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Environment and Planning B : Urban Analytics and City Science
Journal Prestige (SJR): 0.653
Citation Impact (citeScore): 2
Number of Followers: 42  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 2399-8083 - ISSN (Online) 2399-8091
Published by Sage Publications Homepage  [1176 journals]
  • AI and design

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      Authors: Michael Batty
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.

      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-02-24T02:31:37Z
      DOI: 10.1177/23998083241236619
       
  • Unpacking urban scaling and socio-spatial inequalities in mobility:
           Evidence from England

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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
       
  • Exploring the new frontier of information extraction through large
           language models in urban analytics

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      Authors: Andrew Crooks, Qingqing Chen
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.

      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-02-17T10:51:54Z
      DOI: 10.1177/23998083241235495
       
  • Estimating annual ambient air pollution using structural properties of
           road networks

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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
       
  • Peter Allen (1944–2023)

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      Authors: Michael Batty
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.

      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-02-13T01:29:16Z
      DOI: 10.1177/23998083241234776
       
  • Evaluating relocation behavior of establishments: Evidence for the
           short-term effects of COVID-19

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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
       
  • Resilience analysis of global agricultural trade

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      Authors: Chunzhu Wei, Xufeng Liu, Lupan Zhang, Yuanmei Wan, Gengzhi Huang, Yang Lu, Xiaohu Zhang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study examined the transport network of global marine dry bulk carriers for agricultural trade during the period from 2018 to 2021. Firstly, the resilience of agricultural trade network is noteworthy throughout the COVID-19 pandemic. Agricultural trade initially plunged by 10.15% from 2019 to 2020 and bounced by a remarkable 11.45% in 2021, ultimately restoring trade volumes to the average level observed in the pre-pandemic year of 2019. However, the ports in Brazil and Argentina displayed less resilience in their agricultural trade with a continued decline in agricultural trade quantities in 2021. Additionally, the outbound trips increased in Ukraine, Canada, and Russia and decreased in Brazil and Argentina, leading to a more tightly knit agricultural network since 2020. Overall, this study provided evidence in comprehensively assessing the capacity and resilience of global food supply chains, especially in the context of constantly evolving circumstances and challenges.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-01-27T02:36:13Z
      DOI: 10.1177/23998083241229846
       
  • A behavioral explanation of the activity-space segregation: Individuals’
           preference of choosing an activity destination

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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
       
  • A Python package for the local multiscale analysis of spatial point
           processes (LomPy)

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      Authors: Janka Lengyel, François Sémécurbe, Stéphane G. Roux
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The consideration of multiscale characteristics has emerged as a popular component of geospatial data analysis and modeling. However, the practical implementation of such analysis tasks involves time-consuming and computationally intensive processes that require the integration of knowledge and methods from different disciplines (e.g., quantitative geography, signal processing, and natural sciences) and in which large amounts of data have to be processed. Yet, to date, there is few open-source software that enables an efficient and transparent computational workflow. This paper introduces a Python package for the local multiscale analysis of spatial point processes (LomPy). LomPy is specifically designed for processing and analyzing data that is either irregularly spaced or has large data holes over the spatial territory: a common and methodologically challenging property of geoprocessing operations. The first main function of the package computes the multiresolution quantities, while the second applies them to the extraction of fractal and multifractal features at arbitrarily “local” spatial scales. The third function extends the univariate analysis option to multivariate settings. Powerful tools for regression modeling, density estimation, and visualization are also provided. It should be emphasized that the package is efficient on both vector and raster data structures, ensuring a wide range of applicability in urban data science and beyond.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-01-19T08:42:54Z
      DOI: 10.1177/23998083231225990
       
  • Can spatial patterns mitigate the urban heat island effect' Evidence from
           German metropolitan regions

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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
       
  • Visualizing the North–South Divide of international visitors: Evidence
           from the Yangtze River Delta in China

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      Authors: Yao Wang, Xiaohua Lin, Liushan Lin, Xinyi Niu
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The North–South Divide, which historically divided global national development, is now being questioned due to the rise of the Global South represented by China. We analyze the impact of this divide using global connections observed through international visitors to the Yangtze River Delta (YRD) region in China. We visualized the global origins of international visitors to the YRD region and their spatial distribution within the region. The cartogram depicts a significant contribution of the Global North to the scale of international visitors in the YRD region, indicating a closer functional connection between mainland China and the Global North. This suggests that the influence of the North–South Divide on mainland China persists. Despite mainland China strengthening South–South Cooperation through the Belt and Road national strategy, the crucial role played by the Global North in China’s economic globalization is challenging to reverse in the short term. Additionally, the successful path taken by China may not be universally applicable to all of the Global South.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-01-18T10:47:32Z
      DOI: 10.1177/23998083241228718
       
  • Visualizing urban slum population across the globe

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      Authors: Feiran Ren, Yuheng Zhang, Qi Zhou
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Monitoring the percentage of urban residents dwelling in slums (referred to as the urban slum population percentage) is crucial for enhancing living conditions in cities. Despite its importance, there is a notable lack of research that maps this indicator on a global scale. Addressing this gap, our study aims to graphically represent the urban slum population percentage alongside the absolute numbers of the urban slum population worldwide. This analysis was conducted by synthesizing statistical data from a variety of sources. Our findings indicate that countries with a high urban slum population percentage are predominantly situated in Africa, with India having the most substantial absolute slum-dwelling population. Our research offers a comprehensive global viewpoint, underscoring the imperative to improve urban living conditions.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-01-18T03:46:40Z
      DOI: 10.1177/23998083241228719
       
  • Metro cultural scene: A new community scale in urban scene research

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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
       
  • Making plans findable, accessible, interoperable, and reusable with data
           infrastructure: A search engine for constructing, analyzing, and
           visualizing planning documents

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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
       
  • Modeling the relationship between urban tree canopy, landscape
           heterogeneity, and land surface temperature: A machine learning approach

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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
       
  • The innovative role of cities in solving global problems with local
           solutions

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      Authors: Linda See
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.

      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2024-01-09T08:22:19Z
      DOI: 10.1177/23998083241227294
       
  • An agent-based simulation model for the growth of the Sydney Trains
           network

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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
       
  • Facebook city: Place-named groups as urban communication infrastructure in
           Greater London

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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
       
  • Examining the impact of the urban transportation system on tangible and
           intangible vitality at the city-block scale in Nanjing, China

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      Authors: Liu Yang, Mingbo Wu, Yishan Chen, Chenyang Wu
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Cities’ transportation systems have substantial impacts on urban vitality. Given the increasing availability of data on residents’ activities, cities’ tangible/intangible vitality can be analyzed more accurately. This study examined the associations of tangible and intangible vitality with transportation system features, specifically exploring various transportation modes’ accessibility, features related to block forms, and border vacuums at a block scale across different urban areas. Nanjing, China, was analyzed as a case study. Our findings reveal a declining gradient of urban vitality from the Old Town to the Main City and the New Area. Consequently, we suggest prioritizing efforts to enhance urban vitality in the New Area, particularly in its low-vitality blocks. Strategies for improvement include increasing public transportation accessibility and road density, which can positively influence the overall vitality of the entire city. Improving active travel accessibility has a positive impact on tangible vitality, while enhancing automobile accessibility potentially contributes to intangible vitality. Negative border effects of large transportation projects on tangible vitality should be mitigated. Interestingly, we found that intersection density has opposite effects on tangible and intangible vitality. These insights offer valuable guidance for urban planners aiming to enhance vitality levels across an entire city or within specific areas.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-28T01:11:52Z
      DOI: 10.1177/23998083231223867
       
  • Street characteristics and human activities in commercial districts: A
           clustering-based approach application for Shenzhen

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      Authors: Chendi Yang, Rui Ma, Hongqiang Fang, Siu Ming Lo, Jacqueline TY Lo
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      As a significant public place, the commercial area has a potential correlation between its built environment and human activities. However, the current research primarily concentrates on the internal environment of the store and customer satisfaction, while the impact of some environmental features of the outer space of the business district on visitors is seldom systematically discussed. This study takes four commercial districts in Shenzhen as examples, and the streets were categorized into five types based on street characteristics using the cluster analysis method. The relationship between each type of street and the population distribution in the region was subsequently discussed. To this end, a holistic approach was adopted, integrating multi-source urban data such as street view panorama, points of interest (POI), and street and building vectors to describe the built environment. Furthermore, the distribution of people at different times, based on location-based services (LBS) data, was combined to establish statistical models of various streets in commercial districts and evaluate the relationship between street characteristics and human activities. The results demonstrate that the relationship between population distribution and spatial characteristics is different in the five types of streets. Different types of streets have their own advantages, and human activities in the business district are often not affected by this advantage, but by other characteristics. The impact of these factors varies significantly between weekdays and weekends. By systematically categorizing street types and assessing the impact of environmental factors on pedestrian flow, this study sheds new light on the renewal and development of urban commercial districts in the future.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-26T05:20:26Z
      DOI: 10.1177/23998083231224013
       
  • An inductive method for classifying building form in a city with
           implications for orientation

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      Authors: Jinmo Rhee, Ramesh Krishnamurti
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The utilization of deep learning for form analysis facilitates the classification of an extensive number of forms based on their morphological features. A critical consideration for implementing such analysis methods in architectural or urban forms is whether building orientation should be embedded within the data. Orientation functions as a form variable significantly influenced by environmental, social, and cultural contexts within a city. In contrast to other domains where forms are extrapolated in relation to their context, in the city, domain orientation uniquely characterizes building form. In this paper, we introduce a pipeline for constructing an extensive building form dataset and scrutinizing the morphological identity of building forms, with a particular focus on the implications of building orientation as a manifestation of urban locality. Through a case study situated in Montreal, we engage in a comparative analysis employing two distinct datasets—those with orientation-embedded forms and those with orientation-normalized forms. Our research aims to investigate the typo-morphological characteristics of the building forms of the city and to examine how building orientation contributes to the identification of these traits and mirrors urban locality.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-26T04:34:14Z
      DOI: 10.1177/23998083231224505
       
  • Inferring “high-frequent” mixed urban functions from telecom
           traffic

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      Authors: Jintong Tang, Ximeng Cheng, Aihan Liu, Qian Huang, Yinsheng Zhou, Zhou Huang, Yu Liu, Liyan Xu
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom traffic data to infer mixed urban functions at spatiotemporal granularities as fine as buildings and hours. A time-series decomposition method is designed to map the mixture of urban functions, which is further refined by a hierarchical agglomerative clustering method taking urban textures as an additional source of information. In a case study in Shenzhen, China, we find the function of urban buildings can be decomposed into the mixture of three basic functions, namely dwelling, work, and recreation. We further find that the introduction of urban texture information helps identify particular forms of functional combination, which indicate special-function buildings such as urban villages and roadside shops. This study implies ways to improve urban management through methodological contributions in mixed urban function identification alongside the introduction of the telecom traffic, a kind of “high-frequency” urban data, and also helps inspire a rethinking of the form/function dichotomy in the era of “High-frequent” cities.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-12T01:48:40Z
      DOI: 10.1177/23998083231221867
       
  • Evolvable case-based design: An artificial intelligence system for urban
           form generation with specific indicators

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      Authors: Yubo Liu, Kai Hu, Qiaoming Deng
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This research proposes a design system that combines a case-based learning algorithm with a rule-based optimization algorithm to automatically generate and revise urban form prototypes based on historical cases and user requirements. The system aims to address the challenges of existing generative methods for urban forms, such as the lack of flexibility and organicity of rule-based methods and the insufficient manipulability and interpretability of the newest GAN-integrated case-based methods. It can help designers generate multiple solutions with specific indicators in the conceptual stage and has the potential to facilitate citizen participation in urban planning and design. This research demonstrates the feasibility and effectiveness of the system through a case study in Shenzhen. The research further extends the discussion about the application of the proposed system and the alternative evolution approach for the next generation of automatic design methods.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-08T08:15:19Z
      DOI: 10.1177/23998083231219364
       
