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Environment and Planning B : Urban Analytics and City Science
Journal Prestige (SJR): 0.653
Citation Impact (citeScore): 2
Number of Followers: 42  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 2399-8083 - ISSN (Online) 2399-8091
Published by Sage Publications Homepage  [1176 journals]
  • Physical distancing and its association with travel behavior in daily
           pre-pandemic urban life: An analysis utilizing lifelogging images and
           composite survey and mobility data

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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      Authors: Will B. Payne, Evangeline McGlynn
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      When mapping relationships across multiple spatial scales, prevailing visualization techniques treat every mile of distance equally, which may not be appropriate for studying phenomena with long-tail distributions of distances from a common point of reference (e.g., retail customer locations, remittance flows, and migration data). While quantitative geography has long acknowledged that non-Cartesian spaces and distances are often more appropriate for analyzing and visualizing real-world data and complex spatial phenomena, commonly available GIS software solutions make working with non-linear distances extremely difficult. Our Relational Reprojection Platform (RRP) fills this gap with a simple stereographic projection engine centering any given data point to the rest of the set, and transforming great circle distances from this point to the other locations using a set of broadly applicable non-linear functions as options. This method of reprojecting data allows users to quickly and easily explore how non-linear distance transformations (including square root and logarithmic reprojections) reveal more complex spatial patterns within datasets than standard projections allow. Our initial release allows users to upload comma separated value (CSV) files with geographic coordinates and data columns and minimal cleaning and explore a variety of spatial transformations of their data. We hope this heuristic tool will enhance the exploratory stages of social research using spatial data.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-16T03:09:23Z
      DOI: 10.1177/23998083231215463
       
  • Analyzing urban parks’ spatial integration in Budapest to understand
           changes in visitation patterns during the COVID-19 pandemic

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      Authors: Zoltán Bereczki, György Csomós, Jenő Zsolt Farkas
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      Globally, dramatic changes in park visitation have accompanied the COVID-19 pandemic. In general, cities have experienced an overall increase in park visitation after strict lockdowns imposed in the pandemic’s first wave have been removed. However, previous research conducted in Hungary has demonstrated that park visitation varied across parks with different sizes and locations in the city. We hypothesized that the degree of the park’s integration into the urban fabric significantly affected changes in visitation. To test this hypothesis, we conducted a space syntax analysis. Findings show that community parks with an area of 10.01–50.00 hectares and a mean spatial integration of 83.37 experienced the highest increase in the number of visitors (based on mobile devices’ GNSS data). Surprisingly, large metropolitan parks providing highly complex ecosystem services lost many visitors during the pandemic, which might be due to their low spatial integration.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-16T02:12:20Z
      DOI: 10.1177/23998083231217012
       
  • Crossing intersections: A tool for investigating road user pathways

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      Authors: Heather Anne Kaths
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      The pathways used by cyclists, pedestrians, and users of micromobility to cross intersections do not always align with those planned by traffic engineers. Observing actual usage patterns could lead to a better understanding of the tactical behavior of users of active and micromobility, allowing planners and engineers to create urban environments specifically for these road users. An open-source Python tool is introduced that uses clustering to automatically identify the forms of pathways used by road users. The tool was used to cluster trajectories from five intersections in Germany. The exemplar of each cluster is selected to represent the average shape of each pathway type. The open-source Python tool RoadUserPathways is introduced, the case studies are examined and use cases are presented.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-11-15T09:07:32Z
      DOI: 10.1177/23998083231215462
       
  • Exploring the plot patterns of the retail landscape: The case of the
           Helsinki Metropolitan area

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

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

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

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

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

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

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

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      Authors: Xiaohua Lin, Yao Wang, Meilin Zhu, Yang Xiao
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.
      It should be recognized that women in long-distance commuting in China may suffer from unequal treatment. This study visualized gender differences and uneven reactions of women in long-distance commuting in Suzhou, China. We speculate on the potential spatial impact of employment equity among female long-distance commuters in Suzhou. The cartogram revealed that women’s average long-distance commuting is longer, and their work destinations are mainly concentrated in Kunshan and the central city, where international companies gather.
      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-28T04:09:34Z
      DOI: 10.1177/23998083231210613
       
  • The Michael Breheny Prize 2023

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

      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-25T05:39:54Z
      DOI: 10.1177/23998083231210914
       
  • The role of procedural utility in land market dynamics in Greater Cairo:
           An agent based model application

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

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

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      Authors: Levi John Wolf
      Abstract: Environment and Planning B: Urban Analytics and City Science, Ahead of Print.

