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Authors:Michael Batty Pages: 1707 - 1710 Abstract: Environment and Planning B: Urban Analytics and City Science, Volume 50, Issue 7, Page 1707-1710, September 2023.
Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-09-23T04:48:12Z DOI: 10.1177/23998083231202903 Issue No:Vol. 50, No. 7 (2023)
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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
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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
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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
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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
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Authors:Hadrien Salat, Dustin Carlino, Fernando Benitez-Paez, Anna Zanchetta, Daniel Arribas-Bel, Mark Birkin Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The Synthetic Population Catalyst (SPC) is an open-source tool for the simulation of populations. Building on previous efforts, synthetic populations can be created for any area in England, from a small geographical unit to the entire country, and linked to geolocalised daily activities. In contrast to most transport models, the output is focussed on the population itself and the way people socially interact together, rather than on a precise modelling of the volume of transport trips from one area to another. SPC is therefore particularly well suited, for example, to study the spread of a pandemic within a population. Other applications include identifying segregation patterns and potential causes of inequality of opportunity amongst individuals. It is fast, thanks to its Rust codebase. The outputs for each lieutenancy area in England are directly available without having to run the code. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-09-25T12:42:28Z DOI: 10.1177/23998083231203066
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Authors:María del Carmen Dircio-Palacios-Macedo, Paula Cruz-García, Fausto Hernández-Trillo, Emili Tortosa-Ausina Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In this paper, we present a gridded population cartogram illustrating a recently constructed financial inclusion index for Mexico 2020 (Dircio-Palacios-Macedo et al., 2023). Its importance relies in that it allows us to identify the municipalities with different levels of financial inclusion in a geographical disaggregated manner, which can be used for the ultimate goal of relevant policy analysis. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-09-23T02:12:52Z DOI: 10.1177/23998083231203990
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Authors:Sugam Agarwal, Smruti Ranjan Behera Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The COVID-19 pandemic has exerted unprecedented impacts on school education. Despite improvements in school enrolment numbers, students’ performance on learning outcomes lagged, especially in low- and middle-income countries. This study visualizes the geographical disparity in student learning outcomes across various Indian districts pre- and post-COVID-19 pandemic. The map visualizes that only a sample of districts in a few Indian states enlarged students’ learning outcomes in proportion to the number of children. Furthermore, the effects of the COVID-19 pandemic weaken students' learning outcomes at the district level. This knowledge is essential for the policymakers to implement the most effective monitoring systems and strategies for improving the student’s learning outcomes. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-09-21T08:49:11Z DOI: 10.1177/23998083231204125
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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
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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
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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
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Authors:Andrew Price, Gary Higgs, Mitchel Langford Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. It is estimated that over half a million people visited one network of ‘warm rooms’ during the winter of 2022 in the UK, a figure that may rise to 2.5 million people if other networks are considered. As well as offering a means to try to limit exposure to cold temperatures and reduce household energy costs, these winter warm hubs also address wider concerns such as social isolation, loneliness and mental well-being as the cost-of-living bites. This graphic demonstrates how geospatial approaches can be used to explore access via public transport and walking to a network of warm spaces in the capital city of Cardiff, Wales. By accounting for site opening times in relation to existing bus timetables for those for whom walking may not be an option, we illustrate how such information can be included in three commonly applied access measures (floating catchment area, cumulative opportunity and shortest distance) to highlight potential spatio-temporal gaps in provision across the city. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-08-31T09:59:14Z DOI: 10.1177/23998083231196397
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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
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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
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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
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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
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Authors:Lemir Teron, Theodore Endreny Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-08-16T09:54:50Z DOI: 10.1177/23998083231195189
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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
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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
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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
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Authors:Carlos Sandoval Olascoaga Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In this article we present, the conceptual and technical background of a tool for mapping and geo-computing, Painting with Data (PWD). PWD was inspired by early computer mapping algorithms that focused on drawing as an intuitive interface between spatial data and architectural practice. PWD embraces the potential of such techniques, coupled with alternative computational representations, modes of interaction, and computational interfaces, to encourage public participation in planning and design. Essential to this task, is PWD’s integration of open-source, collaborative, interactive, and web-based technologies to create an online software with a visually based approach to spatial analysis and mapping that dramatically reduces the steep learning curve required for GIS software. As a high-level graphical interface, PWD allows users to iterate by intuitively creating spatial models on-the-fly based on their subjective understanding of information. In PWD, we deploy voxels, a data structure that organizes information as 3D pixels, which allows users to compute with spatial information visually, to potentially inform the ways in which users build quantitative models. In PWD, multiple layers of information can be visualized concurrently, and visual correlations can be made instantly. Users build spatial models by directly manipulating the map itself instead of manipulating a database that then produces a map. PWD’s high-level interaction is made possible by custom data structures that leverage GPU processing, which makes them significantly faster than traditional topological data structures. Computationally, user interactions generate visualization specifications and declarative queries that are compiled and executed by the platform. Lastly, PWD introduces a visual programming language, which enables intuitive geospatial modeling and visualization. Practical work has shown the value of PWD’s approach to design students, planning agencies and community non-profits. Throughout the process, PWD’s open-source spatial models generated a user community with more than 3000 users, including designers, students, and educators. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-07-31T09:59:05Z DOI: 10.1177/23998083231193321
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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
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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
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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
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Authors:Demetrio Carmona-Derqui, Dolores Gutiérrez-Mora, Daniel Oto-Peralías Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Street names constitute a rich source of data for quantitative analysis in social sciences. We gather and process street-name data from OpenStreetMap to create an accessible and readily analyzable street names database for the US and a large part of Europe. We also develop a web app to visualize the spatial distribution of street names and download the underlying data from users’ queries. These tools will continue to expand its geographic coverage by including additional countries. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-07-21T10:14:07Z DOI: 10.1177/23998083231190711
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Authors:Meng Cai, Huiqing Huang, Travis Decaminada Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Municipal websites serve as central platforms for local governments to share information with the public. They offer authoritative, up-to-date, and free access for researchers to collect city-level data. However, until now, a comprehensive and accurate database of municipal web addresses did not exist. Here, we introduce a complete and manually verified dataset containing information on whether a municipality has an official website and, if so, what its web address is, of all 19,518 municipalities in the United States. With this dataset, researchers can easily conduct systematic searches on municipal official websites for self-defined keywords. The search results are well-suited for text-based analytics. This new data source benefits urban scholars who struggle to access high-quality local data for nationwide studies and contributes to narrowing the data gap. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-07-21T02:59:59Z DOI: 10.1177/23998083231190961
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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