  • Physical distancing and its association with travel behavior in daily
           pre-pandemic urban life: An analysis utilizing lifelogging images and
           composite survey and mobility data

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      Authors: Piyushimita (Vonu) Thakuriah, Christina Boididou, Jinhyun Hong
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study analyzed physical distancing in people’s daily lives and its association with travel behavior and the use of transportation modes before the COVID-19 outbreak. We used data from photographic images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s physical distancing and travel behavior and transportation choices. The multidisciplinary research approach to understand these associations accounted for a number of social, economic, and cultural factors that potentially influenced their physical isolation levels. We found that participants spend a significant amount of time physically separated from others, without anyone else within 2 meters. The use of public transportation, automobiles, active travel, and an increase in trip frequency, including trips to transportation facilities, reduced the extent of physical distancing, with public transportation having the most significant impact. Higher incomes, strong social networks, and a sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable about climate change issues increased the likelihood of physical distancing. The paper addresses a crucial gap in our understanding of how these factors intersect to create the dynamics of physical distancing in non-emergency situations and highlights their planning and operational implications while showcasing the use of unique person-based physical distancing measures derived from autonomously collected image data.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-07T03:57:50Z
      DOI: 10.1177/23998083231215822
       
  • Actionable descriptors of spatiotemporal urban dynamics from large-scale
           mobile data: A case study in Lisbon city

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      Authors: Miguel G Silva, Sara C Madeira, Rui Henriques
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Mobile phones share location records, offering the opportunity to monitor and understand emerging population dynamics in urban centers. With the aim of supporting urban planning, this study introduces a scalable methodology grounded on extracting and organizing spatiotemporal statistics from decomposed population density data. The proposed methodology serves three major purposes: (i) assess the predictability of spatiotemporal citizen density patterns; (ii) detect emerging spatiotemporal trends in population density; and (iii) uncover multi-level seasonality patterns with guarantees of actionability. Additionally, it makes available an open-access tool for deploying the proposed methodology and analyzing mobile phone network data with easy-to-use spatiotemporal visualization and navigation facilities. The results obtained from real-world, large-scale mobile data in Lisbon, Portugal, demonstrate the effectiveness and validity of the proposed methodology in extracting actionable statistics in linear time to guide both tactic and strategic urban planning.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-05T09:47:50Z
      DOI: 10.1177/23998083231219048
       
  • Towards a study of everyday geographic information: Bringing the everyday
           into view

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      Authors: Stefano De Sabbata, Katy Bennett, Zoe Gardner
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Events are the driving force behind social media, whether we try to create them or keep up with them. A wide range of studies has focused on how content from social media can be used to detect, model and predict events and identify key topics of discussion. At the same time, very limited attention has been given so far to the quantitative study of the everyday, which has fascinated qualitative human geography research in the past few decades. That is partly due to the lack of a formal definition of what constitutes the everyday. In this paper, we aim to advance our understanding of the everyday, not by reducing it to any kind of definition but by bringing it into view through a quantitative analysis. We hypothesise that the by-products of current methods focused on event detection might be used to quantitatively explore everyday geographies as represented through Twitter data. We consider the use of both statistical approaches based on term frequency and state-of-the-art large language models, and we conduct a case study on content posted on Twitter and geolocated in the city of Leicester. Our paper makes two key advances for research concerned with the everyday and the analysis of geographic information. First, we illustrate how large language models combined with spatial analysis and visualisation can foster the study of everyday geographies, providing an insight into the still elusive concept of the everyday, representing what other approaches to the everyday have struggled to qualify. Secondly, we showcase the potential held by large language models and visual analytics in democratising sophisticated natural language processing and thus providing new tools for research in human geography.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-05T08:29:09Z
      DOI: 10.1177/23998083231217606
       
  • Examining the impact of neighborhood environment factors on residents’
           emotions during COVID-19 lockdown and reopening: A Wuhan study on
           mediation and moderation

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      Authors: Minghao Liu, Zhonghua Gou
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Due to COVID-19, the urban lockdown has caused a significant impact on the mental health of residents. However, limited research investigates the role of neighborhood factors on residents’ mental health during and after the lockdown. This study examines Wuhan, the first city to experience the COVID-19 outbreak, employing multiple linear regression and XGBoost algorithms to analyze the emotional status and distribution of Wuhan residents. The goal of this study is to identify the moderating effect of the neighborhood environment scale on emotional positivity and the marginal effect of the neighborhood environment on residents’ emotions. The results of the study indicate that specific neighborhood environmental characteristics have varying effects on residents’ positive emotions, both before and after the COVID-19 lockdown. The green space ratio, attraction density, waterfront space density, and service facility density all positively affected mood within different distance ranges. Shopping facilities, on the other hand, had mainly positive effects during the open period, with negative effects during the closed period. Furthermore, this study determined scale thresholds where neighborhood environments had a positive effect on mood. For instance, attractions and waterfront areas improved the mood of residents in residential areas, up to at least 3 km away. Medical facilities had a positive effect on residents’ mood beyond 2.2 km. This study highlights crucial implications for planning and managing neighborhoods to promote resilience during future public health crises.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-04T12:53:35Z
      DOI: 10.1177/23998083231219322
       
  • Urban dispersion indicator to assess the Italian settlement pattern

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      Authors: Lucia Saganeiti, Lorena Fiorini, Francesco Zullo, Beniamino Murgante
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The 2022 United Nations Climate Change Conference (COP27) reaffirmed the most urgent need to build actions to accelerate the restoration of policies to arrest and reverse the loss of natural ecosystems by 2030 and move towards full ecosystem recovery by 2050. Land take is a significant source of emissions and contributes to global warming and biodiversity loss in natural ecosystems. Consequently, it is crucial to act on it by investigating the phenomenon quantitatively and formally, thus contributing to the goal of zero net land take. In recent years, land take worldwide has become massive, leading in some cases to forming compact, high-density urban settlements. In other cases, it has led to dispersed, low-density urban settlements. The basic assumption underlying this research is that a compact context is more sustainable (environmentally, economically, and socially) than a dispersed urban one. Consequently, this research aims to investigate the evolution of land take from the point of view of the pattern of urban settlements and their dispersion over the Italian territory. The spatial configuration of the Italian settlement pattern at the regional and provincial level was analyzed through a Spatio-temporal analysis of the global Moran index and other quantitative variables. The results provide, for each territory, a reading of the main expansion dynamics that occurred from the ‘50s to nowadays: compact city, urban sprawl, or urban sprinkling.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-12-04T04:23:18Z
      DOI: 10.1177/23998083231218779
       
  • Place-bound planning support systems for deliberation: Affording better
           communication and comprehension

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      Authors: Raz Weiner, Filipe Mello Rose, Batel Yossef Ravid, Jörg Rainer Noennig, Meirav Aharon-Gutman
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Despite planning support systems (PSS) becoming increasingly useful for citizen participation processes, the effects of such systems’ material and spatial setup on citizen participation processes still need to be studied. PSS have long been equated to software- and data-based technologies, and only little attention has been put on place-bound PSS that prescribe onsite face-to-face collaboration. As closing the ‘implementation gap’ requires extensive conceptualisation, description, and critical analysis of different ideal types, workings, and use cases of PSS, this study researches this understudied place-bound type of PSS. More precisely, this study uses empirical material from Haifa’s 3 S Lab to contribute to closing the implementation gap by identifying place-bound PSS – an under-studied type of PSS – as useful for deliberative decision-making – an overlooked implementation context. This research advances the conceptualisation of PSS by discussing place-bound PSS and their hypothesised utility, practical setup, and empirically tested benefits for deliberative citizen participation. We find that the benefits of place-bound PSS for planning lie in deliberative affordances that ease the communication and comprehension deficiencies that often plague deliberative citizen participation processes. As place-bound PSS, the 3 S Lab provides an immersive shared space that improves communication, while its interactive visualisation techniques afford improved comprehension of complex urban issues.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-30T03:29:32Z
      DOI: 10.1177/23998083231217784
       
  • Effect of greening vacant houses on improvement in thermal environment
           using ENVI-met simulation: A case study on Busan metropolitan city

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      Authors: Yoko Kamata, Jung Eun Kang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study aimed to analyze the improvement in outdoor thermal environment by greening of persistently vacant housing and open areas in a densely built old downtown area of Busan Metropolitan City using ENVI-met. Simulation was performed for a summer day by constructing four scenarios for four areas considering the building density and slope direction. The results indicate that compared to the current scenario, the concrete scenario had the worst thermal environment, where the average temperature, mean radiant temperature (MRT), and physiological equivalent temperature (PET) increased by 0.04°C, 1.49°C, and 0.51°C, respectively. In contrast, the tree scenario exhibited the most significant improvement. The average temperature, MRT, and PET decreased by 0.03°C, 1.66°C, and 0.65°C, respectively. Moreover, the removal of vacant houses in dense residential areas improved ventilation, and PET decreased by approximately 8°C locally. Planting trees higher than the demolished vacant houses mitigated the thermal environment considerably. The effect of greening was the strongest in the residential areas located on the south-facing slope with the worst thermal environment. This study provides essential data for implementing greening as a smart reduction strategy in the sustainable management of vacant houses.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-28T10:35:42Z
      DOI: 10.1177/23998083231217349
       
  • Evaluating the impact of social housing policies: Measuring accessibility
           changes when individuals move to social housing projects

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      Authors: Flavia Lopes, Lucas Figueiredo, Jorge Gil, Edja Trigueiro
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Addressing housing deficits and inequalities remains a key challenge for cities in promoting more sustainable urban development. In response to these challenges, governments around the world, particularly in the Global South, have made substantial investments in housing policies for middle- and low-income individuals. Nevertheless, while these initiatives increase housing provision, they often face criticism for not adequately considering the location of new residences. This oversight has far-reaching effects on the accessibility to essential facilities, which play a pivotal role in determining spatial advantages and disadvantages, and consequently, in the degree of inclusion of individuals in both the city and society. Addressing this critical role of accessibility, this paper introduces a methodology for assessing the potential impact of housing policies on the lives of their beneficiaries, by quantifying changes in cumulative accessibility levels between individuals' former house locations and the location of the housing projects into which they moved. Accessibility is calculated for three distinct transport modes: walking, cycling, and public transport, using unimodal and multimodal urban network models. A case study was conducted in Natal, northeastern Brazil, on the implementation of the Minha Casa, Minha Vida (My House, My Life, MCMV) housing policy, initiated in 2009 and still active today. The results of the study revealed a significant decrease in accessibility across all transportation modes when individuals moved to the new housing estates. The decline was particularly pronounced among individuals with lower incomes, potentially raising their regular expenses after relocation and, ultimately, leading to spatial isolation and social exclusion. These findings demonstrate the contribution of the methodology to capturing the impacts of housing policies on the everyday accessibility of their beneficiaries, while emphasizing the importance of re-evaluating these policies with a particular focus on fostering the social and urban inclusion of beneficiaries.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-28T10:07:02Z
      DOI: 10.1177/23998083231218774
       
