Pub Date : 2025-08-13DOI: 10.1016/j.cacint.2025.100234
Ali Najah Ahmed , Nouar AlDahoul , Nurhanani A. Aziz , Y.F. Huang , Mohsen Sherif , Ahmed El-Shafie
With the global population now exceeding 8 billion and 4.5 billion of whom residing in urban areas, rapid urbanization has contributed to a range of environmental and ecological challenges, notably the Urban Heat Island (UHI) effect. According to statistical data, the ten hottest years on record occurred between 2013 and 2022, underscoring the urgency of addressing urban heat issues. This study provides a comprehensive review of research on the UHI effect, analysing and classifying studies that utilize a variety of input–output datasets. It also examines predictive methods used to estimate UHI intensity, categorizing them into conventional machine learning (ML) algorithms, deep learning (DL) models, and hybrid approaches. While conventional ML algorithms remain widely used, DL and hybrid models have shown superior performance in predictive accuracy. This review aims to enhance understanding of recent advancements in UHI prediction techniques, identify limitations in current methodologies, and propose directions for future research.
{"title":"The urban heat Island effect: A review on predictive approaches using artificial intelligence models","authors":"Ali Najah Ahmed , Nouar AlDahoul , Nurhanani A. Aziz , Y.F. Huang , Mohsen Sherif , Ahmed El-Shafie","doi":"10.1016/j.cacint.2025.100234","DOIUrl":"10.1016/j.cacint.2025.100234","url":null,"abstract":"<div><div>With the global population now exceeding 8 billion and 4.5 billion of whom residing in urban areas, rapid urbanization has contributed to a range of environmental and ecological challenges, notably the Urban Heat Island (UHI) effect. According to statistical data, the ten hottest years on record occurred between 2013 and 2022, underscoring the urgency of addressing urban heat issues. This study provides a comprehensive review of research on the UHI effect, analysing and classifying studies that utilize a variety of input–output datasets. It also examines predictive methods used to estimate UHI intensity, categorizing them into conventional machine learning (ML) algorithms, deep learning (DL) models, and hybrid approaches. While conventional ML algorithms remain widely used, DL and hybrid models have shown superior performance in predictive accuracy. This review aims to enhance understanding of recent advancements in UHI prediction techniques, identify limitations in current methodologies, and propose directions for future research.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100234"},"PeriodicalIF":3.8,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144866523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-07DOI: 10.1016/j.cacint.2025.100231
Avikal Somvanshi, Joachim Schulze, Shahrzad Talebsafa
Climate change has made heatwaves common during German summers. The phenomenon of urban heat islands (UHIs) only worsens the adverse effects of heatwaves, especially for the elderly, defined as population aged 65+ for this study. These issues need immediate attention and redress to avoid catastrophic consequences. For this research investigation, the city of Darmstadt in Germany has been mapped for heatwaves and UHIs. An assessment has been done to derive interrelationships between them and characteristics of the built environment. Rather than applying a generic Local Climate Zones classification, the built environment has been assessed by clustering the city of Darmstadt based on the UrbanReNet catalogue—a more contextual and nuanced urban typologies-based classification developed specifically for German cities. The findings of this study show that certain urban typologies are more prone to overheating, and can also significantly influence the thermal conditions of their surroundings. The latter is established by a novel multivariate regression that employs the H3 hierarchical geospatial indexing system. Further, mapping of the elderly in Darmstadt revealed that about half of them reside in settings that are at risk of overheating during a heatwave. This study provides a novel methodology to delineate areas at maximum risk of overheating. This can help prioritize heatproofing efforts to minimize the risk of excessive heat stress by focusing on the most vulnerable.