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Authors:Ze Zhang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. As a global phenomenon of political and economic geography, a partial “decoupling” of U.S. and Chinese technology ecosystems is well under way. It should be noted that the biggest manifestation of this decoupling is the U.S. crackdown on Chinese technology companies. The U.S. government has placed over 1000 Chinese firms on the Entity List, Unverified List, Chinese Military firms Sanitations List, and NS-CMIC as a representative action to stifle the Chinese technology industry. The spatial patterns of the technological decoupling between China and the United States can be summed up by looking at the spatial distribution of blacklisted firms. Beyond China, the United States largely works to prevent China from forging indirect business links with U.S. high-tech companies through its allies (for example, the United Kingdom). Core cities like Beijing, Shanghai, and Shenzhen in China, which are also hubs for Chinese high-tech businesses, are the major targets of industrial suppression in the United States. The U.S.-China rivalry will continue to shape the global economic geography in the future. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-07-13T08:53:19Z DOI: 10.1177/23998083231189938
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Authors:Guiyu Chen, Chaosu Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The increasing population exposure to heat extremes in recent decades represents a formidable challenge to urban sustainability. Yet, less is known about the spatial and temporal dynamics of extreme heat events accompanied by the changing climate and the associated human exposure. In this study, we create a series of cartograms to reveal the spatial and temporal changes of population exposure to extreme heat in the contiguous United States from 2001 to 2020. Findings demonstrate a notable spatial shift in exposure from northern to southern regions over the two-decade period, with the worrying trend of prolonged extreme heat in some counties with large populations. While the majority of the population experienced fewer than 18 days of extreme heat annually, the spatial shift was accompanied by increasing population exposure to prolonged extreme heat. Results underscore the urgent need for spatially targeted climate adaptation policies to effectively mitigate the adverse impacts of heat extremes. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-07-12T02:56:26Z DOI: 10.1177/23998083231189594
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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
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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
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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
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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
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Authors:Xinyu Wang, Ying Long Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Shrinking cities have become increasingly prevalent worldwide due to various factors, which pose serious challenges to affected areas in terms of population decline, economic decline, and spatial deterioration. While existing research studies have focused on identifying shrinking cities, there is a need for global projections to mitigate uncertainties in their growth trajectories. Spatially explicit population grids offer a new approach to identifying potentially shrinking cities with sufficient spatial resolution. By utilizing a global gridded population dataset from 2020 to 2100 under the SSP2 (Middle of the Road) scenario, we produce a global projection map for future shrinking cities. Among the total 19,024 natural cities, 9682 cities (50.9%) will face population decline and 1751 cities (9.2%) may lose more than half population by 2100. Cities in East Asia and East Europe may face serious population decline. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-06-27T01:18:01Z DOI: 10.1177/23998083231186153
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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
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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
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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
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Authors:Luce Prignano, Lluc Font-Pomarol, Ignacio Morer, Sergi Lozano Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Terrestrial Transportation Infrastructures (TTIs) are shaped by both socio-political and geographical factors, hence encoding crucial information about how resources and power are distributed through a territory. Therefore, analysing the structure of pathway, railway or road networks allows us to gain a better understanding of the political and social organization of the communities that created and maintained them. Network science can provide extremely useful tools to address quantitatively this issue. Here, focussing on passengers transport, we propose a methodology to shed light on the processes and forces that moulded transportation infrastructures into their current configuration, without having to rely on any additional information besides the topology of the network and the distribution of the population. Our approach is based on a simple mechanistic model that implements a wide spectrum of decision-making mechanisms (representing different power distributions) which could have driven the growth of a TTI. Thus, by adjusting a few model parameters, it is possible to generate several synthetic transportation networks, and compare across them and against the empirical system under study. An illustrative case study (i.e. the railway system in Catalonia, a region in Spain) is also provided to showcase the application of the proposed methodology. Our preliminary results highlight the potential of our approach, thus calling for further research. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-06-16T03:31:17Z DOI: 10.1177/23998083231174024
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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
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Authors:Ehsan Dorostkar, Mahsa Najarsadeghi Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Metaverse is a unique space in a virtual world that with many capabilities and features shows us a world in which we can experience life in all its dimensions. The presence of Metaverse in the global arena and the use of the facilities of this technology are effective in improving living conditions in developing cities. In this study, two sample projects that Metaverse can support are presented in India and Ghana. The remarkable thing is its innovative method that can solve many problems in the cities of the world. One of the most important challenges in the world today is saving cities from climate change and its adverse effects. Metaverse is one of the innovative methods to reduce the effects of greenhouse gas emissions in the cities of the world. The question is whether Metaverse can influence the urban planning of many cities in the world and change urban planning in the world internationally' And can Metaverse challenge the theoretical foundations in the city' The purpose of this study is to clarify the effects of the Metaverse on the city level. This study aims to introduce a new way to solve the problems of today and the future of world cities by examining Upland and its effects on urban sustainability and ways to prevent climate change. In this context, Metaverse technology has been used as a new solution to provide jobs, reduce poverty, increase public health, prevent climate change and, as a result, realize a better life for future generations. The results of this study, due to being cross-border and creating a global perspective on the Metaverse in urban planning, can create a new scientific perspective based on technological advances in urban studies. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-06-08T12:01:03Z DOI: 10.1177/23998083231181596
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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
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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
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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
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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
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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
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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
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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
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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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Authors:Dimitra V Chondrogianni, Yorgos J Stephanedes Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. To achieve urban resilience, assessment of the bioclimatic impact of various planning solutions should be given high priority in the decision-making process for the implementation of urban planning interventions. To aid in this process and improve the creation of resilient open spaces, the Bioclimatic Index has been developed as an evaluation tool and simple guide for local stakeholders. The assessment of the indicator is essential to determine the likelihood of its use in other Mediterranean cities as the methodological framework was based on the microclimate simulation results of the case study area of Patras Old Port, which is a seaside open space with a Mediterranean climate. In this framework, the Bioclimatic Index is used to rate the regenerated open spaces in Thessaloniki, Malaga, and Genoa, three Mediterranean seaside areas. The indicator values are compared to the microclimate simulation results created based on their planning solutions, aiming to test the accuracy, transferability and scalability of the indicator. The research result showed that the seaside space of Malaga, which has been evaluated as the optimal regeneration plan based on the Bioclimatic Index, creates the most favorable microclimate conditions through seasons, supporting the use of the indicator for evaluating the bioclimatic impact of regeneration plans in Mediterranean cities. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-05-15T08:58:27Z DOI: 10.1177/23998083231175894
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Authors:André Hartmann, Martin Behnisch, Robert Hecht, Gotthard Meinel Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Building usage is an important variable in modelling the energetic, material and social properties of a building stock. Gathering this data on large geographical scale, and in the necessary temporal and spatial resolution, that means, on building level, is a challenging task. Machine Learning algorithms like Random Forest have proven useful in predicting building-related features in the past but often resort to training sets of limited geographic scope, for example, cities. This study presents a workflow of predicting the semantic attribute of usage on the level of individual buildings. Based on screening data of the previous ENOB:dataNWG project, a novel building ground-truth data set distributed across Germany, a Random Forest algorithm is used to assess how the German building stock can be classified according to its residential or non-residential use. Different sampling strategies had been applied in order to find a robust evaluation metric for the classifier. Furthermore, the relevance of the feature set is highlighted and it is examined whether regional differences in classification quality exist. Results show that a classification of residential and non-residential building footprints has good prospects with an AUC of up to 0.9. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-05-15T08:45:07Z DOI: 10.1177/23998083231175680
