Computers Environment and Urban Systems最新文献

GeoAvatar: A big mobile phone positioning data-driven method for individualized pseudo personal mobility data generation

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-03-10 DOI: 10.1016/j.compenvurbsys.2025.102252

Peiran Li , Haoran Zhang , Wenjing Li , Dou Huang , Zhiling Guo , Jinyu Chen , Junxiang Zhang , Xuan Song , Pengjun Zhao , Jinyue Yan , Shibasaki Ryosuke , Noboru Koshizuka

The importance of personal mobility data is widely recognized in various fields. However, the utilization of real personal mobility data raises privacy concerns. Therefore, it is crucial to generate pseudo personal mobility data that accurately reflects real-world mobility patterns while safeguarding user privacy. Nevertheless, existing methods for generating pseudo mobility data, mostly focusing on trip or trajectory generation, have limitations in capturing sufficient individual heterogeneity. To address these gaps, taking pseudo-person(avatar) as ground-zero, a novel individual-based human mobility generator named GeoAvatar has been proposed – which considering individual heterogeneity in spatial and temporal decision-making, incorporates demographic characteristics. Our method utilizes a deep generative model to generate heterogeneous individual life patterns, a variation inference model for inferring individual demographic characteristics, and a Bayesian-based approach for generating spatial choices considering individual demographic characteristics. Through our method, we have achieved generating realistic pseudo personal human mobility data - we evaluated the proposed method based on physical features – obeying common law of human mobility, activity features – showing diverse and realistic activities, and spatial-temporal characteristics – presenting high-accuracy in terms of temporal grid population and od-count, demonstrating its good performance, with both a big mobile phone GPS trajectory dataset from Tokyo Metropolis and a big mobile phone CDR dataset from Shanghai. Furthermore, this method maintains extensibility for broader applications, making it a promising framework for generating pseudo personal human mobility data.

{"title":"GeoAvatar: A big mobile phone positioning data-driven method for individualized pseudo personal mobility data generation","authors":"Peiran Li , Haoran Zhang , Wenjing Li , Dou Huang , Zhiling Guo , Jinyu Chen , Junxiang Zhang , Xuan Song , Pengjun Zhao , Jinyue Yan , Shibasaki Ryosuke , Noboru Koshizuka","doi":"10.1016/j.compenvurbsys.2025.102252","DOIUrl":"10.1016/j.compenvurbsys.2025.102252","url":null,"abstract":"<div><div>The importance of personal mobility data is widely recognized in various fields. However, the utilization of real personal mobility data raises privacy concerns. Therefore, it is crucial to generate pseudo personal mobility data that accurately reflects real-world mobility patterns while safeguarding user privacy. Nevertheless, existing methods for generating pseudo mobility data, mostly focusing on trip or trajectory generation, have limitations in capturing sufficient individual heterogeneity. To address these gaps, taking pseudo-person(avatar) as ground-zero, a novel individual-based human mobility generator named GeoAvatar has been proposed – which considering individual heterogeneity in spatial and temporal decision-making, incorporates demographic characteristics. Our method utilizes a deep generative model to generate heterogeneous individual life patterns, a variation inference model for inferring individual demographic characteristics, and a Bayesian-based approach for generating spatial choices considering individual demographic characteristics. Through our method, we have achieved generating realistic pseudo personal human mobility data - we evaluated the proposed method based on physical features – obeying common law of human mobility, activity features – showing diverse and realistic activities, and spatial-temporal characteristics – presenting high-accuracy in terms of temporal grid population and od-count, demonstrating its good performance, with both a big mobile phone GPS trajectory dataset from Tokyo Metropolis and a big mobile phone CDR dataset from Shanghai. Furthermore, this method maintains extensibility for broader applications, making it a promising framework for generating pseudo personal human mobility data.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102252"},"PeriodicalIF":7.1,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143577239","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}

引用次数: 0

Modelling active travel accessibility at the micro-scale using multi-source built environment data

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-03-07 DOI: 10.1016/j.compenvurbsys.2025.102270

Corin Staves , Irena Itova , Belen Zapata-Diomedi , Audrey de Nazelle , Jenna Panter , Lucy Gunn , Alan Both , Yuchen Li , Ismail Saadi , James Woodcock , S.M. Labib

Accessibility models explore how land use and transport systems interact to facilitate access to activities and daily needs. Existing applications generally model accessibility based on distance or travel time. For pedestrians and cyclists, the street-level environment (e.g., green visibility, streetside amenities, dedicated infrastructure) significantly influences people's willingness and ability to travel. Incorporating these features into accessibility models can help them to be more representative of active travellers' experienced environment.