{"title":"Urban heat typologies: impact of heatwaves on urban built environment and heat stress risk to the elderly in Darmstadt, Germany","authors":"Avikal Somvanshi, Joachim Schulze, Shahrzad Talebsafa","doi":"10.1016/j.cacint.2025.100231","DOIUrl":"10.1016/j.cacint.2025.100231","url":null,"abstract":"<div><div>Climate change has made heatwaves common during German summers. The phenomenon of urban heat islands (UHIs) only worsens the adverse effects of heatwaves, especially for the elderly, defined as population aged 65+ for this study. These issues need immediate attention and redress to avoid catastrophic consequences. For this research investigation, the city of Darmstadt in Germany has been mapped for heatwaves and UHIs. An assessment has been done to derive interrelationships between them and characteristics of the built environment. Rather than applying a generic Local Climate Zones classification, the built environment has been assessed by clustering the city of Darmstadt based on the UrbanReNet catalogue—a more contextual and nuanced urban typologies-based classification developed specifically for German cities. The findings of this study show that certain urban typologies are more prone to overheating, and can also significantly influence the thermal conditions of their surroundings. The latter is established by a novel multivariate regression that employs the H3 hierarchical geospatial indexing system. Further, mapping of the elderly in Darmstadt revealed that about half of them reside in settings that are at risk of overheating during a heatwave. This study provides a novel methodology to delineate areas at maximum risk of overheating. This can help prioritize heatproofing efforts to minimize the risk of excessive heat stress by focusing on the most vulnerable.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100231"},"PeriodicalIF":3.8,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-05DOI: 10.1016/j.cacint.2025.100232
M. Matias , G. Mills , T. Silva , C. Girotti , A. Lopes
The urban heat island (UHI), which describes the warmer temperature over urban landscape, is the most studied climate effect of cities. Most studies focus on the surface and canopy layers, particularly in common urban configurations such as street canyons. The causes of the UHI include aspects of physical form, fabric and of functions and, while urban forms are treated as fixed (over short time periods), functions are considered dynamic. In this context, the thermal and radiative properties of street facets like roads are critical urban canopy parameters (UCPs) that are used to understand heat storage and surface-air exchanges. However, the role of vehicles, especially parked ones, in modifying these surface properties and associated UCPs has been largely overlooked. This short contribution examines the impact of parked and mobile vehicles in cities using data from Lisbon, Portugal. Our findings highlight that parked vehicles significantly alter surface thermal properties in densely built areas, where road coverage is extensive and UHI intensity is greatest. These insights underscore the need to consider parked vehicles in urban heat island studies and the potential for spatially targeted mitigation strategies, such as restricting parking in identified hotspots, constructing shading structures, and promoting light, over dark, coloured vehicles.
{"title":"The underestimated impact of parked cars in urban warming.","authors":"M. Matias , G. Mills , T. Silva , C. Girotti , A. Lopes","doi":"10.1016/j.cacint.2025.100232","DOIUrl":"10.1016/j.cacint.2025.100232","url":null,"abstract":"<div><div>The urban heat island (UHI), which describes the warmer temperature over urban landscape, is the most studied climate effect of cities. Most studies focus on the surface and canopy layers, particularly in common urban configurations such as street canyons. The causes of the UHI include aspects of physical form, fabric and of functions and, while urban forms are treated as fixed (over short time periods), functions are considered dynamic. In this context, the thermal and radiative properties of street facets like roads are critical urban canopy parameters (UCPs) that are used to understand heat storage and surface-air exchanges. However, the role of vehicles, especially parked ones, in modifying these surface properties and associated UCPs has been largely overlooked. This short contribution examines the impact of parked and mobile vehicles in cities using data from Lisbon, Portugal. Our findings highlight that parked vehicles significantly alter surface thermal properties in densely built areas, where road coverage is extensive and UHI intensity is greatest. These insights underscore the need to consider parked vehicles in urban heat island studies and the potential for spatially targeted mitigation strategies, such as restricting parking in identified hotspots, constructing shading structures, and promoting light, over dark, coloured vehicles.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100232"},"PeriodicalIF":3.8,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Worldwide, flood resilience strategies are increasingly moving from theoretical frameworks to on-the-ground application, especially in regions grappling with climate change and rapid urbanization. North African coastal cities face heightened flood risks driven by intensified rainfall, sea-level rise, and significant land-use transitions. This paper introduces a contextual flood resilience framework − encompassing governance, socio-economic, and environmental dimensions − and applies it to the city of Jijel, Algeria. Integrating GIS-based land-use analyses with stakeholder surveys and policy reviews, the study identifies institutional fragmentation, outdated urban planning, and informal housing as key drivers of vulnerability. Results suggest that strengthening legal and institutional frameworks, investing in adaptive infrastructure, and fostering collaborative governance are critical for long-term flood resilience. In offering targeted recommendations for North African coastal settings, this research underscores the value of a multidimensional, context-sensitive approach to addressing flood risks across a rapidly changing urban landscape.