  • Mobility and transit segregation in urban spaces

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      Authors: Nandini Iyer, Ronaldo Menezes, Hugo Barbosa
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Segregation is a highly nuanced concept that researchers have worked to define and measure over the past several decades. Conventional approaches tend to estimate segregation based on residential patterns. However, the residential dimension does not fully comprise individuals’ interactions with their environment and, consequently, can misrepresent individuals’ lived experiences. To address this gap, we analyse how segregation extends to other dimensions of the urban life. We accomplish this by using the Index of Concentration at the Extremes (ICE) to measure socioeconomic segregation at amenities and on public transit lines. Moreover, we consider the pivotal role that transport plays in democratising access to opportunities. Using transport networks, amenity visitations, and census data, we leverage agent-based models to approximate socioeconomic composition at amenities and on transit lines. Consequently, we can estimate socioeconomic segregation within the United States, for various aspects of urban life. We find that neighbourhoods that are segregated in the residential domain tend to exhibit similar levels of segregation in amenity visitation patterns and transit usage, albeit to a lesser extent. Moreover, we discover that low-income neighbourhoods experience a greater decrease from residential to amenity segregation, than their high-income segregated counterparts, highlighting how mobility can be used as a tool for overcoming residential inequalities, given the proper infrastructure. We identify inequalities embedded into transit service, which impose constraints on residents from segregated areas, limiting the neighbourhoods that they can access within an hour to areas that are similarly disadvantaged. By exploring socioeconomic segregation from a transit perspective, we underscore the importance of conceptualising experiential segregation, while also highlighting how transport systems can contribute to a cycle of disadvantage.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-28T06:03:59Z
      DOI: 10.1177/23998083231219294
       
  • Where is it complex to reallocate road space'

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      Authors: Gabriel Valença, Filipe Moura, Ana Morais de Sá
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Road space distribution has traditionally been based on the hierarchical classification of streets. In arterials, the majority of space is dedicated to traffic lanes, whereas local streets typically have fewer traffic lanes and more space for parking or sidewalks. Within urban areas, road space is contested between two main types of spaces: corridors of movement, and places for access and standing/stillness/staying. Given the limited availability of urban space, particularly in central areas, deciding how to allocate space for these functions poses a dilemma and requires tradeoffs. Nonetheless, certain areas experience underutilization and inefficiencies in space utilization over time. In this context, we propose a site selection methodology to identify complex zones within a city where different types of users and demands compete for space. These zones present the potential for dynamically allocating road space based on fluctuating demands and policy objectives. This methodology serves as an initial guide for planners to identify zones that require a thorough evaluation of activities and diverse temporal-spatial demands when reallocating road space. We use network centrality, land use indicators, traffic, and public transport dynamics indicators to detect complex zones and apply them to a Lisbon case study.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-27T07:10:08Z
      DOI: 10.1177/23998083231217770
       
  • Proximity or opportunity' Spatial and market determinants of private
           individuals’ buy-to-let investments

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      Authors: Antoine Peris, Laure Casanova Enault
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This paper contributes to the debate on the liquidity of real estate investment in the context of financialisation. Using microdata built from tax registers, we analyse the geography of rental housing purchases by private individuals from three French cities. We develop a modelling approach in order to better understand the respective roles of space and market characteristics in determining buy-to-let investment flows. Considering the distribution of the data and our objective of integrating both intra- and intercity housing investments in a single model, we use an adaptive zoning approach. This approach allows high spatial resolution where interactions are strong to be kept and the aggregation of more distant, less populated areas. We demonstrate that geographical proximity is highly determinant in explaining flows of buy-to-let investments from private individuals. We also uncover striking facts related to the geography of rental investments, such as the convergence of investments from rich suburbs toward the centre of agglomerations and preferential flows from the Paris region to southern and coastal cities. Finally, we find that investors tend to buy in upmarket areas and in places that are more expensive than their market of residence. Our results indicate that geographical proximity and safety of investments are key factors in housing wealth accumulation by private individuals.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-23T03:07:19Z
      DOI: 10.1177/23998083231217014
       
  • Identifying urban functional zones by analysing the spatial distribution
           of amenities

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      Authors: Wei Chien Benny Chin, Yuming Fu, Kwan Hui Lim, Thomas Schroepfer, Lynette Cheah
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Comprehending urban form and function is crucial for effective urban planning and redevelopment. However, delineating urban functional zones and understanding the diverse activities within a city poses challenges. To address this, our study proposes a comprehensive analysis framework utilising point-of-interest (POI) data to identify and characterise urban functional zones and associated amenities. Leveraging geocoded user-generated content enables an effective capture of the spatial structure of human activities. We applied our framework in case studies in two Singapore locations, demonstrating its effectiveness in identifying urban functional zone shapes and examining associated amenities. The findings reveal a spatial configuration of amenities across the urban area, highlighting a diversity often differing from planned land-uses. This underscores the complex and non-uniform nature of the urban living experience, as observed variations in mixed uses challenge the homogeneity of intended land-use types. Our study and analysis framework provide a foundation for further investigations, including exploring travel behaviour patterns and assessing the vibrancy and vitality of urban areas.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-21T03:47:38Z
      DOI: 10.1177/23998083231217376
       
  • Discontinuities in regional economic development due to administrative
           boundaries: Examining the mechanisms of the boundary effect

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      Authors: Jindo Jeong
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Although administrative boundaries are non-physical, they can cause regional inequalities through boundary effects that result in discontinuities between areas. The boundary effect refers to the disparities in policy, economic, and social aspects between areas caused by administrative boundaries, which can lead to regional differences. This study aims to identify the mechanisms that induce discontinuities in regional development due to administrative boundaries. The boundary effect mechanism assumed to include the spillover, fragmentation, and hierarchy effects were examined using six scenarios, each modeled using a spatial economic model. Through the comparison of various scenarios, we have demonstrated the potential validity of the three components comprising the assumed boundary effect. Furthermore, we have confirmed that the model incorporating all effects that we assumed in our research, namely spillover, fragmentation, and hierarchy effects, provides the best fit. We hypothesized and verified the mechanism of boundary effects that disrupt regional development, thereby enhancing the understanding of these effects.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-21T03:33:35Z
      DOI: 10.1177/23998083231217013
       
  • Synthetic population data for small area estimation in the United States

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      Authors: Yue Lin
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Small area estimation is critical for a wide range of applications, including urban planning, funding distribution, and policy formulation. Individual-level population data, which typically include each individual’s socio-demographic characteristics and small area location, are a rich source of information for small area estimation. However, individual-level population data are often not made public due to confidentiality concerns. This paper describes the development of a public-use synthetic individual-level population dataset in the United States that can be useful for small area estimation. This dataset contains characteristics of housing type, age, sex, race, and Hispanic or Latino origin for all 308,745,538 individuals in the United States at the census block group level, based on publicly available aggregated data from the 2010 Census. Experimental results suggest the validity of the synthetic data by comparing it to different data sources, and we show examples of how this dataset can be used in small area estimation.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-16T10:46:50Z
      DOI: 10.1177/23998083231215825
       
  • Relational Reprojection Platform: Non-linear distance transformations of
           spatial data in R

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      Authors: Will B. Payne, Evangeline McGlynn
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      When mapping relationships across multiple spatial scales, prevailing visualization techniques treat every mile of distance equally, which may not be appropriate for studying phenomena with long-tail distributions of distances from a common point of reference (e.g., retail customer locations, remittance flows, and migration data). While quantitative geography has long acknowledged that non-Cartesian spaces and distances are often more appropriate for analyzing and visualizing real-world data and complex spatial phenomena, commonly available GIS software solutions make working with non-linear distances extremely difficult. Our Relational Reprojection Platform (RRP) fills this gap with a simple stereographic projection engine centering any given data point to the rest of the set, and transforming great circle distances from this point to the other locations using a set of broadly applicable non-linear functions as options. This method of reprojecting data allows users to quickly and easily explore how non-linear distance transformations (including square root and logarithmic reprojections) reveal more complex spatial patterns within datasets than standard projections allow. Our initial release allows users to upload comma separated value (CSV) files with geographic coordinates and data columns and minimal cleaning and explore a variety of spatial transformations of their data. We hope this heuristic tool will enhance the exploratory stages of social research using spatial data.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-16T03:09:23Z
      DOI: 10.1177/23998083231215463
       
  • Exploring the plot patterns of the retail landscape: The case of the
           Helsinki Metropolitan area

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      Authors: Hulusi Eren Efeoglu, Anssi Joutsiniemi, Skirmante Mozuriunaite
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study examines the impact of the morphological characteristics of plots in the Helsinki Metropolitan Area (HMA) on the retail landscape, with a focus on understanding the ways in which the morphological characteristics potentially influence the distribution, agglomeration and diversity of retail businesses. Although frequently underestimated in contemporary placemaking practices, this research emphasizes the role of the dual nature of plots as an element of urban form and an element of control over the retail landscape of the city. In this sense, the role of the morphological characteristics of plots in shaping the retail landscape of the city was investigated. The compositional (size, frontage ratio) and configurational (integration, betweenness, frequency) features of the plot in the HMA (n = 77,736) were measured. Thereafter an unsupervised two-step clustering method was applied to reveal the subtle morphological regions through plot patterns. Computational plot characterization with open data sets yielded six plot types having different morphological characteristics and geographic distribution patterns. The spatial capacities of each plot type for retail distribution, agglomeration and diversity were then analysed and compared. This research argues that the interrelationship of the dual nature of plot plays an important role in placemaking processes. The results suggest that the spatial capacity of plots to accommodate street-based retail clusters is improved with spatially integrated, fine-grained urban fabric with independent micro-businesses involving a diversity of uses and actors. The study argues that these spatial conditions might also increase retail resilience and contribute to the vitality and viability of the retail landscape.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-08T08:56:36Z
      DOI: 10.1177/23998083231213695
       
  • Extracting real estate values of rental apartment floor plans using graph
           convolutional networks

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      Authors: Atsushi Takizawa
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Access graphs that indicate adjacency relationships from the perspective of flow lines of rooms are extracted automatically from a large number of floor plan images of a family-oriented rental apartment complex in Osaka Prefecture, Japan, based on a recently proposed access graph extraction method with slight modifications. We define and implement a graph convolutional network (GCN) for access graphs and propose a model to estimate the real estate value of access graphs as the floor plan value. The model, which includes the floor plan value and hedonic method using other general explanatory variables, is used to estimate rents, and their estimation accuracies are compared. In addition, the features of the floor plan that explain the rent are analyzed from the learned convolution network. The results show that the proposed method significantly improves the accuracy of rent estimation compared to that of conventional models, and it is possible to understand the specific spatial configuration rules that influence the value of a floor plan by analyzing the learned GCN.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-08T01:57:17Z
      DOI: 10.1177/23998083231213894
       
  • Inequalities in experiencing urban functions. An exploration of human
           digital (geo-)footprints

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      Authors: Alessia Calafiore, Krasen Samardzhiev, Francisco Rowe, Martin Fleischmann, Daniel Arribas-Bel
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Studies on mobility inequalities have so far mostly relied on Survey data or Censuses. While such studies have demonstrated that inequalities strongly influence everyday mobility choices, these data sources lack granular information on people’s movements on a daily basis. By capitalising on high spatio-temporal resolution data provided by Spectus.ai, this study aims at investigating how the deprivation level of the area where people live influences the kinds of urban environment they are more likely to use for their everyday activities. To do this, raw GPS trajectories collected in 2019 in Great Britain (GB) are transformed into semantic trajectories where short-time changes and the functional nature of urban contexts are acknowledged as two key dimensions to understand human spatial behaviours. Hourly sequences of stops are extracted from GPS trajectories and enriched with contextual information based on a new area-based classification detecting urban functions. The data exploration shows that some human patterns are widely common across all levels of deprivation, such as the tendency to be mostly exposed to the urban context near the home location. At the same time, we show that differences exist, especially between those who live in the most deprived areas and those who live in the least deprived areas of GB. It appears that people living in the most deprived areas tend to have a less regular working pattern and be more exposed to urban-based functions and well-served areas, while those living in the least deprived areas have a more regular working patterns and are mostly exposed to the countryside and low-density areas. Our approach and results provide new insights on the temporal and contextual dimensions of mobility inequalities, informing on who is exposed to issues characterising certain urban environments.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-07T12:02:54Z
      DOI: 10.1177/23998083231208507
       
