Pub Date : 2024-08-12DOI: 10.1177/23998083241271453
Mingzhi Zhou, Shuyu Lei, Jiangyue Wu, Hanxi Ma, David M Levinson, Jiangping Zhou
Using multiday continuous smartcard data in 2020, we investigate group-based travel in Hong Kong metro system by identifying metro riders intentionally traveling in groups (ITGs). ITGs serve as our proxies for citywide physical social interactions. Considering ITG members are interrelated through group-based trips, we construct a social network (an ITG network) formed by ITGs to explore the network properties and structures of ITG activities. Examining ITGs both before and during the COVID-19 pandemic, we measure the spatial patterns of ITGs and their dynamics across locales and over time. We find that the degree of the ITG network follows a heavy-tailed distribution. The network size and interconnections vary across time. Some ITG members are more influential vertices than others in maintaining the networks’ topological properties. We illustrate how new data and methods can be used to explore in-person interactions and social activity patterns in transit-reliant cities.
{"title":"Intentional travel group and social network: Identification and dynamics during a pandemic","authors":"Mingzhi Zhou, Shuyu Lei, Jiangyue Wu, Hanxi Ma, David M Levinson, Jiangping Zhou","doi":"10.1177/23998083241271453","DOIUrl":"https://doi.org/10.1177/23998083241271453","url":null,"abstract":"Using multiday continuous smartcard data in 2020, we investigate group-based travel in Hong Kong metro system by identifying metro riders intentionally traveling in groups (ITGs). ITGs serve as our proxies for citywide physical social interactions. Considering ITG members are interrelated through group-based trips, we construct a social network (an ITG network) formed by ITGs to explore the network properties and structures of ITG activities. Examining ITGs both before and during the COVID-19 pandemic, we measure the spatial patterns of ITGs and their dynamics across locales and over time. We find that the degree of the ITG network follows a heavy-tailed distribution. The network size and interconnections vary across time. Some ITG members are more influential vertices than others in maintaining the networks’ topological properties. We illustrate how new data and methods can be used to explore in-person interactions and social activity patterns in transit-reliant cities.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"57 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182934","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 : 2024-08-09DOI: 10.1177/23998083241272664
Rubén Cordera, Soledad Nogués, Esther González-González, José Luis Moura
Cities may undergo important changes in the coming years driven by various economic, social, and technological innovations, such as those related to autonomous mobility. Among other effects, autonomous vehicles may affect morpho-functional patterns of urban development and, especially, may reinforce or reduce dispersed development patterns, which have been relevant in many cities, particularly in the last decades. In order to offer an assessment of these possible effects, we propose a new urban sprawl index to measure the degree of dispersion/concentration of settlements in the medium-sized urban area of a Spanish city (Santander, Cantabria). Further, we explain the distribution of this index by means of a regression model, showing that variables such as average household income, trip time to the main urban centre, or the percentage of people using cars to commute to work are relevant factors that correlate positively with urban sprawl. Finally, we apply the proposed model to different scenarios to examine how the development of autonomous mobility could affect the characteristics of the analysed settlements. The results obtained suggest that, in scenarios with higher car usage and longer trip times to the urban centre because of the larger number of circulating vehicles, the form of urban settlements, especially those at an intermediate distance from the urban core, could experience an increase in sprawl. Therefore, Autonomous Vehicles could promote, under certain conditions, an urban form with more sustainability problems.