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Authors:Yun Han, Chunpeng Qin, Longzhu Xiao, Yu Ye Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The relationship between the built environment and urban street vitality, as a key issue of contemporary urban design, has been discussed over decades. However, most existing studies relying on linear regression models do not reveal the complicated impacts of built environment features and often neglect their threshold effects. As a response, this study applies the gradient boosting decision tree (GBDT) model with a large amount of new urban data to explore the in-depth understanding of urban street vitality. Based on the street samples from 12 Chinese cities, a series of morphological, functional, and human-scale features were analyzed together with socioeconomic indicators as control variables. The street vitality is measured by street activity intensity computed from billions of location-based service records. The results show that the nonlinear model brings an overall improvement in resolution. Specifically, compared with the functional and human-scale features, the morphological characteristics, especially the street intersection density, average block size, and building density, are dominant contributors to street vitality. It is also worth noting that most built-up environment features obtain the threshold effects on street vitality, which means there is a turning point where the effect of features changes. The interaction between built environment characteristic variables is common and can be divided into two typical types. Insights achieved in this study help to indicate an effective interval of built environment characteristics on vitality, which was missed in previous studies, and thus contribute to more precise urban design practices. Moreover, by clarifying the interaction influence mechanism, this study emphasizes the need for the planner to exploit synergies between variables through optimal combinations while avoiding their antagonistic effects. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-05-05T01:54:15Z DOI: 10.1177/23998083231172985
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Authors:Estelle Mennicken, Rémi Lemoy, Geoffrey Caruso Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The form and the size of cities influence their social, economic and environmental outcomes. The form of a city is itself influenced by the shape of its road network, but this relationship and how it is affected by city size are unclear. We analyse how road distances to the main centre vary across 300 European cities and how radial physical detours (i.e. the distance on the road network compared to the Euclidean distance) are affected by city size and extent. We use landuse and population data to sample potential residences and compute the fastest routes to the main centre. We find a linear relationship between road and Euclidean distances, and for the first time document an average radial physical detour of 1.343 across Europe. We then rescale distance bands so to make cities of different population size comparable and show the effect of different urban delineations. We find that physical detour ratios increase when core cities only are considered without suburbs. At the urban region scale, radial physical detours increase with city size, especially when other significant geographical factors (latitude, longitude, elevation change and proximity to coast) are controlled for. When the central part of cities only is considered, larger cities have smaller radial physical detours. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-05-04T11:54:17Z DOI: 10.1177/23998083231168870
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Authors:Taehyun Kim, Youngre Noh Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Energy-efficient urban development and carbon footprints (CFs) are often discussed in relation to climate change. The optimal level of urban density from a carbon reduction perspective at the city level has been much debated. However, considering possible trade-offs or co-benefits for CFs in the housing and travel sectors, it remains difficult to evaluate how intra-urban/residential densities and mixed land-use patterns relate to individual CFs at a community level in different seasons. The study objective was to demonstrate the changes in the CFs of residents in summer and winter according to spatiotemporal changes in urban forms, such as intra-urban/residential densities and mixed land-use patterns. Based on geographical data and CF survey results from Seoul and Gyeonggi in 2009 and 2018, four path analysis models were used to verify the spatiotemporal variances of the relationships between urban forms and the CFs of the housing/travel sectors (HCF/TCF). Path analysis with a set of mediation variables enables the evaluation of possible trade-offs, or co-benefits, when investigating the impacts of different measures of intra-urban densities and mixed land-use patterns on the CFs. Furthermore, the moderating effects of different cooling and heating patterns in different seasons on CFs were verified by comparing the four path analysis models in different spatiotemporal contexts. The results showed spatiotemporal changes in urban density and different impacts of urban and residential densities on the TCF. It was also revealed that a low percentage of residential land use in urbanized areas offsets the advantage of high density in reducing TCF and HCF. Seasonal differences were also observed in the effects of residential density and HCF. The results of this study help us understand the spatiotemporal characteristics of TCF and HCF in urban settings, which can assist efforts to achieve carbon neutrality goals. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-05-02T02:29:57Z DOI: 10.1177/23998083231172990
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Authors:Roei Yosifof, Dafna Fisher-Gewirtzman Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. To improve pedestrians’ wellbeing and walkability in urban environments, designs must address a range of factors. To enhance such designs, spatial assessments of urban attributes are important, as they may contribute to our understanding of the impact of the urban setting on peoples’ perceptions when traversing these areas. This research proposes a novel hybrid tool for conducting mesoscale analyses that enables the capturing of parameters that influence pedestrians’ visual perceptions, and in turn, generates opportunities for examining specific urban attributes. Such analysis is based on empirical, data-driven methodologies, bridging the gap between microscale and macroscale evaluations. A comparative analysis of three walkable New York City case studies is conducted to demonstrate the hybrid analysis tool, that is comprised of three models: dynamic visibility analysis for predicting perceived density (DVA-D); dynamic visibility analysis for predicting potential interactions with the defining street facades (DVA-I); and dynamic enclosure street section analysis (DESSA). Combined, these models simulate the pedestrians’ perceptions of the urban scape. While all three environments are similarly ranked in Walk Score®, they inherently differ in their perceived density, potential interactions, and enclosure. The hybrid assessment highlights the physical urban attributes of each case study with regards to pedestrians’ visual perception. The readability and visibility of this analysis results may provide architects, urban planners, and stakeholders with a valuable tool for urban decision-making. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-29T04:33:59Z DOI: 10.1177/23998083231171398
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Authors:Giulia Reggiani, Trivik Verma, Winnie Daamen, Serge Hoogendoorn Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Bicycle networks are made up of different types of infrastructure for cars, bikes and mixed use, which has resulted in various definitions being used to describe them. However, it’s crucial to bring these definitions together to understand the structural differences among them and the impact of choices and investments in bike infrastructure. This study examines different definitions of bicycle networks in 47 cities, analysing scaling effects and various network metrics for four different definitions. Understanding structural characteristics of different bicycle networks definitions contributes to the body of knowledge necessary for design interventions by policymakers. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-26T06:06:27Z DOI: 10.1177/23998083231170637
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Authors:Vincent Verbavatz, Marc Barthelemy Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban inequality is a major challenge for cities in the 21st century. This inequality is reflected in the spatial income structure of cities which evolves in time through various processes. Gentrification is a well-known illustration of these dynamics in which the population of a low-income area changes as wealthier residents arrive and old-settled residents are expelled. Less understood but very important is the reverse process of gentrification through which areas of cities get impoverished. Gentrification has been widely studied among social sciences, especially in case studies, but there have been fewer quantitative analyses of this phenomenon, and more generally about the spatial dynamics of income in cities. Here, we first propose a quantitative analysis of these income dynamics in cities based on household incomes in 45 American and nine French Functional Urban Areas (FUA). We found that an important ingredient that determines the evolution of the income level of an area is the income level of its immediate neighboring areas. This empirical finding leads to the idea that these dynamics can be modeled by the voter model of statistical physics. We show that such a model constitutes an interesting tool for both describing and predicting evolution scenarios of urban areas with a very limited number of parameters (two for the United States and one for France). We illustrate our results by computing the probability that areas will change their income status in the case of Boston and Paris at the horizon of 2030. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-20T09:53:56Z DOI: 10.1177/23998083231171397