  • Relative spatial variability in building heights and its spatial
           association: Application for the spatial clustering of harmonious and
           inharmonious building heights in Tokyo

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      Authors: Hiroyuki Usui
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Whether or not a streetscape skeleton – the 3D spaces of streets defined by the arrangement of surrounding buildings – is vertically harmonious depends to a large extent on the degree of difference between the heights of buildings adjacent to one another, known as the relative spatial variability in building heights. Surprisingly, this subject has been overlooked in previous studies examining the harmony of streetscapes. Data on precise building heights are indispensable for evaluating the relative spatial variability in building heights and its spatial association. The recent relaxation of data limitations on precise building heights in Tokyo enabled us to identify the relative spatial variability in building heights and quantify its spatial association. Therefore, in this paper we aim to answer the following question: where are harmonious or inharmonious building heights locally clustered' To this end, we computed the spatial association of the relative spatial variability in building heights as a set of edges whose indices enabled us to evaluate the local indicator of spatial association (LISA). Subsequently, we statistically demarcated locally harmonious and inharmonious building heights without having to set predetermined basic spatial units. In this respect, our methods and findings are novel and can contribute to establishing a new method for measuring the variability in vertical streetscape skeletons, which is important for developing urban design policies.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-07T02:50:54Z
      DOI: 10.1177/23998083231204691
       
  • User-generated content may increase urban park use: Evidence from
           multisource social media data

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      Authors: Di Wei, Yuan Wang, Mengyang Liu, Yi Lu
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Given the numerous health benefits that urban parks and greenspaces provide, it is critical to grasp the key factors that improve park use. Despite the pervasive impact of user-generated content (UGC) in modern society, little is known about the influence of UGC on park use. Therefore, this study examined the effect of UGC on park use based on 613,858 pieces of UGC related to the 251 urban parks in two metropolitans in China, Guangzhou and Shenzhen. After controlling for the confounders, the hierarchical linear regression revealed that the quantity, rating, sentiment, and exposure were significantly associated with park use. Then, we distinguished three distinct relationships between UGC variables and park use. We proposed that the effects of UGC rating, sentiment, and exposure were more reliable predictors of park use because bidirectional associations may not affect them. Furthermore, we found the heterogeneity in the UGC-park use link by UGC and urban park types. The geotagged UGC had a larger effect size on park use than the keyword UGC. Visits to comprehensive parks were significantly affected by UGC, while visits to community parks were not. This study sheds new light on increasing park use from the perspective of digital information, which benefits future research and policy development in modern society.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-31T12:06:06Z
      DOI: 10.1177/23998083231210412
       
  • Unraveling transit service and land use components of the socio-spatial
           inequality of access

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      Authors: Fatemeh Janatabadi, Alireza Ermagun
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study proposes a framework that delineates the mobility and place components of access to help identify areas potentially suffering from insufficient transit service, limited job opportunities, or both. The framework introduces Spatial Inequality of Transit Services (SITS) and Spatial Inequality of Opportunities (SIO) measures to guide the structural reform of transit development policies through the lens of equity, equality, and need. It is tested on transit access to employment opportunities at the block group level in the Washington Metropolitan Area. Three observations are perceived. First, mobility and place components of access should be untangled to tailor effective transit and land use plans and policies. Second, transit services are less equally distributed than employment opportunities and disproportionately serve the residents of core cities. Third, carless and low-income households disproportionally reside in areas with better transit services regardless of their proximity to employment opportunities, and African Americans are discriminated against the most by the unequal distribution of employment opportunities. The findings serve as an essential input for developing regional transit plans and may be utilized to evaluate and prioritize proposed interventions based on their potential to reduce observed access deficiencies. However, further targeted research on residential location choice is necessary to delve into the decision-making processes, understand underlying motivations, identify potential barriers in seeking alternative options, and determine if it is a result of self-selection.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-30T08:43:38Z
      DOI: 10.1177/23998083231207534
       
  • Spatiotemporal gender differences in urban vibrancy

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      Authors: Thomas Collins, Riccardo Di Clemente, Mario Gutiérrez-Roig, Federico Botta
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Urban vibrancy is the dynamic activity of humans in urban locations. It can vary with urban features and the opportunities for human interactions, but it might also differ according to the underlying social conditions of city inhabitants across and within social surroundings. Such heterogeneity in how different demographic groups may experience cities has the potential to cause gender segregation because of differences in the preferences of inhabitants, their accessibility and opportunities, and large-scale mobility behaviours. However, traditional studies have failed to capture fully a high-frequency understanding of how urban vibrancy is linked to urban features, how this might differ for different genders, and how this might affect segregation in cities. Our results show that (1) there are differences between males and females in terms of urban vibrancy, (2) the differences relate to ‘Points of Interest’ as well as transportation networks, and (3) there are both positive and negative ‘spatial spillovers’ existing across each city. To do this, we use a quantitative approach using Call Detail Record data – taking advantage of the near-ubiquitous use of mobile phones – to gain high-frequency observations of spatial behaviours across Italy’s seven most prominent cities. We use a spatial model comparison approach of the direct and ‘spillover’ effects from urban features on male-female differences. Our results increase our understanding of inequality in cities and how we can make future cities fairer.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-28T04:38:58Z
      DOI: 10.1177/23998083231209073
       
  • The role of procedural utility in land market dynamics in Greater Cairo:
           An agent based model application

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      Authors: Yahya Gamal, Nuno Pinto, Deljana Iossifova
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      In land use analyses, procedural utility is the sense of well-being achieved by an actor while participating in a land market. Such utility has not been explored as an indicator of market preferences beyond applying exploratory Agent Based Models (ABMs) and hypothetical scenarios. This paper presents an empirical approach to procedural utility and applies it in the context of Greater Cairo (GC) – a context with different formal/informal markets that lead to different market preferences for buyers. We integrate the observed market preferences in GC in an ABM incorporating procedural utility. We explore the contribution of such utility on formal/informal urban segregation and urban expansion in GC. Our findings indicate that market preferences contribute to (1) the formulation of urban enclaves and lower socio-economic diversity and (2) making the urban system in GC more attractive, leading to higher urban growth. These findings validate the relevance of procedural utility in contexts where market regulations are distinct enough to trigger buyer market preferences – specifically formal/informal contexts of the Global South.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-23T07:00:03Z
      DOI: 10.1177/23998083231207077
       
  • Shrinking homes' The geographies of small domestic properties in
           London, 2010–2021

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      Authors: Phil Hubbard, Jon Reades, Hendrik Walter, Catrin Preston
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      In the last decade, the UK’s media have highlighted an apparent rise in the number of homes below the recommended Nationally Described Space Standard for a one-person, one-bed home. However, evidence for the growth of ‘micro-apartments’ is mixed, with existing data making it difficult to map the geographies of sub-standard homes below the Local Authority scale. Focussing on London, this paper uses Energy Performance Certificates (EPCs) as a source of floorspace data, matching this to the Land Registry’s Price Paid Data (PPD) and information from the London Planning Database. It quantifies the number of sub-standard homes in London registered for an EPC 2010–21, maps their location at the MSOA (neighbourhood) level, and compares property prices for small and larger homes. Focusing on newly-built homes, it shows that the numbers of small homes doubled across this period with growth in select outer London ‘hotspots’ accounting for much of this. It also demonstrates the overall numbers of small homes rose despite the formal incorporation of NDSS in the London Plan 2016, with the by-passing of space standards in property conversions under Permitted Development Rights, 2013–21 appearing relatively insignificant in explaining these temporal and spatial trends. Finally, it shows that the price per square metre of small homes often far exceeds that of much larger homes in the same area. While recognising the limitations of EPC data, our findings point to the need for further exploration of the enforcement of space standards, not least because it is often assumed that building more, smaller homes in the capital will create more affordable homes for Londoners.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-21T05:54:45Z
      DOI: 10.1177/23998083231208732
       
  • Understanding the spatial statistical properties of a real estate listings
           point pattern in San José, Costa Rica

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      Authors: Eduardo Pérez-Molina
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      One should expect real estate sales, and properties listed as for sale, to be concentrated on market hotspots. Using data of real estate listings from San José, Costa Rica, this expected clustering is examined using point pattern processes of detached housing, apartments, and vacant lots. Non-stationary G and J functions describe the patterns and their interactions. Potential determinants of the point pattern were selected based on previous studies and theory. Their effect on the point pattern was estimated using an inhomogeneous Poisson model, with its intensity a lognormal function of the determinants. Results show detached houses, apartments, and lots are all clustered point patterns. The cross density (joint G function) of houses with apartments and with lots exhibits clustering, suggesting the patterns are related; however, the cross density of apartments and lots is no different from a Poisson distribution (they are not related). The inhomogeneous Poisson model with Euclidean distance to the central business district (CBD), nearest municipal center, and nearest main road, as well as elevation and slope, proved better than homogeneous Poisson models in explaining the point patterns of houses, apartments, and lots.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-19T07:28:57Z
      DOI: 10.1177/23998083231208230
       
  • Toward a more socially equitable stormwater management fee: The case of
           Corpus Christi in Texas, USA

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      Authors: Jim Lee, Hua Zhang, Yuxia Huang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study evaluates a popular approach to assessing stormwater utility fees in the context of social equity. Analyses are based on comparing single-family land parcels in different neighborhoods of Corpus Christi, a U.S. city in the state of Texas that recently introduced a stormwater fee program. The stormwater fees are based on the same stormwater runoff factor for all single-family residential land parcels. We instead derive stormwater runoff estimates from parcel-scale impervious area measurements through the application of a machine-learning model to high-resolution remote sensing data. The difference between the official runoff factor and our estimate tends to be larger among land parcels in census tracts with proportionally more low-income and Hispanic households. This finding at odds with the ability-to-pay principle is attributable to the association of different neighborhoods’ sociodemographic compositions with their housing development patterns. Our work not only contributes to the design of a stormwater fee program that better characterizes the generation of stormwater runoff but it also helps city officials alleviate social inequity for homeowners in economically disadvantaged communities.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-18T10:28:21Z
      DOI: 10.1177/23998083231207535
       
  • Gravity-based models for evaluating urban park accessibility: Why does
           localized selection of attractiveness factors and travel modes matter'

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      Authors: Peng Chen, Wei Wang, Chong Qian, Mengqiu Cao, Tianren Yang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Gravity-based models have been extensively utilized in urban studies for measuring geographic disparities in access to urban parks over the past several decades. However, despite methodological advancements incorporating various aspects of accessibility, there has been limited focus on the impact of variable selection (e.g., attractiveness factors) and transport modes on accessibility evaluations. This study investigates the differences in gravity-based models for assessing park accessibility based on varying assumptions about attractiveness factors and travel impedance. Semi-structured interviews with local residents were conducted to identify the reasons for park visits in Shanghai. Our bivariate correlation analyses reveal that factors such as park openness and access to public transport were crucial, in addition to conventional factors identified in the literature (i.e., park size and driving accessibility). This insight led to the development of localized accessibility measurements that incorporate park inclusiveness (i.e., entrance fees and opening hours) and multimodal travel options (based on multinomial logistic mode choice models). The results indicate that the refined model produces lower and more varied accessibility levels, which can better capture accessibility gaps across different geographic contexts. This accurate and practical identification of accessibility gaps can assist local planners and decision-makers in formulating effective policies and strategies to promote equitable access to urban public parks.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-17T07:31:44Z
      DOI: 10.1177/23998083231206168
       
  • Semantic enrichment of building functions through geospatial data
           integration and ontological inference