This study presents a methodology for incorporating the street-level environment into active mode accessibility. First, micro-scale built environment data from multiple sources are harmonised into a high-resolution digital representation of the land use and transport system. Second, a compute-optimised framework is developed for modelling accessibility at the micro-scale (i.e., each dwelling separately) incorporating the street-level environment. The methods build upon the open geodatabase OpenStreetMap and open-source MATSim project, facilitating expandability and transferability to other contexts. We apply this methodology to develop policy-relevant accessibility indicators for Greater Manchester.

In the results, we observe that the street-level environment can cause accessibility indicators to vary at the micro-scale, especially in less connected neighbourhoods where the choice of routes is limited. We also observed that for cyclists, the accessibility advantage over walking reduces substantially when traffic stress is considered. Our findings support further adoption of micro-scale built environment data and high-resolution analysis methods for active travel accessibility modelling in research and practice.

{"title":"Modelling active travel accessibility at the micro-scale using multi-source built environment data","authors":"Corin Staves , Irena Itova , Belen Zapata-Diomedi , Audrey de Nazelle , Jenna Panter , Lucy Gunn , Alan Both , Yuchen Li , Ismail Saadi , James Woodcock , S.M. Labib","doi":"10.1016/j.compenvurbsys.2025.102270","DOIUrl":"10.1016/j.compenvurbsys.2025.102270","url":null,"abstract":"<div><div>Accessibility models explore how land use and transport systems interact to facilitate access to activities and daily needs. Existing applications generally model accessibility based on distance or travel time. For pedestrians and cyclists, the street-level environment (e.g., green visibility, streetside amenities, dedicated infrastructure) significantly influences people's willingness and ability to travel. Incorporating these features into accessibility models can help them to be more representative of active travellers' experienced environment.</div><div>This study presents a methodology for incorporating the street-level environment into active mode accessibility. First, micro-scale built environment data from multiple sources are harmonised into a high-resolution digital representation of the land use and transport system. Second, a compute-optimised framework is developed for modelling accessibility at the micro-scale (i.e., each dwelling separately) incorporating the street-level environment. The methods build upon the open geodatabase OpenStreetMap and open-source MATSim project, facilitating expandability and transferability to other contexts. We apply this methodology to develop policy-relevant accessibility indicators for Greater Manchester.</div><div>In the results, we observe that the street-level environment can cause accessibility indicators to vary at the micro-scale, especially in less connected neighbourhoods where the choice of routes is limited. We also observed that for cyclists, the accessibility advantage over walking reduces substantially when traffic stress is considered. Our findings support further adoption of micro-scale built environment data and high-resolution analysis methods for active travel accessibility modelling in research and practice.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102270"},"PeriodicalIF":7.1,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563599","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}

引用次数: 0

From theory to deep learning: Understanding the impact of geographic context factors on traffic violations

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-27 DOI: 10.1016/j.compenvurbsys.2025.102268

Hao Yang , X. Angela Yao , Farnoosh Roozkhosh , Ruowei Liu , Gengchen Mai

Traffic violations pose significant threats to road safety, often leading to accidents, injuries, fatalities, and property damage. While driving behavior results from a dynamic interaction among drivers, vehicles, and the environment, most studies have primarily focused on human factors, neglecting the critical influence of social and environmental elements of the geographical context. To address this gap, we first develop a theoretical framework that explores how vehicles, drivers, and environmental factors interact, with particular emphasis on the environmental impact on driving behavior and road safety. Building on this framework, we introduce TraVio, a multitask deep learning model that combines Graph Convolutional Networks (GCNs) with Multilayer Perceptrons (MLPs) to detect and classify traffic violations. Through a case study in Montgomery County, Maryland, we demonstrate that TraVio outperforms traditional machine learning baselines in both binary and multi-label classification tasks. The results underscore the critical role of geographic context and time factors in shaping traffic violations. This study not only provides a robust modeling technique for predicting traffic violations but also offers a theoretical foundation that can be extended to other driving-related themes, contributing to safer road environments through informed urban planning and policymaking.

