Pub Date : 2024-12-24DOI: 10.1016/j.compenvurbsys.2024.102244
Hanchen Yu
This paper proposes Generalized Geographically and Temporally Weighted Regression (GGTWR) to address the limitations of Geographically and Temporally Weighted Regression (GTWR). The proposed GGTWR framework encompasses various generalized linear models, e.g. Poisson regression, negative binomial regression, and other models of the exponential distribution family. The paper also shows the classic GTWR bandwidth search algorithm is not suitable for GGTWR and proposes a new bandwidth search algorithm for GGTWR. Several simulation experiments are used to prove that GGTWR can effectively capture spatiotemporal non-stationary. The GGTWR framework enables the estimation of varying regression coefficients that capture spatial and temporal heterogeneity for generalized linear relationships, providing a comprehensive understanding of how predictor variables influence the response variable across different locations and time periods. An application to interprovincial population migration in China using 2005–2020 census data demonstrates the interpretability of the GGTWR framework. GGTWR provides a flexible modeling approach that more accurately explains real-world phenomena.
{"title":"Generalized geographically and temporally weighted regression","authors":"Hanchen Yu","doi":"10.1016/j.compenvurbsys.2024.102244","DOIUrl":"10.1016/j.compenvurbsys.2024.102244","url":null,"abstract":"<div><div>This paper proposes Generalized Geographically and Temporally Weighted Regression (GGTWR) to address the limitations of Geographically and Temporally Weighted Regression (GTWR). The proposed GGTWR framework encompasses various generalized linear models, e.g. Poisson regression, negative binomial regression, and other models of the exponential distribution family. The paper also shows the classic GTWR bandwidth search algorithm is not suitable for GGTWR and proposes a new bandwidth search algorithm for GGTWR. Several simulation experiments are used to prove that GGTWR can effectively capture spatiotemporal non-stationary. The GGTWR framework enables the estimation of varying regression coefficients that capture spatial and temporal heterogeneity for generalized linear relationships, providing a comprehensive understanding of how predictor variables influence the response variable across different locations and time periods. An application to interprovincial population migration in China using 2005–2020 census data demonstrates the interpretability of the GGTWR framework. GGTWR provides a flexible modeling approach that more accurately explains real-world phenomena.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102244"},"PeriodicalIF":7.1,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141425","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 : 2024-12-20DOI: 10.1016/j.compenvurbsys.2024.102242
S. Somanath , L. Thuvander, J. Gil, A. Hollberg
Urban planners use static analysis techniques like network and proximity analysis to evaluate a neighbourhood's accessibility. However, these techniques do not adequately capture the distributional effects of accessibility on individuals. This paper introduces an activity-based model that simulates residents' daily activities to assess the distributional effects of the built environment (BE) on their accessibility. The model consists of a pipeline to generate a synthetic population covering 96 neighbourhoods in Gothenburg, Sweden, performs origin and destination assignment, and supports four travel modes and different activity types. The synthetic population and the travel demand model are validated across demographic and travel survey data. Additionally, we introduce Trip Completion Rate (TCR), an indicator of distributional accessibility and apply our model to a proposed redevelopment plan for a neighbourhood in Gothenburg to demonstrate its utility.
The results show that techniques used in transportation research can be effectively applied to neighbourhood planning, providing planners with insights into residents' ability to fulfil their daily needs. An advantage of our model is its ability to generate synthetic residents for a neighbourhood and then simulate how changes in the BE affect the resident's ability to achieve their daily needs, thus switching the focus of the analysis from the neighbourhood BE to including the residents that live in it. This paper extends the application of techniques used in transportation planning to neighbourhood planning, thereby empowering urban planners to create more equitable neighbourhoods.