{"title":"Modelling sprawl in a medium-sized urban area considering the future arrival of autonomous vehicles","authors":"Rubén Cordera, Soledad Nogués, Esther González-González, José Luis Moura","doi":"10.1177/23998083241272664","DOIUrl":"https://doi.org/10.1177/23998083241272664","url":null,"abstract":"Cities may undergo important changes in the coming years driven by various economic, social, and technological innovations, such as those related to autonomous mobility. Among other effects, autonomous vehicles may affect morpho-functional patterns of urban development and, especially, may reinforce or reduce dispersed development patterns, which have been relevant in many cities, particularly in the last decades. In order to offer an assessment of these possible effects, we propose a new urban sprawl index to measure the degree of dispersion/concentration of settlements in the medium-sized urban area of a Spanish city (Santander, Cantabria). Further, we explain the distribution of this index by means of a regression model, showing that variables such as average household income, trip time to the main urban centre, or the percentage of people using cars to commute to work are relevant factors that correlate positively with urban sprawl. Finally, we apply the proposed model to different scenarios to examine how the development of autonomous mobility could affect the characteristics of the analysed settlements. The results obtained suggest that, in scenarios with higher car usage and longer trip times to the urban centre because of the larger number of circulating vehicles, the form of urban settlements, especially those at an intermediate distance from the urban core, could experience an increase in sprawl. Therefore, Autonomous Vehicles could promote, under certain conditions, an urban form with more sustainability problems.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"39 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141969778","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 : 2024-08-08DOI: 10.1177/23998083241271460
Anna Kajosaari, Martina Schorn, Kamyar Hasanzadeh, Tiina Rinne, Saana Rossi, Marketta Kyttä
Despite the emergence of virtual spaces as arenas for public participation, the geographies of digital participation have gained relatively little attention. Besides considering who participates and why, there is an evident gap in research considering the spatial relationships between the participants of digital urban planning processes and the spaces that are the subject of their participation. This paper proposes a working concept of the spatiality of participation that distinguishes between the spaces in which participation occurs, the spatial realities of the participants, and the spaces as objects of participatory planning. Relationships between these dimensions are investigated empirically with a Public Participation GIS study set in Espoo, Finland, involving 1,731 citizens and over 6,800 future planning and development ideas mapped across the city. The results of the study support prior research observing that e-participation has the potential to spatially expand participation processes both in terms of the involved public and the spatial knowledge they produce. However, our results also show that online participation may capture spatial ties between people and places that differ from those of traditional participation modes, ranging from place-protective behaviors close to the residential location to more casual spatial attachments.
{"title":"Beyond the backyard: Unraveling the geographies of citizens’ engagement in digital participatory planning","authors":"Anna Kajosaari, Martina Schorn, Kamyar Hasanzadeh, Tiina Rinne, Saana Rossi, Marketta Kyttä","doi":"10.1177/23998083241271460","DOIUrl":"https://doi.org/10.1177/23998083241271460","url":null,"abstract":"Despite the emergence of virtual spaces as arenas for public participation, the geographies of digital participation have gained relatively little attention. Besides considering who participates and why, there is an evident gap in research considering the spatial relationships between the participants of digital urban planning processes and the spaces that are the subject of their participation. This paper proposes a working concept of the spatiality of participation that distinguishes between the spaces in which participation occurs, the spatial realities of the participants, and the spaces as objects of participatory planning. Relationships between these dimensions are investigated empirically with a Public Participation GIS study set in Espoo, Finland, involving 1,731 citizens and over 6,800 future planning and development ideas mapped across the city. The results of the study support prior research observing that e-participation has the potential to spatially expand participation processes both in terms of the involved public and the spatial knowledge they produce. However, our results also show that online participation may capture spatial ties between people and places that differ from those of traditional participation modes, ranging from place-protective behaviors close to the residential location to more casual spatial attachments.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"13 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948803","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 : 2024-08-07DOI: 10.1177/23998083241272705
Xianchun Zhang, Yucheng Zou, Chang Xia, Ya’nan Lu
Existing scholarship extensively explores the dynamics, determinants, and consequences of urban expansion, yet there is scant literature examining the impact of regional cooperation upon the directions and spatial forms of urban expansion amidst the fast-urbanizing process. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a developed megacity-region in southern China, to probe whether the notable urban expansion observed in contemporary China has been profoundly influenced by collaborative efforts among jurisdictions. Through the spatial metrics and panel data regression spanning the period from 2010 to 2018, this study unveils that regional cooperation has extended from coastal cities towards hinterland cities within the GBA. Consequently, urban land in most cities has undergone expansion in diverse directions. Furthermore, in contrast to economic and social cooperation, regional institutional cooperation emerges as the most influential factor driving external urban expansion. Additionally, heterogeneous results reveal that regional cooperation drives the external expansion of ordinary cities towards core cities. In contrast, the inertia within the urban system demonstrates strong path dependence on the pattern of adjacent expansion, contrasting with the external expansion facilitated by regional cooperation. In summary, this study illuminates the genesis and dynamics of urban expansion amid the city-regionalization process, going beyond interpretations confined to the municipal scale.
