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Authors:Yichun Xie, Xining Yang Pages: 1187 - 1194 Abstract: Environment and Planning B: Urban Analytics and City Science, Volume 51, Issue 6, Page 1187-1194, July 2024.
Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-22T06:29:11Z DOI: 10.1177/23998083241243124 Issue No:Vol. 51, No. 6 (2024)
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Authors:Qiqi Huang, Changying Xiang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The ambition towards net-zero emission fuels the significance of electric vehicle charging infrastructure (EVCI) as a strategic asset. Yet, a conspicuous gap remains in the comprehensive quantitative analysis of its impact on carbon emissions stemming from fossil fuel combustion, referred to as ODIAC-CE. This study embarks on a longitudinal comparison of EVCI and ODIAC-CE data in 2018 and 2020, further classifying cities by scale to analyze the association between expansion of EVCI and ODIAC-CE change. Utilizing a battery of analytical tools, including correlation analysis, spatial autocorrelation, and coupling coordination analysis, the study dissects the evolving relationship between EVCI and ODIAC-CE within Yangtze River Delta in China. The results underscore a growing interdependence between EVCI expansion and ODIAC-CE change, yet pronounced heterogeneities in coupling coordination are evident across urban scales. Megacity and supercity exhibit quality coordination between rapid expansion of EVCI and ODIAC-CE dynamics. However, in most large, medium-sized, and small cities, the impact of EVCI growth on ODIAC-CE change has proven to be inconsistent or mismatched, affected by various factors such as location and infrastructure, industrial and technological patterns, and social practice and awareness. The study provides systematic insights into potential solutions for decarbonization through EVCI deployment at regional and city levels. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-26T09:00:44Z DOI: 10.1177/23998083241277865
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Authors:Yuanzhao Wang, ChengHe Guan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Various spatial indices have been used by scholars to evaluate the built environment of towns. However, previous analysis has fallen short in systematically addressing the distribution of green space in future town planning. This paper fills the gap by integrating green space indices in an expanded urban intensity framework and comparing existing conditions (2018) and future planning schemes (2030) of eleven towns in Zhejiang Province, China. In this paper, we computed spatial indices in ARCGIS and FRAGSTATS, used correlation analysis in STATA for statistical analysis, and adopted demographic, economic, and environmental variables to validate the selected indices. The results show that: (1) The future planning schemes can result in either reduction of green spaces in town centers or uneven distribution of green spaces; (2) Validation of green space indicators reveals observable association with the normalized difference vegetation index (NDVI), which implies that the chosen framework can effectively reflect the condition of greenery; and (3) The regulatory detailed planning does not always improve the future spatial layout of towns, especially after considering green space distributions. These findings emphasize the importance of suitable spatial layouts of green spaces over large monolithic blocks for effective planning. Moreover, achieving optimal urban intensity necessitates a balanced distribution of the built and green spaces. Finally, the integration of green space factors and the adoption of a comprehensive approach, as highlighted in this study, can serve as a valuable guide for town planners and policymakers in different jurisdictions to achieve more desirable spatial layouts. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-24T08:01:07Z DOI: 10.1177/23998083241274913
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Authors:Federico Botta Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Accessibility of different places, such as hospitals or areas with jobs, is important in understanding transportation systems, urban environments, and potential inequalities in what services and opportunities different people can reach. Often, research in this area is framed around the question of whether people living in an area are able to reach certain destinations within a prespecified time frame. However, the cost of such journeys, and whether they are affordable, is often omitted or not considered to the same level. Here, we present a Python package and an associated data set which allows to analyse the cost of train journeys in Great Britain. We present the original data set we used to construct this, the Python package we developed to analyse it, and the output data set which we generated. We envisage our work to allow researchers, policy makers, and other stakeholders, to investigate questions around the cost of train journeys, any geographical or social inequalities arising from this, and how the transport system could be improved. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-24T05:24:44Z DOI: 10.1177/23998083241276569
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Authors:Hui Jeong Ha, Youngbin Lee, Kyusik Kim, Sohyun Park, Jinhyung Lee Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This paper introduces {spnaf} (spatial network autocorrelation for flows), an R package designed for the hotspot analysis of flow (e.g., human mobility, transportation, and animal movement) datasets based on Berglund and Karlström’s G index. We demonstrate the utility of the {spnaf} package through two example analyses by data forms: 1) bike-sharing trip patterns in Columbus, Ohio, USA, using polygon data, and 2) U.S. airports’ passenger travel patterns, using point data. The {spnaf} is available for download from the Comprehensive R Archive Network (CRAN), which contains a vignette and sample data/code for immediate use. This package addresses limitations in existing spatial analysis packages and emphasizes its efficiency in detecting flow hotspots. It is highly applicable in various urban and geographic data science applications. {spnaf} is still in its early stages and we hope that interested readers can contribute to the development and enhancement of the package. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-21T03:30:11Z DOI: 10.1177/23998083241276021
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Authors:Haruka Kato Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Many developed countries need to plan urban policies based on multidimensional factors related to population change. However, empirical research has been inconsistent with respect to identifying these factors, including economic-, social-, and urban-planning-related factors. The purpose of this study is to clarify the nonlinear multidimensional factors that are correlated with population changes according to the city size. In the analysis, the population change rate was defined as the outcome variable, and 269 economic, social, and educational index (ESE index) were used as predictor variables. Data were stratified according to three city sizes. Using the ESE index, the XGBoost algorithm was used to analyze the nonlinear relationship between the population change rate and multidimensional data. As a key result, population changes were strongly correlated with social-related indicators, such as the population change rate among persons ages 0–14 years in small-sized cities, the natural population change rate in medium-sized cities, and the migration change rate in large-sized cities. Regarding the population decline, Japan has 1304 shrinking cities, which are primarily comprised of medium-sized and small-sized cities. In such cities, other than social-related factors, population changes correlated with the financial strength index as an economic-related factor in medium-sized cities and the designation of underpopulated areas as an urban-planning-related factor in small-sized cities. Among the multidimensional factors, cities of different sizes were characterized by factors other than social-related indicators. These multifaceted factors could provide preliminary insights for urban policymakers to explore various policy measures on which they need to focus, depending on the city’s size. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-19T08:05:50Z DOI: 10.1177/23998083241274381
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Authors:Xiaojiang Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. With the rise of global temperature, many cities are suffering from more and more frequent extreme heat in hot summers. Quantitative information on the spatial distributions of urban heat has become more and more important for extreme heat mitigation and adaptation in cities. This study first investigated the fine-level heat hazard distributions at the sidewalk and building block level from the pedestrian perspective in Philadelphia, Pennsylvania. The urban microclimate modeling based on a high-resolution urban geometrical model was used to generate the 1m resolution outdoor heat hazard map in the study area. The sidewalk map was overlaid on the generated high-resolution heat hazard map to estimate the sidewalk level heat hazard. Based on the sidewalk level heat hazard map, this study further calculated the heat hazard level in the 400m walkshed along sidewalks for each building block. The building level hazard data were then aggregated at the census tract level to compare with the socioeconomic and racial/ethnic variables. The result shows that neighborhoods with higher proportion of African Americans have a higher heat hazard level in Philadelphia. This study would provide new insights for developing more thermally comfortable and pedestrian-friendly neighborhoods in the context of climate change. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-19T07:56:41Z DOI: 10.1177/23998083241274391
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Authors:Mikhail Sirenko, Tina Comes, Alexander Verbraeck Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban vulnerability is affected by changing patterns of hazards due to climate change, increasing inequalities, rapid urban growth and inadequate infrastructure. While we have a relatively good understanding of how urban vulnerability changes in space, we know relatively little about the temporal dynamics of urban vulnerability. This paper presents a framework to assess urban vulnerability over time and space to address this gap. We apply the framework to Amsterdam, Rotterdam, and The Hague, the Netherlands. Using high-resolution, anonymised ambulance calls and socio-economic, built environment, and proximity data, we identify three temporal patterns: ’Midday Peaks’, ’Early Birds’, and ’All-Day All-Night’. Each pattern represents a unique rhythm of risk arising from the interaction of people with diverse demographic and socio-economic backgrounds and the temporal flow of their daily activities within various urban environments. Our findings also highlight the polycentric nature of modern Dutch cities, where similar rhythms emerge in areas with varying population densities. Through these case studies, we demonstrate that our framework uncovers the spatio-temporal dynamics of urban vulnerability. These insights suggest that a more nuanced approach is necessary for assessing urban vulnerability and enhancing preparedness efforts. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-14T12:19:42Z DOI: 10.1177/23998083241272095
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Authors:Linas Fathima A, Chithra K Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The mana/illam is a courtyard house typology found in the southern Indian state of Kerala, specific to the elite Namboothiri Brahmin community, who are members of the highest caste in the Hindu hierarchy, noted for their scholarship and wealth. These are highly decorated palatial houses built using timber or exposed laterite with sloped gable roofing designed to survive the heavy monsoons, expressive of Kerala’s rich vernacular and traditional architecture. This paper describes the language of mana/illam using shape grammar. To formulate the shape grammar, 36 samples of mana/illams across Kerala were architecturally documented, analysed, and their characteristics and differences in typology were determined. Three typologies of mana/illams are identified; spatial configurations, proportions, and hierarchy are examined, from which the vocabulary and rulesets for the shape grammar are formulated. Sixty-eight shape rules are defined across 20 stages. Sixty plan typologies of mana/illam are generated to illustrate the grammar. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-13T10:45:56Z DOI: 10.1177/23998083241271457
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Authors:Mingzhi Zhou, Shuyu Lei, Jiangyue Wu, Hanxi Ma, David M Levinson, Jiangping Zhou Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Using multiday continuous smartcard data in 2020, we investigate group-based travel in Hong Kong metro system by identifying metro riders intentionally traveling in groups (ITGs). ITGs serve as our proxies for citywide physical social interactions. Considering ITG members are interrelated through group-based trips, we construct a social network (an ITG network) formed by ITGs to explore the network properties and structures of ITG activities. Examining ITGs both before and during the COVID-19 pandemic, we measure the spatial patterns of ITGs and their dynamics across locales and over time. We find that the degree of the ITG network follows a heavy-tailed distribution. The network size and interconnections vary across time. Some ITG members are more influential vertices than others in maintaining the networks’ topological properties. We illustrate how new data and methods can be used to explore in-person interactions and social activity patterns in transit-reliant cities. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-12T11:58:20Z DOI: 10.1177/23998083241271453
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Authors:Luyu Liu, Harvey J Miller Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Accessibility is one of the most essential objectives of public transit systems as the transit service’s useability and service quality. The accessibility of transit systems as a whole is well understood; however, it is still unclear how each route contributes to the system-wide accessibility. Meanwhile, with a higher risk of disruptions and more uncertainties from climate change and other disruptions, there is an urgent need to systematically study the impacts of service change with the contribution of each route to general accessibility. To address this gap, we introduce the accessibility derivative, a model-agnostic measure of the contribution of each route in a transit system. We define the derivative of a route as the systemwide change in accessibility after removing the route from the system. We demonstrate how to calculate the derivative numerically from widely available public transit schedule and automated passenger count data. The measure reflects the inherent structure of a transit system and reveals the impacts of potential route-level service changes. The results provide firsthand evidence on public transit assessment and planning, including performance assessment, schedule redesign, and planning transfers. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-12T06:33:38Z DOI: 10.1177/23998083241272098
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Authors:Shruthy Nair, Clio Andris Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. We mapped Facebook’s Social Connectedness Index (SCI) between adjacent counties in the Contiguous 48 U.S. States. The index is calculated as the number of Facebook friends between counties, divided by the product of active Facebook users in the two counties. The results follow regional science principles that tell us that fewer flows may occur across political (administrative) borders such as state boundaries, and between economic zones, including transition zones between metropolitan areas and hinterland boundaries. We also found low connectivity between adjacent counties that are divided by interstate highways and low connectivity within densely populated areas. High connectivity is found in rural areas, and areas of cultural significance, such as highly African American regions in the U.S. South and isolated regions in Appalachia. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-12T01:38:40Z DOI: 10.1177/23998083241272094
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Authors:Cai Wu, Mingshu Wang, Jiong Wang, Duncan Smith, Menno-Jan Kraak Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Master plans are pivotal in strategising urban development, dictating land use, building height, and development intensity. These plans influence the spatial arrangement of urban infrastructure and activities, shaping the morphological and dynamic urban spatial structure. This study evaluates Singapore’s master plan’s effect on urban spatial structure, utilising data extracted from the master plan to project future land use and spatial interaction scenarios. By simulating the changing commercial floor space in different urban centres and its impact on commuting patterns, we evaluate the master plan’s impact on urban spatial structure and promoting polycentric urban development. Singapore’s master plan, with a clear vision towards polycentricity through the ‘Local hubs and global gateways’ strategy, is examined for its impact on urban spatial structure. The study uses urban mobility data and spatial network analysis to reveal how the master plan aims to decentralise development from the Central Business District (CBD) and distribute economic activities across various regions. Our findings indicate that regional centres and local hubs are becoming more autonomous while the CBD remains dominant. The results also highlight the importance of integrating morphological and functional polycentricity measurements and socioeconomic indicators to comprehensively evaluate urban development strategies. This study contributes to understanding urban spatial structure and offers practical insights for future urban planning. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-09T12:59:32Z DOI: 10.1177/23998083241267070