{"title":"Advancing flood resilience in North African coastal Cities: A contextual analysis of Jijel, Algeria","authors":"Omayma Chabou , Youcef Lazri , Simona Mannucci , Adriana Ciardiello , Federica Rosso , Marco Ferrero","doi":"10.1016/j.cacint.2025.100233","DOIUrl":"10.1016/j.cacint.2025.100233","url":null,"abstract":"<div><div>Worldwide, flood resilience strategies are increasingly moving from theoretical frameworks to on-the-ground application, especially in regions grappling with climate change and rapid urbanization. North African coastal cities face heightened flood risks driven by intensified rainfall, sea-level rise, and significant land-use transitions. This paper introduces a contextual flood resilience framework − encompassing governance, socio-economic, and environmental dimensions − and applies it to the city of Jijel, Algeria. Integrating GIS-based land-use analyses with stakeholder surveys and policy reviews, the study identifies institutional fragmentation, outdated urban planning, and informal housing as key drivers of vulnerability. Results suggest that strengthening legal and institutional frameworks, investing in adaptive infrastructure, and fostering collaborative governance are critical for long-term flood resilience. In offering targeted recommendations for North African coastal settings, this research underscores the value of a multidimensional, context-sensitive approach to addressing flood risks across a rapidly changing urban landscape.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100233"},"PeriodicalIF":3.8,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-25DOI: 10.1016/j.cacint.2025.100229
Gideon Baffoe , Philip Antwi-Agyei
Urban agriculture (UA) is increasingly recognized as a potent strategy for addressing contemporary urban challenges, particularly in the developing world, where rapid urbanization and climate change impacts are rising. However, the role and the extent to which UA has been integrated into national strategic policies and programmes remain underexplored in African cities, especially Accra, Ghana. This paper explores the role of UA in building climate resilience in Accra, Ghana, from the perspective of policymakers. It critically examines the extent to which UA has been integrated into national policy frameworks and identifies the barriers undermining its broader adoption and implementation. We employ a mixed-methods approach, drawing on stakeholder interviews, document analysis, and policy reviews to provide empirical insights while guided by resilience theory and the political ecology framework as analytical lenses. Stakeholder perspectives indicate that UA contributes to resilience through localized food production that supports nutritional security, the use of treated wastewater to enhance water efficiency, and the maintenance of green cover that buffers against urban heat and flooding. However, its integration into policy frameworks remains limited due to challenges such as weak institutional coordination, competing economic priorities, and governance inefficiencies. The study notes that UA holds transformative potential for climate resilience in Ghana, but its success hinges on strategic policy integration and robust implementation mechanisms. The paper proposes actionable strategies, including the formal recognition of UA zones, improved cross-agency and ministerial coordination, and the incorporation of farmer associations into decision-making processes.
{"title":"Rethinking the potential of urban agriculture as a climate resilience strategy: Evidence from Accra, Ghana","authors":"Gideon Baffoe , Philip Antwi-Agyei","doi":"10.1016/j.cacint.2025.100229","DOIUrl":"10.1016/j.cacint.2025.100229","url":null,"abstract":"<div><div>Urban agriculture (UA) is increasingly recognized as a potent strategy for addressing contemporary urban challenges, particularly in the developing world, where rapid urbanization and climate change impacts are rising. However, the role and the extent to which UA has been integrated into national strategic policies and programmes remain underexplored in African cities, especially Accra, Ghana. This paper explores the role of UA in building climate resilience in Accra, Ghana, from the perspective of policymakers. It critically examines the extent to which UA has been integrated into national policy frameworks and identifies the barriers undermining its broader adoption and implementation. We employ a mixed-methods approach, drawing on stakeholder interviews, document analysis, and policy reviews to provide empirical insights while guided by resilience theory and the political ecology framework as analytical lenses. Stakeholder perspectives indicate that UA contributes to resilience through localized food production that supports nutritional security, the use of treated wastewater to enhance water efficiency, and the maintenance of green cover that buffers against urban heat and flooding. However, its integration into policy frameworks remains limited due to challenges such as weak institutional coordination, competing economic priorities, and governance inefficiencies. The study notes that UA holds transformative potential for climate resilience in Ghana, but its success hinges on strategic policy integration and robust implementation mechanisms. The paper proposes actionable strategies, including the formal recognition of UA zones, improved cross-agency and ministerial coordination, and the incorporation of farmer associations into decision-making processes.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100229"},"PeriodicalIF":3.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144750286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-21DOI: 10.1016/j.cacint.2025.100228
Lihua Yang , Jie Wang , Songwen Yang , Mingming Wang , Long Li , Tie Chen , Liang Feng
