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}
Pub Date : 2024-07-24DOI: 10.1177/23998083241263898
Hansol Mun, Jaeweon Yeom, Jiwoon Oh, Juchul Jung
Evidence to prove that compact cities, the core of smart growth strategies, are the vision for carbon-neutral cities has been insufficiently explored because analyses have not distinguished between production- and consumption-based carbon emissions. Empirically analyzing the relationship with compact cities by estimating the final demand and investigating carbon emissions generated from the consumption of goods is essential. This study estimated consumption-based carbon emissions in South Korea using nighttime satellite imagery. Subsequently, using spatial analysis, K-means clustering analysis, and a regression model, we comprehensively confirmed whether a compact city to reduce consumption-based carbon emissions should be pursued. The results showed that (1) based on the clustering analysis, consumption-based carbon emissions were the lowest in clusters with the most desirable development form from a compact city perspective; and (2) the OLS regression analysis showed that the higher the complex land use (diversity), population density (density), congestion frequency intensity (transit access), green area ratio (environment), and agricultural area ratio (environment), the lower the consumption-based carbon emissions. However, the results confirmed that the greater the Vehicle Kilometers Traveled (street accessibility) and the poorer the accessibility of high-speed rail, the higher the consumption-based carbon emissions. Therefore, we recommend pursuing a compact city to reduce consumption-based carbon emissions.
{"title":"Does a compact city really reduce consumption-based carbon emissions? The case of South Korea","authors":"Hansol Mun, Jaeweon Yeom, Jiwoon Oh, Juchul Jung","doi":"10.1177/23998083241263898","DOIUrl":"https://doi.org/10.1177/23998083241263898","url":null,"abstract":"Evidence to prove that compact cities, the core of smart growth strategies, are the vision for carbon-neutral cities has been insufficiently explored because analyses have not distinguished between production- and consumption-based carbon emissions. Empirically analyzing the relationship with compact cities by estimating the final demand and investigating carbon emissions generated from the consumption of goods is essential. This study estimated consumption-based carbon emissions in South Korea using nighttime satellite imagery. Subsequently, using spatial analysis, K-means clustering analysis, and a regression model, we comprehensively confirmed whether a compact city to reduce consumption-based carbon emissions should be pursued. The results showed that (1) based on the clustering analysis, consumption-based carbon emissions were the lowest in clusters with the most desirable development form from a compact city perspective; and (2) the OLS regression analysis showed that the higher the complex land use (diversity), population density (density), congestion frequency intensity (transit access), green area ratio (environment), and agricultural area ratio (environment), the lower the consumption-based carbon emissions. However, the results confirmed that the greater the Vehicle Kilometers Traveled (street accessibility) and the poorer the accessibility of high-speed rail, the higher the consumption-based carbon emissions. Therefore, we recommend pursuing a compact city to reduce consumption-based carbon emissions.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"5 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772986","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: 10.1177/23998083241261100
Olaf Mumm, Majd Murad, Vanessa Miriam Carlow
The accessibility and quality of urban mobility networks (UMN) depend on a multitude of static and dynamic conditions for each individual. Promoting sustainable mobility, such as walking, requires a very specific assessment of UMN’s qualities given the specific needs of pedestrians. The objective of this research is to provide a new approach for the comprehensive, mode-specific understanding of a UMN as a base for good planning and decision making. With the Accessibility Score (AccessS), we propose an integrated, indicator-based, holistic geospatial framework for the quantified assessment of qualitative UMN attributes identified in an extensive literature review.
{"title":"Accessibility Score – Data analytics for the holistic assessment of urban mobility networks and the case of Braunschweig","authors":"Olaf Mumm, Majd Murad, Vanessa Miriam Carlow","doi":"10.1177/23998083241261100","DOIUrl":"https://doi.org/10.1177/23998083241261100","url":null,"abstract":"The accessibility and quality of urban mobility networks (UMN) depend on a multitude of static and dynamic conditions for each individual. Promoting sustainable mobility, such as walking, requires a very specific assessment of UMN’s qualities given the specific needs of pedestrians. The objective of this research is to provide a new approach for the comprehensive, mode-specific understanding of a UMN as a base for good planning and decision making. With the Accessibility Score (AccessS), we propose an integrated, indicator-based, holistic geospatial framework for the quantified assessment of qualitative UMN attributes identified in an extensive literature review.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"17 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772989","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 : 2024-07-23DOI: 10.1177/23998083241263124
Patrick Ballantyne, Cillian Berragan
Point of Interest data that is globally available, open access and of good quality is sparse, despite being important inputs for research in a number of application areas. New data from the Overture Maps Foundation offers significant potential in this arena, but accessing the data relies on computational resources beyond the skillset and capacity of the average researcher. In this article, we provide a processed version of the Overture places (POI) dataset for the UK, in a fully queryable format, and provide accompanying code through which to explore the data, and generate other national subsets. In the article, we describe the construction and characteristics of this new open data product, before evaluating its quality in relation to ISO standards, through direct comparison with Geolytix supermarket data. This dataset can support new and important research projects in a variety of different thematic areas, and foster a network of researchers to further evaluate its advantages and limitations, through validation against other well-established datasets from domains external to retail.
尽管兴趣点数据是许多应用领域研究的重要投入，但全球可用、可公开访问且质量上乘的兴趣点数据却非常稀少。来自 Overture 地图基金会的新数据为这一领域提供了巨大的潜力，但访问这些数据所依赖的计算资源超出了普通研究人员的技能和能力范围。在本文中，我们以完全可查询的格式提供了经过处理的英国 Overture 地点（POI）数据集版本，并提供了用于探索数据和生成其他国家子集的配套代码。在文章中，我们介绍了这一新的开放数据产品的构造和特点，然后通过与 Geolytix 超市数据的直接比较，评估了其质量是否符合 ISO 标准。该数据集可支持各种不同主题领域的新的重要研究项目，并通过与零售业以外的其他成熟数据集进行验证，促进研究人员网络进一步评估其优势和局限性。
{"title":"Overture Point of Interest data for the United Kingdom: A comprehensive, queryable open data product, validated against Geolytix supermarket data","authors":"Patrick Ballantyne, Cillian Berragan","doi":"10.1177/23998083241263124","DOIUrl":"https://doi.org/10.1177/23998083241263124","url":null,"abstract":"Point of Interest data that is globally available, open access and of good quality is sparse, despite being important inputs for research in a number of application areas. New data from the Overture Maps Foundation offers significant potential in this arena, but accessing the data relies on computational resources beyond the skillset and capacity of the average researcher. In this article, we provide a processed version of the Overture places (POI) dataset for the UK, in a fully queryable format, and provide accompanying code through which to explore the data, and generate other national subsets. In the article, we describe the construction and characteristics of this new open data product, before evaluating its quality in relation to ISO standards, through direct comparison with Geolytix supermarket data. This dataset can support new and important research projects in a variety of different thematic areas, and foster a network of researchers to further evaluate its advantages and limitations, through validation against other well-established datasets from domains external to retail.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"36 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141772988","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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