{"title":"From theory to deep learning: Understanding the impact of geographic context factors on traffic violations","authors":"Hao Yang , X. Angela Yao , Farnoosh Roozkhosh , Ruowei Liu , Gengchen Mai","doi":"10.1016/j.compenvurbsys.2025.102268","DOIUrl":"10.1016/j.compenvurbsys.2025.102268","url":null,"abstract":"<div><div>Traffic violations pose significant threats to road safety, often leading to accidents, injuries, fatalities, and property damage. While driving behavior results from a dynamic interaction among drivers, vehicles, and the environment, most studies have primarily focused on human factors, neglecting the critical influence of social and environmental elements of the geographical context. To address this gap, we first develop a theoretical framework that explores how vehicles, drivers, and environmental factors interact, with particular emphasis on the environmental impact on driving behavior and road safety. Building on this framework, we introduce TraVio, a multitask deep learning model that combines Graph Convolutional Networks (GCNs) with Multilayer Perceptrons (MLPs) to detect and classify traffic violations. Through a case study in Montgomery County, Maryland, we demonstrate that TraVio outperforms traditional machine learning baselines in both binary and multi-label classification tasks. The results underscore the critical role of geographic context and time factors in shaping traffic violations. This study not only provides a robust modeling technique for predicting traffic violations but also offers a theoretical foundation that can be extended to other driving-related themes, contributing to safer road environments through informed urban planning and policymaking.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102268"},"PeriodicalIF":7.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509561","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}

引用次数: 0

A planning support framework to enable smart mobility: Integrating multi-objective spatial optimization and GIS to enhance commuting efficiency

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-27 DOI: 10.1016/j.compenvurbsys.2025.102269

Reza Mortaheb , Piotr Jankowski , Alan Murray

Planning support approaches can play a transformative role in shaping sustainable, resilient, and equitable urban landscapes that promote efficient mobility patterns. This paper develops a prescriptive framework that integrates Geographic Information Systems with a variant of the transportation problem to evaluate planning initiatives and inform land use policies and growth management strategies aimed at enhancing commuting efficiency at both local and regional levels. A multi-step approach is structured including 1) a multi-objective spatial optimization model that simulates the impacts of alterations to urban locational structure on work trip durations, capturing normative commuting patterns across three major workforce groups under varying urban growth scenarios; and 2) a modified gravity model that estimates regional commuting efficiency at the economic-sector level based on optimized inputs. Results indicate that this framework enables a critical evaluation of urban spatial configurations and corresponding commuting efficiency indicators under both conventional and alternative planning systems. The proposed framework also supports tactical and strategic land use and transportation planning, allowing planners and policymakers to analyze potential urban forms across different development scenarios, dissect commuting efficiency outcomes by industry, and identify sectors with critical spatial mismatches between job locations and housing. The ability to guide more balanced urban development and foster more efficient commuting patterns is demonstrated for Central Florida.