In the calibration process of urban flood and non-point source pollution models, obtaining sufficient site-specific sensitive parameters and their variation trends remains challenging. In this study, a modified Morris screening method was established and used to evaluate the parameters of the green roof module in the SWMM model. This method involved fixing the values of all other parameters while varying a selected parameter X(i) within its defined range, applying multiple iterations of changes at a fixed percentage (10 %) to compute the corresponding model output values. The aim was to specify the impact and significance of hydrological parameters on runoff volume cut-off and water quality indicators under different return periods, and to further improve the prediction accuracy of the SWMM in simulating real rainfall events. Results revealed that Soil-T (soil layer thickness) and Surface-BH (surface layer storage depth) exhibited the highest sensitivity to total runoff production. Specifically, the sensitivity values of Soil-T exceeded 1.0 under 0.5-year and 1-year return periods, indicating its dominant role in runoff generation, while Surface-BH demonstrated a sensitivity value close to 2.0 at 0.5-year return period, showing its strong impact on peak flow. For these high-sensitivity parameters, the manual trial-and-error method was used for parameter refinement. Optimal simulation accuracy (ENS > 0.75) was achieved when Soil-T and Surface-BH were set within ranges of 86–95 mm and 18–22 mm, respectively, across six representative rainfall events. This study provides a new method to determine the optimal parameter combinations for calibrating the SWMM model, and its high accuracy offers a scientific basis for design and optimization of urban drainage systems, particularly in response to extreme rainfall events, which is helpful to the sustainability and resilience of cities.
{"title":"A modified Morris screening protocol for sensitivity analysis and calibration of green roof parameters in SWMM","authors":"Lihua Yang , Jie Wang , Songwen Yang , Mingming Wang , Long Li , Tie Chen , Liang Feng","doi":"10.1016/j.cacint.2025.100228","DOIUrl":"10.1016/j.cacint.2025.100228","url":null,"abstract":"<div><div>In the calibration process of urban flood and non-point source pollution models, obtaining sufficient site-specific sensitive parameters and their variation trends remains challenging. In this study, a modified Morris screening method was established and used to evaluate the parameters of the green roof module in the SWMM model. This method involved fixing the values of all other parameters while varying a selected parameter X(i) within its defined range, applying multiple iterations of changes at a fixed percentage (10 %) to compute the corresponding model output values. The aim was to specify the impact and significance of hydrological parameters on runoff volume cut-off and water quality indicators under different return periods, and to further improve the prediction accuracy of the SWMM in simulating real rainfall events. Results revealed that Soil-T (soil layer thickness) and Surface-BH (surface layer storage depth) exhibited the highest sensitivity to total runoff production. Specifically, the sensitivity values of Soil-T exceeded 1.0 under 0.5-year and 1-year return periods, indicating its dominant role in runoff generation, while Surface-BH demonstrated a sensitivity value close to 2.0 at 0.5-year return period, showing its strong impact on peak flow. For these high-sensitivity parameters, the manual trial-and-error method was used for parameter refinement. Optimal simulation accuracy (E<sub>NS</sub> > 0.75) was achieved when Soil-T and Surface-BH were set within ranges of 86–95 mm and 18–22 mm, respectively, across six representative rainfall events. This study provides a new method to determine the optimal parameter combinations for calibrating the SWMM model, and its high accuracy offers a scientific basis for design and optimization of urban drainage systems, particularly in response to extreme rainfall events, which is helpful to the sustainability and resilience of cities.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100228"},"PeriodicalIF":3.9,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-16DOI: 10.1016/j.cacint.2025.100227
Ruci Wang , Yuji Murayama , Fei Liu , Xinmin Zhang , Hao Hou , Takehiro Morimoto , Ahmed Derdouri
The spatial composition and three-dimensional (3D) configuration of buildings significantly influence land surface temperature (LST), playing a key role in urban heat island (UHI) mitigation and sustainable urban development. However, systematically quantifying these effects remains challenging due to the limitations in data resolution. This study addresses this gap by analyzing LST variations in six representative urban areas in central Tokyo, incorporating multi-source remote sensing data and detailed building information. We applied spatial analysis and a random forest regression model to assess the relative importance of building characteristics on LST across different urban morphologies. The results indicate that building height and volume are negatively correlated with LST, suggesting that taller buildings with larger volumes may contribute to lower surface temperatures primarily through increased shading. In central Tokyo, urban planning regulations require that taller buildings meet specific Floor Area Ratio (FAR) and setback standards, particularly along major roads. These regulations ensure greater spacing and access to sunlight, which can also facilitate localized airflow. As such, the observed cooling effect may result from a combination of shading and planning-induced ventilation conditions, contingent upon building arrangement and surrounding open space. In contrast, higher building density and greater building coverage lead to increased LST, particularly in compact, low-rise residential areas. Among all variables, building height, volume, and density emerged as the most influential factors affecting LST, highlighting the critical role of urban morphology in regulating thermal environments. These findings provide quantitative insights into how 3D urban structures impact LST, offering evidence-based guidance for optimizing urban planning strategies to mitigate UHI effects. The insights gained from central Tokyo can be extended to inform sustainable urban development in other high-density metropolitan areas worldwide.