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Authors:Meen Wook Jung, Mônica A Haddad, Brian K Gelder Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban heat islands (UHIs) are one of the major global issues that need to be addressed because of the negative effects that higher temperatures can cause to people and the environment, such as health issues and higher energy consumption. Within the literature on climate justice, specifically heat inequity, there are very few studies about Global South urban areas. Our study examines the spatial relationships between heat risk, urban form composition, and vulnerable social groups in Belo Horizonte Metropolitan Region (BHMR), in Brazil. We evaluated the spatial pattern of heat risk and concluded that the study area was experiencing UHIs in 2015. We estimated spatial regressions and found that the non-White population, low-income residents, and the elderly population were statistically significantly associated with heat risk. This case study indicates that even though Global South urban areas have the opposite spatial distribution of social groups (i.e., high-income residents living in the center and low-income living in the periphery) when compared to the Global North, areas where vulnerable social groups reside are experiencing similar inequities concerning the UHI effects in both South and North. Our case study exemplifies that climate justice is not taking place in BHMR, and specifically, heat inequity is being experienced by vulnerable social groups. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-20T05:11:58Z DOI: 10.1177/23998083231170634
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Authors:Padraig Corcoran, Rhyd Lewis Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In this paper, we propose novel local and global models of street network entropy that measure levels of navigability given only limited local directional information. These models are defined for individual locations and entire street networks. Both models are derived using a generalised model of entropy from the field of game theory, which considers a decision-maker attempting to perform a task in the presence of incomplete information. We argue that the proposed models are more interpretable and useful than existing models of street network entropy since they measure the uncertainty of navigation, which is the task street networks are intended to facilitate. We demonstrate this utility by performing an empirical analysis of the entropy properties of UK city street networks. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-17T02:49:13Z DOI: 10.1177/23998083231170191
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Authors:Ling Yang, Xin Yang, Yue Li, Sijin Li Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The skyline is a comprehensive display of the morphological characteristics and cultural features of a city, and its quantitative evaluation is remarkable for perceiving the city and assisting in its planning. However, previous studies focused on the overall profile or tall buildings, lacking a perspective that considers the composition of skyline objects. This paper proposes a new quantitative method to evaluate the skyline based on the object-based analysis method and the constitution theory. Firstly, the skyline objects, that is, buildings, vegetation and mountains are extracted by using the object-based image analysis method. Secondly, the buildings are further classified into four classes according to their relative height. Then, two quantitative indicators, namely, richness of the object category variety and complexity of the object category spatial distribution, are proposed by considering the constitution theory. Finally, this method is applied to typical urban skylines in Shanghai, Hong Kong, New York and Vancouver. Results show that the new indicators can effectively represent the differences of city skylines when their profile indicators are relatively similar. The method can quantitatively evaluate the composition and spatial distribution of skyline objects. This paper is expected to provide a new perspective on the study of skyline aesthetics. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-15T01:45:17Z DOI: 10.1177/23998083231168873
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Authors:David Rey-Blanco, Pelayo Arbués, Fernando A. López, Antonio Páez Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Identifying market segments can improve the fit and performance of hedonic price models. In this paper, we present a novel approach to market segmentation based on the use of machine learning techniques. Concretely, we propose a two-stage process. In the first stage, classification trees with interactive basis functions are used to identify non-orthogonal and non-linear submarket boundaries. The market segments that result are then introduced in a spatial econometric model to obtain hedonic estimates of the implicit prices of interest. The proposed approach is illustrated with a reproducible example of three major Spanish real estate markets. We conclude that identifying market sub-segments using the approach proposed is a relatively simple and demonstrate the potential of the proposed modelling strategy to produce better models and more accurate predictions. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-13T06:16:59Z DOI: 10.1177/23998083231166952
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Authors:Junyang Gao, Helin Liu, Yongwei Tang, Mei Luo Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Implementing carbon mitigation through urban spatial optimisation is a possible solution for alleviating global warming. However, the relationship between urban carbon emissions and urban spatial structure has not been well clarified, as adequate mapping of high-spatial-resolution urban carbon emissions from different sectors (particularly residential sectors), a precondition to solving the problem, has yet to be achieved. This study proposes a hybrid method of mapping the spatial distribution of urban residential carbon emissions on a 1 km × 1 km scale using multi-source data and exemplifies it via a case study of the Chinese city of Suzhou. The purpose of using this method is to differentiate residential carbon emissions by commuter population and home-based population, as the time they spend at home differs. The mobile signalling data of Suzhou were used to identify commuter and home-based populations. The number and spatial distribution of these two groups were then calibrated by referring to land use and O-D data. Using calibrated data, the proportion of electricity consumed by the two groups in different residential districts across the city was calculated. Total urban residential carbon emissions were then proportionally allocated to 1 km × 1 km grids. By validating estimated data against the data from the Statistical Yearbook, we found that the proximity level is higher than 93%. Furthermore, comparing these outcomes against the results estimated by using NTL data and the size of the identified population as the proxy data, it was observed that the results estimated by the hybrid method are of higher accuracy and stability. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-13T05:53:23Z DOI: 10.1177/23998083231167167
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Authors:Ayodele Adekunle Faiyetole, Victor Ayodeji Adewumi Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The unprecedented increase in population and urbanization dynamics, particularly without the requisite road infrastructure on the African continent, necessitates a more contextual understanding of the interaction between urban expansion and transportation in its cities. The study used Landsat and Google Earth, two readily available data in a resource-constrained context, and population data from 1999 to 2018 to estimate the interactions among roads stock, urban size and corresponding population changes in Akure, a mid-sized capital city in Nigeria, with substantial federal road connectivity. The results suggest strong positive relationships among all the variables of interest. At α = 10%, an increase in road stock causes a significant (p = 0.064) increase in population. The study reveals a heavier road density as the city expanded, slightly reduced from the core, with an increased stock of roads toward the periphery. These findings could significantly inform how cities evolve and can guide urban and transportation planners on complementary road infrastructure for growing cities. The study recommends that, irrespective of the political dispensation, the government could increase connective and motorable road stock toward the periphery each fiscal year, with promises of sustainability and resilience in the urban system despite the ever-increasing population. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-04-07T12:50:41Z DOI: 10.1177/23998083231169427
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Authors:Flavia Ioana Patrascu, Ali Mostafavi Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The ability to proactively monitor the trajectory of post-disaster recovery is valuable for resource allocation prioritization. Existing knowledge, however, lacks models and insights for quantifying and proactively monitoring post-disaster community recovery. This study examines models that could predict population activity recovery at the scale of the census block group (CBG). Population activity recovery is measured by using location-based human mobility visitation patterns to essential points-of-interest (POIs) in the context of the 2017 Hurricane Harvey in Harris County, Texas. The study examined the association between the population activity recovery duration and 32 features split into four categories: (1) physical vulnerability and access, (2) hazard exposure and impact, (3) proactive actions and (4) population features. Several types of spatial regression models were evaluated to determine their ability to capture this relationship. The Spatial Durbin Model was identified as the best fit for assessing direct, spillover, and total effects of features on population activity recovery at the CBG level. The results show the extent of physical vulnerability, measured by road network density, prolongs the duration of population activity recovery by a combination of direct and spillover effects. Also, the extent of access to essential facilities, measured based on the number of POIs, shortens the duration of population activity recovery. Correspondingly, the extent of flooding is not a significant feature in explaining the population recovery duration in CBGs. The results show that better preparedness, measured by extent of POIs visitations prior to hurricane landing, is associated with faster population activity recovery. In terms of population attributes, the total number of people, the percentage of minorities, and the percentage of Black and Asian subpopulations are significant features in the model for predicting the duration of population activity recovery. The study outcome offers data-driven insights for understanding the determinants of population activity recovery and provides a new model tool for predictive recovery monitoring based on evaluating the direct, spillover, and total effects of features. These findings can identify areas with slower or more rapid recovery to inform emergency managers and public officials in ensuring equitable resource allocation prioritization. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-28T05:26:35Z DOI: 10.1177/23998083231167433
