Pub Date : 2025-04-17DOI: 10.1016/j.compenvurbsys.2025.102291
Pilvi Nummi , Anni Hapuoja
The digitalization of urban planning entails a shift to information model-based planning, where plans are produced in a machine-readable and interoperable format. In Finland, a nationally interoperable information model for land use plans has been applied for the first time to digital planning tools in the recently completed project KAATIO. In this article, we apply socio-technical approach to assess how co-development in this project was perceived by municipal planners and software developers, and how did the tools developed meet the needs of planners and planning practice. The results show that a technology-driven culture dominates the national development and hampers the socio-technical approach. Despite the challenges, co-development is beneficial for both software developers and municipal actors. In conclusion, we argue that, in this context, empowering users, facilitating the discussion on information model-based planning, future-oriented understanding of planning tasks, and accepting the diversity of practices while harmonizing the plan data are essential for promoting human factors in the development.
{"title":"Does co-development facilitate achieving useful planning tools? A socio-technical approach to the development of information model-based land use planning in Finland","authors":"Pilvi Nummi , Anni Hapuoja","doi":"10.1016/j.compenvurbsys.2025.102291","DOIUrl":"10.1016/j.compenvurbsys.2025.102291","url":null,"abstract":"<div><div>The digitalization of urban planning entails a shift to information model-based planning, where plans are produced in a machine-readable and interoperable format. In Finland, a nationally interoperable information model for land use plans has been applied for the first time to digital planning tools in the recently completed project KAATIO. In this article, we apply socio-technical approach to assess how co-development in this project was perceived by municipal planners and software developers, and how did the tools developed meet the needs of planners and planning practice. The results show that a technology-driven culture dominates the national development and hampers the socio-technical approach. Despite the challenges, co-development is beneficial for both software developers and municipal actors. In conclusion, we argue that, in this context, empowering users, facilitating the discussion on information model-based planning, future-oriented understanding of planning tasks, and accepting the diversity of practices while harmonizing the plan data are essential for promoting human factors in the development.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"120 ","pages":"Article 102291"},"PeriodicalIF":7.1,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847917","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 : 2025-04-10DOI: 10.1016/j.compenvurbsys.2025.102289
Katarzyna Kopczewska
This paper deals with the local spatial density of human activity. By understanding and quantifying the spatial distribution of interrelated phenomena such as business location and population settlement at the micro level, it is possible to track local under- and over- spatial representation in socio-economic development. The modelling of spatial density using point data is crucial for territorially targeted policies and business decisions. Weak stream of studies in this field is a consequence of lack of methods. This study presents quick density clustering (QDC), a novel algorithm for classifying geolocated point data into low, medium and high density clusters. QDC uses two spatial features - the sum of distances to k-nearest neighbours (kNN) and the number of neighbours within a fixed radius (frNN) - to generate parameter robust, interpretable clusters. By normalising these metrics and applying K-means clustering, QDC captures both local and global density variations, making it suitable for analysing human activity at urban and regional scales. Empirical validation demonstrates its accuracy and effectiveness in partitioning point data into density clusters and comparing density groups in grids. The QDC provides a robust framework for advancing density-based studies in socio-economic research as well as environmental science and spatial statistics
{"title":"Analysing local spatial density of human activity with quick density clustering (QDC) algorithm","authors":"Katarzyna Kopczewska","doi":"10.1016/j.compenvurbsys.2025.102289","DOIUrl":"10.1016/j.compenvurbsys.2025.102289","url":null,"abstract":"<div><div>This paper deals with the local spatial density of human activity. By understanding and quantifying the spatial distribution of interrelated phenomena such as business location and population settlement at the micro level, it is possible to track local under- and over- spatial representation in socio-economic development. The modelling of spatial density using point data is crucial for territorially targeted policies and business decisions. Weak stream of studies in this field is a consequence of lack of methods. This study presents quick density clustering (QDC), a novel algorithm for classifying geolocated point data into low, medium and high density clusters. QDC uses two spatial features - the sum of distances to k-nearest neighbours (kNN) and the number of neighbours within a fixed radius (frNN) - to generate parameter robust, interpretable clusters. By normalising these metrics and applying K-means clustering, QDC captures both local and global density variations, making it suitable for analysing human activity at urban and regional scales. Empirical validation demonstrates its accuracy and effectiveness in partitioning point data into density clusters and comparing density groups in grids. The QDC provides a robust framework for advancing density-based studies in socio-economic research as well as environmental science and spatial statistics</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":""},"PeriodicalIF":7.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815252","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}
To evaluate human exposure to environmental factors, sufficient population-targeted sensing power of sensor carriers is crucial. However, the traditional static sensing approach is constrained by its limited coverage. Recently, equipping moving vehicles with sensors has emerged as a new approach. However, a quantitative comparison between mobile and traditional static sensing is still lacking. Using empirical taxi trajectory and population data in Beijing and Xiamen, we found that while a small number of taxi-based mobile sensors can cover a larger portion of the population, well-located static sensors eventually surpass mobile sensors in coverage as their number increases. In addition, a higher required frequency reduces the coverage of mobile sensors, whereas a higher cost ratio between static and mobile sensors reduces the coverage of static sites. Taxis provide extensive spatial coverage but with some uncertainty, especially in peripheral areas, whereas static sensors ensure localized and stable coverage. Based on the advantage of taxis and static sites, we propose an effective greedy-add-guided strengthen elitist genetic algorithm to determine the optimal combination of static and mobile sensors. The key idea is to position static sensors in areas with relatively low taxi visit probabilities but high population density. The results indicate that this optimal combination achieves higher population coverage compared to using taxis alone. It addresses the uncertainty in taxi coverage and significantly reduces the number of sensors required. These results support the feasibility of using taxis as a sensing paradigm and further highlight the potential of combining these two sensing paradigms in population-targeted sensing applications.