{"title":"Impact of urban morphology on land surface temperature: A case study of the central Tokyo, Japan","authors":"Ruci Wang , Yuji Murayama , Fei Liu , Xinmin Zhang , Hao Hou , Takehiro Morimoto , Ahmed Derdouri","doi":"10.1016/j.cacint.2025.100227","DOIUrl":"10.1016/j.cacint.2025.100227","url":null,"abstract":"<div><div>The spatial composition and three-dimensional (3D) configuration of buildings significantly influence land surface temperature (LST), playing a key role in urban heat island (UHI) mitigation and sustainable urban development. However, systematically quantifying these effects remains challenging due to the limitations in data resolution. This study addresses this gap by analyzing LST variations in six representative urban areas in central Tokyo, incorporating multi-source remote sensing data and detailed building information. We applied spatial analysis and a random forest regression model to assess the relative importance of building characteristics on LST across different urban morphologies. The results indicate that building height and volume are negatively correlated with LST, suggesting that taller buildings with larger volumes may contribute to lower surface temperatures primarily through increased shading. In central Tokyo, urban planning regulations require that taller buildings meet specific Floor Area Ratio (FAR) and setback standards, particularly along major roads. These regulations ensure greater spacing and access to sunlight, which can also facilitate localized airflow. As such, the observed cooling effect may result from a combination of shading and planning-induced ventilation conditions, contingent upon building arrangement and surrounding open space. In contrast, higher building density and greater building coverage lead to increased LST, particularly in compact, low-rise residential areas. Among all variables, building height, volume, and density emerged as the most influential factors affecting LST, highlighting the critical role of urban morphology in regulating thermal environments. These findings provide quantitative insights into how 3D urban structures impact LST, offering evidence-based guidance for optimizing urban planning strategies to mitigate UHI effects. The insights gained from central Tokyo can be extended to inform sustainable urban development in other high-density metropolitan areas worldwide.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100227"},"PeriodicalIF":3.9,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144654092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-11DOI: 10.1016/j.cacint.2025.100226
Gomez Raimundo Elias , Maria Gabriela Miño
The article investigates the spatial footprints of anthropogenic emissions and infrastructures in Arcos de Valdevez, Portugal, and their association with the social composition of its parishes during the last years (2021–2023). Through the analysis of air quality, electric nightlight radiation, building age, and road networks, the research establishes connections between these physical footprints and the economic and social composition of the resident population. The study employs satellite imagery, open-access data, and the Portuguese Census of 2021 to conduct a Principal Component Analysis (PCA) and a Mixed Classification (MC), allowing for the spatial mapping of these relationships. By examining nightlight radiance intensity, road and building density, and pollutants such particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), Carbon Monoxide (CO), ozone (O3, tropospheric), and ammonia (NH3), the study highlights the unequal distribution of physical imprints of social and economic activities shaping the environment. The findings examine the transformed environment affecting quality of life, identifying distinct classes of areas characterised by specific configurations of air pollution, infrastructure development, nocturnal electric radiance, and the social composition of residents.
