Pub Date : 2023-12-26DOI: 10.1177/23998083231224013
Chendi Yang, Rui Ma, Hongqiang Fang, Siu Ming Lo, Jacqueline TY Lo
As a significant public place, the commercial area has a potential correlation between its built environment and human activities. However, the current research primarily concentrates on the internal environment of the store and customer satisfaction, while the impact of some environmental features of the outer space of the business district on visitors is seldom systematically discussed. This study takes four commercial districts in Shenzhen as examples, and the streets were categorized into five types based on street characteristics using the cluster analysis method. The relationship between each type of street and the population distribution in the region was subsequently discussed. To this end, a holistic approach was adopted, integrating multi-source urban data such as street view panorama, points of interest (POI), and street and building vectors to describe the built environment. Furthermore, the distribution of people at different times, based on location-based services (LBS) data, was combined to establish statistical models of various streets in commercial districts and evaluate the relationship between street characteristics and human activities. The results demonstrate that the relationship between population distribution and spatial characteristics is different in the five types of streets. Different types of streets have their own advantages, and human activities in the business district are often not affected by this advantage, but by other characteristics. The impact of these factors varies significantly between weekdays and weekends. By systematically categorizing street types and assessing the impact of environmental factors on pedestrian flow, this study sheds new light on the renewal and development of urban commercial districts in the future.
{"title":"Street characteristics and human activities in commercial districts: A clustering-based approach application for Shenzhen","authors":"Chendi Yang, Rui Ma, Hongqiang Fang, Siu Ming Lo, Jacqueline TY Lo","doi":"10.1177/23998083231224013","DOIUrl":"https://doi.org/10.1177/23998083231224013","url":null,"abstract":"As a significant public place, the commercial area has a potential correlation between its built environment and human activities. However, the current research primarily concentrates on the internal environment of the store and customer satisfaction, while the impact of some environmental features of the outer space of the business district on visitors is seldom systematically discussed. This study takes four commercial districts in Shenzhen as examples, and the streets were categorized into five types based on street characteristics using the cluster analysis method. The relationship between each type of street and the population distribution in the region was subsequently discussed. To this end, a holistic approach was adopted, integrating multi-source urban data such as street view panorama, points of interest (POI), and street and building vectors to describe the built environment. Furthermore, the distribution of people at different times, based on location-based services (LBS) data, was combined to establish statistical models of various streets in commercial districts and evaluate the relationship between street characteristics and human activities. The results demonstrate that the relationship between population distribution and spatial characteristics is different in the five types of streets. Different types of streets have their own advantages, and human activities in the business district are often not affected by this advantage, but by other characteristics. The impact of these factors varies significantly between weekdays and weekends. By systematically categorizing street types and assessing the impact of environmental factors on pedestrian flow, this study sheds new light on the renewal and development of urban commercial districts in the future.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"21 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139155314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-25DOI: 10.1177/23998083231224505
Jinmo Rhee, Ramesh Krishnamurti
The utilization of deep learning for form analysis facilitates the classification of an extensive number of forms based on their morphological features. A critical consideration for implementing such analysis methods in architectural or urban forms is whether building orientation should be embedded within the data. Orientation functions as a form variable significantly influenced by environmental, social, and cultural contexts within a city. In contrast to other domains where forms are extrapolated in relation to their context, in the city, domain orientation uniquely characterizes building form. In this paper, we introduce a pipeline for constructing an extensive building form dataset and scrutinizing the morphological identity of building forms, with a particular focus on the implications of building orientation as a manifestation of urban locality. Through a case study situated in Montreal, we engage in a comparative analysis employing two distinct datasets—those with orientation-embedded forms and those with orientation-normalized forms. Our research aims to investigate the typo-morphological characteristics of the building forms of the city and to examine how building orientation contributes to the identification of these traits and mirrors urban locality.