{"title":"Activity-based simulations for neighbourhood planning towards social-spatial equity","authors":"S. Somanath , L. Thuvander, J. Gil, A. Hollberg","doi":"10.1016/j.compenvurbsys.2024.102242","DOIUrl":"10.1016/j.compenvurbsys.2024.102242","url":null,"abstract":"<div><div>Urban planners use static analysis techniques like network and proximity analysis to evaluate a neighbourhood's accessibility. However, these techniques do not adequately capture the distributional effects of accessibility on individuals. This paper introduces an activity-based model that simulates residents' daily activities to assess the distributional effects of the built environment (BE) on their accessibility. The model consists of a pipeline to generate a synthetic population covering 96 neighbourhoods in Gothenburg, Sweden, performs origin and destination assignment, and supports four travel modes and different activity types. The synthetic population and the travel demand model are validated across demographic and travel survey data. Additionally, we introduce Trip Completion Rate (TCR), an indicator of distributional accessibility and apply our model to a proposed redevelopment plan for a neighbourhood in Gothenburg to demonstrate its utility.</div><div>The results show that techniques used in transportation research can be effectively applied to neighbourhood planning, providing planners with insights into residents' ability to fulfil their daily needs. An advantage of our model is its ability to generate synthetic residents for a neighbourhood and then simulate how changes in the BE affect the resident's ability to achieve their daily needs, thus switching the focus of the analysis from the neighbourhood BE to including the residents that live in it. This paper extends the application of techniques used in transportation planning to neighbourhood planning, thereby empowering urban planners to create more equitable neighbourhoods.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102242"},"PeriodicalIF":7.1,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141408","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 : 2024-12-14DOI: 10.1016/j.compenvurbsys.2024.102241
Huimin Liu , Miao Li , Qingming Zhan , Zhengyue Ma , Bao-Jie He
Many cities are under intense heat challenges with severe environmental, social, and economic consequences, sparking great concern on heat-resilient urban planning, yet normally with biased focus on limited (e.g., diurnal) mitigation needs. Particularly, the recognition of urban thermal hotspots is crucial for adding effective cooling interventions for mitigation and avoiding overheating in newly built areas. However, the hotspots and associated drivers vary across time and space, bringing challenges to urban planners to make win-win decisions to synchronously address diurnal and nocturnal heat stresses through an integrated set of cooling strategies. This study aims to recognize the homogeneity and heterogeneity of diurnal and nocturnal hotspots and interpret principal and synergetic drivers behind them by developing a robust methodological scheme in addressing uncertainties associated with temperature data and analytical models. It explicitly 1) identified summer diurnal and nocturnal hotspots using rigorously screened satellite data; 2) recognized the typical typologies of hotspot-prone urban landscape according to urban composition, morphology, and function; 3) explored the day-night similarities and disparities in major urban factors and their robust effective ranges for synergetic mitigation through multi-model non-linear analysis with diverse machine learning techniques covering random forest, gradient boosting machines, and boosted regression trees. Results revealed that the specific locations and typical urban landscape features varied between diurnal and nocturnal hotspots. Among the six typologies recognized, industrial-dominated ones were more inclined to emerge as diurnal hotspots, while mid- to high-rise and mid-density blocks, with diversified land uses (mostly residential-dominated), tended to become diurnal, and more likely, nocturnal hotspots. All three models reached robust conclusion that urban morphology exhibited significant influence on both diurnal and nocturnal hotspot formation. Although trade-offs remained unavoidable in many cases, synergetic mitigation could be achieved through optimizing area averaged building height below 15 m or above 25 m, and building volume density under 2 % for Wuhan, China. Overall, this study responds to the emerging multidimensional urban science and praxis and extends the conventional one-dimensional planning against urban heat to win-win decisions over both diurnal and nocturnal hotspots. The empirical findings can benefit the development of complete, unbiased, and implementable actions for enhanced climate-resilience.
