Pub Date : 2025-07-01Epub Date: 2025-03-28DOI: 10.1016/j.compenvurbsys.2025.102285
Wen Ji , Ke Han , Qian Ge
Vehicle-based mobile sensing is a new paradigm for urban data collection. Certain urban sensing scenarios require sensing vehicles for highly targeted monitoring, such as air pollutant and accident site investigation. A hallmark of these scenarios is that the points of interest (POIs) need to be repeatedly visited by a set of agents, whose routes should provide sufficient sensing coverage with coordinated overlap at certain important POIs. For these applications, this paper presents the open team orienteering problem with repeatable visits (OTOP-RV) and specifically tailors an adaptive large neighborhood search (ALNS) algorithm to address it. Test results on randomly generated datasets show that the ALNS significantly outperforms the greedy algorithm (by 7.2 % to 32.4 %), and a heuristic based on sequential orienteering problem (by about 6 %). Finally, a real-world air pollution sensing case study demonstrates the unique applicability of the OTOP-RV and the effectiveness of the proposed algorithms in enhancing sensing capabilities.
{"title":"Route planning of mobile sensing fleets for repeatable environmental monitoring tasks","authors":"Wen Ji , Ke Han , Qian Ge","doi":"10.1016/j.compenvurbsys.2025.102285","DOIUrl":"10.1016/j.compenvurbsys.2025.102285","url":null,"abstract":"<div><div>Vehicle-based mobile sensing is a new paradigm for urban data collection. Certain urban sensing scenarios require sensing vehicles for highly targeted monitoring, such as air pollutant and accident site investigation. A hallmark of these scenarios is that the points of interest (POIs) need to be repeatedly visited by a set of agents, whose routes should provide sufficient sensing coverage with coordinated overlap at certain important POIs. For these applications, this paper presents the <em>open team orienteering problem with repeatable visits</em> (OTOP-RV) and specifically tailors an adaptive large neighborhood search (ALNS) algorithm to address it. Test results on randomly generated datasets show that the ALNS significantly outperforms the greedy algorithm (by 7.2 % to 32.4 %), and a heuristic based on sequential orienteering problem (by about 6 %). Finally, a real-world air pollution sensing case study demonstrates the unique applicability of the OTOP-RV and the effectiveness of the proposed algorithms in enhancing sensing capabilities.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102285"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714890","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 : 2025-07-01Epub Date: 2025-04-08DOI: 10.1016/j.compenvurbsys.2025.102287
Clinton Stipek, Daniel Adams, Philipe Dias, Taylor Hauser, Viswadeep Lebakula, Alexander Sorokine, Justin Epting, Jessica Moehl, Robert Stewart
Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modeling building height at scale are hindered by two pervasive issues. First, there is no consistent approach to quantify what a high-rise building is at a macro scale, leaving researchers unable to accurately compare results across geographies and domains. Second, high-rise buildings represent a small fraction of the built environment, implying data imbalance challenges that negatively affect current approaches. This is a problem of practical relevance since information on high-rise buildings is important for studies on urban heat islands, population dynamics, and pollution dispersion. Here, we introduce a novel approach to map building height which first identifies two distinct distributions within the built environment, with one being composed of low-rise buildings and one composed of high-rise buildings. We then develop an ensemble scheme where discrete specialist models are trained for each subset of low-rise buildings and high-rise buildings to infer building height from morphology features. For experiments mapping heights of 4.85 million buildings in Japan, we show an increase of 34 % in accuracy within error when compared to the current state-of-the-art when modeling high-rise buildings, which based on KNN experimentation we define as any building . Our findings show that such an ensemble framework outperforms the current state-of-the-art approaches, which is especially relevant in relation to inferring height for high-rise buildings, a prominent issue of existing approaches for mapping the built environment.