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Authors:Rubén Cordera, Soledad Nogués, Esther González-González, José Luis Moura Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Cities may undergo important changes in the coming years driven by various economic, social, and technological innovations, such as those related to autonomous mobility. Among other effects, autonomous vehicles may affect morpho-functional patterns of urban development and, especially, may reinforce or reduce dispersed development patterns, which have been relevant in many cities, particularly in the last decades. In order to offer an assessment of these possible effects, we propose a new urban sprawl index to measure the degree of dispersion/concentration of settlements in the medium-sized urban area of a Spanish city (Santander, Cantabria). Further, we explain the distribution of this index by means of a regression model, showing that variables such as average household income, trip time to the main urban centre, or the percentage of people using cars to commute to work are relevant factors that correlate positively with urban sprawl. Finally, we apply the proposed model to different scenarios to examine how the development of autonomous mobility could affect the characteristics of the analysed settlements. The results obtained suggest that, in scenarios with higher car usage and longer trip times to the urban centre because of the larger number of circulating vehicles, the form of urban settlements, especially those at an intermediate distance from the urban core, could experience an increase in sprawl. Therefore, Autonomous Vehicles could promote, under certain conditions, an urban form with more sustainability problems. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-09T12:17:27Z DOI: 10.1177/23998083241272664
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Authors:Lanqing Gu, Annika Dimitrov-Discher, Martin Knöll, Jenny Roe Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Ground murals have been increasingly applied as a tactical urban design strategy to improve place quality. However, limited research has explored how ground mural design may impact mental health. This study applied a 3 × 2 × 2 mixed design to explore how design features of sidewalk ground murals, specifically color (warm, cool, or achromatic) and pattern (rectilinear or curvilinear), influence mood states and perceived restorativeness of stressed or non-stressed individuals. Students (n = 112) were assigned into two groups, one with stress induction and the other without. They were asked to view images showing six design conditions and the uncolored condition. For each condition, mood states, including pleasure level, energetic arousal, and relaxation, were assessed using statements, along with perceived restorativeness as measured by the Perceived Restorativeness Scale—short version. The results reveal that presence of sidewalk murals improved mood states, including hedonic tone and energetic arousal, and perceived restorativeness compared to the uncolored sidewalk. Cool colors had the strongest effects in promoting a restorative experience, particularly for stressed subgroup. Warm colors significantly reduced relaxation across all participants and were perceived as less restorative for stressed individuals. Achromatic colors reduced energetic arousal and were perceived as the least restorative across all participants. Pattern features did not contribute to mood enhancement, but curvilinear patterns were perceived as more restorative than rectilinear patterns. This study provides empirical evidence to support urban public space design aiming to benefit mental health through ground murals with a more systematic color and pattern use. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-09T05:04:16Z DOI: 10.1177/23998083241272100
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Authors:Tianyuan Wang, Li Wan, Helen XH Bao Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The unprecedented urbanisation observed in leading developing countries has placed immense pressure on effective and efficient land management. The significance of land-use efficiency in the Chinese context has been addressed in the literature, particularly on the measurements of land-use efficiency and key influencing factors. However, quantifying the interdependence between land-use efficiency, local government revenue, employment and infrastructure development whilst controlling for significant cross-city differences remains a gap in the literature. Based on data for 272 prefecture-level Chinese cities between 2012 and 2017, this study employs a novel modelling approach, combining latent class analysis (LCA) in a generalised structural equation model. The incorporation of LCA helps to control for the significant, non-linear heterogeneity across city samples. The empirical model identifies both the direct (one-off land conveyance fee and transaction-related tax revenue from land transactions) and indirect (corporate and personal taxes generated from employment and business growth) channels, through which land development contributes to local government revenue. It also provides one of the first quantified evidence, confirming that employment growth provides higher long-term return than a one-off, land conveyance fee to government revenue in China, controlling for significant cross-city heterogeneity in land-use efficiency and wage. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-09T04:21:59Z DOI: 10.1177/23998083241272092
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Authors:Anna Kajosaari, Martina Schorn, Kamyar Hasanzadeh, Tiina Rinne, Saana Rossi, Marketta Kyttä Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Despite the emergence of virtual spaces as arenas for public participation, the geographies of digital participation have gained relatively little attention. Besides considering who participates and why, there is an evident gap in research considering the spatial relationships between the participants of digital urban planning processes and the spaces that are the subject of their participation. This paper proposes a working concept of the spatiality of participation that distinguishes between the spaces in which participation occurs, the spatial realities of the participants, and the spaces as objects of participatory planning. Relationships between these dimensions are investigated empirically with a Public Participation GIS study set in Espoo, Finland, involving 1,731 citizens and over 6,800 future planning and development ideas mapped across the city. The results of the study support prior research observing that e-participation has the potential to spatially expand participation processes both in terms of the involved public and the spatial knowledge they produce. However, our results also show that online participation may capture spatial ties between people and places that differ from those of traditional participation modes, ranging from place-protective behaviors close to the residential location to more casual spatial attachments. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-08T10:07:02Z DOI: 10.1177/23998083241271460
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Authors:Xianchun Zhang, Yucheng Zou, Chang Xia, Ya’nan Lu Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Existing scholarship extensively explores the dynamics, determinants, and consequences of urban expansion, yet there is scant literature examining the impact of regional cooperation upon the directions and spatial forms of urban expansion amidst the fast-urbanizing process. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a developed megacity-region in southern China, to probe whether the notable urban expansion observed in contemporary China has been profoundly influenced by collaborative efforts among jurisdictions. Through the spatial metrics and panel data regression spanning the period from 2010 to 2018, this study unveils that regional cooperation has extended from coastal cities towards hinterland cities within the GBA. Consequently, urban land in most cities has undergone expansion in diverse directions. Furthermore, in contrast to economic and social cooperation, regional institutional cooperation emerges as the most influential factor driving external urban expansion. Additionally, heterogeneous results reveal that regional cooperation drives the external expansion of ordinary cities towards core cities. In contrast, the inertia within the urban system demonstrates strong path dependence on the pattern of adjacent expansion, contrasting with the external expansion facilitated by regional cooperation. In summary, this study illuminates the genesis and dynamics of urban expansion amid the city-regionalization process, going beyond interpretations confined to the municipal scale. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-07T09:25:01Z DOI: 10.1177/23998083241272705
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Authors:Zhiying Lu, Yang Yang, Danlin Ou, Dazhi Gu Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The outbreak of the COVID-19 pandemic has precipitated food crises worldwide, prompting a re-examination of the resilience of the urban food environment. While previous research on the urban food environment has predominantly focused on Western contexts, scant attention has been given to China. This study takes Shenzhen, China as an example to establish a food environment evaluation framework centered on accessibility, diversity, and healthiness factors, aiming to analyze the dynamic changes of the food environment during normal and pandemic periods. By using the GA optimization algorithm, some convenience stores are transformed into self-pickup points (SPPs), which is expected to eliminate the deserts risk areas (DRAs) with low cost and high efficiency. The findings reveal a distinctive “center-periphery” spatial structure characterizing the food environment in Shenzhen, and the improvement of healthiness plays a crucial role in sustaining food oases and ameliorating food swamps. This research provides methods for improving the resilience of the food environment during the pandemic across diverse nations, bolstering the security of urban lifeline systems. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-06T07:06:11Z DOI: 10.1177/23998083241272101
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Authors:Anirudh Govind, Ate Poorthuis, Ben Derudder Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Although it is generally accepted that street configurations may influence people’s intra-urban travel, capturing the exact nature of that influence remains challenging. We frame this challenge as one of operationalization and measurement and attempt to quantify and analyze the impact of street configurations more precisely. We draw on geographic data science tools to suggest that street configurations may be captured using catchment area polygons. To illustrate our approach, we derive these polygons for every building in Singapore and show that catchment area sizes spatially cluster, thus acting as proxies for street configurations. Using a spatial error model, we demonstrate that these catchment area sizes partially explain people’s intra-urban travel, conceptualized as their activity spaces. That is, as street configurations lead to larger catchment areas, people’s activity spaces tend to shrink. We show that the explanatory power of catchment area sizes is distinct from, albeit correlated with, other built environment variables (such as amenity density and land use diversity) typically used to explain people’s travel. We conclude by considering the potential of our approach in broader urban geographical research agendas drawing on street configurations and other morphological influences in the study of socio-spatial processes. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-05T04:08:04Z DOI: 10.1177/23998083241272093
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Authors:Léo Leplat, Claudia López-Alfaro, Arne Styve, Ricardo da Silva Torres Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The implementation of sustainable urban lighting infrastructure is of paramount importance to promote healthy habits, mitigate the impact of light pollution on humans and wildlife, and balance out energy consumption. However, the analysis of alternatives for implementing lighting interventions in urban spaces is a laborious and time-consuming task, often involving the use of multiple tools. Also, the design of lighting infrastructure often demands a balance of conflicting needs and variables (e.g., aesthetics, human perception, impact on wildlife, and energy consumption). In this paper, we introduce NorDark-DT, an urban digital twin to support urban lighting infrastructure planning and analysis. We present the main requirements addressed in its design and development, its architecture and components, and illustrate its use in compelling usage scenarios related to the assessment of lighting intervention options in two study areas in Ålesund (Norway) and Uppsala (Sweden). We also discuss challenges and lessons learned during the development of NorDark-DT, providing valuable insights to developers, stakeholders, and practitioners interested in the creation of urban digital twins or similar tools. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-05T03:43:46Z DOI: 10.1177/23998083241272099
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Authors:Xinyu Fu, Catherine Brinkley, Thomas W Sanchez, Chaosu Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Cities worldwide are commonly aspiring to transition from inefficient urban sprawl patterns to more compact and sustainable urban forms. However, urban densification efforts often face significant public resistance or skepticism, hindering at-scale implementation. There is a scarcity of empirical studies identifying the rationale and mechanisms underpinning public opposition to urban density. This study aims to bridge this gap by leveraging novel natural language processing techniques (NLP), combined with mixed-methods analysis of a unique, highly detailed public dataset on urban intensification in Hamilton. This research stands out by proposing a transferable model for rapidly generating insights from large public feedback datasets, and also unveils the polarized and complex, self-interest-driven mechanisms, including NIMBYism (Not In My Back Yard), behind public support or opposition to urban densification. NLP techniques, such as sentiment analysis, topic modeling, and ChatGPT, can be used to offer rapid insights into a large, unstructured public feedback dataset. When combined with submitters’ individual interest representation and identifies, these AI-generated summaries can offer important insights into the hidden rationales behind public opinions, and, more importantly, be used to design tailored public engagement activities to obtain community buy-in. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-08-02T09:56:14Z DOI: 10.1177/23998083241272097
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Authors:Yuling Xie, Xiao Fu, Yi Long, Mingyang Pei Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban functions often diverge from initial planning due to changes driven by residents’ behaviors. Effective urban planning and renewal require accurately identifying urban functional regions based on residents’ behavior data (including activity and travel data). However, previous methods have primarily relied on either point of interest (POI) data or a single source of traffic data, and often ignore the combined influence of residents’ activities and travel behaviors. In this study, we introduce a novel framework that integrates multiple sources of traffic data (such as metro smart card data and car-hailing data) with POI data to identify urban functional regions. This approach is unique because it simultaneously considers two critical dimensions of residents’ behavior: travel and activity behaviors. By combining these dimensions, we extract a comprehensive set of characteristics, including travel time, travel flow, origin-destination patterns, activity types, and activity time, which are then aggregated at the regional level (i.e., traffic analysis zone). To process these characteristics, we use latent Dirichlet allocation (LDA) to extract high-level semantic features from each data type. Additionally, to handle the sparse data from metro smart cards, we employ a specialized clustering technique. The integration of diverse and complementary information from multiple data sources enables more accurate and nuanced identification of urban functional regions than single data source and k-means clustering algorithm, providing valuable insights for urban planners. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-30T09:45:19Z DOI: 10.1177/23998083241267370
