Pub Date : 2024-11-22DOI: 10.1016/j.compenvurbsys.2024.102218
Matti Pönkänen , Henrikki Tenkanen , Miloš Mladenović
During the last two decades, accessibility has begun to take a more central role in transport planning and decision making, as its importance has been recognized in many different policy agendas. Although environmental and social sustainability are central in contemporary public policy, the social exclusion effects related to access of opportunities are rarely measured at the national level. In this study, we analyze spatial accessibility to various opportunities in Finland at 1 km resolution and assess accessibility inequalities using the Palma ratio. Furthermore, we test how a web-based tool can be used in stakeholder communication and investigate the usefulness of the accessibility indicators and the tool for planning practice based on focus group discussions with Finnish transport planners. Our results show significant variation in the levels of access to different opportunities across Finnish municipalities. The Palma ratios reveal that the largest disparities are typically located in municipalities surrounding large city regions, where wealthier residents tend to have better access to opportunities compared to low-income populations. Finally, the insights from Finnish planning practitioners reveal that communicating national-level accessibility information via an online tool has high communicative and learning value for various planning and policy processes.
{"title":"Spatial accessibility and transport inequity in Finland: Open source models and perspectives from planning practice","authors":"Matti Pönkänen , Henrikki Tenkanen , Miloš Mladenović","doi":"10.1016/j.compenvurbsys.2024.102218","DOIUrl":"10.1016/j.compenvurbsys.2024.102218","url":null,"abstract":"<div><div>During the last two decades, accessibility has begun to take a more central role in transport planning and decision making, as its importance has been recognized in many different policy agendas. Although environmental and social sustainability are central in contemporary public policy, the social exclusion effects related to access of opportunities are rarely measured at the national level. In this study, we analyze spatial accessibility to various opportunities in Finland at 1 km resolution and assess accessibility inequalities using the Palma ratio. Furthermore, we test how a web-based tool can be used in stakeholder communication and investigate the usefulness of the accessibility indicators and the tool for planning practice based on focus group discussions with Finnish transport planners. Our results show significant variation in the levels of access to different opportunities across Finnish municipalities. The Palma ratios reveal that the largest disparities are typically located in municipalities surrounding large city regions, where wealthier residents tend to have better access to opportunities compared to low-income populations. Finally, the insights from Finnish planning practitioners reveal that communicating national-level accessibility information via an online tool has high communicative and learning value for various planning and policy processes.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"116 ","pages":"Article 102218"},"PeriodicalIF":7.1,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The flow of essential elements such as people, goods, and information through complex networks has become a critical factor in shaping urban dynamics and regional development. Quantifying location centrality plays an indispensable role not only in urban infrastructure planning but also in National central city planning. Two vital aspects should be considered for central nodes in flow-based complex networks: their impact on adjacent nodes and the diversity of nodes they affect. In this paper, we present a centrality measure index (C-index) that accounts for flow volume and flow directions, offering a high degree of interpretability. We applied the C-index to four public weighted complex networks, demonstrating that our method outperforms classical methods. Furthermore, we validated the effectiveness and advantages of C-index on quantifying location centrality both in inter-city and intra-city population mobility network. The centrality findings from the perspective of population mobility can reinforce guidelines for understanding National central cities and polycentric structure of cities, thereby facilitating policy-making of sustainable urban development.