{"title":"A planning support framework to enable smart mobility: Integrating multi-objective spatial optimization and GIS to enhance commuting efficiency","authors":"Reza Mortaheb , Piotr Jankowski , Alan Murray","doi":"10.1016/j.compenvurbsys.2025.102269","DOIUrl":"10.1016/j.compenvurbsys.2025.102269","url":null,"abstract":"<div><div>Planning support approaches can play a transformative role in shaping sustainable, resilient, and equitable urban landscapes that promote efficient mobility patterns. This paper develops a prescriptive framework that integrates Geographic Information Systems with a variant of the transportation problem to evaluate planning initiatives and inform land use policies and growth management strategies aimed at enhancing commuting efficiency at both local and regional levels. A multi-step approach is structured including 1) a multi-objective spatial optimization model that simulates the impacts of alterations to urban locational structure on work trip durations, capturing normative commuting patterns across three major workforce groups under varying urban growth scenarios; and 2) a modified gravity model that estimates regional commuting efficiency at the economic-sector level based on optimized inputs. Results indicate that this framework enables a critical evaluation of urban spatial configurations and corresponding commuting efficiency indicators under both conventional and alternative planning systems. The proposed framework also supports tactical and strategic land use and transportation planning, allowing planners and policymakers to analyze potential urban forms across different development scenarios, dissect commuting efficiency outcomes by industry, and identify sectors with critical spatial mismatches between job locations and housing. The ability to guide more balanced urban development and foster more efficient commuting patterns is demonstrated for Central Florida.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102269"},"PeriodicalIF":7.1,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509560","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}

引用次数: 0

Interpreting core forms of urban morphology linked to urban functions with explainable graph neural network

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-24 DOI: 10.1016/j.compenvurbsys.2025.102267

Dongsheng Chen , Yu Feng , Xun Li , Mingya Qu , Peng Luo , Liqiu Meng

Understanding the high-order relationship between urban form and function is essential for modeling the underlying mechanisms of sustainable urban systems. Nevertheless, it is challenging to establish an accurate data representation for complex urban forms that are readily explicable in human terms. This study proposed the concept of core urban morphology representation and developed an explainable deep learning framework for explicably symbolizing complex urban forms into the novel representation, which we call CoMo. By interpretating the well-trained deep learning model with a stable weighted F1-score of 89.14 %, CoMo presents a promising approach for revealing links between urban function and urban form in terms of core urban morphology representation. Using Boston as a study area, we analyzed the core urban forms at the individual-building, block, and neighborhood level that are important to corresponding urban functions. The residential core forms follow a gradual morphological pattern along the urban spine, which is consistent with a center-urban-suburban transition. Furthermore, we prove that urban morphology directly affects land use efficiency, which has a significantly strong correlation with the location (R² = 0.721, p < 0.001). Overall, CoMo can explicably symbolize urban forms, provide evidence for the classic urban location theory, and offer mechanistic insights for digital twins.

{"title":"Interpreting core forms of urban morphology linked to urban functions with explainable graph neural network","authors":"Dongsheng Chen , Yu Feng , Xun Li , Mingya Qu , Peng Luo , Liqiu Meng","doi":"10.1016/j.compenvurbsys.2025.102267","DOIUrl":"10.1016/j.compenvurbsys.2025.102267","url":null,"abstract":"<div><div>Understanding the high-order relationship between urban form and function is essential for modeling the underlying mechanisms of sustainable urban systems. Nevertheless, it is challenging to establish an accurate data representation for complex urban forms that are readily explicable in human terms. This study proposed the concept of <strong>co</strong>re urban <strong>mo</strong>rphology representation and developed an explainable deep learning framework for explicably symbolizing complex urban forms into the novel representation, which we call <strong>CoMo</strong>. By interpretating the well-trained deep learning model with a stable weighted F1-score of 89.14 %, CoMo presents a promising approach for revealing links between urban function and urban form in terms of core urban morphology representation. Using Boston as a study area, we analyzed the core urban forms at the individual-building, block, and neighborhood level that are important to corresponding urban functions. The residential core forms follow a gradual morphological pattern along the urban spine, which is consistent with a center-urban-suburban transition. Furthermore, we prove that urban morphology directly affects land use efficiency, which has a significantly strong correlation with the location (R<sup>2</sup> = 0.721, <em>p</em> < 0.001). Overall, CoMo can explicably symbolize urban forms, provide evidence for the classic urban location theory, and offer mechanistic insights for digital twins.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"118 ","pages":"Article 102267"},"PeriodicalIF":7.1,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143478709","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}

引用次数: 0

Future scenarios for urban agriculture and food security in sub-Saharan Africa: Modelling the urban land-food system in an agent-based approach