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Authors:Wuyang Hong, Renzhong Guo Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Building coverage in urban areas is gradually increasing, inducing a lack of green spaces—a common problem for sustainable urban development. Greenery on buildings has significant low-carbon effects and it becomes an innovative approach to reduce loss of urban green spaces. This paper focused on the planning methodology for urban building greening and established the content framework including the investigation and analysis, planning proposal, and management policies. In addition, the key issues that affect planning scientificity and implementation were discussed. Quantitative models on greening potential were developed, and a combined policy system comprising incentives and mandatory measures was established. Shenzhen is a typical Chinese city densely built-up with a shortage of green spaces. The city was taken as the empirical research object to analyze the current scale and compositional, and the distributional characteristics of building greening planning. Method of estimating the low-carbon effects of building greening was proposed. The results indicate that the carbon reduction effect of existing building greening was 1.96%, which reached 5.55% under the planning scenario. Finally, the paper emphasized the need for a planning methodology to realize the large-scale refurbishment of existing buildings, and discussed the issue of planning implementation being highly dependent on public policies. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-23T04:11:14Z DOI: 10.1177/23998083231165294
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Authors:Roger Beecham, Yuanxuan Yang, Caroline Tait, Robin Lovelace Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Bikeability, the extent to which a route network enables cycling for everyday travel, is a frequently cited theme for increasing and diversifying cycling uptake and therefore one that attracts much research attention. Indexes designed to quantify bikeability typically generate a single bikeability value for a single locality. Important to transport planners making and evaluating infrastructure decisions, however, is how well-connected by bike are pairs of localities. For this, it is necessary to estimate the bikeability of plausible routes connecting different parts of a city. We approximate routes for all origin-destination trips cycled in the London Cycle Hire Scheme for 2018 and estimate the bikeability of each route, linking to the newly released London Cycle Infrastructure Database. We then divide the area of inner London covered by the bikeshare scheme into ‘villages’ and profile how bikeability varies for trips connecting those villages – we call this connected bikeability. Our bikeability scores vary geographically with certain localities in London better connected by bike than others. A key finding is that higher levels of connected bikeability are conferred to origin-destination village pairs of strategic importance, aligning with the stated ambition of recent cycling infrastructure interventions. The geography of connected bikeability maps to the commuting needs of London’s workers and we find some evidence that connected bikeability has a positive association with observed cycling activity, especially so when studying patterns of cycling to job-rich villages. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-22T06:33:32Z DOI: 10.1177/23998083231165122
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Authors:Emre Tepe Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Spatio-temporal land-use change (LUC) modeling provides vital information about land development dynamics. However, accounting for such dynamics faces methodological challenges. This research introduces a Dynamic Spatial Panel Data (DSPD) modeling framework for LUC, incorporating spatial and temporal dependencies. A continuous response variable is introduced to take advantage of traditional spatial regression models. The DSPD model is applied to balanced spatial panel data at the block-group level covering Florida between 2010 and 2019 and incorporating both new and previously used proxy variables. The urban growth impacts of site-specific, proximity, neighborhood, socio-economic, and transportation factors are investigated. This study contributes to the literature by providing extensive insights into spatial autocorrelation, spillover, heterogeneity, and temporal lag effects in urban growth. Also, the study reveals the importance of mobility and mortgage financing in land development. The proposed modeling framework achieves high accuracy. The dynamic structure of this model provides an opportunity to predict future urban growth without the need for a land development scenario. Such predictions provide insights about future land development to practitioners and policymakers. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-21T12:29:39Z DOI: 10.1177/23998083231164397
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Authors:Svitlana Pyrohova, Jiafei Hu, Jonathan Corcoran Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The land use mosaic that characterises our urban environments is complex and subject to regular and on-going change and transition. Land use change takes place as cities seek to meet ever evolving population, economic, social, and environmental objectives. However, our empirical capacity to map, measure and monitor the geographical shifts in land use at a fine spatial granularity and how these aggregate across the urban environment remain very limited. In this paper, we draw on parcel level land use data for a large metropolitan region in Australia for a 19-year period and employ sequence analysis to delineate the location and timing of shifts in land use. Results reveal both similarities between jurisdictional regions alongside the unique land use transitions that go some way to highlight context specific mechanisms. This study demonstrates the utility of our empirical approach in its capacity to inform regional development strategies through revealing the type, timing and location of land use change in relation to land use policy and planning goals. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-11T01:43:42Z DOI: 10.1177/23998083231163569
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Authors:Ryun Jung Lee, Galen Newman, Shannon Van Zandt Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Vacant and abandoned land can be public eyesores that can potentially result in neighborhood distress in the long term. In some cases, the contextual conditions of a neighborhood have been shown to have more of a negative effect on communities than the vacant property itself. Maximum opportunities to actually reuse vacant and abandoned land is known to primarily exist in cases where the surrounding area has locational benefits or when local economic conditions are hopeful. This study examines and compares neighborhood socioeconomic characteristics around vacant lots in Minneapolis, Minnesota, USA, to identify spatial heterogeneity within vacancy types and neighborhood characteristics. Specifically, we examine 1) if the socioeconomic characteristics of a neighborhood can predict existing vacant lots and 2) what neighborhood characteristics are associated with certain vacant lot types. Three logistic regressions were tested with different buffers around each vacant lot, and a total of eighteen regressions were performed to capture the effects on six vacancy types. Results suggest that there are various types of vacancies interacting differently at the neighborhood scale, and that a large-scale neighborhood context matters when predicting vacancy types. The results also indicate three salient points. First, minority populations are a strong predictor of residential and commercial vacancies. Second, high-income areas tend to predict vacancies with potential investment opportunities or vacancies as a part of an existing park or recreational system. Third, vacant properties designated for institutional land uses tend to be found in lower-income areas, yet, not necessarily in areas with high minority populations. Managing and repurposing vacant and abandoned land should be handled more progressively with a better understanding of the socioeconomic characteristics of neighborhoods. Further, examining vacancy types by community can be a way to diagnose potential neighborhood risks associated with vacant and abandoned land. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-10T11:08:52Z DOI: 10.1177/23998083231160542
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Authors:Colin Ferster, Trisalyn Nelson, Kevin Manaugh, Jeneva Beairsto, Karen Laberee, Meghan Winters Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. High-quality and consistent cycling infrastructure data are needed to advance research into equity and safety and for planning active transportation. With recent growth in cycling and investments in cycling infrastructure, there are concerns that these investments have not been equitable across communities. There is no consistent and complete national dataset for cycling infrastructure in Canada. Our goal is to develop a national cycling infrastructure dataset by (1) classifying OpenStreetMap (OSM) using the Canadian Bikeway Comfort and Safety Classification System (Can-BICS) as consistent criteria and categorisation for comfort class and infrastructure type; (2) performing a site-specific accuracy assessment by comparing the classification with more than 2000 reference points from a stratified random sample in 15 cities; and (3) presenting summary results from the national dataset. Based on reference data collected in 15 test cities, the classification had an estimated accuracy of 76 ± 3% for presence or absence of infrastructure, 71 ± 4% for comfort class and 69 ± 4% (by length) for infrastructure type. High comfort infrastructure was slightly underestimated (since bike paths were sometimes confused with multi-use paths) and low comfort infrastructure was slightly overestimated. Nationally, we identified 22,992 km of cycling infrastructure meeting Can-BICS standards and 48,953 km of non-conforming infrastructure. Multi-use paths are the most common infrastructure type by length (16.6%), followed by painted bike lanes (11.0%), and then high comfort infrastructure (cycle tracks, local street bikeways and bike paths) (4.3%). There was a wider range in access to cycling infrastructure in small cities than in medium and large cities. To reduce repeated effort assembling data and increase reproducible active transportation research, we encourage contribution to OSM. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-09T03:44:45Z DOI: 10.1177/23998083231159905