{"title":"Homogeneity and heterogeneity of diurnal and nocturnal hotspots and the implications for synergetic mitigation in heat-resilient urban planning","authors":"Huimin Liu , Miao Li , Qingming Zhan , Zhengyue Ma , Bao-Jie He","doi":"10.1016/j.compenvurbsys.2024.102241","DOIUrl":"10.1016/j.compenvurbsys.2024.102241","url":null,"abstract":"<div><div>Many cities are under intense heat challenges with severe environmental, social, and economic consequences, sparking great concern on heat-resilient urban planning, yet normally with biased focus on limited (e.g., diurnal) mitigation needs. Particularly, the recognition of urban thermal hotspots is crucial for adding effective cooling interventions for mitigation and avoiding overheating in newly built areas. However, the hotspots and associated drivers vary across time and space, bringing challenges to urban planners to make win-win decisions to synchronously address diurnal and nocturnal heat stresses through an integrated set of cooling strategies. This study aims to recognize the homogeneity and heterogeneity of diurnal and nocturnal hotspots and interpret principal and synergetic drivers behind them by developing a robust methodological scheme in addressing uncertainties associated with temperature data and analytical models. It explicitly 1) identified summer diurnal and nocturnal hotspots using rigorously screened satellite data; 2) recognized the typical typologies of hotspot-prone urban landscape according to urban composition, morphology, and function; 3) explored the day-night similarities and disparities in major urban factors and their robust effective ranges for synergetic mitigation through multi-model non-linear analysis with diverse machine learning techniques covering random forest, gradient boosting machines, and boosted regression trees. Results revealed that the specific locations and typical urban landscape features varied between diurnal and nocturnal hotspots. Among the six typologies recognized, industrial-dominated ones were more inclined to emerge as diurnal hotspots, while mid- to high-rise and mid-density blocks, with diversified land uses (mostly residential-dominated), tended to become diurnal, and more likely, nocturnal hotspots. All three models reached robust conclusion that urban morphology exhibited significant influence on both diurnal and nocturnal hotspot formation. Although trade-offs remained unavoidable in many cases, synergetic mitigation could be achieved through optimizing area averaged building height below 15 m or above 25 m, and building volume density under 2 % for Wuhan, China. Overall, this study responds to the emerging multidimensional urban science and praxis and extends the conventional one-dimensional planning against urban heat to win-win decisions over both diurnal and nocturnal hotspots. The empirical findings can benefit the development of complete, unbiased, and implementable actions for enhanced climate-resilience.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102241"},"PeriodicalIF":7.1,"publicationDate":"2024-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141411","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 : 2024-12-13DOI: 10.1016/j.compenvurbsys.2024.102238
Lior Wolpert, Itzhak Omer
Pedestrian flow distributions can inform planning for walkability and improve understanding of factors that influence pedestrian activity. However, detailed data is rarely available so pedestrian volume models, commonly relying on the Space Syntax framework, are often utilized to predict pedestrian volumes. This study compares the performance and dominant variables of three modelling families – multiple regression analyses, machine learning models, and agent-based models – in Tel Aviv-Yafo, Israel. Using 247 flow observations, optimal models from each family were fitted and validated for 3 separate areas that differ in their urban growth and morphological characteristics, as well for the whole city. Results showed that ensemble-based machine learning models were best for city-wide predictions while agent-based models had an advantage at the local scale of neighborhoods – especially in neighborhoods that did not develop in a self-organized process. Regression analyses fell short for all areas, even when using principal component analysis to reduce multicollinearity and overfitting. These differences are attributed to the relative influence of cognitive-behavioral and structural factors on pedestrian flows: agent-based models outperform statistical models in individual areas, where behavior is captured more accurately using a small set of cognitive-behavioral parameters. Statistical models are dominant in the city-wide context, where structural variables can predict aggregate patterns. This is crucially important when evaluating the distribution of pedestrians in a planned urban environment. Overall, our results indicate that stepwise regression are not sufficient for pedestrian volume modelling, that agent-based models better capture complex interactions between independent variables, and that machine learning models have a strong potential for city-wide pedestrian volume modelling.