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-18 DOI: 10.1016/j.compenvurbsys.2025.102258

Lisa-Marie Hemerijckx , Koen De Vos , Joseph Oseko Kaunda , Anton Van Rompaey

Food systems in sub-Saharan African cities are increasingly pressured by rapid urban sprawl and socio-economic changes. While land conversion from cropland to built-up area is limiting the opportunity for urban agriculture, food demand is rising because of population growth and changing diets. Meanwhile, socio-economic segregation – often associated with urbanization - can hinder access to food. For the case study of Kampala (Uganda), we spatiotemporally model the land-food system using an agent-based approach. Based on 747 household surveys, we recalibrated the Agent-based model of Social Segregation and Urban Expansion (ASSURE) by Vermeiren et al. (2016) and included food system dynamics to assess future trajectories (2020–2040) of Kampala's dependency on urban agriculture. While food that is both produced and consumed within the city is often considered the most resilient food source in times of crisis, we show that it is particularly this source that is threatened. Overall, about 10 % of the urban and peri-urban agricultural land in Kampala is projected to disappear by 2040. This may lead to decreased levels of food security and dietary diversity, particularly for households that strongly rely on urban agriculture. Information on the links between urban growth and local food provision is essential for planners who aim to develop strategies for more secure, just and sustainable African urban food systems.

{"title":"Future scenarios for urban agriculture and food security in sub-Saharan Africa: Modelling the urban land-food system in an agent-based approach","authors":"Lisa-Marie Hemerijckx , Koen De Vos , Joseph Oseko Kaunda , Anton Van Rompaey","doi":"10.1016/j.compenvurbsys.2025.102258","DOIUrl":"10.1016/j.compenvurbsys.2025.102258","url":null,"abstract":"<div><div>Food systems in sub-Saharan African cities are increasingly pressured by rapid urban sprawl and socio-economic changes. While land conversion from cropland to built-up area is limiting the opportunity for urban agriculture, food demand is rising because of population growth and changing diets. Meanwhile, socio-economic segregation – often associated with urbanization - can hinder access to food. For the case study of Kampala (Uganda), we spatiotemporally model the land-food system using an agent-based approach. Based on 747 household surveys, we recalibrated the Agent-based model of Social Segregation and Urban Expansion (ASSURE) by Vermeiren et al. (2016) and included food system dynamics to assess future trajectories (2020–2040) of Kampala's dependency on urban agriculture. While food that is both produced and consumed within the city is often considered the most resilient food source in times of crisis, we show that it is particularly this source that is threatened. Overall, about 10 % of the urban and peri-urban agricultural land in Kampala is projected to disappear by 2040. This may lead to decreased levels of food security and dietary diversity, particularly for households that strongly rely on urban agriculture. Information on the links between urban growth and local food provision is essential for planners who aim to develop strategies for more secure, just and sustainable African urban food systems.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"118 ","pages":"Article 102258"},"PeriodicalIF":7.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427783","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}

引用次数: 0

Plot-scale population estimation modeling based on residential plot form clustering and locational attractiveness analysis

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-18 DOI: 10.1016/j.compenvurbsys.2025.102257

Youmei Peng, Quan Liu

In many regions, urbanization has advanced to a stage that requires urban renewal, making precise population data essential for effective regional renewal and sustainable development. Therefore, this paper aims to disaggregate Jiedao-level (an administrative unit under the district) census population data to the Plot level. From an urban morphology perspective, the Gaussian Mixture Model (GMM) clustering algorithm was applied to classify the form of residential plots, assigning a type parameter for each type: the per capita housing area, to describe population density differences among the types. We then used Pearson correlation analysis to assess the relationship between POI density and population density at various bandwidths, identifying the optimal bandwidth for different POI types and calculating the overall POI density for each plot to evaluate its locational attractiveness. A regression model was established using per capita housing area, POI density, and total building area to derive population weight layers for estimating population at the plot level. The results of accuracy assessment show that using the morphological type parameter can effectively improve the estimation accuracy at plot scale, especially in areas with diverse land-use patterns and lower population density. However, our optimized locational attractiveness calculation method shows only a slight improvement to the method using a fixed bandwidth. This study develops a more accurate population estimation method of plot-level based on morphological classification， and highlights the population distribution characteristics of different types of residential plots, aiding urban decision-makers in developing targeted strategies for housing optimization and community resource allocation.