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Authors:Somwrita Sarkar, Clémentine Cottineau-Mugadza, Levi J Wolf Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This special issue of Environment and Planning B focuses on Spatial Inequalities and Cities. As the world progresses to almost a fully urban state, locations, networks, and access shape the everyday lives lived in cities, alongside being the movers and shapers of the future of sustainable and equitable urbanization. This special issue brings together a set of peer-reviewerd papers spanning urban science, urban analytics, geographic information / spatial science, network science, and quantitative socio-economic-spatial analysis, to explore and examine how the morphological, structural and spatial form of cities is linked to the production, maintenance and exacerbation of socio-economic inequalities and injustices. The issue also presents a critical angle on data, methods, and their use, and on how novel data and methods can help shed light on new dimensions of spatial inequalities. This editorial presents a brief critical review of the field of urban spatial inequalities and a summary of the special issue. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-25T04:35:21Z DOI: 10.1177/23998083241263422
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Authors:Olaf Mumm, Majd Murad, Vanessa Miriam Carlow Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The accessibility and quality of urban mobility networks (UMN) depend on a multitude of static and dynamic conditions for each individual. Promoting sustainable mobility, such as walking, requires a very specific assessment of UMN’s qualities given the specific needs of pedestrians. The objective of this research is to provide a new approach for the comprehensive, mode-specific understanding of a UMN as a base for good planning and decision making. With the Accessibility Score (AccessS), we propose an integrated, indicator-based, holistic geospatial framework for the quantified assessment of qualitative UMN attributes identified in an extensive literature review. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-24T12:35:45Z DOI: 10.1177/23998083241261100
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Authors:Hansol Mun, Jaeweon Yeom, Jiwoon Oh, Juchul Jung Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Evidence to prove that compact cities, the core of smart growth strategies, are the vision for carbon-neutral cities has been insufficiently explored because analyses have not distinguished between production- and consumption-based carbon emissions. Empirically analyzing the relationship with compact cities by estimating the final demand and investigating carbon emissions generated from the consumption of goods is essential. This study estimated consumption-based carbon emissions in South Korea using nighttime satellite imagery. Subsequently, using spatial analysis, K-means clustering analysis, and a regression model, we comprehensively confirmed whether a compact city to reduce consumption-based carbon emissions should be pursued. The results showed that (1) based on the clustering analysis, consumption-based carbon emissions were the lowest in clusters with the most desirable development form from a compact city perspective; and (2) the OLS regression analysis showed that the higher the complex land use (diversity), population density (density), congestion frequency intensity (transit access), green area ratio (environment), and agricultural area ratio (environment), the lower the consumption-based carbon emissions. However, the results confirmed that the greater the Vehicle Kilometers Traveled (street accessibility) and the poorer the accessibility of high-speed rail, the higher the consumption-based carbon emissions. Therefore, we recommend pursuing a compact city to reduce consumption-based carbon emissions. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-24T09:58:11Z DOI: 10.1177/23998083241263898
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Authors:Federico Botta, Robin Lovelace, Laura Gilbert, Arthur Turrell Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. However, open data is not enough in many cases: publicly available datasets need to be accessible in an analysis-ready form from popular data science tools, such as R and Python, for them to realise their full potential. This paper explores ways to maximise the impact of open data with reference to a case study of packaging code to facilitate reproducible analysis. We present the jtstats project, which consists of a main Python package, and a smaller R version, for importing, processing, and visualising large and complex datasets representing journey times, for many transport modes and trip purposes at multiple geographic levels, released by the UK Department for Transport (DfT). jtstats shows how domain specific packages can enable reproducible research within the public sector and beyond, saving duplicated effort and reducing the risks of errors from repeated analyses. We hope that the jtstats project inspires others, particularly those in the public sector, to add value to their data sets by making them more accessible. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-24T09:11:51Z DOI: 10.1177/23998083241267331
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Authors:Patrick Ballantyne, Cillian Berragan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-23T10:08:33Z DOI: 10.1177/23998083241263124
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Authors:Cherifa Ben Farhat, Nicola Pontarollo Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In our contribution, by using data from European Social Survey, we show that the percentage of happy people in European countries has increased over the last two decades, and that Eastern European countries are catching up. However, considerable differences between nations remain. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-22T01:20:49Z DOI: 10.1177/23998083241263153
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Authors:Francisco J Bahamonde-Birke, Niek Mouter Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Participatory value evaluation (PVE) is a novel method aiming at the evaluation of projects from a societal perspective. It aims at establishing which investment projects or investment portfolios (with a portfolio containing multiple investment projects) are likely to be favored by the population, given a limited budget. The approach emulates the decision-making problem faced by policy-makers. The ultimate end of PVE is to evaluate and establish how individuals value attributes of public projects in order to construct social trade-offs between different attributes and investments projects. However, it is important to consider that when investment portfolios consist of more than one investment project, significant synergies (positive and negative) may exist among the projects. This paper proposes a new evaluation framework that allows addressing synergies among projects in the context of PVE, while also offering a highly flexible structure. The approach is tested making use of one synthetic and two real datasets. Results show that neglecting synergies among the utilities of the projects included in the chosen portfolio majorly reduces the model fit and biases the estimators. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-21T10:17:39Z DOI: 10.1177/23998083241264321
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Authors:Kanta Sayuda, Hiroyuki Usui, Yasushi Asami, Kimihiro Hino Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In Japan, shrinking densely built-up cities face the dual issue of lacking open spaces and increasing underutilized lands, such as vacant lots, lots with vacant houses, and parking lots. These unused land patches can be temporarily repurposed as open spaces for evacuation and recreation. However, identifying such clump is methodologically challenging. To address this issue, lot geometry is utilized. The study thus aims to investigate the frequency and size of contiguous underutilized lands, called the contiguity of underutilized lands, at a specific time point and under their temporary uses. A densely built-up area in Kobe city, Japan, was selected for the empirical case study. A comparison with simulation results shows that the observed static contiguity of underutilized lands tends to be more substantial than a uniformly random distribution. It shows a certain feasibility of a temporary use policy conducted in the case site. Specifically, when considering the temporary uses of underutilized lands, the maximum area of contiguous temporary open spaces is 583 m2, meeting the area requirement for a redevelopment project in Kobe. Utilizing parking lots can further extend the maximum area up to 945 m2. Nevertheless, policy makers need to promote the joint development of privately owned lots facing a wide roadway, as these are unlikely to become temporary open spaces. This study contributes not only to providing new methods for land use change simulation using lot geometry to analyse the contiguity of underutilized lands under their temporariness but also to demonstrating the feasibility and limitations of a temporary use policy. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-21T05:54:22Z DOI: 10.1177/23998083241265735
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Authors:Ate Poorthuis, Qingqing Chen, Matthew Zook Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In this article, we present a historical dataset of activity spaces, originally based on publicly posted and geotagged social media sent within the United States from 2012 to 2019. The dataset, which contains approximately 2 million users and 1.2 billion data points, is de-identified and spatially aggregated to enable ethical and broad sharing across the research community. By publishing the dataset, we hope to help researchers to quickly access and filter data to study people’s activity spaces across a range of places. In this article, we first describe the construction and characteristics of this dataset and then highlight certain limitations of the data through an illustrative analysis of potential bias—an important consideration when using data not collected through representative sampling. Our goal is to empower researchers to create novel, insightful research projects of their own design based on this dataset. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-07-18T01:01:10Z DOI: 10.1177/23998083241264051
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Authors:Quan Gao, Nan Wei, Qian Zhang, Siyu Zhou, Jing Xie Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-22T06:52:01Z DOI: 10.1177/23998083241264075
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Authors:Susan A Phillips, Michael C McCarthy Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-21T12:24:35Z DOI: 10.1177/23998083241262553
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Authors:Richard Burke, Raja Sengupta, Alistair Ford Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The use of land parcel data, 3D visualisation and urban theories offers a significant opportunity for advancing simulations of urban densification. This paper presents a 3D agent-based model (ABM) to explore future urban densification dynamics in Toronto based on stakeholder behaviour and interactions, the impact of zoning regulations, and profit expectations. The ABM establishes residents, developers, landowners, and the local zoning authority as primary actors involved in urban densification. This model replicates the Toronto urban development process through a structured framework of submodels which represent different stages of this process, based on the literature and gentrification theories. Three different scenarios are developed which show the city is projected to experience between 46 and 98 new developments by the year 2040. Average building height could increase by 17% to 56%, and the city could have 10,238 to 25,070 new units to meet future population demand. These simulations characterise Toronto’s future capacity for urban densification, realise the levels of densification required to meet Toronto’s growing population, and ultimately provide a more comprehensive understanding of the city’s future transformation. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-21T08:54:11Z DOI: 10.1177/23998083241261762
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Authors:Patrizia Sulis, Paola Proietti Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The scarcity or lack of access to essential services at the local and neighbourhood levels in cities can result in significant spatial inequalities, as some areas and their residents can deal with disadvantages and a lower quality of daily life. In particular, the spatial distribution and the variety of amenities at the local scale represent an important feature of the liveliness of places. The local availability and access to essential services are particularly relevant for some demographic groups experiencing limited mobility or mobility poverty, such as older adults living in cities, and spatial disparities have been further exacerbated by the COVID-19 pandemic, which highlighted severe difficulties in accessing essential services. This work explores the issue focussing on the following question: who can access what depending on where they live in cities' Using Machine Learning and Spatial Autocorrelation applied to different data sources for spatial information on the location of urban amenities and Internet access, this work aims to identify the most underserved places in terms of the variety of available amenities and access to quality broadband in three European capital cities. A comparison to urban areas where high percentages of older adults reside makes it possible to identify where residents can locally access several essential services (green spaces, health care, and local shopping) and where this need cannot be satisfied because of a lack in the amenity variety available at walking distance to their home. The combination of underserved areas with a high concentration of senior residents identifies left-behind areas in these cities, where interventions on inequalities are most needed. Results can inform policies aiming at favouring fair access to services at the local scale, possibly including slow and active mobility modes, and in general to develop comprehensive and sustainable planning strategies for cities, leaving no place and no person behind. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-21T07:52:18Z DOI: 10.1177/23998083241260757
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Authors:Yiming Tan, Zifeng Chen Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-20T12:21:40Z DOI: 10.1177/23998083241263385
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Authors:Víctor Cano-Ciborro, Ana Medina, Alejandro Burgueño, Mario González-Rodríguez, Daniel Díaz, María Rosa Zambrano Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This study evaluates the spatial behavior of an intermodal transportation hub in Carapungo, one of the densest neighborhoods in Quito, Ecuador. This public infrastructure is deficient and lacks adequate equipment for the people who use, occupy, and transit within and around it, as well as for the numerous activities that occur, particularly at Carapungo’s Entry Park. Traditional methods for analyzing urban dynamics and land use are typically rigid and fail to grasp the complex and nonlinear nature of public spaces, especially in informal Global South cities. However, recent advancements in Artificial Intelligence and Machine Learning, combined with aerial drone videos, have enabled the modeling and prediction of urban dynamics beyond state regulations and formal planning. In this context, we developed a model using Computer Vision Technology and the YOLOv5 algorithm, incorporating Deep Learning training. The objective is twofold: firstly, to detect people, their movement and speed; and secondly, to produce “Occupancy” and “Count & Speed” cartographies that highlight commuters’ spatial patterns. These situated cartographies provide valuable insights into urban design, mobility, and interaction within a conflicted public space’s-built environment. The generated data offer planners and policymakers quantitative spatial information to consider local practices and dynamics in urban planning, particularly in situations of informality and insufficient urban infrastructure. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-20T11:19:06Z DOI: 10.1177/23998083241262548
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Authors:Anqi Wang, Wei Zheng, Zheng Tan, Mingqing Han, Edwin HW Chan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban renewal in high-density cities presents a complex challenge when it comes to balancing social-environmental performance and economic benefits; improvements to the built environment and social wellbeing may be associated with substantial costs and economic loss, and particularly so where land resources are scarce and highly valued. The interplay that takes place between sustainable targets tends to be very complicated. This study proposes a decision-making support framework that can quantify the synergies and trade-offs between economic, environmental, and social targets pertaining to land use change and public open space (OS) provision in urban renewal processes. The proposed decision-making support framework operates at both neighbourhood and building levels, and is comprised of three analytical components: a redevelopment trend analysis module, a three-dimensional land use simulation module, and a sustainable performance evaluation module. One high-density and ageing district in Hong Kong, Yau Mong district, was selected as the case study area for this work. Six planning scenarios were built which reflect various priorities and principles including economic benefits, environmental benefits, the equal distribution of OS provision and enhancing the quality of OS. The findings suggest that there is a trade-off relationship between economic-environmental targets, a synergic relationship between social-environmental targets, and a mediational relationship between economic-social targets. Planning strategies such as rezoning, land use reconfigurations, plot ratio adjustment and the transfer of development rights could be triangulated as strategic approaches by which to maximising the synergies and achieve better sustainability. The study not only contributes to theory by introducing a prototype of a comprehensive decision-making framework to evaluate sustainability performance, but also provides important insights into reconciling the divergent sustainable targets inherent in urban renewal. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-20T01:39:52Z DOI: 10.1177/23998083241261750