本文调查了葡萄牙阿尔科斯·德瓦尔德韦兹（Arcos de Valdevez）人为排放和基础设施的空间足迹，以及它们与过去几年（2021-2023）教区社会构成的关系。通过对空气质量、夜间电灯辐射、建筑年龄和道路网络的分析，研究建立了这些物理足迹与常住人口的经济和社会构成之间的联系。该研究利用卫星图像、开放获取数据和2021年葡萄牙人口普查数据进行主成分分析（PCA）和混合分类（MC），从而对这些关系进行空间映射。通过考察夜光辐射强度、道路和建筑密度，以及颗粒物（PM2.5和PM10）、二氧化氮（NO2）、二氧化硫（SO2）、一氧化碳（CO）、臭氧（O3，对流层）和氨（NH3）等污染物，该研究强调了社会和经济活动对环境形成的物理印记分布不均。研究结果考察了改变后的环境对生活质量的影响，根据空气污染、基础设施发展、夜间电辐射和居民社会构成的特定配置，确定了不同类别的地区。
{"title":"Extensive objectified footprints: A multidimensional approach to spatial inequalities","authors":"Gomez Raimundo Elias , Maria Gabriela Miño","doi":"10.1016/j.cacint.2025.100226","DOIUrl":"10.1016/j.cacint.2025.100226","url":null,"abstract":"<div><div>The article investigates the spatial footprints of anthropogenic emissions and infrastructures in Arcos de Valdevez, Portugal, and their association with the social composition of its parishes during the last years (2021–2023). Through the analysis of air quality, electric nightlight radiation, building age, and road networks, the research establishes connections between these physical footprints and the economic and social composition of the resident population. The study employs satellite imagery, open-access data, and the Portuguese Census of 2021 to conduct a Principal Component Analysis (PCA) and a Mixed Classification (MC), allowing for the spatial mapping of these relationships. By examining nightlight radiance intensity, road and building density, and pollutants such particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), Carbon Monoxide (CO), ozone (O3, tropospheric), and ammonia (NH3), the study highlights the unequal distribution of physical imprints of social and economic activities shaping the environment. The findings examine the transformed environment affecting quality of life, identifying distinct classes of areas characterised by specific configurations of air pollution, infrastructure development, nocturnal electric radiance, and the social composition of residents.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100226"},"PeriodicalIF":3.9,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-08DOI: 10.1016/j.cacint.2025.100225
Berfin Eren, Mehmet Emin Şalgamcıoğlu
Diyarbakır, located in southeastern Turkey, is known for its rich history and unique urban layout. The Suriçi region, which functions as the historic heart of Diyarbakır, has undergone significant changes over the years. In particular, developments over the last century have begun to reshape Suriçi’s spatial identity, which has evolved through historical processes influenced by spatial experiences. As a result, two distinct morphologies have emerged in the city: formation and deterioration. The shift between these two morphologies has fostered urban resilience. This paper introduces comprehensive, multi-faceted methods for measuring resilience based on space syntax theory and investigates resilience concepts through the relationships between space and society across various scales and time periods. Examining resilience at the urban scale through the street networks of different historical periods, produced via space syntax analysis, facilitates the formulation and analysis of patterns in urban movement, interactions, and past socio-economic activities. At the building scale, space syntax analysis reveals the spatial patterns of the altered morphological characteristics of traditional houses. It evaluates how these modified layouts reflect social, cultural, and political realities, and how they differ from the originally designed houses in spatial terms. The analysis of the city shows that while the overall position of the central area remains relatively stable, its morphology undergoes transformations. Traditional houses have retained certain features from their original designs; however, they have experienced modifications, such as subdivisions into multiple houses and changes in spatial arrangement. The study’s innovative integration of diachronic spatial analysis with socio-political context enriches the field by providing a more comprehensive model for assessing and forecasting urban resilience in historically significant areas, potentially guiding more effective preservation and development strategies.