{"title":"Plot-scale population estimation modeling based on residential plot form clustering and locational attractiveness analysis","authors":"Youmei Peng, Quan Liu","doi":"10.1016/j.compenvurbsys.2025.102257","DOIUrl":"10.1016/j.compenvurbsys.2025.102257","url":null,"abstract":"<div><div>In many regions, urbanization has advanced to a stage that requires urban renewal, making precise population data essential for effective regional renewal and sustainable development. Therefore, this paper aims to disaggregate Jiedao-level (an administrative unit under the district) census population data to the Plot level. From an urban morphology perspective, the Gaussian Mixture Model (GMM) clustering algorithm was applied to classify the form of residential plots, assigning a type parameter for each type: the per capita housing area, to describe population density differences among the types. We then used Pearson correlation analysis to assess the relationship between POI density and population density at various bandwidths, identifying the optimal bandwidth for different POI types and calculating the overall POI density for each plot to evaluate its locational attractiveness. A regression model was established using per capita housing area, POI density, and total building area to derive population weight layers for estimating population at the plot level. The results of accuracy assessment show that using the morphological type parameter can effectively improve the estimation accuracy at plot scale, especially in areas with diverse land-use patterns and lower population density. However, our optimized locational attractiveness calculation method shows only a slight improvement to the method using a fixed bandwidth. This study develops a more accurate population estimation method of plot-level based on morphological classification， and highlights the population distribution characteristics of different types of residential plots, aiding urban decision-makers in developing targeted strategies for housing optimization and community resource allocation.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"118 ","pages":"Article 102257"},"PeriodicalIF":7.1,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427782","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}

引用次数: 0

Model-based estimation of the isolated impacts of urban expansion on projected streamflow values under varied climate scenarios

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-16 DOI: 10.1016/j.compenvurbsys.2025.102259

A. Botero-Acosta , M.L. Chu , C.L. Wu , G.F. McIsaac , J.H. Knouft

The use of computationally intensive hydrologic models under future climate scenarios has become a common practice to project water resource concerns in the coming decades. Under this approach, hydrologic models are parameterized and run under various climate forcings. Although urban areas are expected to expand during the time frame of these simulations, potentially impacting watershed hydrology, the uncertainty of forecasted streamflow is usually estimated based on the ensemble of climate scenarios, with minimal (if any) attention given to the uncertainty introduced by land transformations. The objective of this study is to quantify the Isolated Impacts on Projected Streamflow (IIPS) caused by urban expansion as climate changes in a watershed in the midwestern United States. IIPS time series were estimated as the difference between projected streamflows under future climate scenarios with and without urban expansion and weighted by the historical (1980–2010) monthly average. Two gradual and two abrupt urbanization scenarios, having equivalent developed areas by the end of the 21st century, were implemented. Results indicate that gradual urbanization could result in both increased (up to 26 %) and decreased (up to 16 %) projected streamflows, suggesting the increase in variability of extremes, with potential impacts on human and natural systems. Yearly minimum and maximum IIPS for all scenarios were found to be more likely to occur in summer and fall months, respectively. Impacts of the abrupt urban expansion were mainly observed in the cumulative IIPS and the ensemble variability of extreme IIPS. These results provide insights into the uncertainty of future streamflow estimates attributable to urban expansion.