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Authors:Anat Talmor Blaistain, Dafna Fisher-Gewirtzman Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The field of generative design strives to enable the automated generation of multiple suitable urban design alternatives. However, quantitative analysis is typically conducted in the later design stages, when options are limited, and modifications are costly. Generative methods enable the creating of designs that bridge traditional methods, through systematic examination of numerous options during the early design stages. Yet their utilization in architectural practices is limited, partly due to antagonism that stems from the perceived lack of involvement of urban designers in such methods (which often behave like black boxes). The objective of this study was to develop a generative design process that combines rule-based automated algorithmic processes with the active involvement of urban designers, thereby offering a wide range of design alternatives during the planning phase. Additionally, the study aims at ensuring the key involvement of urban designers throughout the design process. The methodology employed in this study is comprised of both quantitative and subjective evaluations that the urban designers can conduct themselves. This result is an interactive process that utilizes generative tools and computerized analytical measures for creating, evaluating, and screening multiple urban design options at the urban neighborhood level, with an emphasis on residents’ well-being, and based on the designers’ preferred parameters. This parametric workflow could assist designers in the early decision-making process and may be integrated into current urban design processes. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-18T11:47:34Z DOI: 10.1177/23998083241261767
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Authors:Céline Van Migerode, Ate Poorthuis, Ben Derudder Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This paper describes flexurba, a software library written in R, with the first open reconstruction of the Degree of Urbanisation algorithm to classify cities, towns, and rural areas. The R package offers enhanced flexibility and facilitates constructing alternative versions of the Degree of Urbanisation classification by customising parameters such as the minimum population size required for a city, and more ‘hidden’ implementation details including the contiguity rules and edge smoothing procedures. To illustrate how the package can be employed, we briefly demonstrate the grid classification and spatial units classification for Belgium. In addition, we compare results generated by the flexurba package with the official classification and discuss potential use cases. The package enables a broad range of analyses beyond the Degree of Urbanisation’s original application, including evaluating alternative urban delineations, sensitivity analyses, and comparative research. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-18T07:48:21Z DOI: 10.1177/23998083241262545
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Authors:İrem Kale, Tutku Didem Altun Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban design is a complex and challenging process that encompasses many disciplines, such as urban planning, architecture, and landscape. On the urban design scale, it’s important to consider short paths compatible with pedestrian behaviors, especially in public spaces, in terms of designing healthier and more accessible urban areas. But conventional design processes, which are mostly based on the designer’s observations or intuitions, fail to satisfy user behaviors in usage. Studies reveal that P. polycephalum slime molds have the potential to guide good urban planning. However, it is noteworthy that there are not enough studies yet in the disciplines of architecture and urban design on the design of pedestrian ways in small-scale areas, and there is a need for research. In this direction, this study is the first step of a method that is aimed at being developed for the disciplines of architecture and urban design in the long process. The main hypothesis is that a design template can be created as a result of graphical analysis of living slime mold experiments on the map, especially in undesigned empty urban areas, for architects and planners, and it can provide creative suggestions to the designer. In this direction, in this study, a graphic design template proposal was created based on live cell experiments at a site in Izmir, Turkey, which the local government aims to design as a pedestrianized zone and open for competition. Slime molds are used directly as a computer rather than as inspiration for a computer, and a pedestrian-oriented design template proposal develops with the behavioral models of slime molds. Compared to the award-winning designs developed for the same area, it has been shown that even within the framework of simple graphical measurements, it gives better results and creates more optimal routes on main and side roads. It is envisaged that this study can play a pioneering role for studies involving more detailed mathematical models that can begin in the field of architecture and urban design. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-17T10:18:31Z DOI: 10.1177/23998083241239104
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Authors:Thiago H Silva, Daniel Silver Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban research has long recognized that neighbourhoods are dynamic and relational. However, lack of data, methodologies, and computer processing power have hampered a formal quantitative examination of neighbourhood relational dynamics. To make progress on this issue, this study proposes a graph neural network (GNN) approach that permits combining and evaluating multiple sources of information about internal characteristics of neighbourhoods, their past characteristics, and flows of groups among them, potentially providing greater expressive power in predictive models. By exploring a public large-scale dataset from Yelp, we show the potential of our approach for considering structural connectedness in predicting neighbourhood attributes, specifically to predict local culture. Results are promising from a substantive and methodologically point of view. Substantively, we find that either local area information (e.g. area demographics) or group profiles (tastes of Yelp reviewers) give the best results in predicting local culture, and they are nearly equivalent in all studied cases. Methodologically, exploring group profiles could be a helpful alternative where finding local information for specific areas is challenging, since they can be extracted automatically from many forms of online data. Thus, our approach could empower researchers and policy-makers to use a range of data sources when other local area information is lacking. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-14T12:04:30Z DOI: 10.1177/23998083241262053
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Authors:Jue Yang, Lan Mu, Diana S Grigsby-Toussaint Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Outdoor activities confer mental, physical, and social health benefits to children. In urban areas, parks are vital for children to engage in outdoor activities. Understanding public perceptions and expectations of how urban park environments influence children’s activity could attenuate social and physical inequities and promote park use. Previous studies have used surveys and observational data to analyze preferences for children’s activity in parks. Crowdsourced online data and volunteered geographic information (VGI) could offer a valuable addition to or substitute for traditional approaches. Our study uses social media as a passive data collection method to ascertain public perceptions and expectations about children’s activity in Atlanta parks with a shorter timeframe, compared to a regular survey analysis. We collected 4026 Google Map reviews from 2017 to 2022 that related to children and applied text mining analysis to understand how people perceive children’s activity in Atlanta parks and how perceptions change across different environments. Ten topics were extracted that related to children’s activity in Atlanta parks: attitude, amenity, safety, social, pet, nature, recreational, water sports, water recreational, and sports. The attitude, social, and water recreational topics were the most widely discussed topics in all settings. However, in racially diverse and low-crime environments, discussions centered on pet, recreational, and sports, suggest the importance of tailored strategies to promote children’s activities in parks. Park planners and policymakers can use this approach and findings to evaluate and advocate for children’s activities in urban parks. Also, our work helps to expand survey analysis with passive data collection methods from small geographic scales to larger areas and to apply geoanalytics of big data and social media data in investigation research. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-14T10:57:25Z DOI: 10.1177/23998083241260484
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Authors:Wenfei Xu Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Mid-20th century urban renewal in the United States was transformational for the physical urban fabric and socioeconomic trajectories of neighborhoods and its displaced residents. However, there is little research that systematically investigates its impacts due to incomplete national data. This article uses a multiple-model machine learning method to discover 204 new Census tracts that were likely sites of federal urban renewal, highway construction related demolition, and other urban renewal projects between 1949 and 1970. It also aims to understand the factors motivating the decision to “renew” certain neighborhoods. I find that race, housing age, and homeownership are all determinants of renewal. Moreover, by stratifying the analysis along neighborhoods perceived to be more or less risky, I also find that race and housing age are two distinct channels that influence renewal. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-14T05:53:39Z DOI: 10.1177/23998083241260778
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Authors:Lin Liu, Yi Sun, Wanwu Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The degree of urban development depends on the degree of closeness between cities, which is reflected in the strength of interactions between cities, such as traffic and information flows. In this research, we compare and analyze the characteristics of urban interaction networks (UINs) at two spatial scales in Shandong Province and the whole country from two different perspectives of traffic flow and information flow and validate the spatial interaction characteristics reflected in the material space traffic flow from the perspective of textual spatial information flow. The UIN is constructed based on Tencent migration big data, and the assortative coefficient method is introduced to explore the assortative and interaction characteristics between core cities and edge cities in the traffic flow network. Introducing deep learning methods on a larger scale, the GCN_CD model is proposed for semi-supervised classification of nodes to realize community discovery for both traffic flow and information flow networks. The spatial interaction intensity prediction model [math] is constructed taking into account geographic features, which improves the prediction accuracy. The results show that from the perspectives of traffic flow and text information flow, the urban interactive network in Shandong Province has the characteristics of Scale-Free and Small-World, showing certain homogeneity and strong spatial interaction. Shandong Province’s UIN formed Jinan, Qingdao, and other cities as the core, satellite cities around, the east and west ends of the remote echo structure. From the perspective of traffic flow, the national urban interactive network presents a jumping distribution, with a single core city as the dominant distribution structure. From the perspective of information flow, the dividing line between the eastern and western communities is obvious, and the internal aggregation of the community is strong. Distance attenuation effects have an impact on the strength of spatial interactions. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-12T11:55:26Z DOI: 10.1177/23998083241259814
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Authors:Andres Sevtsuk, Raul Kalvo Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The global climate-change crisis, along with public health and economic competitiveness challenges in cities around the world have underscored the need for analytic tools to examine the relationship between city design and sustainable mobility. Car-centered travel demand models and land-use-transportation interaction models have historically analyzed zone-to-zone trips along major roadways, largely omitting pedestrian and bicycle trips and creating a gap in the ability for planners and urban designers to systematically assess non-motorized outcomes of development interventions. We present the Urban Network Analysis tools to address this gap. UNA tools offer an accessibility-based framework for analyzing how built environments influence pedestrian travel in both existing and newly planned built environments. Developed as a free plugin for Rhinoceros 3D since 2015 and applied in several cities and research projects internationally, this paper describes the current Urban Network Analysis modeling framework and discusses the unique contributions the framework offers compared to existing pedestrian modeling approaches. Using Somerville, MA as case example, we demonstrate several commonly used functions for planners: examining pedestrian accessibility over networks; identifying critical walking routes to destinations; estimating foot-traffic on street segments; identifying frustration points for pedestrians; and evaluating how development changes may impact pedestrian activity in their vicinity. Such analyses can provide analytic evidence to pedestrian infrastructure planning and investment, and enable planners, designers, and policy makers to prioritize projects that increase sustainable mobility outcomes. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-12T08:19:16Z DOI: 10.1177/23998083241261766
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Authors:Bisong Hu, Bin Jiang, Jin Luo, Tingting Wu, Hui Lin Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The term “natural cities” refers to human settlements or human activities in general that are naturally or objectively defined. In this study, we utilized the head/tail breaks method to identify natural cities and characterize their living structures in mainland China from 2000 to 2020. This identification was based on nighttime light (NTL) and gridded population distribution (GPD) data, which, respectively, indicate urban areas and residential settlements. Furthermore, we explicitly identified the evolutions of two distinct categories of natural cities and the dynamics of their inherent living structures. Our objective was to verify that the head/tail breaks method is a powerful approach to deriving natural cities that signify various types of human settlements from diverse data sources, while also effectively characterizing their living structures. Also, this article contributes a novel perspective to examine the inequality between the urban-area expansion and the evolution of residential settlements in urban areas (or urban settlements). Our findings reveal a substantial increase in both the number and sizes of urban areas over time. However, there was an intriguing trend observed in urban settlements, where an increasing number corresponded to a gradual decrease in size. Additionally, the inequality exhibited regional disparities and rapidly developing regions showed a higher potential to enter a turning year of urban areas surpassing urban settlements. The dynamics of living structures show that urban settlements always had more hierarchical levels, more substructures, a much higher degree of order, and a much more living (orderly or beautiful) structure than urban areas. The urban-area expansion in mainland China was extremely rapid over the past decades, but the evolution of urban settlements was more “natural” than that (i.e., it consistently generated a living structure characterized by a higher degree of order). Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-12T07:31:13Z DOI: 10.1177/23998083241261764
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Authors:Nick Malleson, Rachel Franklin, Daniel Arribas-Bel, Tao Cheng, Mark Birkin Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-12T02:28:13Z DOI: 10.1177/23998083241262893