{"title":"Scale, state and the city: Transformation of Diyarbakır, Suriçi region through the framework of spatial morphology and urban resilience","authors":"Berfin Eren, Mehmet Emin Şalgamcıoğlu","doi":"10.1016/j.cacint.2025.100225","DOIUrl":"10.1016/j.cacint.2025.100225","url":null,"abstract":"<div><div>Diyarbakır, located in southeastern Turkey, is known for its rich history and unique urban layout. The Suriçi region, which functions as the historic heart of Diyarbakır, has undergone significant changes over the years. In particular, developments over the last century have begun to reshape Suriçi’s spatial identity, which has evolved through historical processes influenced by spatial experiences. As a result, two distinct morphologies have emerged in the city: formation and deterioration. The shift between these two morphologies has fostered urban resilience. This paper introduces comprehensive, multi-faceted methods for measuring resilience based on space syntax theory and investigates resilience concepts through the relationships between space and society across various scales and time periods. Examining resilience at the urban scale through the street networks of different historical periods, produced via space syntax analysis, facilitates the formulation and analysis of patterns in urban movement, interactions, and past socio-economic activities. At the building scale, space syntax analysis reveals the spatial patterns of the altered morphological characteristics of traditional houses. It evaluates how these modified layouts reflect social, cultural, and political realities, and how they differ from the originally designed houses in spatial terms. The analysis of the city shows that while the overall position of the central area remains relatively stable, its morphology undergoes transformations. Traditional houses have retained certain features from their original designs; however, they have experienced modifications, such as subdivisions into multiple houses and changes in spatial arrangement. The study’s innovative integration of diachronic spatial analysis with socio-political context enriches the field by providing a more comprehensive model for assessing and forecasting urban resilience in historically significant areas, potentially guiding more effective preservation and development strategies.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100225"},"PeriodicalIF":3.9,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144623304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-01DOI: 10.1016/j.cacint.2025.100223
Feng Han , Meiqu Lu , Donghong Qin , Guitao Zheng , Guihong Zeng , Yan Tan , Zhongyang Wu , Haijian Lu , Jun Wang , Yirong Deng , Hui He
Interpreting the drivers of housing price dynamics is essential for promoting sustainable urban development, particularly in rapidly urbanizing cities in China. We adopted a data-driven approach by integrating Random Forest (RF) with SHAP (SHapley Additive Explanations) to enhance model interpretability and uncover non-linear relationships. A comprehensive dataset of 2,508 residential communities in South China was compiled using web-crawled property attributes and GIS-derived geospatial indicators. The RF model achieved a robust performance, with an average training R2 of 0.965 and testing R2 of 0.742. SHAP values were used to quantify the marginal contribution of each feature to housing price predictions, revealing that location-based factors and environmental attributes were the most influential. The model also identified price volatility in regions with high standard deviations, offering a new dimension for spatial housing risk assessment. The findings offer practical implications for policymakers aiming to stabilize housing markets, improve affordability, and guide data-informed infrastructure investments. The study also demonstrates the utility of explainable AI techniques in advancing sustainable urban development research.
{"title":"Exploring housing price dynamics in sustainable cities through a cooperated big data driven machine learning method: case study on a typical city in China","authors":"Feng Han , Meiqu Lu , Donghong Qin , Guitao Zheng , Guihong Zeng , Yan Tan , Zhongyang Wu , Haijian Lu , Jun Wang , Yirong Deng , Hui He","doi":"10.1016/j.cacint.2025.100223","DOIUrl":"10.1016/j.cacint.2025.100223","url":null,"abstract":"<div><div>Interpreting the drivers of housing price dynamics is essential for promoting sustainable urban development, particularly in rapidly urbanizing cities in China. We adopted a data-driven approach by integrating Random Forest (RF) with SHAP (SHapley Additive Explanations) to enhance model interpretability and uncover non-linear relationships. A comprehensive dataset of 2,508 residential communities in South China was compiled using web-crawled property attributes and GIS-derived geospatial indicators. The RF model achieved a robust performance, with an average training R<sup>2</sup> of 0.965 and testing R<sup>2</sup> of 0.742. SHAP values were used to quantify the marginal contribution of each feature to housing price predictions, revealing that location-based factors and environmental attributes were the most influential. The model also identified price volatility in regions with high standard deviations, offering a new dimension for spatial housing risk assessment. The findings offer practical implications for policymakers aiming to stabilize housing markets, improve affordability, and guide data-informed infrastructure investments. The study also demonstrates the utility of explainable AI techniques in advancing sustainable urban development research.</div></div>","PeriodicalId":52395,"journal":{"name":"City and Environment Interactions","volume":"28 ","pages":"Article 100223"},"PeriodicalIF":3.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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