{"title":"Advancing population-targeted urban sensing: A comparative study on mobile and static sensing paradigms","authors":"Yuan-Qiao Hou , Xiao-Jian Chen , Zhou Huang , Xia Peng , Yu Liu","doi":"10.1016/j.compenvurbsys.2025.102288","DOIUrl":"10.1016/j.compenvurbsys.2025.102288","url":null,"abstract":"<div><div>To evaluate human exposure to environmental factors, sufficient population-targeted sensing power of sensor carriers is crucial. However, the traditional static sensing approach is constrained by its limited coverage. Recently, equipping moving vehicles with sensors has emerged as a new approach. However, a quantitative comparison between mobile and traditional static sensing is still lacking. Using empirical taxi trajectory and population data in Beijing and Xiamen, we found that while a small number of taxi-based mobile sensors can cover a larger portion of the population, well-located static sensors eventually surpass mobile sensors in coverage as their number increases. In addition, a higher required frequency reduces the coverage of mobile sensors, whereas a higher cost ratio between static and mobile sensors reduces the coverage of static sites. Taxis provide extensive spatial coverage but with some uncertainty, especially in peripheral areas, whereas static sensors ensure localized and stable coverage. Based on the advantage of taxis and static sites, we propose an effective greedy-add-guided strengthen elitist genetic algorithm to determine the optimal combination of static and mobile sensors. The key idea is to position static sensors in areas with relatively low taxi visit probabilities but high population density. The results indicate that this optimal combination achieves higher population coverage compared to using taxis alone. It addresses the uncertainty in taxi coverage and significantly reduces the number of sensors required. These results support the feasibility of using taxis as a sensing paradigm and further highlight the potential of combining these two sensing paradigms in population-targeted sensing applications.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102288"},"PeriodicalIF":7.1,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791196","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 : 2025-04-08DOI: 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":"2025-04-08","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 : 2025-04-03DOI: 10.1016/j.compenvurbsys.2025.102286
Hui Jeong Ha, Jed A. Long
In recent years, the intensity and occurrence of wildfires have risen globally, driven by climate change triggering extreme dry weather conditions. This study focuses on the 2023 McDougall Creek wildfire in British Columbia, highlighting the vulnerability of urban communities to severe wildfires. Using aggregated and de-identified network mobility data from a Canadian telecommunications provider, we quantified neighborhood-level evacuation rates and examined inter-regional travel patterns during the wildfire event. We applied a spatial difference-in-difference (DID) model to understand how neighborhood characteristics influenced evacuation rates. Our findings suggest that formal evacuation orders were positively associated with evacuation rates. We also found that the distance to the wildfire perimeter was a strong and significant predictor of evacuation rates, while socio-demographic variables previously identified as strong predictors of evacuation rates were not significant in this particular context. The analysis of travel patterns before and during the wildfire event reveals distinct directional patterns and variations in inter-regional travel across spatial scales. This research contributes to the understanding of wildfire evacuation dynamics and the application of human mobility data into disaster management, enhancing our knowledge of the human response to natural disasters.