{"title":"An inductive method for classifying building form in a city with implications for orientation","authors":"Jinmo Rhee, Ramesh Krishnamurti","doi":"10.1177/23998083231224505","DOIUrl":"https://doi.org/10.1177/23998083231224505","url":null,"abstract":"The utilization of deep learning for form analysis facilitates the classification of an extensive number of forms based on their morphological features. A critical consideration for implementing such analysis methods in architectural or urban forms is whether building orientation should be embedded within the data. Orientation functions as a form variable significantly influenced by environmental, social, and cultural contexts within a city. In contrast to other domains where forms are extrapolated in relation to their context, in the city, domain orientation uniquely characterizes building form. In this paper, we introduce a pipeline for constructing an extensive building form dataset and scrutinizing the morphological identity of building forms, with a particular focus on the implications of building orientation as a manifestation of urban locality. Through a case study situated in Montreal, we engage in a comparative analysis employing two distinct datasets—those with orientation-embedded forms and those with orientation-normalized forms. Our research aims to investigate the typo-morphological characteristics of the building forms of the city and to examine how building orientation contributes to the identification of these traits and mirrors urban locality.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"13 2","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139157910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-19DOI: 10.1177/23998083231224151
Dani Arribas-Bel, Martin Fleischmann
{"title":"In praise of (spatial) bundles","authors":"Dani Arribas-Bel, Martin Fleischmann","doi":"10.1177/23998083231224151","DOIUrl":"https://doi.org/10.1177/23998083231224151","url":null,"abstract":"","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":" 632","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138960374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom traffic data to infer mixed urban functions at spatiotemporal granularities as fine as buildings and hours. A time-series decomposition method is designed to map the mixture of urban functions, which is further refined by a hierarchical agglomerative clustering method taking urban textures as an additional source of information. In a case study in Shenzhen, China, we find the function of urban buildings can be decomposed into the mixture of three basic functions, namely dwelling, work, and recreation. We further find that the introduction of urban texture information helps identify particular forms of functional combination, which indicate special-function buildings such as urban villages and roadside shops. This study implies ways to improve urban management through methodological contributions in mixed urban function identification alongside the introduction of the telecom traffic, a kind of “high-frequency” urban data, and also helps inspire a rethinking of the form/function dichotomy in the era of “High-frequent” cities.
{"title":"Inferring “high-frequent” mixed urban functions from telecom traffic","authors":"Jintong Tang, Ximeng Cheng, Aihan Liu, Qian Huang, Yinsheng Zhou, Zhou Huang, Yu Liu, Liyan Xu","doi":"10.1177/23998083231221867","DOIUrl":"https://doi.org/10.1177/23998083231221867","url":null,"abstract":"Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above limitations, and present an analytical framework which utilizes anonymized and aggregated telecom traffic data to infer mixed urban functions at spatiotemporal granularities as fine as buildings and hours. A time-series decomposition method is designed to map the mixture of urban functions, which is further refined by a hierarchical agglomerative clustering method taking urban textures as an additional source of information. In a case study in Shenzhen, China, we find the function of urban buildings can be decomposed into the mixture of three basic functions, namely dwelling, work, and recreation. We further find that the introduction of urban texture information helps identify particular forms of functional combination, which indicate special-function buildings such as urban villages and roadside shops. This study implies ways to improve urban management through methodological contributions in mixed urban function identification alongside the introduction of the telecom traffic, a kind of “high-frequency” urban data, and also helps inspire a rethinking of the form/function dichotomy in the era of “High-frequent” cities.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"106 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138979529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.1177/23998083231219364
Yubo Liu, Kai Hu, Qiaoming Deng
This research proposes a design system that combines a case-based learning algorithm with a rule-based optimization algorithm to automatically generate and revise urban form prototypes based on historical cases and user requirements. The system aims to address the challenges of existing generative methods for urban forms, such as the lack of flexibility and organicity of rule-based methods and the insufficient manipulability and interpretability of the newest GAN-integrated case-based methods. It can help designers generate multiple solutions with specific indicators in the conceptual stage and has the potential to facilitate citizen participation in urban planning and design. This research demonstrates the feasibility and effectiveness of the system through a case study in Shenzhen. The research further extends the discussion about the application of the proposed system and the alternative evolution approach for the next generation of automatic design methods.