{"title":"Quantifying centrality using a novel flow-based measure: Implications for sustainable urban development","authors":"Yanzhong Yin , Qunyong Wu , Zhiyuan Zhao , Xuanyu Chen","doi":"10.1016/j.compenvurbsys.2024.102217","DOIUrl":"10.1016/j.compenvurbsys.2024.102217","url":null,"abstract":"<div><div>The flow of essential elements such as people, goods, and information through complex networks has become a critical factor in shaping urban dynamics and regional development. Quantifying location centrality plays an indispensable role not only in urban infrastructure planning but also in National central city planning. Two vital aspects should be considered for central nodes in flow-based complex networks: their impact on adjacent nodes and the diversity of nodes they affect. In this paper, we present a centrality measure index (C-index) that accounts for flow volume and flow directions, offering a high degree of interpretability. We applied the C-index to four public weighted complex networks, demonstrating that our method outperforms classical methods. Furthermore, we validated the effectiveness and advantages of C-index on quantifying location centrality both in inter-city and intra-city population mobility network. The centrality findings from the perspective of population mobility can reinforce guidelines for understanding National central cities and polycentric structure of cities, thereby facilitating policy-making of sustainable urban development.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"116 ","pages":"Article 102217"},"PeriodicalIF":7.1,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.compenvurbsys.2024.102219
Eleanor S. Smith , Christopher Fleet , Stuart King , William Mackaness , Hannah Walker , Catherine E. Scott
We present a new end-to-end methodology for extracting symbols from historical maps and demonstrate an application of the method to extract details of the urban forests of Leeds and Edinburgh in the UK using Ordnance Survey maps from the 1890s. The methods presented allow tree symbols on 1:500 scale maps to be efficiently extracted, with our object detection model achieving an F1-score of 0.945. The results for each city are presented on the National Library of Scotland website and have been used to generate an estimate of 37 ± 1 tree symbols per hectare for Leeds in 1888–90 and 40 ± 1 tree symbols per hectare for Edinburgh in 1893–94. This is the first time that quantitative data has been obtained for historical urban tree counts in these two cities. The method presented can be expanded to other UK towns and cities and is a valuable tool for learning about the past, and changes to both the natural and built environment over time, aiding decisions on future tree planting. We discuss the process used to automate the generation of training data and to train a machine learning model to extract the symbols, comparing it with other possible models. This discussion provides context on how best to tackle similar problems of symbol extraction from historical maps and the issues that may arise in such automated analysis, alongside factors that must be considered when using historical maps as a data source.
{"title":"Estimating the density of urban trees in 1890s Leeds and Edinburgh using object detection on historical maps","authors":"Eleanor S. Smith , Christopher Fleet , Stuart King , William Mackaness , Hannah Walker , Catherine E. Scott","doi":"10.1016/j.compenvurbsys.2024.102219","DOIUrl":"10.1016/j.compenvurbsys.2024.102219","url":null,"abstract":"<div><div>We present a new end-to-end methodology for extracting symbols from historical maps and demonstrate an application of the method to extract details of the urban forests of Leeds and Edinburgh in the UK using Ordnance Survey maps from the 1890s. The methods presented allow tree symbols on 1:500 scale maps to be efficiently extracted, with our object detection model achieving an <em>F</em><sub><em>1</em></sub>-score of 0.945. The results for each city are presented on the National Library of Scotland website and have been used to generate an estimate of 37 ± 1 tree symbols per hectare for Leeds in 1888–90 and 40 ± 1 tree symbols per hectare for Edinburgh in 1893–94. This is the first time that quantitative data has been obtained for historical urban tree counts in these two cities. The method presented can be expanded to other UK towns and cities and is a valuable tool for learning about the past, and changes to both the natural and built environment over time, aiding decisions on future tree planting. We discuss the process used to automate the generation of training data and to train a machine learning model to extract the symbols, comparing it with other possible models. This discussion provides context on how best to tackle similar problems of symbol extraction from historical maps and the issues that may arise in such automated analysis, alongside factors that must be considered when using historical maps as a data source.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"115 ","pages":"Article 102219"},"PeriodicalIF":7.1,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.compenvurbsys.2024.102214
Ziwei Zhang , Han Zhang , Xing Meng , Yuxia Wang , Yuanzhi Yao , Xia Li
Despite the global concern about the chronic toxic effects of fine particulate matter (PM2.5) on human health, particularly in urban areas, the impact of urban form on PM2.5 pollution remains incompletely understood. This study established panel regression models for two resolutions (1 km and 30 m), covering 320 cities in China from 2000 to 2015, using landscape metrics and natural and socioeconomic variables to explore the urban form-PM2.5 relationship. The findings suggest that while the effects of fragmentation and agglomeration are opposite, the impact of urban scale on PM2.5 remains consistent across different resolutions. To unveil its mechanism, we compared authentic urban land use data under varying resolutions in detail and discovered that the coarse-resolution data lacked certain small patches, in addition to exhibiting edge deformation. As a result, we conducted counterfactual experiments on high-resolution land use data (30 m), simulating changes to urban patches, including removing small urban patches, dilating urban patch edges, and eroding urban patch edges. The implication of the findings is that the loss of information on small patches is more common in coarse resolution data than the deformation of patch edges, which in turn ultimately alters the results. Therefore, one of the major contributions of this study is exploring the mechanism of how spatial resolution impacts the relationship between urban form and PM2.5 concentration. The results can provide recommendations for sustainable urban development, emphasizing the significance of the scale effect in studies. This recommends urban planners to adopt a satellite urban development approach in which large cities are evenly distributed and minor ones are clustered together, with the aim of reducing PM2.5 pollution and human exposure.