{"title":"Measuring evacuation rates from mobility data during the McDougall Creek wildfire in British Columbia, Canada","authors":"Hui Jeong Ha, Jed A. Long","doi":"10.1016/j.compenvurbsys.2025.102286","DOIUrl":"10.1016/j.compenvurbsys.2025.102286","url":null,"abstract":"<div><div>In recent years, the intensity and occurrence of wildfires have risen globally, driven by climate change triggering extreme dry weather conditions. This study focuses on the 2023 McDougall Creek wildfire in British Columbia, highlighting the vulnerability of urban communities to severe wildfires. Using aggregated and de-identified network mobility data from a Canadian telecommunications provider, we quantified neighborhood-level evacuation rates and examined inter-regional travel patterns during the wildfire event. We applied a spatial difference-in-difference (DID) model to understand how neighborhood characteristics influenced evacuation rates. Our findings suggest that formal evacuation orders were positively associated with evacuation rates. We also found that the distance to the wildfire perimeter was a strong and significant predictor of evacuation rates, while socio-demographic variables previously identified as strong predictors of evacuation rates were not significant in this particular context. The analysis of travel patterns before and during the wildfire event reveals distinct directional patterns and variations in inter-regional travel across spatial scales. This research contributes to the understanding of wildfire evacuation dynamics and the application of human mobility data into disaster management, enhancing our knowledge of the human response to natural disasters.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102286"},"PeriodicalIF":7.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760122","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 : 2025-03-28DOI: 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":"2025-03-28","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 : 2025-03-27DOI: 10.1016/j.compenvurbsys.2025.102284
Yingjie Liu , Zeyu Wang , Siyi Ren , Runying Chen , Yixiang Shen , Filip Biljecki
Urban transformation not only reshapes physical spaces but also impacts public perception, influencing how people experience their environments. This study utilizes Street View Imagery (SVI) as an emerging, human-level data source to assess urban changes, providing perspective beyond traditional datasets. Existing studies often focus on either urban physical changes or human perception changes, without bridging the two. This research integrates both aspects by combining a change detection model, trained on a self-labeled dataset, and a human perception model based on the crowdsourced Place Pulse 2.0 dataset with input from 81,630 online volunteers, to analyze urban transformation in New York City and Memphis from 2007 to 2023. Our findings reveal differences between the two cities: New York City exhibited small, isolated changes often driven by community needs, while Memphis transitioned from concentrated to more dispersed development patterns. This study provides insights into how physical changes influence public perception within these two cities. It demonstrates how thoughtful, well-planned urban transformation can improve neighborhood's perception such as safety and livability, while also pointing out potential challenges like gentrification or social fragmentation. These findings provide policymakers with valuable insights into human perception, aiding in the creation of more inclusive, vibrant, and resilient urban transformation. This helps ensure that urban transformation efforts are based on community desires and align with long-term sustainability goals.
{"title":"Physical urban change and its socio-environmental impact: Insights from street view imagery","authors":"Yingjie Liu , Zeyu Wang , Siyi Ren , Runying Chen , Yixiang Shen , Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102284","DOIUrl":"10.1016/j.compenvurbsys.2025.102284","url":null,"abstract":"<div><div>Urban transformation not only reshapes physical spaces but also impacts public perception, influencing how people experience their environments. This study utilizes Street View Imagery (SVI) as an emerging, human-level data source to assess urban changes, providing perspective beyond traditional datasets. Existing studies often focus on either urban physical changes or human perception changes, without bridging the two. This research integrates both aspects by combining a change detection model, trained on a self-labeled dataset, and a human perception model based on the crowdsourced Place Pulse 2.0 dataset with input from 81,630 online volunteers, to analyze urban transformation in New York City and Memphis from 2007 to 2023. Our findings reveal differences between the two cities: New York City exhibited small, isolated changes often driven by community needs, while Memphis transitioned from concentrated to more dispersed development patterns. This study provides insights into how physical changes influence public perception within these two cities. It demonstrates how thoughtful, well-planned urban transformation can improve neighborhood's perception such as safety and livability, while also pointing out potential challenges like gentrification or social fragmentation. These findings provide policymakers with valuable insights into human perception, aiding in the creation of more inclusive, vibrant, and resilient urban transformation. This helps ensure that urban transformation efforts are based on community desires and align with long-term sustainability goals.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102284"},"PeriodicalIF":7.1,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714949","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 : 2025-03-20DOI: 10.1016/j.compenvurbsys.2025.102283
Koichi Ito , Yihan Zhu , Mahmoud Abdelrahman , Xiucheng Liang , Zicheng Fan , Yujun Hou , Tianhong Zhao , Rui Ma , Kunihiko Fujiwara , Jiani Ouyang , Matias Quintana , Filip Biljecki
Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and image-processing procedures have not been standardized, and solutions have been implemented in isolation, often making it difficult for others to reproduce existing work and carry out new research. Using SVI for research requires multiple technical steps: accessing APIs for scalable data collection, preprocessing images to standardize formats, implementing computer vision models for feature extraction, and conducting spatial analysis. These technical requirements create barriers for researchers in urban studies, particularly those without extensive programming experience. We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of SVI analysis, supporting a wide range of use cases. Its end-to-end pipeline includes downloading SVI from multiple platforms (e.g., Mapillary and KartaView) efficiently, analyzing metadata of SVI, applying computer vision models to extract target features, transforming SVI into different projections (e.g., fish-eye and perspective) and different formats (e.g., depth map and point cloud), visualizing analyses with maps and plots, and exporting outputs to other software tools. We demonstrated its use in Singapore through a case study of data quality assessment and clustering analysis in a streamlined manner. Our software improves the transparency, reproducibility, and scalability of research relying on SVI and supports researchers in conducting urban analyses efficiently. Its modular design facilitates extensions of the package for new use cases. This package is openly available at https://github.com/koito19960406/ZenSVI, and it is supported by documentation including tutorials (https://zensvi.readthedocs.io/en/latest/examples/index.html).