{"title":"Evolvable case-based design: An artificial intelligence system for urban form generation with specific indicators","authors":"Yubo Liu, Kai Hu, Qiaoming Deng","doi":"10.1177/23998083231219364","DOIUrl":"https://doi.org/10.1177/23998083231219364","url":null,"abstract":"This research proposes a design system that combines a case-based learning algorithm with a rule-based optimization algorithm to automatically generate and revise urban form prototypes based on historical cases and user requirements. The system aims to address the challenges of existing generative methods for urban forms, such as the lack of flexibility and organicity of rule-based methods and the insufficient manipulability and interpretability of the newest GAN-integrated case-based methods. It can help designers generate multiple solutions with specific indicators in the conceptual stage and has the potential to facilitate citizen participation in urban planning and design. This research demonstrates the feasibility and effectiveness of the system through a case study in Shenzhen. The research further extends the discussion about the application of the proposed system and the alternative evolution approach for the next generation of automatic design methods.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"11 3","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138590286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-07DOI: 10.1177/23998083231215822
Piyushimita (Vonu) Thakuriah, Christina Boididou, Jinhyun Hong
This study analyzed physical distancing in people’s daily lives and its association with travel behavior and the use of transportation modes before the COVID-19 outbreak. We used data from photographic images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s physical distancing and travel behavior and transportation choices. The multidisciplinary research approach to understand these associations accounted for a number of social, economic, and cultural factors that potentially influenced their physical isolation levels. We found that participants spend a significant amount of time physically separated from others, without anyone else within 2 meters. The use of public transportation, automobiles, active travel, and an increase in trip frequency, including trips to transportation facilities, reduced the extent of physical distancing, with public transportation having the most significant impact. Higher incomes, strong social networks, and a sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable about climate change issues increased the likelihood of physical distancing. The paper addresses a crucial gap in our understanding of how these factors intersect to create the dynamics of physical distancing in non-emergency situations and highlights their planning and operational implications while showcasing the use of unique person-based physical distancing measures derived from autonomously collected image data.
{"title":"Physical distancing and its association with travel behavior in daily pre-pandemic urban life: An analysis utilizing lifelogging images and composite survey and mobility data","authors":"Piyushimita (Vonu) Thakuriah, Christina Boididou, Jinhyun Hong","doi":"10.1177/23998083231215822","DOIUrl":"https://doi.org/10.1177/23998083231215822","url":null,"abstract":"This study analyzed physical distancing in people’s daily lives and its association with travel behavior and the use of transportation modes before the COVID-19 outbreak. We used data from photographic images acquired automatically by lifelogging devices every 5 seconds, on average, from 170 participants of a 2-day wearable camera study, in order to identify their physical distancing status throughout the day. Using deep-learning computer vision algorithms, we developed three measures which provided a near-continuous quantification of the proportion of time spent without anyone else within a distance of approximately 13 meters, as well as the proportion of time spent without others within approximately 2 meters. These measures are then used as outcomes in beta regression and multinomial logit models to explore the association between the participant’s physical distancing and travel behavior and transportation choices. The multidisciplinary research approach to understand these associations accounted for a number of social, economic, and cultural factors that potentially influenced their physical isolation levels. We found that participants spend a significant amount of time physically separated from others, without anyone else within 2 meters. The use of public transportation, automobiles, active travel, and an increase in trip frequency, including trips to transportation facilities, reduced the extent of physical distancing, with public transportation having the most significant impact. Higher incomes, strong social networks, and a sense of belonging to the community reduced the tendency for physical distancing. In contrast, factors such as age, obesity, dog ownership, intensive use of the Internet, and being knowledgeable about climate change issues increased the likelihood of physical distancing. The paper addresses a crucial gap in our understanding of how these factors intersect to create the dynamics of physical distancing in non-emergency situations and highlights their planning and operational implications while showcasing the use of unique person-based physical distancing measures derived from autonomously collected image data.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"56 6","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138592904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.1177/23998083231217606
Stefano De Sabbata, Katy Bennett, Zoe Gardner
Events are the driving force behind social media, whether we try to create them or keep up with them. A wide range of studies has focused on how content from social media can be used to detect, model and predict events and identify key topics of discussion. At the same time, very limited attention has been given so far to the quantitative study of the everyday, which has fascinated qualitative human geography research in the past few decades. That is partly due to the lack of a formal definition of what constitutes the everyday. In this paper, we aim to advance our understanding of the everyday, not by reducing it to any kind of definition but by bringing it into view through a quantitative analysis. We hypothesise that the by-products of current methods focused on event detection might be used to quantitatively explore everyday geographies as represented through Twitter data. We consider the use of both statistical approaches based on term frequency and state-of-the-art large language models, and we conduct a case study on content posted on Twitter and geolocated in the city of Leicester. Our paper makes two key advances for research concerned with the everyday and the analysis of geographic information. First, we illustrate how large language models combined with spatial analysis and visualisation can foster the study of everyday geographies, providing an insight into the still elusive concept of the everyday, representing what other approaches to the everyday have struggled to qualify. Secondly, we showcase the potential held by large language models and visual analytics in democratising sophisticated natural language processing and thus providing new tools for research in human geography.