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      Authors: Abdulkadir Memduhoglu, Melih Basaraner
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The comprehensive definition of buildings in urban spatial databases (SDBs) and geographic information systems (GISs) is crucial for city management, considering their essential role in cities. Modern urban geospatial applications such as smart cities need a rich-information infrastructure, relying upon urban SDBs and GISs, powered by multiple data sources. In this respect, many geospatial techniques are available for acquiring geometric data of buildings, but their semantic definitions require extra effort. Today, volunteered geographic information (VGI) platforms are assumed to be alternative geospatial data sources and their integration with official datasets provides a new opportunity for the enrichment of urban geospatial datasets. In this context, geospatial semantic web technologies can contribute to the semantic enrichment process. This study presents a methodology for enriching various building datasets using GIS and geospatial semantic web technologies, enabling an enhanced definition of building functions. The proposed method makes use of an OpenStreetMap (OSM) dataset to obtain missing semantic information about building features. The enrichment process was implemented by transferring OSM point of interest (POI) values to associated OSM building features through the application of semantic web rule language (SWRL) rules. Furthermore, the same process was also applied to two additional official datasets at different levels of detail (TOPO1K and TOPO25K), which were subsequently incorporated into the geospatial ontology. The resulting geospatial ontology offers new opportunities for functional definition and logical rule-making for buildings, with the potential to uncover new classes and insights. In addition, it allows for the definition of terms and explanations in multiple languages, covering most of the OSM values used worldwide for selected tags to define building functions.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-17T01:30:54Z
      DOI: 10.1177/23998083231206165
       
  • Characterizing urban lifestyle signatures using motif properties in
           network of places

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      Authors: Junwei Ma, Bo Li, Ali Mostafavi
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The lifestyles of urban dwellers could reveal important insights regarding the dynamics and complexity of cities. The availability of human movement data captured from cell phones enables characterization of distinct and recurrent human daily visitation patterns. Despite growing research on analysis of lifestyle patterns in cities, little is known about the characteristics of people’s lifestyle patterns at urban scale. This limitation is primarily due to challenges in restriction of human movement data to protect the privacy of users. To address this gap, this study constructed networks of places to model cities based on location-based human visitation data. We examined the motifs in the networks of places to map and characterize lifestyle patterns at urban scale. The results show that (1) people’s lifestyles in cities can be well depicted and quantified based on distribution and attributes of motifs in networks of places; (2) motifs show stability in quantity and distance as well as periodicity on weekends and weekdays indicating the stability of lifestyle patterns in cities; (3) networks of places and lifestyle patterns show similarities across different metropolitan areas implying the universality of lifestyle signatures across cities; (4) lifestyles represented by attributed motifs are spatially heterogeneous suggesting variations of lifestyle patterns within different population groups based on where they live in a city. The findings provide deeper insights into urban lifestyle signatures and significant implications for data-informed urban planning and management.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-16T10:51:50Z
      DOI: 10.1177/23998083231206171
       
  • Modelling the impact of urban form on daily mobility energy consumption
           using archetypal cities

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      Authors: Maud Haffner, Olivier Bonin, Gilles Vuidel
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Urban form is regularly identified as a potential key factor for reducing road traffic and the resulting energy consumption, but the nature of the link between mobility and urban form has been subject to considerable debate. This paper employs quantitative research methods to investigate the influence of urban form on daily mobility energy consumption. First, we used archetypal cities, representative of French large-sized cities, which allow us to isolate the impact of urban form and to have more generalised results. Second, we simulated daily mobility in those archetypal cities by using an individual-based daily mobility simulation model, Mobisim-Soft and then calculated the resulting energy consumptions. Our results confirm that the periurban city is the most energy consuming compare to the axialized and the polycentric cities, but it appears that the global structure of an agglomeration is a second-order factor compared to the inner arrangement of its neighbourhoods.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-13T03:37:04Z
      DOI: 10.1177/23998083231206169
       
  • Developing a two-level machine-learning approach for classifying urban
           form for an East Asian mega-city

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      Authors: Chih-Yu Chen, Florian Koch, Christa Reicher
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Having had the most rapid urbanization in the world since the 1990s, mega-cities in East Asia featured highly compact and atomized modernist architecture. With densely built modernist architecture and relatively free building regulations, it is challenging to trace the actual development of the whole city. Compared to European cities, their overall urban landscapes are much denser, higher, and functionally mixed. In order to achieve a quicker and more accurate identification of urban forms in mega-cities, this study proposed a two-level machine-learning approach. At the building level, we extracted features from topographic maps and building licenses to automatically classify building types. Four state-of-the-art multi-class classification models were compared. At the block level, we used building types as input data and compared two methods for block clustering. In total 61,426 buildings from Taipei were classified and grouped into 10 block types. Different from Western cities, many of the block types in Taipei were mixtures of different types of buildings. This approach is efficient in exploring new urban form types, especially for emerging mega-cities where block types are previously unknown. The result not only sheds light on the features of East Asian urban landscapes but also serves as important basis of type-based strategic plans for contemporary urban issues.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-09-29T06:45:57Z
      DOI: 10.1177/23998083231204606
       
  • Explainable spatially explicit geospatial artificial intelligence in urban
           analytics

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      Authors: Pengyuan Liu, Yan Zhang, Filip Biljecki
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph neural networks (GNNs) have become one of the most popular methods in recent years. However, along with the success of GNNs, the black box nature of AI models has led to various concerns (e.g. algorithmic bias and model misuse) regarding their adoption in urban analytics, particularly when studying socio-economics where high transparency is a crucial component of social justice. Therefore, the desire for increased model explainability and interpretability has attracted increasing research interest. This article proposes an explainable spatially explicit GeoAI-based analytical method that combines a graph convolutional network (GCN) and a graph-based explainable AI (XAI) method, called GNNExplainer. Here, we showcase the ability of our proposed method in two studies within urban analytics: traffic volume prediction and population estimation in the tasks of a node classification and a graph classification, respectively. For these tasks, we used Street View Imagery (SVI), a trending data source in urban analytics. We extracted semantic information from the images and assigned them as features of urban roads. The GCN first provided reasonable predictions related to these tasks by encoding roads as nodes and their connectivities and networks as graphs. The GNNExplainer then offered insights into how certain predictions are made. Through such a process, practical insights and conclusions can be derived from the urban phenomena studied here. In this paper we also set out a path for developing XAI in future urban studies.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-09-29T01:19:33Z
      DOI: 10.1177/23998083231204689
       
  • Leveraging newspapers to understand urban issues: A longitudinal analysis
           of urban shrinkage in Detroit

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      Authors: Na Jiang, Andrew T Crooks, Hamdi Kavak, Wenjing Wang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Today we are awash with data, especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to remotely sensed imagery and social media. This has led to the growth of urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, we would argue that social media only provides limited insights when dealing with longer-term urban phenomena, such as the growth and shrinkage of cities. This relates to the fact that social media is a relatively recent phenomenon compared to longer-term urban problems that take decades to emerge. Concerning longer-term coverage, newspapers, which are increasingly becoming digitized, provide the possibility to overcome the limitations of social media and provide insights over a timeframe that social media does not. To demonstrate the utility of newspapers for urban analytics and to study longer-term urban issues, we utilize an advanced topic modeling technique (i.e., BERTopic) on a large number of newspaper articles from 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal insights related to how Detroit shrinks. For example, side effects of 2007 to 2009 economic recessions on Detroit’s automobile industry, local employment status, and the housing market.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-09-28T11:38:59Z
      DOI: 10.1177/23998083231204695
       
  • Transportation and urban spatial structure: Evidence from Paris

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      Authors: Anton Salov, Elena Semerikova
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This paper analyses the role of railroad development in employment subcentre formation in the Paris metropolitan area between 1968 and 2018. Over half a century, approximately 1.5 million new jobs were created; however, their spatial distribution across the Île-de-France region was uneven. Paris intramuros has lost 123,000 jobs for the 50-year period, while the Grande Couronne (outer periphery) accounted for 2/3 of employment growth. These dramatic changes in the geography of employment in the Paris metropolitan region were coupled with cardinal alterations in railroad transportation, whose network has expanded and whose branches have been intertwined in order to improve population mobility and, to some extent, decentralise the capital area in favour of the development of peripheral territories. The construction of the Réseau Express Régional (RER) on the basis of 19th century railroads together with formation of the Transilien network were stepping stones towards today’s efficient rapid transit system. Our investigation, using McMillen (2001) method to identify subcentres as well as the IV approach to determine the role of railway transport development in local employment growth and the evolution of urban spatial structures, corroborates the decisive role of RER in fostering employment and in the emergence of employment subcentres. Specifically, the proximity to a railway station boosts employment in the commune. For RER stations, this effect is more substantial and heterogeneous across space, being of greater magnitude for municipalities more distant from the CBD. Furthermore, the presence of a railway station in a commune increases its probability of being a (part of) subcentre from 19.3% to 41.3% depending on the period. Moreover, this effect is of greater magnitude for the presence of a RER station in a municipality (53.2%–76.1%). Interestingly, we cannot confirm that the influence of a railway station on subcentre formation spills over the edge of the commune where it is located.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-09-26T02:52:35Z
      DOI: 10.1177/23998083231202551
       
  • Developing a TOD assessment model based on node–place–ecology for
           suburban areas of metropolitan cities: A case in Odawara

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      Authors: Weiyao Yang, Wanglin Yan, Lihua Chen, Haichen Wei, Shuang Gan
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Transit-oriented development (TOD) has a close relationship with ecology since its inception and aims to create livable and sustainable environments. However, few studies have examined the key point of ecology in the construction of TOD assessment models. This paper takes Odawara as an example, a city located on the outskirts of the Tokyo metropolitan area. Based on the node–place model, a new dimension of ecology is introduced to expand the two-dimensional model into a three-dimensional model, primarily applied to 18 stations in Odawara. Using this model, the study explores the impact of TOD on the development process of Odawara and proposes historical policy and data-based current condition discussions. The results indicate that the model-based analysis reveals a discrepancy between the current condition of the 18 stations in Odawara and the official positioning of these stations by city managers. Additionally, there is a negative correlation between the node–place value and ecology value of the station areas. We believe that this approach not only directly connects TOD with ecological considerations but also develops a new quantitative assessment model for TOD, particularly in the context of abundant ecological resources in suburban areas of metropolitan areas, arriving at a more refined level of research than before. At the same time, the model continues to maintain good scalability, providing new perspectives for the metabolism of developing areas worldwide.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-09-20T01:23:12Z
      DOI: 10.1177/23998083231202880
       
  • Understanding street-level urban vibrancy via spatial-temporal Wi-Fi data
           analytics: Case LivingLine Shanghai

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      Authors: Yan Zhang, Chengliang Li, Jiajie Li, Zhiyuan Gao, Tianyu Su, Can Wang, Hexin Zhang, Teng Ma, Yang Liu, Weiting Xiong, Ronan Doorley, Luis Alonso, Yongqi Lou, Kent Larson
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Urban vibrancy is a topic of great concern in the field of urban design and planning. However, the definition and measurement of urban vibrancy have not been consistently and clearly followed. With the development of technologies such as big data and machine learning, urban planners have adopted new methods that enable better quantitative evaluation of urban performance. This research attempts to quantify the impact on the urban vibrancy of the urban interventions introduced by the LivingLine project in a residential neighborhood renovation made in Siping Street, Shanghai. We use Wi-Fi probes to process collected mobile phone data and segment people into different categories according to commuting patterns analysis. We use a pre-trained random forest model to determine the specific locations of each person. Subsequently, we analyze the behavior patterns of people from stay points detection and trajectory analysis. Through statistical models, we apply multi-linear regression and find that urban intervention (well-curated and defined lab events deployed in the street) and people’s behavior are positively correlated, which helps us to prove the impact of urban intervention on street dynamics. The research proposes a novel, evidence-based, low-cost methodology for studying granular behavior patterns on a street level without compromising users’ data privacy.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-09-07T08:51:27Z
      DOI: 10.1177/23998083231198721
       
