Pub Date : 2024-05-01DOI: 10.1177/23998083241250265
Liliana S Valverde-Caballero, Luis M Mendoza-Salazar, Cinthya L Butron-Revilla, Ernesto Suarez-Lopez, Jesus S Aguilar-Ruiz
Walkability principles are an important part in the planning process of cities that face urban problems such as gentrification, pollution, and decay of their built heritage. The proposed factors – connectivity, proximity, land use mix, and retail density – form a comprehensive framework for evaluating walkability that transcends the boundaries of historical cities. These factors, while initially identified within historical contexts, possess inherent qualities that render them universally adaptable to various urban landscapes. By leveraging these factors, urban planners gain insights into the intricate fabric of pedestrian experiences in cities. They serve as universal evaluative tools, applicable not only to historical cities but also to burgeoning metropolises and smaller urban centres. This work introduces a novel approach to assessing the Walkability Index for World Heritage Cities, utilizing a Multiple Criteria Spatial Decision Support System (GIS-MCDA) structured in four stages. The approached methodology is particularly valuable for governments and decision-makers in developing countries of the Global South, where limitations in data and available tools are common challenges. The insights gained from this study can guide the improvement of policies, enable more precise implementation of sustainable mobility infrastructure, and motivate the pursuit or maintenance of UNESCO World Heritage nominations. The case study focused on the Historical Centre of Arequipa, Peru, a city designated as a UNESCO World Heritage site. The results of this study demonstrate the effectiveness of the proposed approach in such contexts, owing to its specificity and the integration of both objective and subjective elements.
{"title":"Walkability index for world heritage cities in developing countries","authors":"Liliana S Valverde-Caballero, Luis M Mendoza-Salazar, Cinthya L Butron-Revilla, Ernesto Suarez-Lopez, Jesus S Aguilar-Ruiz","doi":"10.1177/23998083241250265","DOIUrl":"https://doi.org/10.1177/23998083241250265","url":null,"abstract":"Walkability principles are an important part in the planning process of cities that face urban problems such as gentrification, pollution, and decay of their built heritage. The proposed factors – connectivity, proximity, land use mix, and retail density – form a comprehensive framework for evaluating walkability that transcends the boundaries of historical cities. These factors, while initially identified within historical contexts, possess inherent qualities that render them universally adaptable to various urban landscapes. By leveraging these factors, urban planners gain insights into the intricate fabric of pedestrian experiences in cities. They serve as universal evaluative tools, applicable not only to historical cities but also to burgeoning metropolises and smaller urban centres. This work introduces a novel approach to assessing the Walkability Index for World Heritage Cities, utilizing a Multiple Criteria Spatial Decision Support System (GIS-MCDA) structured in four stages. The approached methodology is particularly valuable for governments and decision-makers in developing countries of the Global South, where limitations in data and available tools are common challenges. The insights gained from this study can guide the improvement of policies, enable more precise implementation of sustainable mobility infrastructure, and motivate the pursuit or maintenance of UNESCO World Heritage nominations. The case study focused on the Historical Centre of Arequipa, Peru, a city designated as a UNESCO World Heritage site. The results of this study demonstrate the effectiveness of the proposed approach in such contexts, owing to its specificity and the integration of both objective and subjective elements.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"12 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-30DOI: 10.1177/23998083241247870
Jessica Gosling-Goldsmith, Sarah Elizabeth Antos, Luis Miguel Triveno, Adam R Benjamin, Chaofeng Wang
Those who work in the design, development, and management of cities are often limited by the scarcity of data. Particularly in the Global South, urban databases may be insufficient, out of date, or simply not available. However, digital technology is making it possible to fill gaps and build substantial datasets using “urban clues,” or attributes, gathered in high-resolution imagery by sky- and street-based cameras. Aided by machine learning, it is possible to detect specific building characteristics (purpose, condition, size, material, and construction)—yielding an array of geolocated details about the built environment. The resulting composite view can be made available, as we have done, in an open-source portal for use in urban management. The insights gained in this way may help address common urban management challenges, such as locating homes vulnerable to hazards such as flooding or earthquakes, identifying urban sprawl and informal housing, prioritizing infrastructure investments, and guiding public program support. This approach has been applied in Colombia, Guatemala, Indonesia, Mexico, Paraguay, Peru, St Lucia, and St Maarten.