{"title":"Unraveling the mystery of urban expansion in the Guangdong-Hong Kong-Macao Greater Bay Area: Exploring the crucial role of regional cooperation","authors":"Xianchun Zhang, Yucheng Zou, Chang Xia, Ya’nan Lu","doi":"10.1177/23998083241272705","DOIUrl":"https://doi.org/10.1177/23998083241272705","url":null,"abstract":"Existing scholarship extensively explores the dynamics, determinants, and consequences of urban expansion, yet there is scant literature examining the impact of regional cooperation upon the directions and spatial forms of urban expansion amidst the fast-urbanizing process. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a developed megacity-region in southern China, to probe whether the notable urban expansion observed in contemporary China has been profoundly influenced by collaborative efforts among jurisdictions. Through the spatial metrics and panel data regression spanning the period from 2010 to 2018, this study unveils that regional cooperation has extended from coastal cities towards hinterland cities within the GBA. Consequently, urban land in most cities has undergone expansion in diverse directions. Furthermore, in contrast to economic and social cooperation, regional institutional cooperation emerges as the most influential factor driving external urban expansion. Additionally, heterogeneous results reveal that regional cooperation drives the external expansion of ordinary cities towards core cities. In contrast, the inertia within the urban system demonstrates strong path dependence on the pattern of adjacent expansion, contrasting with the external expansion facilitated by regional cooperation. In summary, this study illuminates the genesis and dynamics of urban expansion amid the city-regionalization process, going beyond interpretations confined to the municipal scale.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"19 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948805","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 : 2024-08-06DOI: 10.1177/23998083241272101
Zhiying Lu, Yang Yang, Danlin Ou, Dazhi Gu
The outbreak of the COVID-19 pandemic has precipitated food crises worldwide, prompting a re-examination of the resilience of the urban food environment. While previous research on the urban food environment has predominantly focused on Western contexts, scant attention has been given to China. This study takes Shenzhen, China as an example to establish a food environment evaluation framework centered on accessibility, diversity, and healthiness factors, aiming to analyze the dynamic changes of the food environment during normal and pandemic periods. By using the GA optimization algorithm, some convenience stores are transformed into self-pickup points (SPPs), which is expected to eliminate the deserts risk areas (DRAs) with low cost and high efficiency. The findings reveal a distinctive “center-periphery” spatial structure characterizing the food environment in Shenzhen, and the improvement of healthiness plays a crucial role in sustaining food oases and ameliorating food swamps. This research provides methods for improving the resilience of the food environment during the pandemic across diverse nations, bolstering the security of urban lifeline systems.
{"title":"Dynamic changes of food environment: In and out of COVID-19 pandemic","authors":"Zhiying Lu, Yang Yang, Danlin Ou, Dazhi Gu","doi":"10.1177/23998083241272101","DOIUrl":"https://doi.org/10.1177/23998083241272101","url":null,"abstract":"The outbreak of the COVID-19 pandemic has precipitated food crises worldwide, prompting a re-examination of the resilience of the urban food environment. While previous research on the urban food environment has predominantly focused on Western contexts, scant attention has been given to China. This study takes Shenzhen, China as an example to establish a food environment evaluation framework centered on accessibility, diversity, and healthiness factors, aiming to analyze the dynamic changes of the food environment during normal and pandemic periods. By using the GA optimization algorithm, some convenience stores are transformed into self-pickup points (SPPs), which is expected to eliminate the deserts risk areas (DRAs) with low cost and high efficiency. The findings reveal a distinctive “center-periphery” spatial structure characterizing the food environment in Shenzhen, and the improvement of healthiness plays a crucial role in sustaining food oases and ameliorating food swamps. This research provides methods for improving the resilience of the food environment during the pandemic across diverse nations, bolstering the security of urban lifeline systems.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"32 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948804","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 : 2024-08-05DOI: 10.1177/23998083241272093
Anirudh Govind, Ate Poorthuis, Ben Derudder
Although it is generally accepted that street configurations may influence people’s intra-urban travel, capturing the exact nature of that influence remains challenging. We frame this challenge as one of operationalization and measurement and attempt to quantify and analyze the impact of street configurations more precisely. We draw on geographic data science tools to suggest that street configurations may be captured using catchment area polygons. To illustrate our approach, we derive these polygons for every building in Singapore and show that catchment area sizes spatially cluster, thus acting as proxies for street configurations. Using a spatial error model, we demonstrate that these catchment area sizes partially explain people’s intra-urban travel, conceptualized as their activity spaces. That is, as street configurations lead to larger catchment areas, people’s activity spaces tend to shrink. We show that the explanatory power of catchment area sizes is distinct from, albeit correlated with, other built environment variables (such as amenity density and land use diversity) typically used to explain people’s travel. We conclude by considering the potential of our approach in broader urban geographical research agendas drawing on street configurations and other morphological influences in the study of socio-spatial processes.