{"title":"The role of data resolution in analyzing urban form and PM2.5 concentration","authors":"Ziwei Zhang , Han Zhang , Xing Meng , Yuxia Wang , Yuanzhi Yao , Xia Li","doi":"10.1016/j.compenvurbsys.2024.102214","DOIUrl":"10.1016/j.compenvurbsys.2024.102214","url":null,"abstract":"<div><div>Despite the global concern about the chronic toxic effects of fine particulate matter (PM2.5) on human health, particularly in urban areas, the impact of urban form on PM2.5 pollution remains incompletely understood. This study established panel regression models for two resolutions (1 km and 30 m), covering 320 cities in China from 2000 to 2015, using landscape metrics and natural and socioeconomic variables to explore the urban form-PM2.5 relationship. The findings suggest that while the effects of fragmentation and agglomeration are opposite, the impact of urban scale on PM2.5 remains consistent across different resolutions. To unveil its mechanism, we compared authentic urban land use data under varying resolutions in detail and discovered that the coarse-resolution data lacked certain small patches, in addition to exhibiting edge deformation. As a result, we conducted counterfactual experiments on high-resolution land use data (30 m), simulating changes to urban patches, including removing small urban patches, dilating urban patch edges, and eroding urban patch edges. The implication of the findings is that the loss of information on small patches is more common in coarse resolution data than the deformation of patch edges, which in turn ultimately alters the results. Therefore, one of the major contributions of this study is exploring the mechanism of how spatial resolution impacts the relationship between urban form and PM2.5 concentration. The results can provide recommendations for sustainable urban development, emphasizing the significance of the scale effect in studies. This recommends urban planners to adopt a satellite urban development approach in which large cities are evenly distributed and minor ones are clustered together, with the aim of reducing PM2.5 pollution and human exposure.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"115 ","pages":"Article 102214"},"PeriodicalIF":7.1,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-09DOI: 10.1016/j.compenvurbsys.2024.102206
Xiaoyan Li , Wenting Zhan , Fumin Deng , Xuedong Liang , Peng Luo
The unclear causal links of carbon emissions among global cities challenge policy development. This study develops two causal discovery algorithms to aid in this understanding. The first, scalable causal discovery, excels in unraveling complex causal relationships within extensive non-Euclidean networks encompassing thousands of nodes. The second, knowledge-injection causal discovery, merges expert expertise with artificial intelligence's data mining capabilities, employing a human-computer interaction approach for precise causal analysis. The proposed algorithms outperform leading causal discovery methods in the Granger causality test and causal structural consistency. This study investigates the emission causal networks across global cities and key international organizations, including the Organization for Economic Cooperation and Development, the Commonwealth, G20, the Belt and Road Initiative, and the Asia-Pacific Economic Cooperation. The analysis encompasses networks, countries, cities, and emission sources, providing insights for developing collaborative urban emission reduction policies. It underscores the tightly interconnected nature of the worldwide emission network, where the effects are rapidly disseminated. Furthermore, sub-networks reveal consistency and variability in their causal patterns, with core cities exerting significant influence over various dynamics. It is essential to leverage the unique structural characteristics inherent in each sub-network to enhance the effectiveness of coordinated emission reduction initiatives.