{"title":"Comparative analysis of pedestrian volume models: Agent-based models, machine learning methods and multiple regression analysis","authors":"Lior Wolpert, Itzhak Omer","doi":"10.1016/j.compenvurbsys.2024.102238","DOIUrl":"10.1016/j.compenvurbsys.2024.102238","url":null,"abstract":"<div><div>Pedestrian flow distributions can inform planning for walkability and improve understanding of factors that influence pedestrian activity. However, detailed data is rarely available so pedestrian volume models, commonly relying on the Space Syntax framework, are often utilized to predict pedestrian volumes. This study compares the performance and dominant variables of three modelling families – multiple regression analyses, machine learning models, and agent-based models – in Tel Aviv-Yafo, Israel. Using 247 flow observations, optimal models from each family were fitted and validated for 3 separate areas that differ in their urban growth and morphological characteristics, as well for the whole city. Results showed that ensemble-based machine learning models were best for city-wide predictions while agent-based models had an advantage at the local scale of neighborhoods – especially in neighborhoods that did not develop in a self-organized process. Regression analyses fell short for all areas, even when using principal component analysis to reduce multicollinearity and overfitting. These differences are attributed to the relative influence of cognitive-behavioral and structural factors on pedestrian flows: agent-based models outperform statistical models in individual areas, where behavior is captured more accurately using a small set of cognitive-behavioral parameters. Statistical models are dominant in the city-wide context, where structural variables can predict aggregate patterns. This is crucially important when evaluating the distribution of pedestrians in a planned urban environment. Overall, our results indicate that stepwise regression are not sufficient for pedestrian volume modelling, that agent-based models better capture complex interactions between independent variables, and that machine learning models have a strong potential for city-wide pedestrian volume modelling.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102238"},"PeriodicalIF":7.1,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141410","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 : 2024-12-09DOI: 10.1016/j.compenvurbsys.2024.102230
Rosa Félix , Filipe Moura , Robin Lovelace
A high proportion of car trips can be replaced by a combination of public transit and cycling for the first-and-last mile. This paper estimates the potential for cycling combined with public transit as a substitute for car trips in the Lisbon metropolitan area and assesses its socio-environmental impacts using open data and open source tools. A decision support tool that facilitates the design and development of a metropolitan cycling network was developed (biclaR). The social and environmental impacts were assessed using Health World Organization tools. The impacts of shifting car trips to public transport were also estimated and monetized. The results show that 10 % of all trips could be made by cycling in combination with public transport. Shifting to cycling for the shorter first and last mile stages can reduce annual CO2eq emissions from 3000 to 7500 tons/year, while for the public transport leg, the transfer from car avoids of up to 20,500 tons of CO2eq emissions per year. The estimated socio-environmental benefits are of €125 million to €325 million over 10 years. This evidence can support policymakers to prioritize interventions that reduce the reliance on private motor vehicles.
{"title":"Reproducible methods for modeling combined public transport and cycling trips and associated benefits: Evidence from the biclaR tool","authors":"Rosa Félix , Filipe Moura , Robin Lovelace","doi":"10.1016/j.compenvurbsys.2024.102230","DOIUrl":"10.1016/j.compenvurbsys.2024.102230","url":null,"abstract":"<div><div>A high proportion of car trips can be replaced by a combination of public transit and cycling for the first-and-last mile. This paper estimates the potential for cycling combined with public transit as a substitute for car trips in the Lisbon metropolitan area and assesses its socio-environmental impacts using open data and open source tools. A decision support tool that facilitates the design and development of a metropolitan cycling network was developed (<em>biclaR</em>). The social and environmental impacts were assessed using Health World Organization tools. The impacts of shifting car trips to public transport were also estimated and monetized. The results show that 10 % of all trips could be made by cycling in combination with public transport. Shifting to cycling for the shorter first and last mile stages can reduce annual CO<sub>2</sub>eq emissions from 3000 to 7500 tons/year, while for the public transport leg, the transfer from car avoids of up to 20,500 tons of CO<sub>2</sub>eq emissions per year. The estimated socio-environmental benefits are of €125 million to €325 million over 10 years. This evidence can support policymakers to prioritize interventions that reduce the reliance on private motor vehicles.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102230"},"PeriodicalIF":7.1,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141423","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 : 2024-12-05DOI: 10.1016/j.compenvurbsys.2024.102228
Yunlei Liang , Jiawei Zhu , Wen Ye , Song Gao
Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from the network and reveal the hidden relationships between nodes, but they usually do not involve the attribute information. To consider edge-based interactions and node attributes together, this study proposed a family of GeoAI-enhanced unsupervised community detection methods called region2vec based on Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN). The region2vec methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering. The proposed GeoAI-based methods are compared with multiple baselines and perform the best when one wants to maximize node attribute similarity and spatial interaction intensity simultaneously within the spatial network communities. It is further applied in the shortage area delineation problem in public health and demonstrates its promise in regionalization problems.