{"title":"Aerial-terrestrial data fusion for fine-grained detection of urban clues","authors":"Jessica Gosling-Goldsmith, Sarah Elizabeth Antos, Luis Miguel Triveno, Adam R Benjamin, Chaofeng Wang","doi":"10.1177/23998083241247870","DOIUrl":"https://doi.org/10.1177/23998083241247870","url":null,"abstract":"Those who work in the design, development, and management of cities are often limited by the scarcity of data. Particularly in the Global South, urban databases may be insufficient, out of date, or simply not available. However, digital technology is making it possible to fill gaps and build substantial datasets using “urban clues,” or attributes, gathered in high-resolution imagery by sky- and street-based cameras. Aided by machine learning, it is possible to detect specific building characteristics (purpose, condition, size, material, and construction)—yielding an array of geolocated details about the built environment. The resulting composite view can be made available, as we have done, in an open-source portal for use in urban management. The insights gained in this way may help address common urban management challenges, such as locating homes vulnerable to hazards such as flooding or earthquakes, identifying urban sprawl and informal housing, prioritizing infrastructure investments, and guiding public program support. This approach has been applied in Colombia, Guatemala, Indonesia, Mexico, Paraguay, Peru, St Lucia, and St Maarten.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"6 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-27DOI: 10.1177/23998083241249592
Xiang Liu, Jing Fan, Zongshi Liu
The geographic assessment of population changes provides fundamental insights into understanding urban development and addressing future urbanization challenges. In this graphic, we produced a Dorling cartogram to geo-visualize population changes at the city level across China between 2010 and 2020. The cartogram illustrates how internal migration fuels China’s growing population concentration and regional disparity, leading to significant population loss in lower administrative-level cities and escalating intercity imbalances across the country.
{"title":"Always growing? Mapping population change in urban China for 2010-2020","authors":"Xiang Liu, Jing Fan, Zongshi Liu","doi":"10.1177/23998083241249592","DOIUrl":"https://doi.org/10.1177/23998083241249592","url":null,"abstract":"The geographic assessment of population changes provides fundamental insights into understanding urban development and addressing future urbanization challenges. In this graphic, we produced a Dorling cartogram to geo-visualize population changes at the city level across China between 2010 and 2020. The cartogram illustrates how internal migration fuels China’s growing population concentration and regional disparity, leading to significant population loss in lower administrative-level cities and escalating intercity imbalances across the country.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"33 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-26DOI: 10.1177/23998083241249322
Bailing Zhang, Jing Kang, Tao Feng
The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.
{"title":"A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning","authors":"Bailing Zhang, Jing Kang, Tao Feng","doi":"10.1177/23998083241249322","DOIUrl":"https://doi.org/10.1177/23998083241249322","url":null,"abstract":"The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"44 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140797784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.1177/23998083241247523
{"title":"Corrigendum to ‘Transportation and urban spatial structure: Evidence from Paris’","authors":"","doi":"10.1177/23998083241247523","DOIUrl":"https://doi.org/10.1177/23998083241247523","url":null,"abstract":"","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"45 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140630232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.1177/23998083241246375
Ana Luisa Maffini, Gustavo Maciel Gonçalves, Clarice Maraschin, Jorge Gil
Accessibility and mobility are key concerns of sustainable cities, especially in the Global South, due to the strong social inequalities. This paper contributes to the literature on mobility segregation by focusing on the potential movement of social groups in the city. We conceptualize potential movement as a network centrality, acting as an indicator of population movement when performing daily activities (working, studying, shopping, etc.). This paper’s objectives are (a) to identify the inequalities in potential movement of different social groups performing their daily activities; (b) to propose a network-based method to enhance our understanding of mobility inequalities; and (c) to address the context of medium-sized Latin American cities. We adopt a modified Betweenness Centrality model (Potential Movement) on a directed and weighted network. Our results show a similar pattern for both cities, with the CBD concentrating the potential movement for all groups; however, several inequalities were found. The high-income and white groups show higher levels of potential movement in the CBD and the low-income and non-white groups have a more distributed potential movement pattern, implying longer journeys to reach jobs and services. Income and race have shown to play a crucial role in those inequalities.