{"title":"Causal discovery and analysis of global city carbon emissions based on data-driven and hybrid intelligence","authors":"Xiaoyan Li , Wenting Zhan , Fumin Deng , Xuedong Liang , Peng Luo","doi":"10.1016/j.compenvurbsys.2024.102206","DOIUrl":"10.1016/j.compenvurbsys.2024.102206","url":null,"abstract":"<div><div>The unclear causal links of carbon emissions among global cities challenge policy development. This study develops two causal discovery algorithms to aid in this understanding. The first, scalable causal discovery, excels in unraveling complex causal relationships within extensive non-Euclidean networks encompassing thousands of nodes. The second, knowledge-injection causal discovery, merges expert expertise with artificial intelligence's data mining capabilities, employing a human-computer interaction approach for precise causal analysis. The proposed algorithms outperform leading causal discovery methods in the Granger causality test and causal structural consistency. This study investigates the emission causal networks across global cities and key international organizations, including the Organization for Economic Cooperation and Development, the Commonwealth, G20, the Belt and Road Initiative, and the Asia-Pacific Economic Cooperation. The analysis encompasses networks, countries, cities, and emission sources, providing insights for developing collaborative urban emission reduction policies. It underscores the tightly interconnected nature of the worldwide emission network, where the effects are rapidly disseminated. Furthermore, sub-networks reveal consistency and variability in their causal patterns, with core cities exerting significant influence over various dynamics. It is essential to leverage the unique structural characteristics inherent in each sub-network to enhance the effectiveness of coordinated emission reduction initiatives.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"115 ","pages":"Article 102206"},"PeriodicalIF":7.1,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142658798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-05DOI: 10.1016/j.compenvurbsys.2024.102205
Jianhui Lai, Yanyan Wang, Yang Yang, Xiaojie Wu, Yue Zhang
Waiting time is a critical metric for evaluating the quality of online car-hailing services, with studies indicating a close association between Online Car-hailing waiting time (CWT) and the urban built environment (BE). Points of interest (POI) data is widely utilized to characterize the built environment. However, quantifying the proper combination or spatial relationships between different types of POIs to capture the functional features of the built environment within a region poses a challenge. This paper proposes a framework for analyzing built environment characteristics based on a semantic probabilistic topic model utilizing Latent Dirichlet Allocation, demonstrating that integrating the building area of POIs along with the travel activity intensity at their respective locations enables a more precise identification of regional functions. Moreover, while some studies have explored the correlation between the built environment and waiting time, few have evaluated the nonlinear interactions between them. On this foundation, employing the machine learning technique XGBoost model in conjunction with online car-hailing order data, we probe the relationship between the built environment and waiting time. The research indicates that CWT is comprehensively affected by multiple factors. Taking weekday evening peak period as an example, the CWT of Central-urban area, where has dense commercial and office land use, positively correlated with commercial and office topics, while negatively correlated with educational, residential, leisure and healthcare topics, leading to a longer CWT in the Central-urban. In addition, the interaction between BEs can weakens their individual effects on CWT. A higher degree of job-residence balance can mitigate the negative impact of residential and office topics on increasing CWT, particularly in Intermediate-urban areas. Additionally, BE topics may also suppress the positive effect of road density on reducing CWT. The relationship between the BE and CWT exhibits threshold effects and a V-shaped relationship, indicating that the BE is significantly associated with CWT only within specific ranges. This correlation also exhibits a gradient pattern from the Central-urban to the Sub-urban, especially concerning office and residential topics. These outcomes elucidate the salient ranges of the built environment that exert substantial impacts on waiting time, informing strategic planning for online car-hailing dispatch and urban development to augment passenger travel satisfaction.