{"title":"Towards a study of everyday geographic information: Bringing the everyday into view","authors":"Stefano De Sabbata, Katy Bennett, Zoe Gardner","doi":"10.1177/23998083231217606","DOIUrl":"https://doi.org/10.1177/23998083231217606","url":null,"abstract":"Events are the driving force behind social media, whether we try to create them or keep up with them. A wide range of studies has focused on how content from social media can be used to detect, model and predict events and identify key topics of discussion. At the same time, very limited attention has been given so far to the quantitative study of the everyday, which has fascinated qualitative human geography research in the past few decades. That is partly due to the lack of a formal definition of what constitutes the everyday. In this paper, we aim to advance our understanding of the everyday, not by reducing it to any kind of definition but by bringing it into view through a quantitative analysis. We hypothesise that the by-products of current methods focused on event detection might be used to quantitatively explore everyday geographies as represented through Twitter data. We consider the use of both statistical approaches based on term frequency and state-of-the-art large language models, and we conduct a case study on content posted on Twitter and geolocated in the city of Leicester. Our paper makes two key advances for research concerned with the everyday and the analysis of geographic information. First, we illustrate how large language models combined with spatial analysis and visualisation can foster the study of everyday geographies, providing an insight into the still elusive concept of the everyday, representing what other approaches to the everyday have struggled to qualify. Secondly, we showcase the potential held by large language models and visual analytics in democratising sophisticated natural language processing and thus providing new tools for research in human geography.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"126 50","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.1177/23998083231219048
Miguel G. Silva, Sara C. Madeira, Rui Henriques
Mobile phones share location records, offering the opportunity to monitor and understand emerging population dynamics in urban centers. With the aim of supporting urban planning, this study introduces a scalable methodology grounded on extracting and organizing spatiotemporal statistics from decomposed population density data. The proposed methodology serves three major purposes: (i) assess the predictability of spatiotemporal citizen density patterns; (ii) detect emerging spatiotemporal trends in population density; and (iii) uncover multi-level seasonality patterns with guarantees of actionability. Additionally, it makes available an open-access tool for deploying the proposed methodology and analyzing mobile phone network data with easy-to-use spatiotemporal visualization and navigation facilities. The results obtained from real-world, large-scale mobile data in Lisbon, Portugal, demonstrate the effectiveness and validity of the proposed methodology in extracting actionable statistics in linear time to guide both tactic and strategic urban planning.
{"title":"Actionable descriptors of spatiotemporal urban dynamics from large-scale mobile data: A case study in Lisbon city","authors":"Miguel G. Silva, Sara C. Madeira, Rui Henriques","doi":"10.1177/23998083231219048","DOIUrl":"https://doi.org/10.1177/23998083231219048","url":null,"abstract":"Mobile phones share location records, offering the opportunity to monitor and understand emerging population dynamics in urban centers. With the aim of supporting urban planning, this study introduces a scalable methodology grounded on extracting and organizing spatiotemporal statistics from decomposed population density data. The proposed methodology serves three major purposes: (i) assess the predictability of spatiotemporal citizen density patterns; (ii) detect emerging spatiotemporal trends in population density; and (iii) uncover multi-level seasonality patterns with guarantees of actionability. Additionally, it makes available an open-access tool for deploying the proposed methodology and analyzing mobile phone network data with easy-to-use spatiotemporal visualization and navigation facilities. The results obtained from real-world, large-scale mobile data in Lisbon, Portugal, demonstrate the effectiveness and validity of the proposed methodology in extracting actionable statistics in linear time to guide both tactic and strategic urban planning.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"129 27","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.1177/23998083231218779
L. Saganeiti, Lorena Fiorini, F. Zullo, B. Murgante
The 2022 United Nations Climate Change Conference (COP27) reaffirmed the most urgent need to build actions to accelerate the restoration of policies to arrest and reverse the loss of natural ecosystems by 2030 and move towards full ecosystem recovery by 2050. Land take is a significant source of emissions and contributes to global warming and biodiversity loss in natural ecosystems. Consequently, it is crucial to act on it by investigating the phenomenon quantitatively and formally, thus contributing to the goal of zero net land take. In recent years, land take worldwide has become massive, leading in some cases to forming compact, high-density urban settlements. In other cases, it has led to dispersed, low-density urban settlements. The basic assumption underlying this research is that a compact context is more sustainable (environmentally, economically, and socially) than a dispersed urban one. Consequently, this research aims to investigate the evolution of land take from the point of view of the pattern of urban settlements and their dispersion over the Italian territory. The spatial configuration of the Italian settlement pattern at the regional and provincial level was analyzed through a Spatio-temporal analysis of the global Moran index and other quantitative variables. The results provide, for each territory, a reading of the main expansion dynamics that occurred from the ‘50s to nowadays: compact city, urban sprawl, or urban sprinkling.