{"title":"GeoAI-enhanced community detection on spatial networks with graph deep learning","authors":"Yunlei Liang , Jiawei Zhu , Wen Ye , Song Gao","doi":"10.1016/j.compenvurbsys.2024.102228","DOIUrl":"10.1016/j.compenvurbsys.2024.102228","url":null,"abstract":"<div><div>Spatial networks are useful for modeling geographic phenomena where spatial interaction plays an important role. To analyze the spatial networks and their internal structures, graph-based methods such as community detection have been widely used. Community detection aims to extract strongly connected components from the network and reveal the hidden relationships between nodes, but they usually do not involve the attribute information. To consider edge-based interactions and node attributes together, this study proposed a family of GeoAI-enhanced unsupervised community detection methods called <em>region2vec</em> based on Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN). The <em>region2vec</em> methods generate node neural embeddings based on attribute similarity, geographic adjacency and spatial interactions, and then extract network communities based on node embeddings using agglomerative clustering. The proposed GeoAI-based methods are compared with multiple baselines and perform the best when one wants to maximize node attribute similarity and spatial interaction intensity simultaneously within the spatial network communities. It is further applied in the shortage area delineation problem in public health and demonstrates its promise in regionalization problems.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102228"},"PeriodicalIF":7.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141422","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 : 2024-12-03DOI: 10.1016/j.compenvurbsys.2024.102229
Shir Gravitz-Sela , Adi Levy , Shani Zehavi , Ori Bryt , Dalit Shach-Pinsly , Pnina Plaut
Rapid urbanization, urban density, and COVID-19 effects have highlighted the need for high-quality urban parks within walking distance. A high-quality urban park maximizes a neighborhood's spatial, safety, and social potential, which are key factors to the well-being of its residents. Most studies evaluating urban parks rely on questionnaires, observations, interviews, and post-occupancy methods. These traditional methods are limited regarding the spatial and temporal dimensions as well as the size of the sample under investigation. In this paper, we demonstrate a new approach to evaluating urban parks by focusing on individuals' activity patterns, using big data extracted from city cameras by utilizing deep learning and computer vision. Our case study is a small urban park, Katznelson Garden, located in Or Yehuda, Israel. The imagery data is analyzed in relation to the gender of the parks' users, along with spatial and temporal analysis. Thus, activities during different hours of the day, days of the week, and in various parts of the urban park are identified. The results of our study revealed that females' and males' activity patterns are different and depend on the hour of the day and the type of park characteristics. Moreover, we found that activity levels and patterns varied according to the day of the week. As many cities seek to design better urban parks tailored to their residents' needs, these study findings can contribute to planning decisions by paving the way to customizing the design of urban parks in accordance with the revealed behavior.
{"title":"Analyzing usage patterns from video data through deep learning: The case of an urban park","authors":"Shir Gravitz-Sela , Adi Levy , Shani Zehavi , Ori Bryt , Dalit Shach-Pinsly , Pnina Plaut","doi":"10.1016/j.compenvurbsys.2024.102229","DOIUrl":"10.1016/j.compenvurbsys.2024.102229","url":null,"abstract":"<div><div>Rapid urbanization, urban density, and COVID-19 effects have highlighted the need for high-quality urban parks within walking distance. A high-quality urban park maximizes a neighborhood's spatial, safety, and social potential, which are key factors to the well-being of its residents. Most studies evaluating urban parks rely on questionnaires, observations, interviews, and post-occupancy methods. These traditional methods are limited regarding the spatial and temporal dimensions as well as the size of the sample under investigation. In this paper, we demonstrate a new approach to evaluating urban parks by focusing on individuals' activity patterns, using big data extracted from city cameras by utilizing deep learning and computer vision. Our case study is a small urban park, Katznelson Garden, located in Or Yehuda, Israel. The imagery data is analyzed in relation to the gender of the parks' users, along with spatial and temporal analysis. Thus, activities during different hours of the day, days of the week, and in various parts of the urban park are identified. The results of our study revealed that females' and males' activity patterns are different and depend on the hour of the day and the type of park characteristics. Moreover, we found that activity levels and patterns varied according to the day of the week. As many cities seek to design better urban parks tailored to their residents' needs, these study findings can contribute to planning decisions by paving the way to customizing the design of urban parks in accordance with the revealed behavior.