研究表明，网约车等候时间（CWT）与城市建筑环境（BE）密切相关。兴趣点（POI）数据被广泛用于描述建筑环境的特征。然而，如何量化不同类型 POI 之间的适当组合或空间关系，以捕捉区域内建筑环境的功能特征，是一项挑战。本文提出了一个基于语义概率主题模型的建筑环境特征分析框架，该框架利用潜在德里希勒分配（Latent Dirichlet Allocation）技术，证明了将 POI 的建筑面积与其所在位置的旅行活动强度相结合，可以更精确地识别区域功能。此外，虽然一些研究探讨了建筑环境与等待时间之间的相关性，但很少有研究对两者之间的非线性相互作用进行评估。在此基础上，我们利用机器学习技术 XGBoost 模型，结合在线叫车订单数据，探究了建筑环境与等候时间之间的关系。研究表明，CWT 受多种因素的综合影响。以工作日晚高峰为例，商业和办公用地密集的中城区的候车时间与商业和办公主题正相关，而与教育、居住、休闲和医疗保健主题负相关，导致中城区的候车时间更长。此外，BE 之间的相互作用会削弱其对 CWT 的单独影响。较高程度的职住平衡可以减轻居住和办公主题对延长 CWT 的负面影响，尤其是在中城区。此外，BE 主题还可能抑制道路密度对降低 CWT 的积极影响。BE 与 CWT 之间的关系表现出阈值效应和 V 型关系，表明 BE 仅在特定范围内与 CWT 显著相关。这种相关性还表现出从中心城区到郊区的梯度模式，尤其是在办公楼和住宅主题方面。这些结果阐明了对等候时间产生重大影响的建筑环境的突出范围，为网约车调度和城市发展的战略规划提供了参考，从而提高乘客的出行满意度。
{"title":"Exploring the built environment impacts on Online Car-hailing waiting time: An empirical study in Beijing","authors":"Jianhui Lai, Yanyan Wang, Yang Yang, Xiaojie Wu, Yue Zhang","doi":"10.1016/j.compenvurbsys.2024.102205","DOIUrl":"10.1016/j.compenvurbsys.2024.102205","url":null,"abstract":"<div><div>Waiting time is a critical metric for evaluating the quality of online car-hailing services, with studies indicating a close association between Online Car-hailing waiting time (CWT) and the urban built environment (BE). Points of interest (POI) data is widely utilized to characterize the built environment. However, quantifying the proper combination or spatial relationships between different types of POIs to capture the functional features of the built environment within a region poses a challenge. This paper proposes a framework for analyzing built environment characteristics based on a semantic probabilistic topic model utilizing Latent Dirichlet Allocation, demonstrating that integrating the building area of POIs along with the travel activity intensity at their respective locations enables a more precise identification of regional functions. Moreover, while some studies have explored the correlation between the built environment and waiting time, few have evaluated the nonlinear interactions between them. On this foundation, employing the machine learning technique XGBoost model in conjunction with online car-hailing order data, we probe the relationship between the built environment and waiting time. The research indicates that CWT is comprehensively affected by multiple factors. Taking weekday evening peak period as an example, the CWT of Central-urban area, where has dense commercial and office land use, positively correlated with commercial and office topics, while negatively correlated with educational, residential, leisure and healthcare topics, leading to a longer CWT in the Central-urban. In addition, the interaction between BEs can weakens their individual effects on CWT. A higher degree of job-residence balance can mitigate the negative impact of residential and office topics on increasing CWT, particularly in Intermediate-urban areas. Additionally, BE topics may also suppress the positive effect of road density on reducing CWT. The relationship between the BE and CWT exhibits threshold effects and a V-shaped relationship, indicating that the BE is significantly associated with CWT only within specific ranges. This correlation also exhibits a gradient pattern from the Central-urban to the Sub-urban, especially concerning office and residential topics. These outcomes elucidate the salient ranges of the built environment that exert substantial impacts on waiting time, informing strategic planning for online car-hailing dispatch and urban development to augment passenger travel satisfaction.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"115 ","pages":"Article 102205"},"PeriodicalIF":7.1,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142586472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.compenvurbsys.2024.102203
Rongxiang Su , Niall Newsham , Somayeh Dodge
Ethnoracial segregation persists as a pressing issue in American cities. Understanding these issues is crucial for promoting social equity and justice, and planning more inclusive cities. Prior research has predominantly emphasized residential ethnoracial diversity but has often overlooked or inadequately addressed ethnoracial diversity and segregation in individuals' daily activities and places they visit, due in part to data limitations. This study leverages a dynamic measure of ethnoracial diversity and dominance at the finest spatial scale, specifically at the Points of Interest (POI) level and various temporal contexts. Using one month of privacy-enhanced mobile phone location data in Los Angeles County, California, this study explores ethnoracial diversity and spatial segregation simultaneously in POI visits in LA County. Our findings confirm that individuals' daily mobility in urban areas enhances ethnoracial mixing at activity locations. Empirical results indicate that the diversity of visitors to a POI is significantly higher than the neighborhood diversity where the same POI is located. A significant positive linear relationship was found between the neighborhood diversity of POIs and the diversity of visitors. About 34 % of the variance in the diversity of visitors to POIs can be explained by the neighborhood diversity of POIs. Our results also suggest significant spatial clusters of isolated/integrated areas regarding ethnoracial mixing in people's daily activity locations. Notably, the Hispanic or Latino population tends to stay in their own communities and experiences a higher level of segregation in their daily activity locations. The findings have significant implications for urban planners and policymakers to design targeted solutions and policies to promote social equity, integration, and equal access to public amenities and opportunities in urban spaces.
