset for the UK, in a fully queryable format, and provide accompanying code through which to explore the data, and generate other national subsets. In the article, we describe the construction and characteristics of this new open data product, before evaluating its quality in relation to ISO standards, through direct comparison with Geolytix supermarket data. This dataset can support new and important research projects in a variety of different thematic areas, and foster a network of researchers to further evaluate its advantages and limitations, through validation against other well-established datasets from domains external to retail.

Socio-spatial segregation of immigrants or other ethno-racial groups in Western cities has been extensively investigated. In the recent decades, China has also witnessed a substantial growth of international immigrants. In the city of Guangzhou, one of the most famous destinations in China for transnational migration, the spatial presence of international migrants has received scholarly attention, mostly focusing on single racial groups. In this study, we present two cartograms using cellphone data to visualize the spatial distributions of multiple groups of international migrants, namely, the African migrants, the European and North American migrants, and the Japanese and Korean migrants, in Guangzhou. The cartograms indicate that the spatial distributions of migrants from Africa and those from the European, North American, and East Asian countries are considerably divided in Guangzhou, suggesting a possible ethno-racial segregation among the international migrants in this Chinese city. Such an issue is largely under-researched in the existing literature.

The academic mobility of Nobel laureates (NLs) epitomises not only the inter-urban knowledge flows and networks but also the spatial evolution of the world’s scientific hubs. Yet the understanding of the mobility and patterns of Nobel laureates’ scholarly migration remains limited. To address this gap, we elucidate the trajectories of academic mobility for 734 Nobel laureates and how their migratory patterns change in different geopolitical eras by establishing a life-course database encompassing NLs in science and economics from 1901 to 2023. First, the migratory patterns of NLs have evolved from multi-cored diversification in Phase A (–1945) to polarisation in Phase B (1946–1991) and to re-diversification in Phase C (1992–2023). Second, the academic mobility of NLs, and especially their international mobility, has significantly declined over the past century, which contrasts with other observations that scientists tend to be more mobile as globalisation advances.