{"title":"ZenSVI: An open-source software for the integrated acquisition, processing and analysis of street view imagery towards scalable urban science","authors":"Koichi Ito , Yihan Zhu , Mahmoud Abdelrahman , Xiucheng Liang , Zicheng Fan , Yujun Hou , Tianhong Zhao , Rui Ma , Kunihiko Fujiwara , Jiani Ouyang , Matias Quintana , Filip Biljecki","doi":"10.1016/j.compenvurbsys.2025.102283","DOIUrl":"10.1016/j.compenvurbsys.2025.102283","url":null,"abstract":"<div><div>Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and image-processing procedures have not been standardized, and solutions have been implemented in isolation, often making it difficult for others to reproduce existing work and carry out new research. Using SVI for research requires multiple technical steps: accessing APIs for scalable data collection, preprocessing images to standardize formats, implementing computer vision models for feature extraction, and conducting spatial analysis. These technical requirements create barriers for researchers in urban studies, particularly those without extensive programming experience. We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of SVI analysis, supporting a wide range of use cases. Its end-to-end pipeline includes downloading SVI from multiple platforms (e.g., Mapillary and KartaView) efficiently, analyzing metadata of SVI, applying computer vision models to extract target features, transforming SVI into different projections (e.g., fish-eye and perspective) and different formats (e.g., depth map and point cloud), visualizing analyses with maps and plots, and exporting outputs to other software tools. We demonstrated its use in Singapore through a case study of data quality assessment and clustering analysis in a streamlined manner. Our software improves the transparency, reproducibility, and scalability of research relying on SVI and supports researchers in conducting urban analyses efficiently. Its modular design facilitates extensions of the package for new use cases. This package is openly available at <span><span><span>https://github.com/koito19960406/ZenSVI</span></span><svg><path></path></svg></span>, and it is supported by documentation including tutorials (<span><span><span>https://zensvi.readthedocs.io/en/latest/examples/index.html</span></span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102283"},"PeriodicalIF":7.1,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143684261","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 : 2025-03-15DOI: 10.1016/j.compenvurbsys.2025.102282
Rui Cao , Wei Tu , Dongsheng Chen , Wenyu Zhang
The shift toward high-quality urbanization has brought increased attention to the issue of “urban villages”, which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and identify challenges and future directions, we have conducted a comprehensive review, which to the best of our knowledge is the first of its kind in this field. Our review begins by providing a clear context for urban villages and elaborating the method for literature review, then summarizes the study areas, data sources, and approaches used for urban village mapping in China. We also address the challenges and future directions for further research. Through thorough investigation, we find that current studies only cover very limited study areas and periods and lack sufficient investigation into the scalability, transferability, and interpretability of identification approaches due to the challenges in concept fuzziness and variances, spatial heterogeneity and variances of urban villages, and data availability. Future research can complement and further the current research in the following potential directions in order to achieve large-area mapping across the whole nation: 1) establish a unified standard of urban villages in China to accommodate significant variances and fuzzy concepts, 2) address the challenges of data availability via flexible use of available multisource data and explore potential use of data-driven image super-resolution approaches, 3) create public benchmarks to ensure fair comparison and focus on the scalability, transferability, and interpretability of urban village recognition approaches, 4) initiate a crowdsourcing program to enable effective and efficient data collection and validation as well as application. This review not only supports urban village-related research in China, but also contributes valuable knowledge from a Chinese perspective to global informal settlements mapping research and the achievement of the United Nations' Sustainable Development Goals (SDGs).