{"title":"Quantifying the effects of Singapore’s street configurations on people’s activity spaces","authors":"Anirudh Govind, Ate Poorthuis, Ben Derudder","doi":"10.1177/23998083241272093","DOIUrl":"https://doi.org/10.1177/23998083241272093","url":null,"abstract":"Although it is generally accepted that street configurations may influence people’s intra-urban travel, capturing the exact nature of that influence remains challenging. We frame this challenge as one of operationalization and measurement and attempt to quantify and analyze the impact of street configurations more precisely. We draw on geographic data science tools to suggest that street configurations may be captured using catchment area polygons. To illustrate our approach, we derive these polygons for every building in Singapore and show that catchment area sizes spatially cluster, thus acting as proxies for street configurations. Using a spatial error model, we demonstrate that these catchment area sizes partially explain people’s intra-urban travel, conceptualized as their activity spaces. That is, as street configurations lead to larger catchment areas, people’s activity spaces tend to shrink. We show that the explanatory power of catchment area sizes is distinct from, albeit correlated with, other built environment variables (such as amenity density and land use diversity) typically used to explain people’s travel. We conclude by considering the potential of our approach in broader urban geographical research agendas drawing on street configurations and other morphological influences in the study of socio-spatial processes.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"73 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141948806","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 : 2024-08-02DOI: 10.1177/23998083241272097
Xinyu Fu, Catherine Brinkley, Thomas W Sanchez, Chaosu Li
Cities worldwide are commonly aspiring to transition from inefficient urban sprawl patterns to more compact and sustainable urban forms. However, urban densification efforts often face significant public resistance or skepticism, hindering at-scale implementation. There is a scarcity of empirical studies identifying the rationale and mechanisms underpinning public opposition to urban density. This study aims to bridge this gap by leveraging novel natural language processing techniques (NLP), combined with mixed-methods analysis of a unique, highly detailed public dataset on urban intensification in Hamilton. This research stands out by proposing a transferable model for rapidly generating insights from large public feedback datasets, and also unveils the polarized and complex, self-interest-driven mechanisms, including NIMBYism (Not In My Back Yard), behind public support or opposition to urban densification. NLP techniques, such as sentiment analysis, topic modeling, and ChatGPT, can be used to offer rapid insights into a large, unstructured public feedback dataset. When combined with submitters’ individual interest representation and identifies, these AI-generated summaries can offer important insights into the hidden rationales behind public opinions, and, more importantly, be used to design tailored public engagement activities to obtain community buy-in.