  • Mapping sidewalks on a neighborhood scale from street view images

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      Authors: Omar Faruqe Hamim, Surendra Reddy Kancharla, Satish V Ukkusuri
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Although reliable and accurate inventorying of sidewalks is time consuming, it can aid urban planners in decision making for infrastructure development. Recent advancements in computer vision and machine learning algorithms have improved the reliability and accuracy of automated inventorying. This research uses a deep learning architecture-based semantic segmentation model (i.e., HRNet + OCR) to automate sidewalk identification using Google Street View (GSV) images. The results show that retraining the model using local training images yields 114.16% and 178.11% higher performance in terms of intersection over union (IoU) metric compared to pretrained model using Cityscapes and Mapillary datasets, respectively. The developed model showed excellent performance in predicting the presence of sidewalks in an image by achieving high accuracy (0.9557), precision (0.9447), recall (0.9900), and F1- score (0.9668). Further, in comparison with EfficientNet, a computationally efficient image classification model, the present model showed superior performance in predicting sidewalk presence at the image level. Therefore, integrating local training images containing minimum required labels (in this study, roads, sidewalks, buildings, and walls) with publicly available training datasets can help increase the performance of the semantic segmentation model for extracting the required features (in this study, roads and sidewalks) from GSV images, especially in developing countries like Bangladesh. This study generates sidewalk maps on a neighborhood scale, which can be useful visualization tools for researchers and practitioners to understand the existing pedestrian infrastructure and plan for future improvements.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-09-01T08:21:52Z
      DOI: 10.1177/23998083231200445
       
  • Operationalizing the open city concept: A case study of Berlin

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      Authors: Grace Abou Jaoude, Majd Murad, Olaf Mumm, Vanessa Miriam Carlow
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Framed by a utopian rhetoric, the Open City emerges as a potential guiding principle to the contradictory tendencies and calamities of cities. As an elusive concept with a panoply of context-bound interpretations, the Open City is an open-ended project that manifests through different situations across the city. The article aims to explore different attributes of the Open City, in the context of Berlin, based on a thorough literature review and operationalizes the concept using a systematized approach. Results revealed that openness in Berlin followed a center-periphery pattern, where areas that fostered a high degree of openness were mostly found in inner-city neighborhoods while a lower potential of openness prevailed along the edges. By analyzing the conditions of openness in relation to the built environment, we sought to contribute toward a better understanding of the Open City concept and provide an approach for analyzing openness that can be adapted to different geographic contexts.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-08-26T07:09:51Z
      DOI: 10.1177/23998083231196016
       
  • Segregated by design' Street network topological structure and the
           measurement of urban segregation

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      Authors: Elijah Knaap, Sergio Rey
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Racial residential segregation is a longstanding topic of focus across the disciplines of urban social science. Classically, segregation indices are calculated based on areal groupings (e.g., counties or census tracts), with more recent research exploring ways that spatial relationships can enter the equation. Spatial segregation measures embody the notion that proximity to one’s neighbors is a better specification of residential segregation than simply who resides together inside the same arbitrarily drawn polygon. Thus, they expand the notion of “who is nearby” to include those who are geographically close to each polygon rather than a binary inside/outside distinction. Yet spatial segregation indices often resort to crude measurements of proximity, such as the Euclidean distance between observations, given the complexity and data requirements of calculating more theoretically appropriate measures, such as distance along the pedestrian travel network. In this paper, we examine the ramifications of such decisions. For each metropolitan region in the U.S., we compute both Euclidean and network-based spatial segregation indices. We use a novel inferential framework to examine the statistical significance of the difference between the two measures and following, we use features of the network topology (e.g., connectivity, circuity, throughput) to explain this difference using a series of regression models. We show that there is often a large difference between segregation indices when measured by these two strategies (which is frequently significant). Further, we explain which topology measures reduce the observed gap and discuss implications for urban planning and design paradigms.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-08-23T05:15:57Z
      DOI: 10.1177/23998083231197956
       
  • Exploring the construction and analysis method of landscape spatial
           structure based on complex networks

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      Authors: Yijing Wang, Shi Cheng, Ziqian Cheng, Yuning Cheng
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Complex network theory proposes a structural network model that abstracts the elements in a complex system into nodes and the relationship between elements into ties, which proved to be a scientific method to study the structural system of complexities. This article aims to apply the complex network theory to interpret the organization of landscape spatial structure and propose a spatial structure network construction and quantitative analysis method suitable for landscape space. The following two questions are addressed in this research: (1) How to extract the structural elements from the landscape space and realize the construction of the structural network model based on the 3D spatial information' (2) How to quantitatively analyze the organization characteristics of a spatial structure according to the particularity of landscape space' The research content of this article includes the following five aspects, which are 3D spatial information acquisition, structural element extraction, structural element description, structural network model diagramming, and quantitative index selection. Taking Nanjing Lovers Park as an example, the feasibility of this method is verified. The results show that the method proposed can guarantee the accuracy of the landscape spatial structure model, visualize the attributes of structural elements, and achieve a refined analysis of the structure organization.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-08-22T09:16:01Z
      DOI: 10.1177/23998083231197496
       
  • New town development and housing affordability: A case study in Hong Kong

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      Authors: Yaoxuan Huang, Victor Jing Li, Daikun Wang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      New towns and housing affordability are the hot topics among city planners, urban researchers, policymakers, and economists in recent years. Yet, whether the existing new town development could mitigate the problem of housing affordability for low-income people is understudied. To fill this important knowledge blank and research gap, this study selects Hong Kong to conduct the empirical analysis. Through examining the data from multiple sources such as Census statistics from the Hong Kong government, market-rate housing transaction records, and private housing transactions from the property agents’ website, we found that new town development mitigates the problems of housing affordability in Hong Kong in general but not for low-income tenants in particular. The low-income people in new towns pay more for living in unsubsidized affordable housing/subdivided units as their last shelters. As more new towns will be developed in the upcoming years, the results also found that the announcement of new development areas increases the housing rent rate by 9.4% in the new development areas. We suggest that the priority of the newly built subsidized housing in the new development areas should be allocated to the local/indigenous low-income people; otherwise, the housing affordability of low-income people may repeat the same pattern associated with the past new towns’ development.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-08-21T04:59:29Z
      DOI: 10.1177/23998083231196405
       
  • A city of gardeners: What happens when policy, planning, and populace
           co-create the food production of a novel peri-urban area'

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      Authors: Jan Eelco Jansma, Sigrid CO Wertheim-Heck
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Urban re-orientation on feeding the city in a city-region context has encouraged local policies to spur urban agriculture by stimulating bottom-up citizen participation in urban food production. However, in real life, tensions occur between policies and practices. The misalignment of policy goals with planning instruments and the needs of practitioners in urban agriculture hampers the development of substantial urban food production. This paper introduces Oosterwold, a new peri-urban area of the Dutch city of Almere that pivots urban agriculture. Oosterwold is a unique experiment in which a top-down policy goal – producing 10% of the future urban food needs – is handed over to the self-organisation of new residents, who are bound by the rule to allocate 51% of their plot to urban agriculture. This study deploys a social practice theory–informed analysis to appraise the performance in urban agriculture. Novel in our methodology – combining an online survey (n=111) with an analysis of aerial photos (n=199) – we unpack the unruly nature in which urban policy and planning are shaping up through bottom-up citizen participation. Our study demonstrates that (i) it takes time for residents to adopt urban agriculture as a substantial practice in their heterogeneous lifestyle and (ii) that a focus on bottom-up approaches, such as Oosterwold residents’ self-organisation, does not imply laissez faire from planning and policy. It is inferred that a balance in policy goals, planning instruments, and the needs of the practitioners requires a shared vision and builds on supportive conditions.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-08-14T01:28:42Z
      DOI: 10.1177/23998083231193802
       
  • Short video-driven deep perception for city imagery

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      Authors: Xiana Chen, Junxian Yu, Yingying Zhu, Ruonan Wu, Wei Tu
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      City imagery is essential for enhancing city characteristics and disseminating city identity. As an emerging medium, short videos can intuitively reflect people’s perception of complex urban environment. In this study, we proposed a short video-driven deep learning perception framework to sense city imagery. To quantitatively deconstruct spatial imagery of urban space, deep neural network is used for pixel-level semantic segmentation. K-means clustering and hierarchical clustering analysis are carried out to extract and reveal the spatial imagery characteristics at the landmark level and the city level. Taking the Guangdong-Hong Kong-Macao Great Bay Area (GBA) as the study area, an experiment was carried out with TikTok short videos. The results showed that (1) the spatial imagery of the GBA cities are divided into four categories: Green Waterfront, including Jiangmen, Huizhou, Zhuhai, Zhaoqing, and Zhongshan; Humanistic Capital, including Hong Kong, Guangzhou, and Foshan; Modern Green City, including Shenzhen and Dongguan; Sky City, that is, Macao; (2) the landmark imagery in GBA can be characterized into five groups: Green Water and Blue Sky, Ancient Architecture of Greenery, Modern Architecture, Staggered Roads, and Urban Green Lung. It further investigated spatial distribution of landmark-level spatial imagery. These results prove the feasibility of sensing city imagery with short videos and provide useful insights into city imagery studies. It provides a new approach for understanding and spreading the city imagery over Internet.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-08-08T12:52:16Z
      DOI: 10.1177/23998083231193236
       
  • Analyzing jogging activity patterns and adaptation to public health
           regulation

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      Authors: Yifeng Liu, Yuan Lai
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Outdoor running is one of the most popular physical activities, with numerous health benefits and minimal cost. Despite such importance, limited scientific understanding of collective behavioral patterns of running activity constraints more evidence-based spatial planning and urban design for promoting an active lifestyle. This study investigates the underlying spatial, temporal, and typological patterns of running activities within a university campus by analyzing a large number of running trajectory data (n = 11088) at high spatial-temporal resolution. Based on classification and pattern identification, the results reveal three major running activity types on streets, tracks, and mixed spatial conditions. This study further investigates data during a specific period when the campus experienced public space regulation as a part of the COVID-19 prevention protocol. Results reveal the disruption, change, and recovery of running activity, revealing local behavioral adaptation and resilience towards spatial intervention. Overall, our findings resonate with classic urban design theory and existing literature, and the proposed analytical workflow can further support more evidence-based and data-informed planning decisions and design actions for promoting physical activity and active living.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-08-05T03:50:32Z
      DOI: 10.1177/23998083231193484
       
  • Quantifying the pedestrian access potential of suburban street network
           retrofits

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      Authors: Rohan L Aras, Nicholas T Ouellette, Rishee K Jain
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The United States largely depends on the automobile for personal transportation. This dominance has significant consequences for society over a range of issues, including the environment, public safety, public health, and equity. The issues associated with the dominance of the automobile are most pressing in the suburbs due to their size and curvilinear street network patterns. Thus, any effort to address the negative consequences of automobile dependency in the US needs to consider retrofitting the suburbs and their street networks. We attempt to better understand the potential for street network retrofits to increase suburban pedestrian access. We consider a class of planar graph augmentation problems that attempt to increase pedestrian access to points of interest (POIs) within the study area by adding new pedestrian paths to the street network that follow existing property lines. Our methodology builds on past work on graph dilation and route directness, from the planar graph and street network communities, respectively, to score the pedestrian access disruption of individual blocks. We apply this methodology to a case study of suburban Seattle. We find that, both in the limit of all possible interventions and with a limited number of untargeted interventions, retrofits can meaningfully increase pedestrian access to POIs. Given this promise, the methods we outline present a useful starting point for discussing the potential of street network retrofits to serve non-automobile mobility in suburban communities across the US.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-07-27T11:31:19Z
      DOI: 10.1177/23998083231190974
       