{"title":"Inequalities in the potential movement of social groups: A network-based indicator","authors":"Ana Luisa Maffini, Gustavo Maciel Gonçalves, Clarice Maraschin, Jorge Gil","doi":"10.1177/23998083241246375","DOIUrl":"https://doi.org/10.1177/23998083241246375","url":null,"abstract":"Accessibility and mobility are key concerns of sustainable cities, especially in the Global South, due to the strong social inequalities. This paper contributes to the literature on mobility segregation by focusing on the potential movement of social groups in the city. We conceptualize potential movement as a network centrality, acting as an indicator of population movement when performing daily activities (working, studying, shopping, etc.). This paper’s objectives are (a) to identify the inequalities in potential movement of different social groups performing their daily activities; (b) to propose a network-based method to enhance our understanding of mobility inequalities; and (c) to address the context of medium-sized Latin American cities. We adopt a modified Betweenness Centrality model (Potential Movement) on a directed and weighted network. Our results show a similar pattern for both cities, with the CBD concentrating the potential movement for all groups; however, several inequalities were found. The high-income and white groups show higher levels of potential movement in the CBD and the low-income and non-white groups have a more distributed potential movement pattern, implying longer journeys to reach jobs and services. Income and race have shown to play a crucial role in those inequalities.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"6 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140623169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1177/23998083241242913
Alex D Singleton, Paul A Longley
This paper outlines the creation of the London Output Area Classification (LOAC) from the 2021 Census, set within the broader context of geodemographic classification systems in the United Kingdom. The LOAC 2021 was developed in collaboration with the Greater London Authority (GLA) and offers an enhanced, statistically robust typology adept at capturing the unique spatial, socio-economic and built characteristics of London’s residential neighbourhoods. The paper asserts the critical importance of nuanced, area-specific geodemographic classifications for urban areas with unique geography relative to the national extent.
{"title":"Classifying and mapping residential structure through the London Output Area Classification","authors":"Alex D Singleton, Paul A Longley","doi":"10.1177/23998083241242913","DOIUrl":"https://doi.org/10.1177/23998083241242913","url":null,"abstract":"This paper outlines the creation of the London Output Area Classification (LOAC) from the 2021 Census, set within the broader context of geodemographic classification systems in the United Kingdom. The LOAC 2021 was developed in collaboration with the Greater London Authority (GLA) and offers an enhanced, statistically robust typology adept at capturing the unique spatial, socio-economic and built characteristics of London’s residential neighbourhoods. The paper asserts the critical importance of nuanced, area-specific geodemographic classifications for urban areas with unique geography relative to the national extent.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"130 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140598054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-10DOI: 10.1177/23998083241240099
Keith Burghardt, Johannes H Uhl, Kristina Lerman, Stefan Leyk
The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data.
{"title":"Analyzing urban scaling laws in the United States over 115 years","authors":"Keith Burghardt, Johannes H Uhl, Kristina Lerman, Stefan Leyk","doi":"10.1177/23998083241240099","DOIUrl":"https://doi.org/10.1177/23998083241240099","url":null,"abstract":"The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"67 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140598169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-08DOI: 10.1177/23998083241243104
Brad Bottoms, Julie Arbit, Earl Lewis, Alford Young
Large-scale socioeconomic vulnerability models commonly used in flood hazard assessments grapple with data limitations and struggle to fully capture diversity in vulnerability and resilience stemming from America’s sociopolitical history. In response, we developed a prototype for a place-based Flood Resilience Assessment Index (FRAI) using tract-level geographies that illustrates human-centric frameworks for quantifying flood resilience in the U.S. For these purposes, we define flood resilience as the likelihood a tract will rebound from a flood disaster. This framework can be used in tandem with flood risk models. We employ mixed methods in geospatial processing, including dasymetric interpolation and network analysis to model access. We also standardize variables by percentage to enable temporal analyses and equity-centered narrative framing. While the resulting scores for a five-county pilot study correlate with those of leading vulnerability indices, FRAI leverages diverse data sources and novel methods to represent the changing landscapes, resources, and needs of urban cores and growing suburbs. Future trajectories for FRAI will continue to define and refine methods for diverse datasets, employ participatory methods for emergency managers and residents of flood-prone communities in value-setting, weighting, and validation, and identify policy and practice avenues.