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Authors:Greg Rybarczyk, Ayse Ozbil, Demet Yesiltepe, Gorsev Argin Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The purpose of this research is to extend our understanding of children’s walking behavior to school in an understudied region of the world, Istanbul, Turkey. Children (aged 11–17) and their parents were surveyed to comprehend subjective and objective factors on walking behavior to school when alone or with someone. Using participatory mapping and GIS, a route detour index was first created to highlight differences in walking behaviors. A robust spatial analysis, consisting of spatial statistics and a hierarchical spatial error model, then signified important survey responses, urban design factors from space syntax, and neighborhood composition and contextual variables on between-group route choices. Empirical and geovisual analysis confirmed that accompanied children deviated more from GIS shortest routes to school than their unaccompanied peers, and “hot-spot” analysis showed it was dependent on where children reside. The spatial error models exhibited notable relations among route choice, children’s age, health, and gender. Parent attitudes concerning greenspace positively affected children’s longer route choices, while street connectivity had the opposite influence. Surprisingly, neighborhood walkability did not impact children’s route choice decisions for either group. The results provide new insights on how to encourage additional walking trips to school. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-03-09T02:26:06Z DOI: 10.1177/23998083231161612
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Authors:Obi Thompson Sargoni, Ed Manley Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Pedestrian navigation decisions take place simultaneously at multiple spatial scales. Yet most models of pedestrian behaviour focus either on local physical interactions or optimisation of routes across a road network. We present a novel hierarchical pedestrian route choice framework that integrates dynamic, perceptual decisions at the street level with abstract, network-based decisions at the neighbourhood level. The framework is based on construal level theory which states that decision makers construe decisions based on their psychological distance from the object of the decision. We implement this route choice framework in a spatial agent-based model in which pedestrian and vehicle agents complete trips in an urban environment. Using global sensitivity analysis techniques, we demonstrate the interaction between route choice components representing decision making at different spatial and temporal scales. Additionally, through comparison to a least cost network model, we demonstrate the increased route heterogeneity produced by this approach. This work could form the basis of an alternative method for producing pedestrian route alternatives. The granularity and scale of the modelled pedestrian trajectories could also help improve appraisals of street infrastructure. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-25T05:34:36Z DOI: 10.1177/23998083231158371
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Authors:Becky PY Loo, Zhuangyuan Fan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Research on the relationship between space and social interaction has focused on indoor spaces, such as museums and offices. However, empirical evidence on the connection between the intensity of social interaction and outdoor public spaces is still lacking. Applying machine learning algorithms to a 9-hour time-lapse video of an urban park, we decipher the effects of two spatial features, edges, and landmarks, on visitors’ activities. We identified dynamic visitor groups in the videos through a graph-based method and mapped the clustering pattern of interaction activities over time and space. In parallel, we used a computer vision algorithm to delineate fixed objects (notably the harbourfront, landside park boundary, a carousel, four benches, three pavilions, and four heart-shaped seating) and dynamic edges (formed by moveable furniture as park visitors repositioned them) onsite. We found that dynamic edges formed by moveable furniture and the fixed edge of a visual landmark consistently attracted more social interaction and group activities. In designing public spaces that encourage group activities, urban planners and designers can leverage the combination of fixed objects and flexible furniture to maximise the choices for visitors and curate a more engaging public open space. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-24T10:48:37Z DOI: 10.1177/23998083231160549
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Authors:Xiaoyan Mu, Xiaohu Zhang, Anthony Gar-On Yeh, Yang Yu, Jiejing Wang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Over the past two years, China has wrested domestic control of the COVID-19 pandemic through non-pharmaceutical interventions. However, the extent to which the pandemic has changed people’s travel behavior in the new normal under the zero-COVID policy is not yet clear. This study investigates the profound effects of the zero-COVID strategy on human mobility in 365 Chinese cities over time. Our results suggest the following: (1) Even between city pairs with no local cases, intercity mobility decreased by an average of 16%, whereas intra-city mobility increased by 9% compared with the pre-pandemic baseline. Long-distance travel decreased substantially, while trips below 100 km increased slightly. These new trends indicate a localized pattern which is presumably caused by the wide adoption of teleworking and virtual classes, as well as concerns about the safety and availability of public transportation. (2) Containment measures caused a considerably acute decline in intercity short-distance trips but exerted a markedly lasting effect on long-distance trips. (3) Cities with lower levels of urbanization, smaller population sizes, less labor force, and lower GDP and GDP per capita experienced greater reductions in mobility, which may be due to their insufficient management capabilities. (4) The Chinese government has adopted adaptive measures to contain outbreaks. Stricter and more timely responses led to faster recoveries in the second half of 2021 compared with the first half. Inter- and intra-city mobility decreased by 41% and 26%, respectively, within the first 2 weeks of an outbreak, and it took 6-7 weeks to return to pre-outbreak levels. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-24T02:00:33Z DOI: 10.1177/23998083231159397
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Authors:Margherita Carlucci, Gloria Polinesi, Luca Salvati Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Economic downturns, social change, and migrations shape population expansion and shrinkage, making city life cycles particularly complex over time and intrinsically diversified over space. Identifying local drivers of population change plays a major role when addressing metropolitan cycles of growth and decline and provides insights to any policy and planning strategy aimed at promoting together local development, economic competitiveness, and socio-environmental sustainability at large. Timing of metropolitan cycles is, however, heterogeneous and reflects the individual development path of any city. Assuming economic downturns and the associated social processes at the base of spatially heterogeneous patterns of population growth and decline in Mediterranean Europe, we adopted a spatial econometric approach investigating short-term and long-term demographic dynamics (1960–2010) in metropolitan Athens (Greece), with the aim at identifying contextual drivers of population change. Spatial regressions evaluated the role of economic and non-economic dimensions of metropolitan growth, quantifying the impact of agglomeration, scale, accessibility, and amenities at different phases of the city life cycle. Settlement models grounded on scale and agglomeration processes—with growing population in high- and medium-density municipalities—were observed under economic expansion. Recession consolidated a settlement model with population growth in socially dynamic and accessible (low density) districts with natural/cultural amenities, reflecting the inherent decline of agglomeration economies. Based on such dynamics, the polarized hierarchy of central and peripheral locations resulting from radio-centric population expansion was replaced with a settlement model grounded on population increase in “intermediate-density,” attractive locations. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-23T12:57:46Z DOI: 10.1177/23998083231159110
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Authors:Yang Yang, Samitha Samaranayake, Timur Dogan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This paper uses a generalizable clustering approach to investigate the effects of socio-demographic features on aggregate urban mobility patterns, including activity distribution and travel modal split. We use K-means via principal component analysis to identify eight representative traveler clusters from the 2017 U.S. National Household Travel Survey. Based on the cluster centroids and the cluster percentages within a neighborhood, we can estimate a Temporal Mobility Choice Matrix (TM) that describes the neighborhood-level aggregate mobility choice pattern. The estimation accuracy is assessed in a case study in LA City. It is found that the neighborhood-level temporal mobility patterns are well-replicated, with an average R2 of 65.47%, 53.15%, and 72.04% among all analyzed neighborhoods in the city. However, we find a moderate to low accuracy in estimating the spatial differences in the mobility patterns across neighborhoods. This could be because factors other than socio-demographics, such as physical and built environment factors like terrain, street quality, or amenity densities, are contributing to the spatial differences but have not been considered in this study. Overall, we show that socio-demographic features alone can approximate the average temporal mobility choice patterns of a given population. Our method and result can serve as the baseline and benchmark for future mobility studies that take the socio-demographics of the traveler population into consideration in modeling. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-22T09:30:46Z DOI: 10.1177/23998083231159909
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Authors:Zhou Huang, Ganmin Yin, Xia Peng, Xiao Zhou, Quanhua Dong Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human–environment relationship and guide urban policymakers in business planning. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-22T06:15:17Z DOI: 10.1177/23998083231158370
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Authors:Jonathan Bourne, Andrea Ingianni, Rex McKenzie Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The UK, particularly London, is a global hub for money laundering, a significant portion of which takes place through residential property. However, understanding the distribution and characteristics of offshore residential property in the UK is a challenge. This paper attempts to remedy that situation by enhancing a publicly available dataset of UK property owned by offshore companies. We create a data-processing pipeline which draws on several datasets and on machine learning techniques to create a parsed set of addresses classified into six use classes. The enhanced dataset contains 138,000 properties – 44,000 more than the original dataset. The majority are residential (95k), with a disproportionate number of those in London (42k). The average offshore residential property in London is worth 1.33 million GBP, and collectively this amounts to approximately 56 billion GBP. We perform an in-depth analysis of offshore residential property in London, comparing the price, distribution and entropy/concentration with Airbnb property, low-use/empty property and conventional residential property. We estimate that the total number of offshore, low-use and Airbnb properties in London is between 144,000 and 164,000, collectively worth between 145–174 billion GBP. Furthermore, offshore residential property is more expensive and has higher entropy/concentration than all other property types. In addition, we identify two different types of offshore property – nested and individual – which have different price and distribution characteristics. Finally, we release the enhanced offshore property dataset, the complete low-use London dataset and the pipeline for creating the enhanced dataset to encourage further research into this topic. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-09T10:20:28Z DOI: 10.1177/23998083231155483