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"117 ","pages":"Article 102229"},"PeriodicalIF":7.1,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143141981","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 : 2024-11-29DOI: 10.1016/j.compenvurbsys.2024.102207
Matthew Danish , S.M. Labib , Britta Ricker , Marco Helbich
Street View Imagery (SVI) is a valuable data source for studies (e.g., environmental assessments, green space identification or land cover classification). While commercial SVI is available, such providers commonly restrict copying or reuse in ways necessary for research. Open SVI datasets are readily available from less restrictive sources, such as Mapillary, but due to the heterogeneity of the images, these require substantial preprocessing, filtering, and careful quality checks. We present a method for automated downloading, processing, cropping, and filtering open SVI, to be used in a survey of human perceptions of the streets portrayed in these images. We demonstrate our open-source reusable SVI preparation and smartphone-friendly perception-survey software with Amsterdam (Netherlands) as the case study. Using a citizen science approach, we collected from 331 people 22,637 ratings about their perceptions for various criteria. We have published our software in a public repository for future re-use and reproducibility.
{"title":"A citizen science toolkit to collect human perceptions of urban environments using open street view images","authors":"Matthew Danish , S.M. Labib , Britta Ricker , Marco Helbich","doi":"10.1016/j.compenvurbsys.2024.102207","DOIUrl":"10.1016/j.compenvurbsys.2024.102207","url":null,"abstract":"<div><div>Street View Imagery (SVI) is a valuable data source for studies (e.g., environmental assessments, green space identification or land cover classification). While commercial SVI is available, such providers commonly restrict copying or reuse in ways necessary for research. Open SVI datasets are readily available from less restrictive sources, such as Mapillary, but due to the heterogeneity of the images, these require substantial preprocessing, filtering, and careful quality checks. We present a method for automated downloading, processing, cropping, and filtering open SVI, to be used in a survey of human perceptions of the streets portrayed in these images. We demonstrate our open-source reusable SVI preparation and smartphone-friendly perception-survey software with Amsterdam (Netherlands) as the case study. Using a citizen science approach, we collected from 331 people 22,637 ratings about their perceptions for various criteria. We have published our software in a public repository for future re-use and reproducibility.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"116 ","pages":"Article 102207"},"PeriodicalIF":7.1,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746440","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 : 2024-11-27DOI: 10.1016/j.compenvurbsys.2024.102221
Sandro M. Reia , Taylor Anderson , Henrique F. de Arruda , Kuldip S. Atwal , Shiyang Ruan , Hamdi Kavak , Dieter Pfoser
The relationship between urban form and function is a complex challenge that can be examined from multiple perspectives. In this study, we propose a method to characterize the urban function of U.S. metropolitan areas by analyzing trip patterns extracted from the 2017 National Household Travel Survey. To characterize urban form, we employ measures that capture road network topology. We cluster cities based on both form and function and subsequently compare these clusters. Our analysis of 52 U.S. metropolitan areas identifies 7 distinct clusters of cities that exhibit similar travel behavior, suggesting that diverse mobility patterns can be effectively grouped into a few universal classes. The observed disparity between the urban-function clustering and the urban-form clustering suggests that travel behavior in the U.S. is not strongly influenced by the physical infrastructure of the city.