种族隔离一直是美国城市的一个紧迫问题。了解这些问题对于促进社会公平和正义以及规划更具包容性的城市至关重要。以往的研究主要强调居住地的人种多样性，但往往忽略了个人日常活动和访问场所的人种多样性和种族隔离问题，或对其关注不够，部分原因在于数据的局限性。本研究在最精细的空间尺度上，特别是在兴趣点（POI）层面和各种时间背景下，对种族多样性和优势进行了动态测量。本研究利用加利福尼亚州洛杉矶县一个月的隐私增强型手机定位数据，同时探讨了洛杉矶县兴趣点访问中的人种多样性和空间隔离问题。我们的研究结果证实，个人在城市地区的日常流动增强了活动地点的人种混合。实证结果表明，一个主要景点的游客多样性明显高于同一主要景点所在社区的多样性。研究发现，主要景点的邻里多样性与游客多样性之间存在明显的正线性关系。POI 附近的多样性可以解释 POI 访客多样性中约 34% 的差异。我们的研究结果还表明，在人们日常活动地点的人种混合方面，存在着明显的孤立/融合区域空间集群。值得注意的是，西班牙裔或拉丁裔人口倾向于留在自己的社区，他们的日常活动地点的隔离程度较高。这些发现对城市规划者和决策者设计有针对性的解决方案和政策，以促进社会公平、融合以及平等享有城市空间的公共设施和机会具有重要意义。
{"title":"Spatiotemporal dynamics of ethnoracial diversity and segregation in Los Angeles County: Insights from mobile phone data","authors":"Rongxiang Su , Niall Newsham , Somayeh Dodge","doi":"10.1016/j.compenvurbsys.2024.102203","DOIUrl":"10.1016/j.compenvurbsys.2024.102203","url":null,"abstract":"<div><div>Ethnoracial segregation persists as a pressing issue in American cities. Understanding these issues is crucial for promoting social equity and justice, and planning more inclusive cities. Prior research has predominantly emphasized residential ethnoracial diversity but has often overlooked or inadequately addressed ethnoracial diversity and segregation in individuals' daily activities and places they visit, due in part to data limitations. This study leverages a dynamic measure of ethnoracial diversity and dominance at the finest spatial scale, specifically at the Points of Interest (POI) level and various temporal contexts. Using one month of privacy-enhanced mobile phone location data in Los Angeles County, California, this study explores ethnoracial diversity and spatial segregation simultaneously in POI visits in LA County. Our findings confirm that individuals' daily mobility in urban areas enhances ethnoracial mixing at activity locations. Empirical results indicate that the diversity of visitors to a POI is significantly higher than the neighborhood diversity where the same POI is located. A significant positive linear relationship was found between the neighborhood diversity of POIs and the diversity of visitors. About 34 % of the variance in the diversity of visitors to POIs can be explained by the neighborhood diversity of POIs. Our results also suggest significant spatial clusters of isolated/integrated areas regarding ethnoracial mixing in people's daily activity locations. Notably, the Hispanic or Latino population tends to stay in their own communities and experiences a higher level of segregation in their daily activity locations. The findings have significant implications for urban planners and policymakers to design targeted solutions and policies to promote social equity, integration, and equal access to public amenities and opportunities in urban spaces.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"114 ","pages":"Article 102203"},"PeriodicalIF":7.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-30DOI: 10.1016/j.compenvurbsys.2024.102204
Liuyi Song , Dong Liu , Mei-Po Kwan , Yang Liu , Yan Zhang
An accurate understanding of noise perception is important for urban planning, noise management and public health. However, the visual and acoustic urban landscapes are intrinsically linked: the intricate interplay between what we see and hear shapes noise perception in the urban environment. To measure this complex and mixed effect, we conducted a mobility-based survey in Hong Kong with 800 participants, recording their noise exposure, noise perception and GPS trajectories. In addition, we acquired Google Street View images associated with each GPS trajectory point and extracted the urban visual environment from them. This study used a multi-sensory framework combined with XGBoost and Shapley additive interpretation (SHAP) models to construct an interpretable classification model for noise perception. Compared to relying solely on sound pressure levels, our model exhibited significant improvements in predicting noise perception, achieving a six-classification accuracy of approximately 0.75. Our findings revealed that the most influential factors affecting noise perception are the sound pressure levels and the proportion of buildings, plants, sky, and light intensity. Further, we discovered non-linear relationships between visual factors and noise perception: an excessive number of buildings exacerbated noise annoyance and stress levels and diminished objective noise perception at the same time. On the other hand, the presence of green plants mitigated the effect of noise on stress levels, but beyond a certain threshold, it led to worsened objective noise perception and noise annoyance instead. Our study provides insight into the objective and subjective perception of noise pressure, which contributes to advancing our understanding of complex and dynamic urban environments.