{"title":"A segmented approach to modeling building height: Delineating high-rise and low-rise buildings for enhanced height estimation","authors":"Clinton Stipek, Daniel Adams, Philipe Dias, Taylor Hauser, Viswadeep Lebakula, Alexander Sorokine, Justin Epting, Jessica Moehl, Robert Stewart","doi":"10.1016/j.compenvurbsys.2025.102287","DOIUrl":"10.1016/j.compenvurbsys.2025.102287","url":null,"abstract":"<div><div>Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modeling building height at scale are hindered by two pervasive issues. First, there is no consistent approach to quantify what a high-rise building is at a macro scale, leaving researchers unable to accurately compare results across geographies and domains. Second, high-rise buildings represent a small fraction of the built environment, implying data imbalance challenges that negatively affect current approaches. This is a problem of practical relevance since information on high-rise buildings is important for studies on urban heat islands, population dynamics, and pollution dispersion. Here, we introduce a novel approach to map building height which first identifies two distinct distributions within the built environment, with one being composed of low-rise buildings and one composed of high-rise buildings. We then develop an ensemble scheme where discrete specialist models are trained for each subset of low-rise buildings and high-rise buildings to infer building height from morphology features. For experiments mapping heights of 4.85 million buildings in Japan, we show an increase of 34 % in accuracy within <span><math><mn>3</mn><mi>m</mi></math></span> error when compared to the current state-of-the-art when modeling high-rise buildings, which based on KNN experimentation we define as any building <span><math><mo>></mo><mn>12</mn><mi>m</mi></math></span>. Our findings show that such an ensemble framework outperforms the current state-of-the-art approaches, which is especially relevant in relation to inferring height for high-rise buildings, a prominent issue of existing approaches for mapping the built environment.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102287"},"PeriodicalIF":7.1,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791057","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 : 2025-07-01Epub Date: 2025-02-27DOI: 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":"2025-07-01","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}
Pub Date : 2025-06-01Epub Date: 2025-02-13DOI: 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.
{"title":"Shortest covering paths and other covering walks: Refinements and prospects for subtour prevention","authors":"T.C. Matisziw","doi":"10.1016/j.compenvurbsys.2024.102247","DOIUrl":"10.1016/j.compenvurbsys.2024.102247","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"118 ","pages":"Article 102247"},"PeriodicalIF":7.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395418","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 : 2025-06-01Epub Date: 2025-02-24DOI: 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.
了解城市形态和功能之间的高阶关系对于建立可持续城市系统的潜在机制至关重要。然而，为复杂的城市形式建立一个准确的数据表示是具有挑战性的，这些形式很容易用人类的术语来解释。本研究提出了核心城市形态表征的概念，并开发了一个可解释的深度学习框架，用于将复杂的城市形态解释为新的表征，我们称之为CoMo。CoMo通过解释训练良好的深度学习模型，其加权f1得分稳定在89.14%，为揭示城市功能和城市形态之间的联系提供了一种很有前途的方法，可以从核心城市形态表征的角度来揭示城市功能和城市形态之间的联系。我们以波士顿为研究区域，分析了个体建筑、街区和社区层面的核心城市形态，这些核心城市形态对相应的城市功能至关重要。住宅核心形式遵循城市脊梁的渐变形态模式，与中心-城市-郊区过渡相一致。此外，我们还证明了城市形态直接影响土地利用效率，其与区位具有显著的强相关性(R2 = 0.721, p <；0.001)。总体而言，CoMo可以解释地象征城市形态，为经典的城市区位理论提供证据，并为数字孪生提供机制见解。
{"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":"2025-06-01","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}
Pub Date : 2025-06-01Epub Date: 2025-02-18DOI: 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":"2025-06-01","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}
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":"2025-06-01","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}
Pub Date : 2025-06-01Epub Date: 2025-02-18DOI: 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":"2025-06-01","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}
Pub Date : 2025-04-01Epub 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":"2025-04-01","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 : 2025-04-01Epub 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":"2025-04-01","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}
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