{"title":"Text mining public feedback on urban densification plan change in Hamilton, New Zealand","authors":"Xinyu Fu, Catherine Brinkley, Thomas W Sanchez, Chaosu Li","doi":"10.1177/23998083241272097","DOIUrl":"https://doi.org/10.1177/23998083241272097","url":null,"abstract":"Cities worldwide are commonly aspiring to transition from inefficient urban sprawl patterns to more compact and sustainable urban forms. However, urban densification efforts often face significant public resistance or skepticism, hindering at-scale implementation. There is a scarcity of empirical studies identifying the rationale and mechanisms underpinning public opposition to urban density. This study aims to bridge this gap by leveraging novel natural language processing techniques (NLP), combined with mixed-methods analysis of a unique, highly detailed public dataset on urban intensification in Hamilton. This research stands out by proposing a transferable model for rapidly generating insights from large public feedback datasets, and also unveils the polarized and complex, self-interest-driven mechanisms, including NIMBYism (Not In My Back Yard), behind public support or opposition to urban densification. NLP techniques, such as sentiment analysis, topic modeling, and ChatGPT, can be used to offer rapid insights into a large, unstructured public feedback dataset. When combined with submitters’ individual interest representation and identifies, these AI-generated summaries can offer important insights into the hidden rationales behind public opinions, and, more importantly, be used to design tailored public engagement activities to obtain community buy-in.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"17 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141883766","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 : 2024-07-30DOI: 10.1177/23998083241267370
Yuling Xie, Xiao Fu, Yi Long, Mingyang Pei
Urban functions often diverge from initial planning due to changes driven by residents’ behaviors. Effective urban planning and renewal require accurately identifying urban functional regions based on residents’ behavior data (including activity and travel data). However, previous methods have primarily relied on either point of interest (POI) data or a single source of traffic data, and often ignore the combined influence of residents’ activities and travel behaviors. In this study, we introduce a novel framework that integrates multiple sources of traffic data (such as metro smart card data and car-hailing data) with POI data to identify urban functional regions. This approach is unique because it simultaneously considers two critical dimensions of residents’ behavior: travel and activity behaviors. By combining these dimensions, we extract a comprehensive set of characteristics, including travel time, travel flow, origin-destination patterns, activity types, and activity time, which are then aggregated at the regional level (i.e., traffic analysis zone). To process these characteristics, we use latent Dirichlet allocation (LDA) to extract high-level semantic features from each data type. Additionally, to handle the sparse data from metro smart cards, we employ a specialized clustering technique. The integration of diverse and complementary information from multiple data sources enables more accurate and nuanced identification of urban functional regions than single data source and k-means clustering algorithm, providing valuable insights for urban planners.
{"title":"Identifying Urban functional regions: A multi-dimensional framework approach integrating metro smart card data and car-hailing data","authors":"Yuling Xie, Xiao Fu, Yi Long, Mingyang Pei","doi":"10.1177/23998083241267370","DOIUrl":"https://doi.org/10.1177/23998083241267370","url":null,"abstract":"Urban functions often diverge from initial planning due to changes driven by residents’ behaviors. Effective urban planning and renewal require accurately identifying urban functional regions based on residents’ behavior data (including activity and travel data). However, previous methods have primarily relied on either point of interest (POI) data or a single source of traffic data, and often ignore the combined influence of residents’ activities and travel behaviors. In this study, we introduce a novel framework that integrates multiple sources of traffic data (such as metro smart card data and car-hailing data) with POI data to identify urban functional regions. This approach is unique because it simultaneously considers two critical dimensions of residents’ behavior: travel and activity behaviors. By combining these dimensions, we extract a comprehensive set of characteristics, including travel time, travel flow, origin-destination patterns, activity types, and activity time, which are then aggregated at the regional level (i.e., traffic analysis zone). To process these characteristics, we use latent Dirichlet allocation (LDA) to extract high-level semantic features from each data type. Additionally, to handle the sparse data from metro smart cards, we employ a specialized clustering technique. The integration of diverse and complementary information from multiple data sources enables more accurate and nuanced identification of urban functional regions than single data source and k-means clustering algorithm, providing valuable insights for urban planners.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"45 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141864352","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 : 2024-07-25DOI: 10.1177/23998083241263422
Somwrita Sarkar, Clémentine Cottineau-Mugadza, Levi J Wolf
This special issue of Environment and Planning B focuses on Spatial Inequalities and Cities. As the world progresses to almost a fully urban state, locations, networks, and access shape the everyday lives lived in cities, alongside being the movers and shapers of the future of sustainable and equitable urbanization. This special issue brings together a set of peer-reviewerd papers spanning urban science, urban analytics, geographic information / spatial science, network science, and quantitative socio-economic-spatial analysis, to explore and examine how the morphological, structural and spatial form of cities is linked to the production, maintenance and exacerbation of socio-economic inequalities and injustices. The issue also presents a critical angle on data, methods, and their use, and on how novel data and methods can help shed light on new dimensions of spatial inequalities. This editorial presents a brief critical review of the field of urban spatial inequalities and a summary of the special issue.