{"title":"Urban dispersion indicator to assess the Italian settlement pattern","authors":"L. Saganeiti, Lorena Fiorini, F. Zullo, B. Murgante","doi":"10.1177/23998083231218779","DOIUrl":"https://doi.org/10.1177/23998083231218779","url":null,"abstract":"The 2022 United Nations Climate Change Conference (COP27) reaffirmed the most urgent need to build actions to accelerate the restoration of policies to arrest and reverse the loss of natural ecosystems by 2030 and move towards full ecosystem recovery by 2050. Land take is a significant source of emissions and contributes to global warming and biodiversity loss in natural ecosystems. Consequently, it is crucial to act on it by investigating the phenomenon quantitatively and formally, thus contributing to the goal of zero net land take. In recent years, land take worldwide has become massive, leading in some cases to forming compact, high-density urban settlements. In other cases, it has led to dispersed, low-density urban settlements. The basic assumption underlying this research is that a compact context is more sustainable (environmentally, economically, and socially) than a dispersed urban one. Consequently, this research aims to investigate the evolution of land take from the point of view of the pattern of urban settlements and their dispersion over the Italian territory. The spatial configuration of the Italian settlement pattern at the regional and provincial level was analyzed through a Spatio-temporal analysis of the global Moran index and other quantitative variables. The results provide, for each territory, a reading of the main expansion dynamics that occurred from the ‘50s to nowadays: compact city, urban sprawl, or urban sprinkling.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"20 24","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138603279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.1177/23998083231219322
Minghao Liu, Zhonghua Gou
Due to COVID-19, the urban lockdown has caused a significant impact on the mental health of residents. However, limited research investigates the role of neighborhood factors on residents’ mental health during and after the lockdown. This study examines Wuhan, the first city to experience the COVID-19 outbreak, employing multiple linear regression and XGBoost algorithms to analyze the emotional status and distribution of Wuhan residents. The goal of this study is to identify the moderating effect of the neighborhood environment scale on emotional positivity and the marginal effect of the neighborhood environment on residents’ emotions. The results of the study indicate that specific neighborhood environmental characteristics have varying effects on residents’ positive emotions, both before and after the COVID-19 lockdown. The green space ratio, attraction density, waterfront space density, and service facility density all positively affected mood within different distance ranges. Shopping facilities, on the other hand, had mainly positive effects during the open period, with negative effects during the closed period. Furthermore, this study determined scale thresholds where neighborhood environments had a positive effect on mood. For instance, attractions and waterfront areas improved the mood of residents in residential areas, up to at least 3 km away. Medical facilities had a positive effect on residents’ mood beyond 2.2 km. This study highlights crucial implications for planning and managing neighborhoods to promote resilience during future public health crises.
{"title":"Examining the impact of neighborhood environment factors on residents’ emotions during COVID-19 lockdown and reopening: A Wuhan study on mediation and moderation","authors":"Minghao Liu, Zhonghua Gou","doi":"10.1177/23998083231219322","DOIUrl":"https://doi.org/10.1177/23998083231219322","url":null,"abstract":"Due to COVID-19, the urban lockdown has caused a significant impact on the mental health of residents. However, limited research investigates the role of neighborhood factors on residents’ mental health during and after the lockdown. This study examines Wuhan, the first city to experience the COVID-19 outbreak, employing multiple linear regression and XGBoost algorithms to analyze the emotional status and distribution of Wuhan residents. The goal of this study is to identify the moderating effect of the neighborhood environment scale on emotional positivity and the marginal effect of the neighborhood environment on residents’ emotions. The results of the study indicate that specific neighborhood environmental characteristics have varying effects on residents’ positive emotions, both before and after the COVID-19 lockdown. The green space ratio, attraction density, waterfront space density, and service facility density all positively affected mood within different distance ranges. Shopping facilities, on the other hand, had mainly positive effects during the open period, with negative effects during the closed period. Furthermore, this study determined scale thresholds where neighborhood environments had a positive effect on mood. For instance, attractions and waterfront areas improved the mood of residents in residential areas, up to at least 3 km away. Medical facilities had a positive effect on residents’ mood beyond 2.2 km. This study highlights crucial implications for planning and managing neighborhoods to promote resilience during future public health crises.","PeriodicalId":11863,"journal":{"name":"Environment and Planning B: Urban Analytics and City Science","volume":"61 14","pages":""},"PeriodicalIF":3.5,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138604871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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