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Authors:Zhu Chen, Hengzhou Xu Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban growth boundaries (UGBs) are commonly used for governance of urban growth. For demarcation of UGBs, the prediction models are prized for their automation and high efficiency in spatial data processing. However, public intentions, which cannot be predicted by prediction models, are ignored. This ignorance affects the performance of UGBs and urban development. From the perspective of regulatory tools, this article proposes public intentions and predicted trend as the two decisive elements for demarcation of UGBs. Following this thought, the study develops a method for coordination of their conflicts by providing a decision criteria for policy makers based on estimation of the cost to intervene predicted trend. This method is incorporated into a process of demarcation of UGBs and applied in the case of Tianjin, China. Results show that firstly, policy makers of UGBs should consider conflicts between public intentions and predicted trend and coordinate them for land development. Secondly, in the method for coordination, the decision criteria based on intervention costs can be used to make choice between public intentions and predicted trend and to choose suitable strategies for two types of conflicts. Thirdly, combined with the method for coordination, the process of demarcation is improved with multiple perspectives and methods for providing sufficient reference. Lastly in the case of Tianjin, China, it is verified that the decisions of land development and UGBs are made differently from the considerations of individual or public interests. To optimize the UGBs based on the method for coordination, four categories of strategies are matched with those conflict areas, and supporting measures are proposed correspondingly. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-10T12:35:58Z DOI: 10.1177/23998083241259200
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Authors:Francisco Escriva Saneugenio, Ahmed Alhussen, Alvaro Marucci, Luca Salvati, Leonardo Salvatore Alaimo, Ioannis Vardopoulos Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The sudden increase of residential swimming pools in fringe landscapes is reflective of urban sprawl and depends on intricate processes of social stratification and economic diversification. They are considered a dominant feature of low-density settlements expanding into rural areas. To achieve a better understanding of the spatial distribution of swimming pools in the context of varying mechanisms of wealth accumulation and income polarizations, a comparative analysis was conducted in three metropolitan regions of Southern Europe, specifically Barcelona (Spain), Rome (Italy), and Athens (Greece). The unique characteristics of the local context in each city were intended as a multivariate predictor of urban diversity and regional heterogeneity in the socio-spatial evolution of human settlements. To achieve this, a Canonical Correlation Analysis was run on five dependent variables (left set) that illustrate the spatial distribution of swimming pools and 50 indicators (right set) that characterize the socioeconomic context and the conditions leading to sustainable (local) development. The empirical results of this analysis delineate a consistent association between swimming pools and urban sprawl, regardless of the specific region/country under investigation. The spatial distribution and density of swimming pools also reflect the distinct local dynamics that underlie recent metropolitan growth, with implications for sustainable development on a broader scale. Due to the spatially varying economic foundations and socio-demographic circumstances, the distribution of swimming pools in Southern Europe reflects unique patterns of suburbanization. These patterns imply different forms of social diversification and economic polarization which, in turn, shape fringe landscapes, ranging from the micro-geography of neighbourhoods to the macro-scale of metropolitan regions. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-08T02:41:13Z DOI: 10.1177/23998083241259817
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Authors:Gitali Mandal, Subbaiyan Gnanasambandam Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Land surface temperature (LST) is an important parameter for investigating surface urban heat islands (UHI) at different scales of urban development. Previous studies on UHI have been limited in quantifying the magnitude, and very few studies explored in-depth relations between built-up density classes and LST. This paper focuses on the land surface cover factors contributing to changes in urban LST. The impact of urban surface cover indicators such as land cover (LC) groups, built-up density classes, and pervious surface fraction (PSF) groups on LST was investigated in this study. Bengaluru metropolitan region was selected as the study area. Seasonal LSTs were calculated using the 2013–14, 2019, and 2023 Landsat 8 images and validated with MODIS LST images. LC for the three time periods were generated using supervised classification and related to respective LSTs. A 500 m × 500 m grid was considered a unit area to calculate the percentage of LC classes. One-way ANOVA tests revealed significant differences in seasonal mean LSTs among seven LC groups, six percentage built-up area (PBA) groups and nine PSF groups. The study has found a moderate negative relation between PBA groups and mean LSTs due to rapid urbanisation towards fringe areas in 2019 and 2023, which led to vacant lands with higher mean LST values converted into built-up areas with comparatively lesser mean LST. The mean LST of 2023 was lesser than in 2013 and 2019 due to an increment of vegetation cover by 16.8% during the last decade, which turned Bengaluru’s central area into a surface urban cool island (SUCI). However, the study has also found that the high-density built-up areas with no vegetation or waterbody located in the periphery of the outer ring road and the central commercial area of Bengaluru generated more urban hot spots in 2019 and 2023 than in 2013. This study, carried out at a local level of urban development, is more relevant to the urban planners’ formulation of guidelines for ameliorating surface UHI. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-07T11:01:07Z DOI: 10.1177/23998083241256497
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Authors:Dakota McCarty, Dongwoo Lee, Yunmi Park, Hyun Woo Kim Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This study addresses the critical issue of road safety in urban environments, with a specific focus on the Greater London Area. Utilizing a novel, theory-driven approach, the study investigates the multifaceted impact of urban fabric factors on road safety, operationalized through a severity-weighted index of road accident frequency per capita. Through factorial analysis, six key factors (Urban Integration, Socioeconomic Challenges, Urban Amenities, Commuter Patterns, Housing and Mobility Barriers, and Major Urban Infrastructure) are identified. These factors are examined in relation to road safety using a structural equation model to uncover theoretical relationships, which inform predictive modeling with an XGBoost machine learning framework, enhanced by SHAP value analysis. Our findings reveal significant insights into the interplay between urban physical and social environments and road safety, revealing that integrated urban development strategies—encompassing improved urban integration, enhanced sustainable density, robust infrastructure development, alleviation of socioeconomic disparities, fostering of local employment, and integration of residents with transportation—are imperative for increasing road safety. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-07T10:25:59Z DOI: 10.1177/23998083241259069
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Authors:Rūta Ubarevičienė, Kadi Kalm, Maarten van Ham, Tautvydas Žinys, Jaak Kliimask, Tiit Tammaru Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This study examines how socio-spatial inequalities are associated with population concentration and de-concentration processes shaped by residential mobility. The study explores whether the patterns of residential mobility vary in different settlement system contexts. It reviews the cyclical urbanization models and the inequality of opportunities they provide in urban, suburban, and counter-urban contexts for individuals in various life stages. The theoretical models are tested by analysing individual-level data covering the entire populations of Estonia and Lithuania – two countries with similar social but different settlement system contexts. The study utilizes linked individual-level data from the 2011 and 2021 censuses, and harmonized variables in the two countries. The results show that individuals engaging in concentration, suburbanization, or de-concentration have distinct characteristics, with little differences between countries characterized with different settlement systems. While the life-course approach assumes that young people are most likely to urbanize (concentrate), those in family ages shift towards suburbanization, and older individuals tend to counter-urbanize (de-concentrate), our findings challenge these assumptions, demonstrating that young adults have a high likelihood of migration in all three directions. These findings call for more in-depth studies on the interplay between age and migration patterns that would go beyond the life-course approach and delve deeper into the residential decision-making of young people. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-06T11:21:56Z DOI: 10.1177/23998083241257013
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Authors:Shu Wang, Debra F Laefer Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This paper introduces an extensible framework to predict small-business closures to inform urban planners, lenders, and business owners as to factors to improve business resilience. This paper couples machine learning with two point of interest (POI) datasets and infrastructure data and uses New York State’s COVID-19 PAUSE as a stressor for investigating small-business resiliency. The study included 2537 food-related, non-chain, retail businesses across select New York City zip codes, of which 17.7% closed permanently. Macro-, meso-, and micro-levels of features included the neighborhood profile, street dynamics, and venue-specific, location-related characteristics. A Gaussian Mixture Neural Network model achieved 74.1% precision, 92.5% recall, and an 82.3% F1-score without use of financial data. High-end restaurants located further than average from public transit were most at risk for closure, while non-restaurant, food businesses in commercially diverse areas having higher-than-average social media ratings were least at risk. This paper introduces a model for timely prediction of pandemic-induced, food-related, small-business closures without reliance on private or protected financial data, and provides insights into urban design to promote small, food business survivability. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-05T10:10:38Z DOI: 10.1177/23998083241254573
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Authors:Guiyu Chen, Chaosu Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban spaces distinctly modulate the mobility patterns of men and women, with new mobility modes manifesting gender differences. In this study, by visualizing bike-sharing mobility patterns in New York City, we reveal significant disparities in cycling usage between males and females. During weekdays, the findings highlight a pattern of male dominance in most areas, particularly in business districts. In some recreational and residential areas, routes with higher proportions of female cyclists are observed. Additionally, weekends experience a surge in the proportion of female cyclists, predominantly in leisure-oriented locations. These findings highlighted the need for urban planning to account for gender differences across space and time to meet diverse mobility needs. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-04T12:10:18Z DOI: 10.1177/23998083241258521
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Authors:Jiaqi Zhang, Longfeng Wu Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Green-space-triggered gentrification, wherein original residents are displaced by wealthier individuals owing to the creation of new green spaces, has been criticized for exacerbating environmental injustice. While previous studies have explored green-space-triggered gentrification in individual cities, few have examined heterogeneities across multiple cities, especially in developing countries where cities are at different stages of expansion. The roles of green space in different stages of urbanization can produce varying outcomes of gentrification. This study investigated the relationship between green space and gentrification in Chinese cities from 2012 to 2020. The normalized difference vegetation index (NDVI), distance to adjacent parks, and area of adjacent parks were used as indicators of green space, while nighttime lights and residential land prices were used as proxies for gentrification. Nationwide analyses indicated that both increasing NDVI and building new parks nearby could lead to gentrification, but the park area had a marginal effect. Stratified analyses further showed that the effect of green space on gentrification was related to the different roles of green space associated with the stage of urbanization. Cities with higher urbanization rates were more affected by NDVI but less affected by park distance. Our findings provide insights for urban planners and decision-makers on developing localized strategies that mitigate the varying outcomes of gentrification at different stages of urban development. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-03T12:10:14Z DOI: 10.1177/23998083241258683
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Authors:Prince M Amegbor, Rikke Dalgaard, Doan Nainggolan, Anne Jensen, Clive E Sabel, Toke E Panduro, Mira SR Jensen, Amanda E Dybdal, Marianne Puig Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Living in urban areas is known to increase the risk of psychosocial disorders, including stress, depression, and anxiety. Existing studies suggest that experiential places, including places of interest or favourite places, can mitigate these negative effects on psychological and physical health often associated with urban living. This study aims to model the spatial patterns of the benefits derived from favourite locations in two cities in Denmark: an urban metropolitan area (the capital city) and a provincial commuter town. Additionally, it examines the influence of individual and household socioeconomic factors on the benefits derived from these favourite places. Employing an online Public Participatory Geographic Information System (PPGIS) approach, data on favourite locations, derived benefits, and socioeconomic characteristics of 1400 respondents were collected. Bayesian modelling with Stochastic Partial Differential Equations under the Integrated Nested Laplace Approximation framework (INLA-SPDE) was utilized to predict the spatial patterns of four types of benefits – restorative, physical activity, socializing, and cultural – associated with enjoying favourite places in the two municipalities. This geostatistical approach allows for the identification of specific locations within the cities with perceived benefits and areas lacking such benefits. The findings provide insights into potential inequalities in the spatial distribution of perceived benefits of favourite places in Copenhagen and Roskilde, thereby informing urban planning policies and programs aimed at addressing these disparities. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-06-01T03:20:30Z DOI: 10.1177/23998083241255984
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Authors:Weiyao Yang, Wanglin Yan, Lihua Chen, Haopeng Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Transit-Oriented Development (TOD) is regarded as a planning concept for urban sustainable development that has been increasingly embraced worldwide in recent years. However, scholars rarely assess the sustainability of Transit-Oriented Development (TOD) station areas over a period of time. This study, building upon the existing TOD assessment model based on node-place-ecology, introduces the concepts of “low-carbon cities” and a “timeline-based.” Focuses on whether the three dimensions of TOD station areas—node, place, and carbon—are dynamically balanced over time towards sustainable development. More specifically, this study starting from a micro-level perspective, takes the 70 stations on the Odakyu line as a clue, a railway that spans Tokyo and Kanagawa, aiming to develop a quantitative assessment model for sustainable TOD based on node-place-carbon, and to summarize the spatial dynamic changes of the 70 station areas in the Tokyo metropolitan area from 2011 to 2019 through the principles of sustainable development line (SDL) and K-means cluster analysis. The results indicate that after 8 years, the overall development of the station areas is moving towards sustainability, but there are still some station areas that deviate to some extent. And also, we observed that the trend of monopolization in the central station areas of the Tokyo metropolitan area is continuously strengthening. We believe that the sustainable assessment model developed in this study can provide constructive reference for the planning and design of cities, especially metropolitan areas, around the world. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-31T04:18:13Z DOI: 10.1177/23998083241258240