{"title":"Model-based estimation of the isolated impacts of urban expansion on projected streamflow values under varied climate scenarios","authors":"A. Botero-Acosta , M.L. Chu , C.L. Wu , G.F. McIsaac , J.H. Knouft","doi":"10.1016/j.compenvurbsys.2025.102259","DOIUrl":"10.1016/j.compenvurbsys.2025.102259","url":null,"abstract":"<div><div>The use of computationally intensive hydrologic models under future climate scenarios has become a common practice to project water resource concerns in the coming decades. Under this approach, hydrologic models are parameterized and run under various climate forcings. Although urban areas are expected to expand during the time frame of these simulations, potentially impacting watershed hydrology, the uncertainty of forecasted streamflow is usually estimated based on the ensemble of climate scenarios, with minimal (if any) attention given to the uncertainty introduced by land transformations. The objective of this study is to quantify the Isolated Impacts on Projected Streamflow (IIPS) caused by urban expansion as climate changes in a watershed in the midwestern United States. IIPS time series were estimated as the difference between projected streamflows under future climate scenarios with and without urban expansion and weighted by the historical (1980–2010) monthly average. Two gradual and two abrupt urbanization scenarios, having equivalent developed areas by the end of the 21st century, were implemented. Results indicate that gradual urbanization could result in both increased (up to 26 %) and decreased (up to 16 %) projected streamflows, suggesting the increase in variability of extremes, with potential impacts on human and natural systems. Yearly minimum and maximum IIPS for all scenarios were found to be more likely to occur in summer and fall months, respectively. Impacts of the abrupt urban expansion were mainly observed in the cumulative IIPS and the ensemble variability of extreme IIPS. These results provide insights into the uncertainty of future streamflow estimates attributable to urban expansion.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"118 ","pages":"Article 102259"},"PeriodicalIF":7.1,"publicationDate":"2025-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422651","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}

引用次数: 0

Shortest covering paths and other covering walks: Refinements and prospects for subtour prevention

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-13 DOI: 10.1016/j.compenvurbsys.2024.102247

T.C. Matisziw

The shortest covering path problem (SCPP) is a network optimization model in which a least-cost route connecting an origin and destination that can be accessed by all demand nodes in a network is sought. Thus, it is applicable to transportation planning tasks such as designing routes for public transit and distribution systems. However, deriving optimal solutions to the SCPP can be challenging as an iterative solution approach is often required. Also, problems can arise in accounting for coverage of network nodes and in the handling of certain types of cycles. To this end, a family of model variants for the SCPP is proposed to remedy these problems and to assist with the identification of covering paths and other walks. Additionally, the case in which the origin node is also the destination node is incorporated into the SCPP framework. A flow-constrained SCPP that does not require an iterative solution process is then proposed to identify optimal walks of different types. The flow-constrained SCPP and its iterative counterpart are solved for all origin-destination pairs in a network and their relative computational characteristics are assessed. The results demonstrate that optimal solutions to the flow-constrained SCPP can be obtained more quickly than those obtained using the iterative approach. The results also provide further evidence of the relevance of cycles, particularly those involving U-turns, in solutions to network routing problems. Together, the proposed refinements, extensions, and documented computational experience will extend the applicability and the utility of the SCPP and its counterpart path covering models.

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引用次数: 0

Simulating household energy behavior diffusion using spatial microsimulation and econometric models

IF 7.1 1区地球科学 Q1 ENVIRONMENTAL STUDIES

Computers Environment and Urban Systems

Pub Date : 2025-02-08 DOI: 10.1016/j.compenvurbsys.2025.102256

Jianhua Zhang , Loes Bouman , Dimitris Ballas , Xiaolong Liu

Analyzing household energy behaviors from a spatial perspective is crucial for both policymakers and network operators. However, conducting spatial analysis at a fine scale presents significant challenges, primarily due to data scarcity. This article demonstrates the potential of integrating spatial microsimulation and spatial econometric models to address this issue. Specifically, we construct a static spatial microsimulation model to create a synthetic population for the entire Netherlands, simulating neighborhood-level spatial distributions of household energy behaviors. The results indicate significant neighborhood-level spatial heterogeneity in the adoption of energy behaviors. Subsequently, this simulated output is used as input for spatial econometric models to analyze the neighborhood-level spatial determinants of household energy behavior adoption. We identify the roles of both spatial endogenous effects and neighborhood-specific characteristics in contributing to the observed spatial variations in household energy behaviors.

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引用次数: 0