  • Urban planning and design with points of interest and visual perception

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      Authors: Asya Natapov, Achituv Cohen, Sagi Dalyot
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Pedestrian navigation is often guided by points-of-interest and visibility, yet most planning and design models ignore these, solely addressing street networks. Our innovative ‘POI VizNet’ tool utilises open-source geographical data for integrating points-of-interest and visibility into network-based framework. The tool was applied to the Fitzrovia redevelopment project in London, to support the reallocation of urban activities based on desired locations of various assets. Our results demonstrate the quantifying of location patterns according to the planning project goals, and the examining of urban activities while controlling visibility and accessibility. The developed method is aimed to assist researchers and developers in making more informed planning decisions intended to promote neighbourhood vibrancy and create a sustainable urban context with mixed land use that is desirable for pedestrians.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-07-26T11:17:32Z
      DOI: 10.1177/23998083231191338
       
  • Is national border weakening in technology space' Analysis of inter-urban
           hierarchy with Chinese patent licensing data

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      Authors: Suyoung Kang, Jung Won Sonn, Ilwon Seo
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The literature on the diffusion of innovation from the 1970s has found that a domestic inter-urban hierarchy was the most common conduit for the innovation diffusion. Has this hierarchy become obsolete in today’s globalized economy' As less-developed cities within a developing country absorb technological innovation directly from overseas, is the nationality of cities becoming less important' Contemporary economic geography literature tends to answer these questions in the affirmative. This study challenges that resounding yes. Through our analysis of Chinese patent licensing data, we find evidence not only for the survival but also for the reinforcement of the domestic inter-urban hierarchy. While it is true that the number of cities licensing patents to import technology from overseas has been increasing, it is being outmatched by the domestic patent licensing from the top-tier cities within China. This development demonstrates that the role of the nation as a spatial unit of knowledge production and application has remained constant throughout, even as the technological level of its cities has improved under the increasing globalization of the national economy.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-07-25T07:04:24Z
      DOI: 10.1177/23998083231168871
       
  • The conflicting geographies of social frontiers: Exploring the asymmetric
           impacts of social frontiers on household mobility in Rotterdam

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      Authors: Dan Olner, Gwilym Pryce, Maarten van Ham, Heleen Janssen
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Social frontiers arise when there are sharp differences in the demographic composition of adjacent communities. This paper provides the first quantitative study of their impact on household mobility. We hypothesise that conflicting forces of white flight and territorial allegiance lead to asymmetrical effects, impacting residents on one side of the frontier more than the other due to differences in the range of housing options available to different groups, and different symbolic interpretations of the frontier. Using Dutch registry data for the city of Rotterdam we identify ethnic social frontier locations using a Bayesian spatial model (Dean et al., 2019), exploiting the data’s one hundred metre resolution to estimate frontiers at a very small spatial scale. Regression analysis of moving decisions finds that the ethnic asymmetry of the frontier matters more than ethnicity of individual households. On the ethnic minority side of the frontier, households of all ethnicities in the 28–37 age range have reduced probability of moving compared to non-frontier parts of the city. The opposite is true on the Dutch native side of the frontier. We supplement this analysis with flow models which again find strong frontier effects. Our findings illustrate how the study of social frontiers can shed light on local population dynamics and neighbourhood change.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-07-19T01:53:09Z
      DOI: 10.1177/23998083231173696
       
  • Modelling urban transition with coupled housing and labour markets

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      Authors: Jiaqi Ge, Bernardo Alves Furtado
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study develops an agent-based model of urban transition, U-TRANS, with coupled housing and labour markets to simulate the transition of a city during a major industrial shift. We propose a dynamic, bottom-up framework incorporating the key interacting factors and micro mechanisms that drive the transition paths of cities. Using U-TRANS, we simulate a number of distinctive urban transition paths, from total collapse to weak recovery to enhanced training to global recruit, and analyse the resulting outcomes on economic growth, employment, inequality, housing price and the local neighbourhoods. We find that poor neighbourhoods benefit the most from growth in the new industry, whereas rich neighbourhoods do better than the rest when growth stagnates and the city declines. We also find there is a subtle trade-off between growth and equality in development strategy. By aggressively recruiting a large number of skilled workers from outside of the city in a short time, the division between local and non-local workers can be widened. The study contributes to the understanding of the dynamic process and micro mechanisms underlying urban transition. It helps explain why some cities starting from seemingly similar initial conditions may go on divergent development paths at critical moments in the history. It also demonstrates the heterogeneous impact of industrial shift on different urban neighbourhoods. The model can be used as a policy testbed for different development strategies to help cities navigate through a major industrial revolution.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-07-05T02:48:52Z
      DOI: 10.1177/23998083231186623
       
  • A centrality measure for grid street network considering sequential route
           choice behaviour

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      Authors: Shota Tabata
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      This study proposes a novel centrality measure for a grid network based on pedestrians’ sequential route choices, which we call sequential choice betweenness centrality (SCBC). Although conventional centralities are popular tools for urban network analysis, we must be aware of their meaning in the context of urban planning. This study reinterprets the centralities at the point of pedestrian flow. We then formulate the pedestrian flow distribution based on sequential route choice and develop the SCBC as a function of the probability of going straight at an intersection. The sensitivity analysis shows the probability of minimising the difference between the SCBC and existing centralities while revealing the numerical and spatial features of the SCBC. The more biased the grid proportion, the less similar the SCBC is to the existing ones. Moreover, the SCBC tends to be larger than conventional centralities around the corner nodes of the grid network. The probability parameterises the SCBC to go straight and is related to the pedestrian’s environmental cognition level. This parameterisation enabled us to adapt to the expected pedestrian attribution and perform an in-depth analysis of street networks.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-07-04T11:02:44Z
      DOI: 10.1177/23998083231186750
       
  • Chinese cities as digital environmental governance innovators: Evidence
           from subnational low-Carbon plans

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      Authors: Angel Hsu, Li Lili, Marco Schletz, Zhitong Yu
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Research examining the rise of digital environmental governance, particularly at the subnational scale in China, is fairly limited. Critical questions regarding how digital technologies applied at the subnational level may shape or transform environmental governance are only beginning to be explored, given cities’ increasing role as sustainability experimenters and innovators. In this study, we investigate how smart city initiatives that incorporate big data, artificial intelligence, 5G, Internet of Things, and information communication technologies, may play a role in the transformation towards a “digital China.” We conceptualize three major pathways by which digital technology could transform environmental governance in China: through the generation of new data to address existing environmental data gaps; by enhancing the policy analytical capacity of environmental actors through the use of automation, digitalization, and machine learning/artificial intelligence; and last, through reshaping subnational-national, and state-society interactions that may shift balances of power. With its dual prioritization of digital technologies and climate change, China presents an opportunity for examining digitalization trends and to identify gaps in governance and implementation challenges that could present obstacles to realizing the transformative potential of digital environmental management approaches.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-07-01T11:18:20Z
      DOI: 10.1177/23998083231186622
       
  • The effects of street environment features on road running: An analysis
           using crowdsourced fitness tracker data and machine learning

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      Authors: Shuyang Zhang, Nianxiong Liu, Beini Ma, Shurui Yan
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Urban streets provide environment for road running. The study proposes a non-parametric approach that uses machine learning models to predict road running intensity. The models were developed using route check-in data from Keep, a mobile exercise application, and street geographic information data in Beijing’s core district. The results show that blue space and trail continuity are the most important factors in improving road running intensity. There is an optimum design value for the sky openness and the street enclosure, which need to be balanced with shade while meeting the light of the road. And it is also important to provide appropriate visual permeability. Furthermore, unlike daily activities, it was found that higher function mixture and function density did not have significant positive effects on the road running intensity. This study provides empirical evidence on road running and highlights the key factors that planners, landscape architects, and city managers should consider when design running-friendly urban streets.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-06-27T05:14:57Z
      DOI: 10.1177/23998083231185589
       
  • Simple agents – complex emergent path systems: Agent-based modelling
           of pedestrian movement

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      Authors: Lei Ma, Sven Anders Brandt, Stefan Seipel, Ding Ma
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      In well-planned open and semi-open urban areas, it is common to observe desire paths on the ground, which shows how pedestrians themselves enhance the walkability and affordance of road systems. To better understand how these paths are formed, we present an agent-based modelling approach that simulates real pedestrian movement to generate complex path systems. By using heterogeneous ground affordance and visit frequency of hotspots as environmental settings and by modelling pedestrians as agents, path systems emerge from collective interactions between agents and their environment. Our model employs two visual parameters, angle and depth of vision, and two guiding principles, global conception and local adaptation. To examine the model’s visual parameters and their effects on the cost-efficiency of the emergent path systems, we conducted a randomly generated simulation and validated the model using desire paths observed in real scenarios. The results show that (1) the angle (found to be limited to a narrow range of 90–120°) has a more significant impact on path patterns than the depth of vision, which aligns with Space Syntax theories that also emphasize the importance of angle for modelling pedestrian movement; (2) the depth of vision is closely related to the scale-invariance of path patterns on different map scales; and (3) the angle has a negative exponential correlation with path efficiency and a positive correlation with path costs. Our proposed model can help urban planners predict or generate cost-efficient path installations in well- and poorly designed urban areas and may inspire further approaches rooted in generative science for future cities.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-06-21T03:49:32Z
      DOI: 10.1177/23998083231184884
       
  • Is the noise still going on' Predicting repeat noise complaints with
           historical time course and random forest classifiers

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      Authors: Zicheng Fan, Valerio Signorelli
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Noise can have serious adverse effects on residents' physical and mental health. Since the COVID-19 pandemic, the City of Westminster in London has seen a continuous increase in noise complaints, with a significant number of repeat complaints from the same address within a short time scale. The authorities' ability to respond to complaints is challenged. This study explores a method for predicting and identifying repeat complaints to improve the efficiency of the authorities in dealing with noise complaints. Taking the noise complaint records of the City of Westminster during 2018–2022 as research objects, the research explores the cumulative distribution characteristics and clustering pattern of noise complaints in different spatial and temporal dimensions. On this basis, for a noise complaint from a specific address, the study fits random forest classifiers to predict whether the same address is likely to have another noise complaint in future time scales. It is found that about 18.5% of all complaints had at least one previous complaint at the same address in the previous 7 days; during the lock-down period caused by the COVID-19 pandemic, areas with active commercial activities and higher housing prices experienced a significant decrease in complaints, while areas adjacent to parks and green spaces can share a similar upward trend in noise complaints. Prediction of repeat noise complaints with random forest classifiers is proved feasible. F1 scores of models to predict repeat complaints within 0 to 2nd days, 0 to 7th days and 0 to 30th days in the future are 0.55, 0.66 and 0.75, respectively. Suggestions are provided for local authorities to improve resource allocation related to noise complaint management.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-06-21T03:34:03Z
      DOI: 10.1177/23998083231184254
       
  • BikeDNA: A tool for bicycle infrastructure data and network assessment

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      Authors: Ane Rahbek Vierø, Anastassia Vybornova, Michael Szell, Michael Szell
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Building high-quality bicycle networks requires knowledge of existing bicycle infrastructure. However, bicycle network data from governmental agencies or crowdsourced projects like OpenStreetMap often suffer from unknown, heterogeneous, or low quality, which hampers the green transition of human mobility. In particular, bicycle-specific data have peculiarities that require a tailor-made, reproducible quality assessment pipeline: For example, bicycle networks are much more fragmented than road networks, or are mapped with inconsistent data models. To fill this gap, we introduce BikeDNA, an open-source tool for reproducible quality assessment tailored to bicycle infrastructure data with a focus on network structure and connectivity. BikeDNA performs either a standalone analysis of one data set or a comparative analysis between OpenStreetMap and a reference data set, including feature matching. Data quality metrics are considered both globally for the entire study area and locally on grid cell level, thus exposing spatial variation in data quality. Interactive maps and HTML/PDF reports are generated to facilitate the visual exploration and communication of results. BikeDNA supports quality assessments of bicycle infrastructure data for a wide range of applications—from urban planning to OpenStreetMap data improvement or network research for sustainable mobility.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-06-17T03:33:56Z
      DOI: 10.1177/23998083231184471
       