{"title":"Machine-based understanding of noise perception in urban environments using mobility-based sensing data","authors":"Liuyi Song , Dong Liu , Mei-Po Kwan , Yang Liu , Yan Zhang","doi":"10.1016/j.compenvurbsys.2024.102204","DOIUrl":"10.1016/j.compenvurbsys.2024.102204","url":null,"abstract":"<div><div>An accurate understanding of noise perception is important for urban planning, noise management and public health. However, the visual and acoustic urban landscapes are intrinsically linked: the intricate interplay between what we see and hear shapes noise perception in the urban environment. To measure this complex and mixed effect, we conducted a mobility-based survey in Hong Kong with 800 participants, recording their noise exposure, noise perception and GPS trajectories. In addition, we acquired Google Street View images associated with each GPS trajectory point and extracted the urban visual environment from them. This study used a multi-sensory framework combined with XGBoost and Shapley additive interpretation (SHAP) models to construct an interpretable classification model for noise perception. Compared to relying solely on sound pressure levels, our model exhibited significant improvements in predicting noise perception, achieving a six-classification accuracy of approximately 0.75. Our findings revealed that the most influential factors affecting noise perception are the sound pressure levels and the proportion of buildings, plants, sky, and light intensity. Further, we discovered non-linear relationships between visual factors and noise perception: an excessive number of buildings exacerbated noise annoyance and stress levels and diminished objective noise perception at the same time. On the other hand, the presence of green plants mitigated the effect of noise on stress levels, but beyond a certain threshold, it led to worsened objective noise perception and noise annoyance instead. Our study provides insight into the objective and subjective perception of noise pressure, which contributes to advancing our understanding of complex and dynamic urban environments.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"114 ","pages":"Article 102204"},"PeriodicalIF":7.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142551897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-17DOI: 10.1016/j.compenvurbsys.2024.102202
Armita Kar , Ningchuan Xiao , Harvey J. Miller , Huyen T.K. Le
Existing accessibility measures mainly focus on the physical limitations of travel and ignore travelers' perceptions, behavior, and socio-economic differences. By integrating approaches in time geography and travel behavior, this study introduces a bottom-up inclusive accessibility concept that aggregates individual-level travel perceptions across socio-economic groups to evaluate their multimodal access to opportunities. We classify accessibility constraints into hard constraints (physical space-time limitations to travel) and soft constraints (perceptual factors influencing travel, such as safety perceptions, comfort, and willingness to travel). We categorize travelers into 12 mutually exclusive socio-economic groups from a mobility survey dataset of 477 travelers. We apply a support vector regressor-based ensemble algorithm to estimate network-level walking perception scores as soft constraints for each social group. We derive group-specific inclusive accessibility measures that consider space-time limitations from transit and sidewalk networks as hard constraints and minimize the group-specific soft constraint to a certain threshold. Finally, we demonstrate the effectiveness of group-specific inclusive accessibility by comparing it with the classic access measure. Our study provides scientific evidence on how people of varying socio-economic statuses perceive the same travel environment differently. We find that socio-economically disadvantaged communities experience higher mobility barriers and lower accessibility while walking and using transit in Columbus, OH. Our study demonstrates a transition from person- to place-based accessibility measures by sequentially quantifying mobility perceptions for individual travelers and aggregating them by social groups for a large geographic scale, making this approach suitable for equity-oriented need-specific transportation planning.