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Authors:Jiarui Qin, Ziyang Wang, Yehua Sheng, Li Xue, Xiaolan Cai, Ka Zhang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban vitality reflects the dynamism, openness, available resources, and linkages between different elements in a city. Many studies have focused on the relationship between urban vitality and the built environment, which can benefit urban planners. Based on multi-source data, this study incorporates the aspects of economy, society, culture, and innovation to comprehensively measure urban vitality. Recognizing the significant influence of the built environment on urban vitality, this paper takes Nanjing as the study area. It explores the relationship between urban vitality and the built environment using ordinary least squares and multi-scale geographically weighted regression models based on multi-source data. The results reveal multi-centered urban vitality within Nanjing’s central urban area, which gradually decreases outwards from the city center. Particularly, the southern region has a higher comprehensive vitality than the northern region. While differences in vitality between dimensions are evident, an overall consistent pattern emerges. The spatial distribution of influence varies across different spatial factor. The method provided in this study gives a new view of urban vitality measurement and evaluation. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-31T02:08:37Z DOI: 10.1177/23998083241256246
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Authors:Achituv Cohen, Sagi Dalyot, Asya Natapov, Trisalyn Nelson Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban planning and design aim to encourage active mobility by promoting various models that assess a city’s transportability and accessibility. In practice, these models are not attuned to a huge part of the population that have mobility impairments, therefore they uphold a flawed city design and prevent these populations from being an equal part of the inclusive city vision. We suggest an approach to develop new visually impaired mobility accessibility indices of urban space using open-source geospatial data and showcase them across different wards and boroughs in Greater London. Results show the various urban accessibility levels for visually impaired pedestrians, pointing to existing problems this community faces when navigating the city, such as challenging street network connectivity and dangerous walking areas. These indices can be used for more inclusive city planning and design, enhancing urban mobility and walkability equality, and improving this community’s quality of life. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-30T04:44:49Z DOI: 10.1177/23998083241256402
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Authors:Weicong Luo, Jing Yao, Richard Mitchell, Xiaoxiang Zhang, Wenqiang Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Emergency Medical Services (EMS) play an essential role in saving lives and improving health outcomes by offering immediate medical care to individuals who experience sudden illnesses or injuries. A complete EMS journey consists of two related trips: one from an EMS station to a scene (Trip 1), and the other from a scene to a definitive care location (Trip 2), where the service is coordinately provided by two types of facilities: EMS stations/ambulances and emergency centers (e.g., trauma centers or stroke centers) that are often affiliated with general hospitals. Current work on EMS location optimization considers only one trip (Trip 1 or Trip 2) which ignores the coordination between EMS stations and emergency centers, or the overall trip alone that overlooks the response time requirement. This paper proposed a spatial optimization model, the maximal coverage location problem based on joint coverage (MCLP-JC), for siting EMS stations and emergency centers simultaneously with a consideration of the two related trips. An empirical study of stroke center planning in Wuhan, China, is implemented to compare the proposed approach with the maximal coverage location problem based on overall coverage (MCLP-OC). The results demonstrate that the MCLP-JC can ensure more people being able to receive the first care from an ambulance within the response time requirement, which is critical to subsequent treatment at emergency centers and the odds of survival. The findings from the two scenarios regarding service relocation and expansion offer insights for future health facility planning. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-29T11:20:52Z DOI: 10.1177/23998083241253108
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Authors:Luiz Pedro Couto Santos Silva, Alexandre Alves Porsse Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Accessibility plays a fundamental role in improving urban economic equity, especially for disadvantaged communities. In many metropolitan areas of Brazil, accessibility relies on infrastructure unequally distributed, creating barriers to access opportunities in the urban labor market. This paper aims to identify sources of spatial mismatch and measure its effects on the labor market of the Curitiba Metropolitan Region (CMR), in Brazil. First, we measured the spatial imbalances of households and jobs by income classes and their effects on the spatial dissimilarity of accessibility to job opportunities. Second, we use econometric tools to evaluate how accessibility to the formal job sector affects individual earnings in the CMR labor market by wage quartile. We find evidence of strong spatial segregation for the poorest individuals from public transit network and from the formal job sector. The econometric results show there was roughly no accessibility premium for individual earnings for the poorest workers and positive effects for the richest workers in the CMR labor market. This set of empirical evidence suggest a spatial concentration of urban amenities that enhance economic inequality for the CMR. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-22T06:19:24Z DOI: 10.1177/23998083241254567
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Authors:Shaojun Liu, Yi Long, Ling Zhang, Jing Yang, Wenfei Dong Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban space vitality is a critical indicator for supporting rational urban spatial planning and updating and formulating sustainable development strategies. However, in many areas (e.g., aging urban areas), there is often a mismatch between the conditions of the physical built environment and its spatial attractiveness. Traditional methods based on physical space design theory often fail to accurately measure the spatial vitality of these areas. Street view images directly reflect the actual construction situation and effectively compensate for the lack of visual, subjective, perception dimension information. This study proposes a novel method that integrates objective and subjective dimensions to measure urban vitality, which is captured by incorporating spatial data of points of interest, building outlines, road networks, and street view images. Then, taking mobile phone signaling data as a source of ground truth validation, we choose Nanjing as a case study to demonstrate that our multidimensional fusion method exhibits higher explanatory power and better alignment with actual conditions by comparing it against single-dimensional methods. The results underscore the importance of integrating subjective and perceptual dimensions in measurements of urban vitality. We believe that the localized samples of the subjective perception survey will further enhance the accuracy and generalizability of this method in the future. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-21T10:56:43Z DOI: 10.1177/23998083241256704
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Authors:Shifa Ma, Dailuo Zhang, Yabo Zhao, Xiwen Zhang, Lingling Wu, Yunnan Cai Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The use of cellular automata (CA) is essential for exploring future urban growth scenarios in spatial planning. However, modeling polycentric urbanization processes with CA is still challenging due to the presence of spatial spillover effects arising from spatial interactions between different regions. This study proposes a hybrid framework that addresses the spatial spillover effect that emerges from multi-centers by coupling a radiation model (RM) and Markov chain (MC) with CA to simulate polycentric urbanization processes. The simulation capabilities of the RM-MC-CA framework were evaluated and validated by simulating Guangzhou’s actual urban growth from 2000 to 2020, and the future urban growth scenarios of 2035 and 2050 were simulated with this coupled model. Results showed this framework provides a spatio-temporal diffusion process consistent with the cooperative mechanism of urbanization from monocentric to polycentric. In terms of simulation accuracy, the proposed RM-MC-CA framework demonstrated the most promising performance compared to MC-CA, GM-MC-CA, and PLUS. Compared to classical MC-CA, the framework improved the Kappa, FOM, and Precision metrics by 0.54%, 3.93%, and 2.38%, respectively. These results indicated that incorporating spatial spillover processes into a CA model can enhance its ability to simulate polycentric patterns that promote high-quality urban development. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-21T06:47:58Z DOI: 10.1177/23998083241255983
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Authors:Ying Lu, Chloe Keel, Rebecca Wickes, Danielle Reynald, Jonathan Corcoran Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The Space-Time Budget (STB) method is used to collect spatial and temporal features of an individual’s activities and is an important technique to explore relationships between the environment and crime. This spatial and temporal approach to the study of everyday activity spaces reveals much about victimisation experiences, the relationship between place, time, and perceptions of safety, and where and when offending may occur. Few studies, however, consider the relationship between living in a criminogenic place (neighbourhood crime) and people’s daily activity patterns. Drawing on disaggregate app-based data for 50 participants tracked over a 7-day period, we use sequence analysis to first delineate time allocation to each activity on a minute-by-minute basis. Next, using result from the sequence analysis we introduce the multiple discrete-continuous extreme value (MDCEV) model to understand how neighbourhood crime and socio-demographic characteristics influence individuals’ time allocation to discrete daily activities. Results reveal that neighbourhood drug and violent incidents exert restrictions on an individual’s propensity to spend time outside the home. Females and older people appear more likely to be constrained by the presence of neighbourhood crime (in particular drug and property incidents) as they allocate less time to outdoor activities. The principal utility of the current study is its methodological advancement and the practical insights for urban planning regarding the design of crime prevention strategies to increase guardianship in public places. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-21T03:41:11Z DOI: 10.1177/23998083241255491
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Authors:Teemu Jama, Henrikki Tenkanen, Henrik Lönnqvist, Anssi Joutsiniemi Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Many scholars and planners emphasise the role of compact cities in sustainable urban development. Compact urban form is seen as a way to encourage people, for example, to drive less and walk more, which reduces transport-related GHG emissions. This argument, however, is strongly dependent on local amenity development that can support such local living. In plan-making, a common practice is to try to ensure the realisation of services with a high Gross Floor Area (GFA) residential infill development to raise the local population density. In this paper we are seeking quantitative insight on the resolution under which urban density, as measured by GFA volume, correlates with the growth of urban amenities and liveability. Specifically, we are seeking the direction in which correlation changes when moving from a larger geographical scale (low resolution) towards a smaller scale (high resolution) of walkable reach. Our study shows a clear correlation between urban amenities and planned GFA at low-resolution scales, but that correlation decreases at higher-resolution scales (walkable neighbourhood level), indicating that urban amenities tend to cluster in different locations than density is planned. Based on these findings, we argue that, if the aim of urban planning is to foster the growth of local amenities, it should shift its focus towards larger patterns of urban development rather than emphasising GFA in detailed plan-making. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-06T09:37:56Z DOI: 10.1177/23998083241250264
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Authors:Liliana S Valverde-Caballero, Luis M Mendoza-Salazar, Cinthya L Butron-Revilla, Ernesto Suarez-Lopez, Jesus S Aguilar-Ruiz Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Walkability principles are an important part in the planning process of cities that face urban problems such as gentrification, pollution, and decay of their built heritage. The proposed factors – connectivity, proximity, land use mix, and retail density – form a comprehensive framework for evaluating walkability that transcends the boundaries of historical cities. These factors, while initially identified within historical contexts, possess inherent qualities that render them universally adaptable to various urban landscapes. By leveraging these factors, urban planners gain insights into the intricate fabric of pedestrian experiences in cities. They serve as universal evaluative tools, applicable not only to historical cities but also to burgeoning metropolises and smaller urban centres. This work introduces a novel approach to assessing the Walkability Index for World Heritage Cities, utilizing a Multiple Criteria Spatial Decision Support System (GIS-MCDA) structured in four stages. The approached methodology is particularly valuable for governments and decision-makers in developing countries of the Global South, where limitations in data and available tools are common challenges. The insights gained from this study can guide the improvement of policies, enable more precise implementation of sustainable mobility infrastructure, and motivate the pursuit or maintenance of UNESCO World Heritage nominations. The case study focused on the Historical Centre of Arequipa, Peru, a city designated as a UNESCO World Heritage site. The results of this study demonstrate the effectiveness of the proposed approach in such contexts, owing to its specificity and the integration of both objective and subjective elements. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-05-01T12:08:01Z DOI: 10.1177/23998083241250265
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Authors:Jessica Gosling-Goldsmith, Sarah Elizabeth Antos, Luis Miguel Triveno, Adam R Benjamin, Chaofeng Wang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Those who work in the design, development, and management of cities are often limited by the scarcity of data. Particularly in the Global South, urban databases may be insufficient, out of date, or simply not available. However, digital technology is making it possible to fill gaps and build substantial datasets using “urban clues,” or attributes, gathered in high-resolution imagery by sky- and street-based cameras. Aided by machine learning, it is possible to detect specific building characteristics (purpose, condition, size, material, and construction)—yielding an array of geolocated details about the built environment. The resulting composite view can be made available, as we have done, in an open-source portal for use in urban management. The insights gained in this way may help address common urban management challenges, such as locating homes vulnerable to hazards such as flooding or earthquakes, identifying urban sprawl and informal housing, prioritizing infrastructure investments, and guiding public program support. This approach has been applied in Colombia, Guatemala, Indonesia, Mexico, Paraguay, Peru, St Lucia, and St Maarten. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-30T11:55:44Z DOI: 10.1177/23998083241247870
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Authors:Xiang Liu, Jing Fan, Zongshi Liu Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The geographic assessment of population changes provides fundamental insights into understanding urban development and addressing future urbanization challenges. In this graphic, we produced a Dorling cartogram to geo-visualize population changes at the city level across China between 2010 and 2020. The cartogram illustrates how internal migration fuels China’s growing population concentration and regional disparity, leading to significant population loss in lower administrative-level cities and escalating intercity imbalances across the country. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-27T01:54:40Z DOI: 10.1177/23998083241249592
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Authors:Bailing Zhang, Jing Kang, Tao Feng Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-26T04:54:14Z DOI: 10.1177/23998083241249322
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Authors:Armand Pons, Olivier Finance, Alexis Conesa Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. A range of accessibility indicators has been developed in the past decade to evaluate equity in transportation within urban areas. Some studies have attempted to incorporate them in transport poverty metrics, focussing on insufficient access to general services and employment. While accessibility measures coupled with statistics have been effective in assessing immediate households’ vulnerability, we argue that an analysis of their adaptive capacity could contribute to a better information of local policies in the long term. This paper aims to develop a methodology for mapping transport poverty risks at the metropolitan scale, while studying the relation between urban segregation and the transport divide. We use the case study of Lyon to operationalise our method and find evidence of vulnerability patterns previously identified in the sociological literature. Beyond the sensitivity of households living in the first-crown neighbourhoods and the growing exposure of medium-income families settling in peripheral municipalities, we emphasise the importance of using mixed methodologies to better capture households’ needs and mobility choices within suburban environments. We conclude by discussing shortcomings and future developments of our research. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-22T06:39:11Z DOI: 10.1177/23998083241246377