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Authors:Gianni Talamini, Ting Liu, Roula El-Khoury, Di Shao Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The global neoliberal cityscape is one of the most iconic products of contemporary urbanization. Inborn to international financial hubs such as New York, London, and Hong Kong, the dense concentration of high-rise bank headquarters became a powerful branding tool in the growing competition to attract foreign investment. Despite the extraordinary international attention this cityscape has attracted, there is a paucity of scientific research on its morphological principles and the gap between its visibility and perception. With a focus on Hong Kong, this study develops an innovative multi-method research design, combining historical investigation, a newly advanced visual impact assessment method, and a survey of a random probability population sample. The historical investigation reveals a volitional attempt to preserve visibility from key vantage points. The comparative assessment of bank headquarters and other corporate buildings, regarding both their visibility and perceived impact on the city’s image, demonstrates a gap between visibility and buildings’ perceived importance. The results illustrate the effective semiotic use of architecture, shedding light on how the symbolism of architectural form can consolidate neoliberal hegemony on the basis of shared perception. This study’s novelty lies in its multi-method approach and methodological advancement in terms of visibility analysis, while its significance is its potential application across a vast geographic area by scholars, designers, planners, and policymakers. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-04T12:23:56Z DOI: 10.1177/23998083231154587
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Authors:Yang Yang, Samitha Samaranayake, Timur Dogan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This paper introduces a joint choice model for travel mode and duration to quantify the mobility impacts of urban design changes on the built environment. The model is formulated as a Random Forest classifier that predicts the mode-duration probabilities of a given trip. A novel series of predictor features are proposed which measure the urban form, demographics, and service densities on different scales of the transportation network. Through a sensitivity analysis and a proof-of-concept case study, we find that a dense, mixed-use environment with good coverage of a multi-modal mobility network can significantly promote active transportation and public transit use. However, we also find that ultra-dense, centralized developments can lead to increased travel time and increased vehicle use in the urban periphery. Our modeling and analysis method provides a simplified and effective way to assess urban design and planning scenarios from different mobility perspectives and facilitates data-driven, mobility-aware urban design and planning that can help identify better solutions more quickly. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-03T05:22:30Z DOI: 10.1177/23998083231154263
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Authors:Yael Nidam, Ali Irani, Jamie Bemis, Christoph Reinhart Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Housing retrofits are essential for meeting societal decarbonization goals, alongside addressing energy insecurity, improving public health, and creating new jobs. Yet, despite their multiple benefits and comprehensive government efforts to incentivize retrofits, adoption rates across the world remain low, usually less than 1% per year. Barriers to adoption among homeowners include lack of knowledge of what combination of energy retrofitting upgrades are most cost effective for their situation given available incentive programs. Similarly, cities lack urban-level analysis tools to optimize uptake of and predict carbon emissions reduction from existing incentive programs. To address the latter gap, we present a census-based Urban Building Energy Modeling framework that combines a technical energy saving potential analysis with a socioeconomic model that includes occupant demographics, local building regulations, and incentive eligibility criteria. We use the framework to evaluate the effectiveness of retrofit programs in two Boston neighborhoods with median incomes of $110,00 and $42,000. Results reveal that for the higher income, neighborhood predicted and actual adoption rates between 2014 and 2017 are comparable. In the lower income neighborhood, the proportion of households that would financially benefit from incentive offerings is higher. However, current participation rates do not reflect this difference suggesting that many viable projects do not happen for reasons that are not yet captured by the model. Urban planners, energy policy designers, and community advocates seeking to plan and evaluate energy incentive programs can use this framework to understand the breakdown of opportunities and barriers for different socio-demographic groups and geographic locations. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-02-01T08:13:38Z DOI: 10.1177/23998083231154576
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Authors:Fereshteh Moradi, Nimish Biloria, Mukesh Prasad Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The accelerated growth of cities and urban populations over recent decades and the complexity and diversity of urban areas demands proficient spatial affordance assessment especially for the vulnerable sections of the society. Lately machine learning and computer vision models have become highly competent in analyzing urban images for assessing the built environment. This study harnesses the potential of computer vision techniques to assess the age-friendliness of urban areas. The developed machine learning model utilizes Google’s Street View images and is trained using lived experience-based image ratings provided by elderly participants. Newly assigned urban images are accordingly rated for their level of age-friendliness by the model with an accuracy of 85%. This paper elaborates upon the associated literature review, explains the data collection approach and the developed machine learning model. The success of the implementation is also demonstrated, confirming the validity of the proposed methodology. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-30T06:34:11Z DOI: 10.1177/23998083231153862
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Authors:Na Li, Steven Jige Quan Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Seoul, the capital city of South Korea, has diverse urban forms developed through its complex history. Previous studies show limitations of strong subjectivity and difficulty in scalability in identifying typical Seoul urban forms with expert knowledge. Data-driven approach offers an opportunity to address those challenges, but previous studies often focused on direct applications of clustering algorithms to a given area with diverse methods and workflows, lacking a systematic framework. This study addressed these issues by developing a new form clustering framework to systematically identify form typologies at a large scale and demonstrated its application in Seoul. With a 500 m × 500 m grid as the basic spatial unit and twelve urban form attributes as learning features, 14 clusters were identified using the Gaussian mixture model. These clusters were further translated into form typologies following a semantic typology naming system, with representative form samples identified. The resulting typologies were then verified and validated through comparisons with previous studies. Their relationships with zoning classes were also examined, emphasizing their role in urban planning and design. Results suggest this new framework is an effective and promising way to identify urban form typologies in complex urban environments to better support urban planning and management. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-24T09:38:56Z DOI: 10.1177/23998083231151688
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Authors:Aynaz Lotfata, George Grekousis, Ruoyu Wang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. In the United States, the rise in hypertension prevalence has been connected to neighborhood characteristics. While various studies have found a link between neighborhood and health, they do not evaluate the relative dependence of each component in the growth of hypertension and, more significantly, how this value differs geographically (i.e., across different neighborhoods). This study ranks the contribution of ten socioeconomic neighborhood factors to hypertension prevalence in Chicago, Illinois, using multiple global and local machine learning models at the census tract level. First, we use Geographical Random Forest, a recently proposed non-linear machine learning regression method, to assess each predictive factor’s spatial variation and contribution to hypertension prevalence. Then we compare GRF performance to Geographically Weighted Regression (local model), Random Forest (global model), and OLS (global model). The results indicate that GRF outperforms all models and that the importance of variables varies by census tract. Household composition is the most important factor in the Chicago tracts, while on the other hand, Housing type and Transportation is the least important factor. While the household composition is the most important determinant around north Lake Michigan, the socioeconomic condition of the neighborhood in Chicago’s mid-north has the most importance on hypertension prevalence. Understanding how the importance of socioeconomic factors associated with hypertension prevalence varies spatially aids in the design and implementation of health policies based on the most critical factors identified at the local level (i.e., tract), rather than relying on broad city-level guidelines (i.e., for entire Chicago and other large cities). Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-20T11:42:55Z DOI: 10.1177/23998083231153401