{"title":"Function and form of U.S. cities","authors":"Sandro M. Reia , Taylor Anderson , Henrique F. de Arruda , Kuldip S. Atwal , Shiyang Ruan , Hamdi Kavak , Dieter Pfoser","doi":"10.1016/j.compenvurbsys.2024.102221","DOIUrl":"10.1016/j.compenvurbsys.2024.102221","url":null,"abstract":"<div><div>The relationship between urban form and function is a complex challenge that can be examined from multiple perspectives. In this study, we propose a method to characterize the urban function of U.S. metropolitan areas by analyzing trip patterns extracted from the 2017 National Household Travel Survey. To characterize urban form, we employ measures that capture road network topology. We cluster cities based on both form and function and subsequently compare these clusters. Our analysis of 52 U.S. metropolitan areas identifies 7 distinct clusters of cities that exhibit similar travel behavior, suggesting that diverse mobility patterns can be effectively grouped into a few universal classes. The observed disparity between the urban-function clustering and the urban-form clustering suggests that travel behavior in the U.S. is not strongly influenced by the physical infrastructure of the city.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"116 ","pages":"Article 102221"},"PeriodicalIF":7.1,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746439","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 : 2024-11-25DOI: 10.1016/j.compenvurbsys.2024.102220
Jinzhou Cao , Xianyu Cao , Wei Tu , Xiaoliang Tan , Tong Wang , Guanzhou Chen , Xiaodong Zhang , Qingquan Li
Traditional socioeconomic censuses rely on manual statistical surveys at the administrative division level, incurring significant costs while also facing the issue of data fabrication. The lack of information at the fine-scale spatial level limits more accurate policy formulation at the local and global levels. Nighttime lights have been proven to reflect human activities and estimate socio-economic indicators. Meanwhile, with the widespread use of smart devices, mobile phone data recorded as sensor data also provide various information about human footprints. This research elucidates the revealing ability of mobile phone footprints (MOB) and nighttime lights (NTL) to estimate various socio-economic indicators at a fine grid scale, establishing them as valuable proxies for understanding complex urban patterns. A comparative analysis within the Pearl River Delta (PRD), China demonstrates MOB's superior capacity in accurately reflecting socio-economic indicators such as population density and gross domestic product (GDP) distribution, effectively mitigating the oversaturation shortcomings of NTL in reflecting socioeconomic conditions. Especially in urban built-up areas, MOB and NTL data synergistically provide a refined depiction of socio-economic conditions, with MOB elucidating urban structure and density, and NTL closely associated with the service sector's footprint. The insights of the study highlight the value of integrating MOB and NTL data to refine the accuracy of socioeconomic indicators, which could be instrumental in the creation of nuanced urban planning and policy interventions. Such data-driven approaches promise to more effectively address socioeconomic inequalities and support sustainable urban development initiatives.
{"title":"Nighttime light imagery or mobile phone footprints: Which better reflects urban socio-economics at the grid level? A case study in the Pearl River Delta, China","authors":"Jinzhou Cao , Xianyu Cao , Wei Tu , Xiaoliang Tan , Tong Wang , Guanzhou Chen , Xiaodong Zhang , Qingquan Li","doi":"10.1016/j.compenvurbsys.2024.102220","DOIUrl":"10.1016/j.compenvurbsys.2024.102220","url":null,"abstract":"<div><div>Traditional socioeconomic censuses rely on manual statistical surveys at the administrative division level, incurring significant costs while also facing the issue of data fabrication. The lack of information at the fine-scale spatial level limits more accurate policy formulation at the local and global levels. Nighttime lights have been proven to reflect human activities and estimate socio-economic indicators. Meanwhile, with the widespread use of smart devices, mobile phone data recorded as sensor data also provide various information about human footprints. This research elucidates the revealing ability of mobile phone footprints (MOB) and nighttime lights (NTL) to estimate various socio-economic indicators at a fine grid scale, establishing them as valuable proxies for understanding complex urban patterns. A comparative analysis within the Pearl River Delta (PRD), China demonstrates MOB's superior capacity in accurately reflecting socio-economic indicators such as population density and gross domestic product (GDP) distribution, effectively mitigating the oversaturation shortcomings of NTL in reflecting socioeconomic conditions. Especially in urban built-up areas, MOB and NTL data synergistically provide a refined depiction of socio-economic conditions, with MOB elucidating urban structure and density, and NTL closely associated with the service sector's footprint. The insights of the study highlight the value of integrating MOB and NTL data to refine the accuracy of socioeconomic indicators, which could be instrumental in the creation of nuanced urban planning and policy interventions. Such data-driven approaches promise to more effectively address socioeconomic inequalities and support sustainable urban development initiatives.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"116 ","pages":"Article 102220"},"PeriodicalIF":7.1,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746438","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}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号