{"title":"Inclusive accessibility: Analyzing socio-economic disparities in perceived accessibility","authors":"Armita Kar , Ningchuan Xiao , Harvey J. Miller , Huyen T.K. Le","doi":"10.1016/j.compenvurbsys.2024.102202","DOIUrl":"10.1016/j.compenvurbsys.2024.102202","url":null,"abstract":"<div><div>Existing accessibility measures mainly focus on the physical limitations of travel and ignore travelers' perceptions, behavior, and socio-economic differences. By integrating approaches in time geography and travel behavior, this study introduces a bottom-up inclusive accessibility concept that aggregates individual-level travel perceptions across socio-economic groups to evaluate their multimodal access to opportunities. We classify accessibility constraints into hard constraints (physical space-time limitations to travel) and soft constraints (perceptual factors influencing travel, such as safety perceptions, comfort, and willingness to travel). We categorize travelers into 12 mutually exclusive socio-economic groups from a mobility survey dataset of 477 travelers. We apply a support vector regressor-based ensemble algorithm to estimate network-level walking perception scores as soft constraints for each social group. We derive group-specific inclusive accessibility measures that consider space-time limitations from transit and sidewalk networks as hard constraints and minimize the group-specific soft constraint to a certain threshold. Finally, we demonstrate the effectiveness of group-specific inclusive accessibility by comparing it with the classic access measure. Our study provides scientific evidence on how people of varying socio-economic statuses perceive the same travel environment differently. We find that socio-economically disadvantaged communities experience higher mobility barriers and lower accessibility while walking and using transit in Columbus, OH. Our study demonstrates a transition from person- to place-based accessibility measures by sequentially quantifying mobility perceptions for individual travelers and aggregating them by social groups for a large geographic scale, making this approach suitable for equity-oriented need-specific transportation planning.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"114 ","pages":"Article 102202"},"PeriodicalIF":7.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-15DOI: 10.1016/j.compenvurbsys.2024.102201
Catherine Trudelle , Christophe Claramunt
The many human interactions within cities inevitably generate relations between different places, civic and political organisations, authorities, and eventually conflictual events. Among all conflicts occurring in urban environments, if some are isolated events, many are connected by strong dependencies that generate networks in space and time. The research presented in this paper introduces a graph-based approach whose objective is to track the intertwined relations and dependencies that are associated with registered conflicts. The approach is experimented with and implemented using a combination of a graph-based database and visual graphics that together provide a series of data query capabilities and analysis specifically adapted to the context of our study. An experimental application to a series of conflicts reported in local media from 1985 to 2007 in the urban area of Montréal in Canada is presented and discussed.
{"title":"A graph-based modelling approach for the representation and analysis of urban conflicts","authors":"Catherine Trudelle , Christophe Claramunt","doi":"10.1016/j.compenvurbsys.2024.102201","DOIUrl":"10.1016/j.compenvurbsys.2024.102201","url":null,"abstract":"<div><div>The many human interactions within cities inevitably generate relations between different places, civic and political organisations, authorities, and eventually conflictual events. Among all conflicts occurring in urban environments, if some are isolated events, many are connected by strong dependencies that generate networks in space and time. The research presented in this paper introduces a graph-based approach whose objective is to track the intertwined relations and dependencies that are associated with registered conflicts. The approach is experimented with and implemented using a combination of a graph-based database and visual graphics that together provide a series of data query capabilities and analysis specifically adapted to the context of our study. An experimental application to a series of conflicts reported in local media from 1985 to 2007 in the urban area of Montréal in Canada is presented and discussed.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"114 ","pages":"Article 102201"},"PeriodicalIF":7.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142442884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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