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Authors:Jing Yang, Xinyu Zhu, Wei Chen, Yizhong Sun, Jie Zhu Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. While many published studies have explored the impact of spatial heterogeneity on land-use change, few have focused on regional differences in land-use transition rules caused by urban spatial structure. In this paper, we measured urban land-use diversity by developing self-adaptive kernel density estimation and entropy weight methods and determine the urban spatial structure (composed of urban regions, inner and outer urban-rural fringes, and a rural hinterland) by applying a spectral clustering method. Combining local neighborhood effects and environmental effects, the land-use transition rules of different types of regions were mined to construct a partitioned vector cellular automata (CA) model that zonally simulates urban land-use change. The proposed model was applied to the simulation of the land-use change process in Jiangyin City, China, from 2007 to 2017. The resulting simulation accuracy was higher than that of other well-accepted CA models that do not consider urban spatial structure, and the conventional neighborhood assimilation rule was found not to be applicable to the conversion of construction land. The results and findings demonstrate that the proposed model is an effective means for urban planners to simulate and analyze urban evolution processes of cities with urban spatial structures that fit a concentric circle model. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-17T11:08:40Z DOI: 10.1177/23998083231152887
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Authors:Deborah Strumsky, Luis Bettencourt, José Lobo Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Agglomeration is the tell-tale sign of cities and urbanization. Identifying and measuring agglomeration economies has been achieved by a variety of means and by various disciplines, including urban economics, quantitative geography, and regional science. Agglomeration is typically expressed as the non-linear dependence of many different urban quantities on city size, proxied by population. The identification and measurement of agglomeration effects is of course dependent on the choice of spatial units. Metropolitan areas (or their equivalent) have been the preferred spatial units for urban scaling modeling. The many issues surrounding the delineation of metropolitan areas have tended to obscure that urban scaling is principally about the measurable consequences of social and economic interactions embedded in physical space and facilitated by physical proximity and infrastructure. These generative processes obviously must exist in the spatial subcomponents of metropolitan areas. Using data for counties and urbanized areas in the United States, we show that the generative processes that give rise to scaling effects are not an artifact of metropolitan definitions and exist at smaller spatial scales. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-16T02:54:49Z DOI: 10.1177/23998083221148198
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Authors:Joan Marull, Mercè Farré, Marta Andreu Espuña, Adrià Prior, Vittorio Galletto, Joan Trullén Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This article uses new methods and evidence from satellite data on night lighting to assess the urban network structure of 100 European metropolitan regions. Its aim was to develop indicators to test the hypothesis that complex urban networks are more efficient economically and less dependent on energy consumption owing to better information organization. It uses NPP-VIIRS NTL satellite data on night lighting (NTL) and employs a topographical representation of NTL intensities to detect urban centers. Based on the distribution of NTL intensities in urban centers represented as a Lorenz curve, it develops two new indicators of monocentricity and polycentricity to evaluate large-scale urban network structures. The results show that polycentric urban networks create more innovation, which allows them to be more economically efficient and less dependent on energy consumption. Further research should study in greater detail the relationships between urban network structures and their social, economic, and ecological performances. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-13T06:51:43Z DOI: 10.1177/23998083231151689
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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. For a variety of environmental, health, and social reasons, there is a pressing need to reduce the automobile dependence of American cities. Bicycles are well suited to help achieve this goal. However, perceptions of rider safety present a large hindrance toward increased bicycle adoption. These perceptions are largely influenced by the design of our current road infrastructure, including the crossing distances of large intersections. In this paper, we examine the role of intersection crossing distances in modifying rider behavior through the construction of a novel dataset integrating street widths and probable trip routes from Chicago’s Divvy bikeshare system. We compare real trips to synthetic trips that are not influenced by the width of intersections and exploit behavior differences that result from the semi-dockless nature of the bikeshare system. Our analysis reveals that bikeshare riders do avoid large intersections in limited circumstances; however, these preferences appear to be heavily outweighed by the relative spatial positions of origins and destinations (i.e., the urban morphology of Chicago). Our results suggest that specific infrastructural investments such as protected intersections could prove feasible alternatives to reduce the perception and safety concerns associated with large road barriers and enhance the attractiveness of non-motorized mobility. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-07T09:27:13Z DOI: 10.1177/23998083221147922
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Authors:Seong-Yun Hong, Yeorim Kim, Yongchae Lee Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. This work presents a novel approach to studying ethnic segregation from the perspective of linguistic landscapes. Numerous street-level images accumulated over the last two decades have enabled the exploration of linguistic landscapes at a larger scale than ever before. Since the prevalence of a specific language in a public space implies the linguistic group inhabiting the area, its careful evaluation can reveal the degree of segregation between linguistically different ethnic groups. To demonstrate the effectiveness of the proposed approach, we applied it to the linguistic landscape of Seoul, South Korea. Using a large set of street-level images collected from an online map platform, we measured the levels of segregation between Korean and Chinese signs from 2010 to 2018. The levels of segregation on the street-level images were different to a certain extent from those of residential segregation. While residential segregation gradually increased between 2010 and 2018, except for 2011, more fluctuations were observed in linguistic segregation. This finding is likely because a linguistic landscape is shaped mainly by advertising signs, banners, and billboards in commercial areas, and such commodified urban spaces change more dynamically to attract inhabitants and visitors. These results suggest that the proposed approach can offer an alternative way of understanding the complex socio-demographic phenomenon from a new perspective, as with other emerging data sources in the era of big data. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-04T08:57:27Z DOI: 10.1177/23998083221150240
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Authors:Kiseong Jeong, Jaebin Lim Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. The COVID-19 pandemic was a significant social disaster that radically affected the paradigm of current urbanization and city-center living. Responses to the disaster varied depending on related experiences, individual status, and attitudes. Thus, this research extends the previous literature by examining the effects of experiences related to the COVID-19 pandemic, socioeconomic status, and how perceptions and attitudes affect preferences for city-center living in the Seoul Metropolitan Area, South Korea. We use data from PSSRAC (Perception Survey of Seoul metropolitan area Residential Awareness during COVID-19) of 2021. A binary logistic regression method is used to examine significant characteristics that affected the residential preference change due to “Experience,” “Status,” and “Attitude” in the COVID-19 era. The findings showed that respondents’ experience, status, and attitude related to the pandemic could have a complex effect on predictions of preference, for central or suburban living tendencies in the post-COVID-19 era. In terms of “Experience,” people who had bad experiences during the pandemic, for example, poor economic conditions were associated with suburban area living trends. For “Status,” socially and economically vulnerable groups preferred suburban residence due to the steep rise in housing prices in the city center after the pandemic. Finally, for “Attitude,” ‘value of housing for investment” was positively associated with a preference for city-center living in the post-COVID-19 era; respondents with a higher priority for maintaining remote work tended not to change their current residence. This study may provide planners, housing developers, and policymakers with meaningful implications for addressing urban changes in the post-COVID-19 era. Additionally, it is expected that this research’s ESA analysis and results can be used as a valid reference for other global cities. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-04T01:45:42Z DOI: 10.1177/23998083221149424
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Authors:Haoran Zeng, Bin Zhang, Haijun Wang Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print. Urban sprawl is a typical geographic dynamic process with spatial heterogeneity and nonlinearity. However, current studies usually focus on only one of them to extract urban sprawl mechanisms and build cellular automata (CA) models. In the current work, the urban CA transition rules are derived by a geographically weighted artificial neural network (GWANN), which can discover the driving mechanism of urban sprawl by considering both spatial heterogeneity and nonlinearity. Taking the urban sprawl of Wuhan and Beijing during 2000–2020 as examples, the advantages of GWANN in deriving transition rules are investigated by comparing it with logistic regression (LR), geographically weighted logistic regression (GWLR), and artificial neural network (ANN). Furthermore, the simulation performance of CA models based on LR, GWLR, ANN, and GWANN is compared and analyzed from the aspects of global and regional simulation accuracy and the morphology of simulated urban patches. The results show that GWANN has better fitting and simulation performance, indicating the validity and necessity of coupling spatial heterogeneity and nonlinearity to establish transition rules. This study is a novel exploration that contributes to deriving CA transition rules through a hybrid modeling approach that couples statistical models with learning models. Citation: Environment and Planning B: Urban Analytics and City Science PubDate: 2023-01-03T10:28:19Z DOI: 10.1177/23998083221149018
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