{"title":"Towards urban place-based resilience modeling: Mixed methods for a flood resilience assessment index","authors":"Brad Bottoms, Julie Arbit, Earl Lewis, Alford Young","doi":"10.1177/23998083241243104","DOIUrl":"https://doi.org/10.1177/23998083241243104","url":null,"abstract":"Large-scale socioeconomic vulnerability models commonly used in flood hazard assessments grapple with data limitations and struggle to fully capture diversity in vulnerability and resilience stemming from America’s sociopolitical history. In response, we developed a prototype for a place-based Flood Resilience Assessment Index (FRAI) using tract-level geographies that illustrates human-centric frameworks for quantifying flood resilience in the U.S. For these purposes, we define flood resilience as the likelihood a tract will rebound from a flood disaster. This framework can be used in tandem with flood risk models. We employ mixed methods in geospatial processing, including dasymetric interpolation and network analysis to model access. We also standardize variables by percentage to enable temporal analyses and equity-centered narrative framing. While the resulting scores for a five-county pilot study correlate with those of leading vulnerability indices, FRAI leverages diverse data sources and novel methods to represent the changing landscapes, resources, and needs of urban cores and growing suburbs. Future trajectories for FRAI will continue to define and refine methods for diverse datasets, employ participatory methods for emergency managers and residents of flood-prone communities in value-setting, weighting, and validation, and identify policy and practice avenues.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"158 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140598059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Studies on urban deprivation date back to the 19th Century but remain important today due to rising levels of inequality and social segregation. However, while social causes of deprivation have been investigated, the role of the built environment remains neglected. Existing studies either provide broad coverage at the expense of detailed morphological descriptions or offer meticulous accounts of small-scale areas without capturing the broader context. This paper addresses this gap by investigating the relationship between urban form, measured at the building level, and deprivation across the entire city of Isfahan, Iran. By doing so, we position this study in the tradition of urban morphology. Operationally, we, first, identify urban types (UTs), that is, distinctive patterns of urban form, by clustering 200+ morphological characters; second, we explore the relationship between proportion of buildings belonging to each UT, in each neighbourhood, and deprivation; third, we offer detailed descriptions of the UTs most strongly associated with deprivation, discuss possible drivers for the observed correlations, and link findings to relevant literature in the field. Twelve UTs are identified, with four showing the strongest impacts on predicting deprivation. This study brings novel insights on the morphology of deprivation of Isfahan, while contextualising them with respect to domain-specific studies, which have predominantly focused on Western cities. The proposed methodology can be replicated to explore morphologies of deprivation in different contexts, further our understanding of the topic, and potentially inform planning and policy making.
{"title":"Urban form and socioeconomic deprivation in Isfahan: An urban MorphoMetric approach","authors":"Alessandro Venerandi, Alessandra Feliciotti, Safoora Mokhtarzadeh, Maryam Taefnia, Ombretta Romice, Sergio Porta","doi":"10.1177/23998083241245491","DOIUrl":"https://doi.org/10.1177/23998083241245491","url":null,"abstract":"Studies on urban deprivation date back to the 19<jats:sup>th</jats:sup> Century but remain important today due to rising levels of inequality and social segregation. However, while social causes of deprivation have been investigated, the role of the built environment remains neglected. Existing studies either provide broad coverage at the expense of detailed morphological descriptions or offer meticulous accounts of small-scale areas without capturing the broader context. This paper addresses this gap by investigating the relationship between urban form, measured at the building level, and deprivation across the entire city of Isfahan, Iran. By doing so, we position this study in the tradition of urban morphology. Operationally, we, first, identify urban types (UTs), that is, distinctive patterns of urban form, by clustering 200+ morphological characters; second, we explore the relationship between proportion of buildings belonging to each UT, in each neighbourhood, and deprivation; third, we offer detailed descriptions of the UTs most strongly associated with deprivation, discuss possible drivers for the observed correlations, and link findings to relevant literature in the field. Twelve UTs are identified, with four showing the strongest impacts on predicting deprivation. This study brings novel insights on the morphology of deprivation of Isfahan, while contextualising them with respect to domain-specific studies, which have predominantly focused on Western cities. The proposed methodology can be replicated to explore morphologies of deprivation in different contexts, further our understanding of the topic, and potentially inform planning and policy making.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"56 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140598476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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