  • A hybrid estimation of carbon footprints for urban commuting
           transportation via path reconstruction

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      Authors: Jun Zhang, Qiannan Ai, Yuling Ye, Shejun Deng
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The transportation sector is a major source of carbon emissions, and it is of great significance to study the estimation method of carbon emissions from urban commuting traffic for energy conservation and emission reduction. In view of the difficulty of collecting detailed trip trajectory data, this paper first reconstructs the trip paths via an improved modal choice model and a modified path planning model based on the O-D trip matrix, taking seven single traffic modals and two combined modals into account. In order to estimate the carbon footprints with theoretical accuracy, the bottom-up method is adopted considering the trip modal, vehicle type, power source, vehicle occupancy, operation characteristics and traffic conditions. Meanwhile, faced with the converted carbon emissions from electric vehicles, factors like charging efficiency, vehicular load, regional power structure and transmission loss are further considered in the estimation function. A case study of Changzhou City has been performed to verify the feasibility of the proposed models, where the volume distribution of commuting trips is predicted upon a modified network traffic assignment by TransCAD, and the spatial distribution of carbon emission intensity has further expanded to the adjacent areas via ArcMap analysis tools. The total carbon emission and the average link emission intensity of daily commuting in the study area are about 14.7 × 105 kg/day and 870 kg/km respectively. The discussion results indicate that the CO2 emission of fuel-driven vehicles accounts for over 86%, and the equivalent carbon emission of electric vehicles accounts for about 14% under given modal choices. The correlations of carbon emissions to road levels and zone attributes get further revealed and discussed based on the estimation results.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-06-13T05:41:56Z
      DOI: 10.1177/23998083231181918
       
  • Three-dimensional land-use configuration and property prices: A spatially
           filtered multi-level modelling perspective

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      Authors: Wei Zheng, Mingshu Wang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The influence of neighbourhood characteristics on housing prices has gained increasing attention from scholars in recent decades. However, studies on the three-dimensional nature of urban space, and particularly the vertical dimension, have remained limited. This study investigates previously unexplored variables that can capture the vertical and horizontal dimensions of land-use configuration. In addition, this study proposes a spatially filtered multi-level approach to modelling variations in property values which can capture both spatial and multi-level effects. The research findings reveal a price premium for housing located in immediate neighbourhoods with more open mid-rise buildings and low plants. The results also demonstrate the varying effects of determinants of house pricing in spatially heterogeneous zones.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-06-08T08:32:46Z
      DOI: 10.1177/23998083231180213
       
  • Identifying sinks and sources of human flows: A new approach to
           characterizing urban structures

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      Authors: Takaaki Aoki, Shota Fujishima, Naoya Fujiwara
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Human flow data are rich behavioral data relevant to people’s decision-making regarding where to live, work, go shopping, etc., and provide vital information for identifying city centers. However, it is not as easy to understand massive relational data, and datasets have often been reduced merely to the statistics of trip counts at destinations, discarding relational information from origin to destination. In this study, we propose an alternative center identification method based on human mobility data. This method extracts the scalar potential field of human trips based on combinatorial Hodge theory. It detects not only statistically significant attractive locations as the sinks of human trips but also significant origins as the sources of trips. As a case study, we identify the sinks and sources of commuting and shopping trips in the Tokyo metropolitan area. This aim-specific analysis leads to a combinatorial classification of city centers based on the distinct aspects of human mobility. The proposed method can be applied to other mobility datasets with relevant properties and helps us examine the complex spatial structures in contemporary metropolitan areas from the multiple perspectives of human mobility.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-06-07T01:54:55Z
      DOI: 10.1177/23998083231180608
       
  • Estimating household demand for transit-oriented development: A two-stage
           hedonic analysis in Kitchener-Waterloo, Canada

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      Authors: Yu Huang, Dawn Cassandra Parker, Paul Anglin
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Interest in mass transit investment and transit-oriented development (TOD) is growing as a way to promote smart growth. These investments and policy changes may imply new housing demands, which are not well understood. Using Kitchener-Waterloo, Canada, as a case study, we address the following questions: (1) Do households in this mid-sized region show preferences for TOD neighborhoods' How do preferences for transit accessibility vary across space' (2) What household characteristics are associated with the demand for housing and neighborhood characteristics' With a combined dataset of household survey and housing transactions, we present a novel application of the two-stage hedonic model to understand the housing demand structure impacted by transit policies. This study provides evidence of demand for TOD and LRT accessibility by households with a range of socio-demographics. We thus recommend the region build complete TODs to satisfy a variety of housing needs.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-05-30T07:31:37Z
      DOI: 10.1177/23998083231180610
       
  • Development and evaluation of probabilistic forecasting methods for small
           area populations

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      Authors: Irina Grossman, Kasun Bandara, Tom Wilson, Michael Kirley
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Planning and development decisions in both the government and business sectors often require small area population forecasts. Unfortunately, current methods often produce forecasts that are inaccurate, particularly for remote areas and those with smaller populations. Such inaccuracy necessitates the development and evaluation of methods to forecast and communicate forecast uncertainty, however, little research has been conducted in this domain for small area populations. In this paper, we evaluate a set of probabilistic forecasting methods which include Autoregressive integrated moving average, Exponential Smoothing, THETA, LightGBM and XGBOOST, to produce point forecasts and 80% prediction intervals for Australian SA2 small area populations. We also investigate methods to combine the intervals to produce ensemble forecasts. Our results show that individual probabilistic methods generally produce prediction intervals which underestimate forecast uncertainty. Combining forecasts improves the overall accuracy of point forecasts and the coverage of their intervals, however, coverage still tends to be less than the expected 80% for all but the most conservative combination method.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-05-25T06:06:21Z
      DOI: 10.1177/23998083231178817
       
  • A visibility-based approach to manage the vertical urban development and
           maintain visual sustainability of urban mountain landscapes: A case of
           Mufu Mountain in Nanjing, China

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      Authors: Guanting Zhang, Shi Cheng, Yuan Gao
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      China has experienced a continuous population increase in urban areas over the last few decades with limited land for construction, which has prompted upward growth in the urban environment. Rapid urbanization has encroached on mountain landscapes and deteriorated the visual landscape in different parts of China. This study aims to investigate and analyze the proper balance between visual landscape protection of urban mountains and vertical urban development using a visibility-based method. An interactive and quantitative method was developed in this research using multiple digital 2D and 3D platforms based on the specification of prohibited spaces for constructive expansion in building height control. A metropolitan area near Mufu Mountain in Nanjing, China, was selected as a case study to implement the proposed method and simulate multiple vertical urban development scenarios. According to the comparison of different scenarios, there is a better building height layout to simultaneously satisfy the requirement of sustaining the Mufu mountain’s visibility and the construction capacity proposed by the documented plan. Two polynomial models were generated to quantitatively investigate the relationship between the protection of mountain landscapes and vertical urban development and served as a reference basis for urban planners to formulate the construction volume control strategy around Mufu Mountain. The proposed method in this study can help planners and urban managers to seek an appropriate approach to control building heights and achieve visual sustainability.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-05-24T10:43:33Z
      DOI: 10.1177/23998083231177058
       
  • Intersectional approach of everyday geography

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      Authors: Julie Vallée, Maxime Lenormand
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Hour-by-hour variations in spatial distribution of gender, age and social class within cities remain poorly explored and combined in the segregation literature mainly centred on home places from a single social dimension. Taking advantage of 49 mobility surveys compiled together (385,000 respondents and 1,711,000 trips) and covering 60% of France’s population, we consider variations in hourly populations of 2572 districts after disaggregating population across gender, age and education level. We first isolate five district hourly profiles (two ‘daytime attractive’, two ‘nighttime attractive’ and one more ‘stable’) with very unequal distributions according to urban gradient but also to social groups. We then explore the intersectional forms of these everyday geographies. Taking as reference the dominant groups (men, middle-age and high educated people) known as concentrating hegemonic power and capital, we analyze specifically whether district hourly profiles of dominant groups diverge from those of the others groups. It is especially in the areas exhibiting strong increase or strong decrease of ambient population during the day that district hourly profiles not only combine the largest dissimilarities all together across gender, age and education level but are also widely more synchronous between dominant groups than between non-dominant groups (women, elderly and low-educated people). These intersectional patterns shed new light on areas where peers are synchronously located over the 24-hour period and thus potentially in better position to interact and to defend their common interests.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-05-24T09:43:59Z
      DOI: 10.1177/23998083231174025
       
  • The effect of the perceptible built environment on pedestrians’ walking
           behaviors in commercial districts: Evidence from Hong Kong

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      Authors: Chendi Yang, Siu Ming Lo, Rui Ma, Hongqiang Fang
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      In order to structure an efficient and comfortable commercial district for pedestrians, we need to understand the interaction between pedestrian walking behavior and the complex elements of the built environment. Previous studies have focused on people’s activities in the context of the neighborhood rather than the commercial district. This study investigates the potential associations between multi-dimensional environmental factors and pedestrians under various temporal distributions in a densely populated commercial district. Multi-source urban data and semantic segmentation technics have been adopted to measure the built environmental quality from four classic dimensions of urban design, and combining the observations of pedestrian volumes of representative streets in the commercial district, we assess the relationship between the two at different times on the basis of a generalized linear model (GLM). The analytical results identify that the Morphology, Visual perception, Function, and Street configuration features of the commercial environment have a significant impact on walking activity, and temporal differences exist. The findings highlight the importance of built environment quality to pedestrians and street attractiveness, and inform designers, stakeholders, and municipalities on the revitalization of traditional commercial districts.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-05-21T01:38:00Z
      DOI: 10.1177/23998083231177699
       
  • Exploring flood mitigation governance by estimating first-floor elevation
           via deep learning and google street view in coastal Texas

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      Authors: Ge Gao, Xinyue Ye, Shoujia Li, Xiao Huang, Huan Ning, David Retchless, Zhenlong Li
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Flood mitigation governance is critical for coastal regions where flooding has caused considerable damage. Raising the First-Floor Elevation (FFE) above the base flood elevation (BFE) is an effective mitigation measure for buildings with a high risk of flooding. In the U.S., measuring FFE is necessary to obtain an Elevation Certificate (E.C.) for the National Flood Insurance Program (NFIP) and has traditionally required labor-consuming field surveys. However, the advances in computer vision technology have facilitated the handling of large image datasets, leading to new FFE measurement approaches. Taking Galveston Island (including the cities of Galveston and Jamaica Beach) in Coastal Texas as a case study, we explore how these new approaches may inform flood risk management and governance, including how FFE estimates may be combined with BFE estimates from flood inundation probability mapping to model the predicted cost of raising buildings’ FFE above their BFE. After establishing the FFE model’s accuracy by comparing its results with previously validated FFE estimates in three districts of Galveston, we generalize the workflow to building footprints across Galveston Island. By combining the FFE data derived from our workflow with multidimensional building information, we further analyze the future flood control and post-disaster maintenance strategies. Our findings present valuable data collection paradigms and methodological concepts that inform flood governance for Galveston Island. The proposed workflow can be extended to flood management and research for other vulnerable coastal communities.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-05-17T02:38:07Z
      DOI: 10.1177/23998083231175681
       
 
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School of Mathematical and Computer Sciences
Heriot-Watt University
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