{"title":"Spatial inequalities and cities: A review","authors":"Somwrita Sarkar, Clémentine Cottineau-Mugadza, Levi J Wolf","doi":"10.1177/23998083241263422","DOIUrl":"https://doi.org/10.1177/23998083241263422","url":null,"abstract":"This special issue of Environment and Planning B focuses on Spatial Inequalities and Cities. As the world progresses to almost a fully urban state, locations, networks, and access shape the everyday lives lived in cities, alongside being the movers and shapers of the future of sustainable and equitable urbanization. This special issue brings together a set of peer-reviewerd papers spanning urban science, urban analytics, geographic information / spatial science, network science, and quantitative socio-economic-spatial analysis, to explore and examine how the morphological, structural and spatial form of cities is linked to the production, maintenance and exacerbation of socio-economic inequalities and injustices. The issue also presents a critical angle on data, methods, and their use, and on how novel data and methods can help shed light on new dimensions of spatial inequalities. This editorial presents a brief critical review of the field of urban spatial inequalities and a summary of the special issue.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"73 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772985","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 : 2024-07-24DOI: 10.1177/23998083241267331
Federico Botta, Robin Lovelace, Laura Gilbert, Arthur Turrell
The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. However, open data is not enough in many cases: publicly available datasets need to be accessible in an analysis-ready form from popular data science tools, such as R and Python, for them to realise their full potential. This paper explores ways to maximise the impact of open data with reference to a case study of packaging code to facilitate reproducible analysis. We present the jtstats project, which consists of a main Python package, and a smaller R version, for importing, processing, and visualising large and complex datasets representing journey times, for many transport modes and trip purposes at multiple geographic levels, released by the UK Department for Transport (DfT). jtstats shows how domain specific packages can enable reproducible research within the public sector and beyond, saving duplicated effort and reducing the risks of errors from repeated analyses. We hope that the jtstats project inspires others, particularly those in the public sector, to add value to their data sets by making them more accessible.
有效、合乎道德地使用数据为决策提供信息，可为公共部门带来巨大价值，尤其是在数据处理工作流程透明、可复制且稳健的情况下。政府释放这种价值的方法之一是公开数据，让更多人和组织获得洞察力。然而，在很多情况下，仅开放数据是不够的：公开数据集需要以可通过 R 和 Python 等流行数据科学工具进行分析的形式访问，这样才能充分发挥其潜力。本文通过一个包装代码以促进可重现分析的案例研究，探讨了如何最大限度地发挥开放数据的影响。我们介绍了 jtstats 项目，该项目由一个主要 Python 软件包和一个较小的 R 版本组成，用于导入、处理和可视化英国交通部 (DfT) 发布的大型复杂数据集，这些数据集代表了多种交通模式和出行目的在多个地理层次上的行程时间。jtstats 展示了特定领域软件包如何在公共部门内外实现可重现研究，从而节省重复劳动并降低重复分析产生错误的风险。我们希望 jtstats 项目能激励其他人，尤其是公共部门的人，通过使数据集更易于访问来增加其价值。
{"title":"Packaging code and data for reproducible research: A case study of journey time statistics","authors":"Federico Botta, Robin Lovelace, Laura Gilbert, Arthur Turrell","doi":"10.1177/23998083241267331","DOIUrl":"https://doi.org/10.1177/23998083241267331","url":null,"abstract":"The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. However, open data is not enough in many cases: publicly available datasets need to be accessible in an analysis-ready form from popular data science tools, such as R and Python, for them to realise their full potential. This paper explores ways to maximise the impact of open data with reference to a case study of packaging code to facilitate reproducible analysis. We present the jtstats project, which consists of a main Python package, and a smaller R version, for importing, processing, and visualising large and complex datasets representing journey times, for many transport modes and trip purposes at multiple geographic levels, released by the UK Department for Transport (DfT). jtstats shows how domain specific packages can enable reproducible research within the public sector and beyond, saving duplicated effort and reducing the risks of errors from repeated analyses. We hope that the jtstats project inspires others, particularly those in the public sector, to add value to their data sets by making them more accessible.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"14 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772987","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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