      Citation: Environment and Planning B: Urban Analytics and City Science
      PubDate: 2023-10-19T11:20:39Z
      DOI: 10.1177/23998083231210180
       
  • Understanding the spatial statistical properties of a real estate listings
           point pattern in San José, Costa Rica

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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
       
  • Quantifying the pedestrian access potential of suburban street network
           retrofits

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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
       
  • Prediction of residential and non-residential building usage in Germany
           based on a novel nationwide reference data set

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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
       
  • The nonlinear relationships between built environment features and urban
           street vitality: A data-driven exploration

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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
       
  • Road network distances and detours in Europe: Radial profiles and city
           size effects

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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
       
  • Planning factors affecting carbon footprints of residents: Density, land
           use, and suburbanization

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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
       
  • Hybrid quantitative mesoscale analyses for simulating pedestrians’
           visual perceptions: Comparison of three New York City streets

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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
       
  • Modeling the spatial dynamics of income in cities

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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
       
  • Examining heat inequity in a Brazilian metropolitan region

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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
       
  • A new quantitative evaluation method of urban skyline based on
           object-based analysis and constitution theory

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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
       
  • Using machine learning to identify spatial market segments. A reproducible
           study of major Spanish markets

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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
       
  • Hybrid method of mapping urban residential carbon emissions with
           high-spatial resolution: A case study of Suzhou, China

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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
       
  • Urban expansion and transportation interaction: Evidence from Akure,
           southwestern Nigeria

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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
       
  • Spatial model for predictive recovery monitoring based on hazard, built
           environment, and population features and their spillover effects

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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
       
  • Empirical research and proposed planning methodology for the greening of
           urban buildings to achieve low-carbon effects

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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
       
  • History, neighborhood, and proximity as factors of land-use change: A
           dynamic spatial regression model

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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
       
  • Urban land use transitions: Examining change over 19 years using sequence
           analysis. The case of South-East Queensland, Australia

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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
       
  • Using neighborhood characteristics to predict vacancy types: Comparing
           multi-scale conditions surrounding existing vacant lots

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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
       
  • Developing a national dataset of bicycle infrastructure for Canada using
           open data sources

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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
       
  • Walking alone or walking together: A spatial evaluation of children’s
           travel behavior to school

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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
       
  • Social interaction in public space: Spatial edges, moveable furniture, and
           visual landmarks

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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
       
  • Structural Changes in Human Mobility Under the Zero-COVID Strategy in
           China

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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
       
  • Agglomeration vs amenities' Unraveling the latent engine of growth in
           metropolitan Greece

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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
       
  • A clustering-based approach to quantifying socio-demographic impacts on
           urban mobility patterns

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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
       
  • Quantifying the environmental characteristics influencing the
           attractiveness of commercial agglomerations with big geo-data

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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
       
  • What’s in the laundromat' Mapping and characterising offshore-owned
           residential property in London

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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
       
  • Visibility and symbolism of corporate architecture: A multi-method
           approach for visual impact assessment

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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
       
  • Assessing impacts of the built environment on mobility: A joint choice
           model of travel mode and duration

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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
       
  • Census-based urban building energy modeling to evaluate the effectiveness
           of retrofit programs

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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
       
  • Identifying urban form typologies in Seoul using a new Gaussian mixture
           model-based clustering framework

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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
       
  • Using geographical random forest models to explore spatial patterns in the
           neighborhood determinants of hypertension prevalence across chicago,
           illinois, USA

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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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