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Authors:Yael Nidam, Reann Gibson, Rebecca Houston-Read, Marcia Picard, Vedette Gavin Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Co-creation of urban data and informatics with community partners facilitates the development of insights and actions that are grounded in residents’ experiences and aimed at achieving social change. Despite rising interest in co-creation strategies, instructive guidance on implementation remains scarce. To address the shortage of instructive guidance, we provide a detailed account of how the resident, community, and institutional partners in the Healthy Neighborhoods Study co-created a dataset on neighborhood health, and a data dashboard that allows for all partners, as well as the public, to access and use the data to support neighborhood-level action and regional planning. We focus on the collaborative and iterative design process used to co-create a digital tool to access, analyze, interpret, and communicate community-generated data. While co-creation strategies require an upfront investment in relationship building and facilitating a more complex process, they carry significant benefits in producing truly representative datasets and data tools and services that advance data inclusion, laying the foundation for social change. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-22T05:30:50Z DOI: 10.1177/23998083241245759
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Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-18T04:53:38Z DOI: 10.1177/23998083241247523
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Authors:Ana Luisa Maffini, Gustavo Maciel Gonçalves, Clarice Maraschin, Jorge Gil Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Accessibility and mobility are key concerns of sustainable cities, especially in the Global South, due to the strong social inequalities. This paper contributes to the literature on mobility segregation by focusing on the potential movement of social groups in the city. We conceptualize potential movement as a network centrality, acting as an indicator of population movement when performing daily activities (working, studying, shopping, etc.). This paper’s objectives are (a) to identify the inequalities in potential movement of different social groups performing their daily activities; (b) to propose a network-based method to enhance our understanding of mobility inequalities; and (c) to address the context of medium-sized Latin American cities. We adopt a modified Betweenness Centrality model (Potential Movement) on a directed and weighted network. Our results show a similar pattern for both cities, with the CBD concentrating the potential movement for all groups; however, several inequalities were found. The high-income and white groups show higher levels of potential movement in the CBD and the low-income and non-white groups have a more distributed potential movement pattern, implying longer journeys to reach jobs and services. Income and race have shown to play a crucial role in those inequalities. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-18T01:06:17Z DOI: 10.1177/23998083241246375
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Authors:Keith Burghardt, Johannes H Uhl, Kristina Lerman, Stefan Leyk Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-10T06:49:12Z DOI: 10.1177/23998083241240099
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Authors:Brad Bottoms, Julie Arbit, Earl Lewis, Alford Young Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Large-scale socioeconomic vulnerability models commonly used in flood hazard assessments grapple with data limitations and struggle to fully capture diversity in vulnerability and resilience stemming from America’s sociopolitical history. In response, we developed a prototype for a place-based Flood Resilience Assessment Index (FRAI) using tract-level geographies that illustrates human-centric frameworks for quantifying flood resilience in the U.S. For these purposes, we define flood resilience as the likelihood a tract will rebound from a flood disaster. This framework can be used in tandem with flood risk models. We employ mixed methods in geospatial processing, including dasymetric interpolation and network analysis to model access. We also standardize variables by percentage to enable temporal analyses and equity-centered narrative framing. While the resulting scores for a five-county pilot study correlate with those of leading vulnerability indices, FRAI leverages diverse data sources and novel methods to represent the changing landscapes, resources, and needs of urban cores and growing suburbs. Future trajectories for FRAI will continue to define and refine methods for diverse datasets, employ participatory methods for emergency managers and residents of flood-prone communities in value-setting, weighting, and validation, and identify policy and practice avenues. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-08T11:21:31Z DOI: 10.1177/23998083241243104
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Authors:Alessandro Venerandi, Alessandra Feliciotti, Safoora Mokhtarzadeh, Maryam Taefnia, Ombretta Romice, Sergio Porta Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Studies on urban deprivation date back to the 19th Century but remain important today due to rising levels of inequality and social segregation. However, while social causes of deprivation have been investigated, the role of the built environment remains neglected. Existing studies either provide broad coverage at the expense of detailed morphological descriptions or offer meticulous accounts of small-scale areas without capturing the broader context. This paper addresses this gap by investigating the relationship between urban form, measured at the building level, and deprivation across the entire city of Isfahan, Iran. By doing so, we position this study in the tradition of urban morphology. Operationally, we, first, identify urban types (UTs), that is, distinctive patterns of urban form, by clustering 200+ morphological characters; second, we explore the relationship between proportion of buildings belonging to each UT, in each neighbourhood, and deprivation; third, we offer detailed descriptions of the UTs most strongly associated with deprivation, discuss possible drivers for the observed correlations, and link findings to relevant literature in the field. Twelve UTs are identified, with four showing the strongest impacts on predicting deprivation. This study brings novel insights on the morphology of deprivation of Isfahan, while contextualising them with respect to domain-specific studies, which have predominantly focused on Western cities. The proposed methodology can be replicated to explore morphologies of deprivation in different contexts, further our understanding of the topic, and potentially inform planning and policy making. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-08T02:00:26Z DOI: 10.1177/23998083241245491
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Authors:Ehsan Hamzei, Laure De Cock, Martin Tomko, Nico Van de Weghe, Stephan Winter Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This paper presents a graph model that simultaneously stores route and configurational information about indoor spaces. Existing indoor information models either capture route information to compute shortest paths and to generate route descriptions (i.e., answering how-to-get-to questions), or they store configurational information about objects and places and their spatial relationships to enable spatial querying and inference (i.e., answering where-questions). Consequently, multiple representations of an indoor environment must be stored in information systems to address the various information needs of their users. In this paper, we propose a graph that can capture both configurational and route information in a unified manner. The graph is the dual representation of connected lines of sight, or views. Views can represent continuous movement in an indoor environment, and at the same time, the visible configurational information of each view can be explicitly captured. In this paper, we discuss the conceptual design of the model and an automatic approach to derive the view graph from floorplans. Finally, we demonstrate the capabilities of our model in performing different tasks such as calculating shortest paths, generating route descriptions, and deriving place graphs. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-04T09:15:04Z DOI: 10.1177/23998083241241598
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Authors:David Rey-Blanco, Pelayo Arbues, Fernando Lopez, Antonio Paez Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This article presents an open data product with large geo-referenced micro-data sets of 2018 real estate listings in Spain. These data were originally published on the idealista.com real estate website. The observations were obtained for the three largest cities in Spain: Madrid (n = 94,815 observations), Barcelona (n = 61,486 observations), and Valencia (n = 33,622 observations). The data sets include the coordinates of properties (latitude and longitude), asking prices of each listed dwelling, and several variables of indoor characteristics. The listings were enriched with official information from the Spanish cadastre (e.g., building material quality) plus other relevant geographical features, such as distance to urban points of interest. Along with the real estate listings, the data product also includes neighborhood boundaries for each city. The data product is offered as a fully documented R package and is available for scientific and educational purposes, particularly for geo-spatial studies. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-04T06:49:20Z DOI: 10.1177/23998083241242844
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Authors:Elena Lutz, Michael Wicki, David Kaufmann Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Densification is a key concept in contemporary urban planning. Yet, there are widespread concerns about densification causing displacement and gentrification. This paper examines densification around train stations—a prevalent form of transit-oriented development (TOD) in cities with established public transit systems—in the Canton of Zurich, Switzerland. We assess the effects of densification around train stations on the socioeconomic population composition in these areas and investigate three different potential displacement effects. Leveraging 1.8 million linked person-housing unit observations for all individuals within our study perimeter, we provide a more nuanced understanding of densification’s effects on the population composition and displacement than prior research. Our findings reveal that even though densification increases the absolute number of low-income residents, it primarily benefits middle- and high-income households. Specifically, there is a decline in the share of low-income residents, attributed to the influx of younger high-income individuals. Moreover, incumbent low-income residents experience an increased risk of direct displacement due to housing demolitions. These outcomes highlight the limitations of TOD strategies in mitigating persistent socioeconomic disparities in public transit access, emphasizing the need for more comprehensive measures to address the challenges of equitable housing and public transit accessibility. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-04-01T11:33:17Z DOI: 10.1177/23998083241242883
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Authors:Xiang Liu, Xiaohong Chen, Scott Orford, Mingshu Tian, Guojian Zou Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Numerous studies have explored the correlations between house prices and spatial accessibility, but few have delved into the nonlinearities between both. This study uses Cardiff (UK) as a case study and applies interpretable machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP), to estimate the nonlinear effects of geometric locational accessibility and street network accessibility on house prices. The findings suggest (1) proximity to the CBD, typically the major determinant of land values in hedonic house price models, does not continuously yield higher prices; (2) street closeness centrality, a network-modelling approach to measuring accessibility, exhibits a more generalised pattern with house prices compared proximity to the CBD regardless of analytical spatial scales. The findings challenge the generalizability of Alonso’s bid-rent theory in accurately portraying the relationship between accessibility and house prices in specific urban contexts, highlighting the importance of re-evaluating classical urban theories in different city contexts using novel measures and modelling techniques. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-29T07:07:11Z DOI: 10.1177/23998083241242212
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Authors:Jiacheng Chang, Guoqi Li, Wenjie Sun, Nannan He, Guopeng Du Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Waybill data can reflect the transport process of express delivery in different cities, providing an important basis for revealing intercity logistics connectivity. However, current research neglects the critical function of routing information in waybill data, making it difficult to realistically represent intercity connectivity in the express delivery network. This study uses waybill big data with routing information to map the spatial distribution and network structures of intercity express delivery networks in China, and identifies three main types of network communities: interprovincial communities, regional hub communities, and corridor pattern communities, so that a more accurate and realistic representation of intercity connectivity can be presented. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-26T04:02:55Z DOI: 10.1177/23998083241241842
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Authors:Priyanka Verma, Grant McKenzie Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In recent years we have witnessed explosive growth in the shared, free-floating, electric scooter industry. While still controversial in many North American cities, a number of large e-scooter operators have managed to carve out a piece of the urban transportation landscape. As these vehicles shift from novelty services to increasingly reliable modes of short personal travel, the discussion has turned to investigating who exactly benefits from these micromobility services and who are being left behind. Though population surveys have been administered to identify the socio-demographic characteristics of e-scooter riders in the past, little work has linked these characteristics through trips, or investigated the regional variation in these demographic factors. In this work we explore the variability and similarities in e-scooter rider characteristics across three major U.S. cities. To accomplish this, we apply a Moran’s Eigenvector Spatial Filtering linear regression model and compare our results to more commonly used spatial regression approaches. Our results indicate that the spatial filtering approach outperforms other methods in identifying socio-demographic characteristics of e-scooter users, across multiple regions. We find that many socio-demographics associated with e-scooter usage are regionally variant, despite younger users making up the core user base in all cities. There are variations in usage based on gender, income, and race across cities with Black and Hispanic populations remaining underserved. The implications of these findings are discussed. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-26T02:00:41Z DOI: 10.1177/23998083241240195
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Authors:Sara Lanini-Maggi, Martin Lanz, Christopher Hilton, Sara Irina Fabrikant Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The colour blue often elicits feelings of calmness and contentment, for which evidence has largely been provided in daytime settings. It is unclear whether pathways illuminated in blue, for example, in urban recreational park areas at night confers the same positive impact on night time park visitors. To tackle this open empirical question, we investigated how adding blue self-luminous pavement to park lighting at night time affects park visitors’ emotions compared to conventional white street light illumination. Our goal is to inform design decisions aimed at enhancing the emotional well-being of people outdoors at night in urban environments. Participants’ emotional response was captured at four different time points while watching a video of a walk in a virtual urban park at night, which was lit with white street lights only or with the addition of blue luminescent pavement on the walked paths (between-subject design). To capture visitor’s emotions, we used a simplified version of the Geneva Emotion Wheel (GEW) instrument and online facial expression recognition technology as subjective (self-reports) and objective (physiological) measures of emotion, respectively. The results of the GEW self-reports showed that the addition of a blue self-luminous pavement in a park during night time yielded more positive affect than standard white lighting in park visitors for the first half of the walk. In the second half of the walk through the park, participants’ affective states seemed to equalize between the two lighting conditions. In contrast, sensory data on facial expressions indicated no difference between participants’ emotional states over the whole walk in the two experimental conditions. Consistent with the positive emotional state perceived in the second half of the walk, the state of relaxation experienced after the walk also did not differ between the two lighting conditions. Furthermore, participants’ relaxation judgements after the park walk were more negative overall for females than the more neutral ratings of males. Our results highlight the importance of lighting colour at night for the design of future affect-smart cities that may consider individual and group characteristics with the ultimate intent of promoting public well-being. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-14T03:02:37Z DOI: 10.1177/23998083241239383
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Authors:Peter Schön, Eva Heinen, Vegar Rangul, Erik R Sund, Bendik Manum Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Active travel to school (ATS) is promoted due to its benefits for health, mental well-being and the environment. Although the impact of the urban form on ATS has been extensively researched, findings have remained contradictory. Existing studies have mainly relied on aggregated, area-based measures, scarcely applying disaggregated, network-based measures of accessibility, street configurations, connectivity or urban density. This study addresses issues related to aggregation and the lack of ATS-specific network measures to evaluate the connectivity of routes to school. We examined the associations of route betweenness, reach and weighted reach with ATS, while adjusting for age, gender, traffic and proximity to school. Population data are disaggregated within a 50-m accuracy of address points. We introduce ‘route betweenness’, a new network-based measure for assessing the connectivity of entire shortest routes. We measured network accessibility around homes as reach (i.e., the number of streets reached through the network), and urban density as weighted reach (i.e., as the floor area or population accessible within walk-/cyclable distances). ATS was measured through self-reported walking or cycling to school (yes or no). The results show positive associations of route betweenness with ATS. The findings further indicate that, whilst higher connectivity and accessibility around home can increase ATS, the connectivity of the network along the way to school, as grasped by route betweenness, is even more important. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-09T08:27:10Z DOI: 10.1177/23998083241235978
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Authors:Valentina Marin, Carlos Molinero, Elsa Arcaute Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Self-organisation in territories leads to the emergence of patterns in urban systems that shape the interactions and dependencies between cities, resulting in a hierarchical organisation. Governance follows a hierarchical structure as well, breaking the territory into smaller units for its management. The possible mismatch between these two organisations may lead to a range of problems, ranging from inefficiencies to insufficient and uneven distribution of resources. This paper seeks to develop a methodology to explore and quantify the correspondence between the hierarchical organisation given by the structure of governance and that given by the structure of the urban systems being governed, where Chile is used as a case study. The urban hierarchical structure is defined according to the connectivity of the system given by the road network. This is extracted through a clustering algorithm defined as a percolation process on the Chilean street network, giving rise to urban clusters at different scales. These are then compared to the spatial scales of the politico-administrative system. This is achieved by using measures of pair-wise distance similarity on the dendrograms, such as the cophenetic distance, by looking at the different clustering membership using Jaccard similarity and by analysing the topological diversity defined as the structural entropy. The results show that the urban sub-national structures present high heterogeneity, while the administrative system is highly homogeneous, replicating the same structure of organisation across the national territory. Such contrasting organisational structures present administrative challenges that can give rise to poor decision-making processes and mismanagement, in addition to impairing the efficient functioning of the systems themselves. Our results can help address these challenges, informing how to rebalance such mismatches through planning and political strategies that consider the complex interdependencies of territories across scales. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-07T01:38:10Z DOI: 10.1177/23998083241234405
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Authors:Matteo Mazzamurro, Weisi Guo Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. A system of cities is morphologically polycentric when its cities are similarly sized and evenly spaced across its territory. In this paper, we adapt established spatial interaction models and entropy-based measures of heterogeneity of weighted networks to the problem of measuring morphological polycentricity. We study the evolution of the morphological polycentricity of the system of English and Welsh towns from 1851 to 1881, a period characterised by rapid urbanisation and expansion of railways. Our approach enables us to account for morphological aspects of the system that are often neglected by existing measures of morphological polycentricity, such as the evolution of transport infrastructure and its impact on travel distances. We show that the trend towards a greater concentration of the population in fewer urban centres in England and Wales was accompanied by a more diverse network of connections and potential intercity interactions. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-06T11:39:04Z DOI: 10.1177/23998083241237308
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Authors:Anthony FJ van Raan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In this paper, we make an attempt to increase our understanding of the urban scaling phenomenon. The aim is to investigate how superlinear scaling emerges if a network increases in size and how this scaling depends on the occurrence of elements that constitute the network. To this end, we consider a city as a complex network structure and simulate this structure by the network of all publications of a research intensive university. In this simulation, the publications take the role of the city inhabitants and the concepts (terms and keywords) in the publications represent all kinds of abilities and qualities of the inhabitants. We use in this experiment all author- and database-given terms of the scientific publications of Leiden University from 2022. We calculate the co-occurrence of terms, and on the basis of these connections, we create a network and let this network grow by successively adding publications from the total set of publications. In this way, we get a series of networks with different sizes and this simulates a series of cities with different number of inhabitants. This procedure is performed for different values of the term occurrence threshold. We then analyze how four important network parameters, namely, number of terms, number of clusters, number of links, and total link strength increase with increasing size of the network. Particularly the number of network links and the total network linkage strength are in our opinion the parameters that dominate the scaling phenomenon and can be considered as a simulation of the socioeconomic strength of a city, that is, its gross urban product. We find a significant power law dependence of these network parameters on network size and the power law exponents for the lowest occurrence threshold are within the range that is typical for urban scaling. In our approach, the number of clusters can be interpreted as a measure of complexity within the network. Since the occurrence threshold determines the diversity of terms, we may expect a special relation between the occurrence threshold and the number of clusters. This is indeed the case: whereas for the three other network parameters the scaling exponent increases with increasing occurrence threshold, the number of clusters is the only network parameter of which the scaling exponent decreases with increasing occurrence threshold. Finally, we discuss how our publication term network approach relates to scaling phenomena in cities. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-03-05T08:22:22Z DOI: 10.1177/23998083241237310
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Authors:Ylenia Casali, Nazli Yonca Aydin, Tina Comes Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban areas are dynamic systems, in which different infrastructural, social and economic subsystems continuously co-evolve. As such, disruptions in one system can propagate to another. However, open challenges remain in (i) assessing the long-term implications of change for resilience and (ii) understanding how resilience propagates throughout urban systems over time. Despite the increasing reliance on data in smart cities, few studies empirically investigate long-term urban co-evolution using data-driven methods, leading to a gap in urban resilience assessments. This paper presents an approach that combines Getis-ord Gi* statistical and correlation analyses to investigate how cities recover from crises and adapt by analysing how the spatial patterns of urban characteristics and their relationships changed over time. We illustrate our approach through a study on Helsinki’s road infrastructure, socioeconomic system and built-up area from 1991 to 2016, a period marked by a major socioeconomic crisis. By analysing this case study, we provide insights into the co-evolution over more than two decades, thereby addressing the lack of longitudinal studies on urban resilience. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-02-27T03:52:55Z DOI: 10.1177/23998083241235246
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Authors:Qi-Li Gao, Chen Zhong, Yikang Wang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Prior research on the scaling of city size and inequality has a primary focus on economic factors such as income. Limited research has addressed socio-spatial disparities in mobility, involving physical activities and social interactions among individuals and population groups. Utilising mobile phone app data, this study measured inequalities using multiple mobility-related indicators (i.e. the number of activity points, the radius of gyration, self-containment, and social interaction indices) and related to population size by scaling models. In England’s context, these indicators unfolding mobility patterns and social issues display different scaling regimes, varying from sublinear to super-linear. It was observed that larger cities are associated with greater social interactions, particularly among socioeconomically advantaged groups; however, they also exhibit exacerbated self-segregation. Due to the radiation effect of big cities, the performances (e.g. travel radius) of small surrounding towns deviate from the predicted values of scaling models. Within cities, the evenness of indicators is independent of population size and produces distinct spatial patterns. The findings expand upon previous research and provide a more nuanced understanding of the complex relationship between city size, urban inequality, and human mobility. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-02-20T06:22:51Z DOI: 10.1177/23998083241234137
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Authors:Liam Berrisford, Eraldo Ribeiro, Ronaldo Menezes Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In recent years, the world has become increasingly concerned about air pollution. Particularly, High-Income Countries (HIC) and Upper Middle-Income Countries (UMIC) are implementing systems to monitor air pollution on a large scale to aid decision-making. Such efforts are essential, but they have at least three shortcomings: (1) they are costly; (2) they are slow to deploy; and (3) they focus on urban areas, which leads to urban-rural inequalities. Here, we show that we can estimate annual air pollution using open-source information about the structural properties of roads; we focus on England and Wales in the United Kingdom (UK) in this paper, although we argue that our methods are independent of specific country features. Our approach is an inexpensive method of estimating annual air pollution concentrations to an accuracy level that can underpin policymakers’ decisions while providing an estimate in all districts, not just urban areas. Furthermore, we contend that our process is interpretable and explainable. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-02-15T08:28:52Z DOI: 10.1177/23998083241230707
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Authors:Ali Riahi Samani, Reza Riahisamani, Sabyasachee Mishra, Mihalis M Golias, David Jung-Hwi Lee Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Due to the significant effects of establishments’ relocations on travel patterns and land-use conditions, investigating establishments’ relocation behavior is an important issue. In recent years, many establishments closed or relocated because of a downturn economy, health concerns, interrupted supply chains, and work-from-home caused by the pandemic. Hence, this study aims to propose a modeling approach to assess and compare the relocation behavior of establishments before, during, and after the pandemic. Establishments’ relocation behavior is modeled in two steps: relocation decision and relocation action. The former provides insights into behavioral factors associated with establishment relocation and the latter models likelihood of spatial relocation choice. Using the data collected from the state of Tennessee, USA, the Random Forest classification approach is incorporated to model both steps, where the model validation results showed the promising accuracy of this modeling approach. Moreover, statistical analyses are applied to evaluate the differences between the spatial relocation choices throughout the time. Results showed that in post-Covid conditions, the importance of establishment characteristics on relocation decisions was reduced by half and relocations occurred more due to office profile and accessibility. Results of modeling relocation action indicated the high importance of accessibility, even though the attractiveness of accessibility was reduced by 20.9% in post-Covid analysis. The findings of this study enrich the knowledge on establishment relocation behavior and provide valuable information regarding the effect of the pandemic, which can be used in policy development and travel behavior modeling by urban and transportation planners. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-31T07:26:37Z DOI: 10.1177/23998083241230580
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Authors:Fei Chen, Suhong Zhou, Junwen Lu, Zhong Zheng Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The difference in the individuals’ preference of activity destination choice is a new explanation for the activity-space segregation. This study investigates individuals’ preference in the destination choice for their daily activities. It uses revealed preference survey for the choice of the activity destination, and mobile phone dataset for the ambient population at the activity destination in Guangzhou, China. It has found that (1) the activity-space segregation is strongly influenced by the residential segregation, but disadvantaged populations are more spatially constrained by the distance decay effect; (2) all individuals prefer a destination with high diversity of built environment; and (3) migrant people tend to be self-segregated at the activity space, but people with higher education status prefer to take activities at an integrated place. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-22T01:08:14Z DOI: 10.1177/23998083241229110
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Authors:Wenzheng Li, Stephan Schmidt Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-18T11:12:11Z DOI: 10.1177/23998083241227500
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Authors:Yuhuan Wang, Yongbo Zhou Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-13T11:54:12Z DOI: 10.1177/23998083241227753
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Authors:Lindsay Poirier, Dexter Antonio, Makenna Dettmann, Tiffany Eng, Jennifer Ganata, Sujoy Ghosh, Mirthala Lopez, Ranesh Karma, Asiya Natekal, Catherine Brinkley Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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). Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-12T06:37:17Z DOI: 10.1177/23998083241227471
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Authors:Bev Wilson, Shakil Bin Kashem, Lily Slonim Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-11T03:59:10Z DOI: 10.1177/23998083241226848
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Authors:Bahman Lahoorpoor, David M Levinson Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-02T07:23:44Z DOI: 10.1177/23998083231224831
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Authors:Andrea Ballatore, Scott Rodgers, Liam McLoughlin, Susan Moore Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. 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. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2024-01-02T04:36:21Z DOI: 10.1177/23998083231224136
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