{"title":"Mapping urban villages in China: Progress and challenges","authors":"Rui Cao , Wei Tu , Dongsheng Chen , Wenyu Zhang","doi":"10.1016/j.compenvurbsys.2025.102282","DOIUrl":"10.1016/j.compenvurbsys.2025.102282","url":null,"abstract":"<div><div>The shift toward high-quality urbanization has brought increased attention to the issue of “urban villages”, which has become a prominent social problem in China. However, there is a lack of available geospatial data on urban villages, making it crucial to prioritize urban village mapping. In order to assess the current progress in urban village mapping and identify challenges and future directions, we have conducted a comprehensive review, which to the best of our knowledge is the first of its kind in this field. Our review begins by providing a clear context for urban villages and elaborating the method for literature review, then summarizes the study areas, data sources, and approaches used for urban village mapping in China. We also address the challenges and future directions for further research. Through thorough investigation, we find that current studies only cover very limited study areas and periods and lack sufficient investigation into the scalability, transferability, and interpretability of identification approaches due to the challenges in concept fuzziness and variances, spatial heterogeneity and variances of urban villages, and data availability. Future research can complement and further the current research in the following potential directions in order to achieve large-area mapping across the whole nation: 1) establish a unified standard of urban villages in China to accommodate significant variances and fuzzy concepts, 2) address the challenges of data availability via flexible use of available multisource data and explore potential use of data-driven image super-resolution approaches, 3) create public benchmarks to ensure fair comparison and focus on the scalability, transferability, and interpretability of urban village recognition approaches, 4) initiate a crowdsourcing program to enable effective and efficient data collection and validation as well as application. This review not only supports urban village-related research in China, but also contributes valuable knowledge from a Chinese perspective to global informal settlements mapping research and the achievement of the United Nations' Sustainable Development Goals (SDGs).</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102282"},"PeriodicalIF":7.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628243","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 : 2025-03-11DOI: 10.1016/j.compenvurbsys.2025.102271
Jiahua Chen , Peter Kedron , Trisalyn Nelson , Dan Willett , Achituv Cohen , Colin Ferster
To motivate people to use bikes for transportation, cities are shifting their focus from constructing isolated bike lanes to building interconnected bike networks. The effectiveness of these networks is measured by their level of connectivity, specifically how easily individuals of all ages and abilities can reach their destinations by bike. While most researchers and policymakers hypothesize that well-connected bike networks will reduce crash risk by offering bicyclists extended protection from traffic, most studies find positive or null associations between network connectivity and bike crashes. This discrepancy may arise either from actual processes, such as increased ridership in high-traffic areas, or from variability in how connectivity is measured. Our study aims to understand relationships between bike safety and various connectivity metrics at the neighborhood level by deconstructing and comparing different metrics. We critique previous constructs of density-based metrics rely solely on bike infrastructure and introduce new density-based and routing-based metrics derived from low-stress networks. Using a negative binomial regression model, we examine the association between bike crashes and connectivity metrics across 125 block groups in Santa Barbara and Goleta, California. We find that increased density-based connectivity in both bike infrastructure and low-stress networks correlates with fewer crashes. In contrast, routing-based connectivity measures, which reflect bike access to key destinations, are positively associated with crashes. We conclude that different connectivity metrics can alter the direction of connectivity-safety associations. Our proposed metrics, which incorporate low-stress networks and routing algorithms, provide a more nuanced understanding of how connectivity is related to bicycling safety.
{"title":"Quantify relationships between bike network connectivity and bike safety: A comparative analysis of connectivity metrics conducted in two California cities","authors":"Jiahua Chen , Peter Kedron , Trisalyn Nelson , Dan Willett , Achituv Cohen , Colin Ferster","doi":"10.1016/j.compenvurbsys.2025.102271","DOIUrl":"10.1016/j.compenvurbsys.2025.102271","url":null,"abstract":"<div><div>To motivate people to use bikes for transportation, cities are shifting their focus from constructing isolated bike lanes to building interconnected bike networks. The effectiveness of these networks is measured by their level of connectivity, specifically how easily individuals of all ages and abilities can reach their destinations by bike. While most researchers and policymakers hypothesize that well-connected bike networks will reduce crash risk by offering bicyclists extended protection from traffic, most studies find positive or null associations between network connectivity and bike crashes. This discrepancy may arise either from actual processes, such as increased ridership in high-traffic areas, or from variability in how connectivity is measured. Our study aims to understand relationships between bike safety and various connectivity metrics at the neighborhood level by deconstructing and comparing different metrics. We critique previous constructs of density-based metrics rely solely on bike infrastructure and introduce new density-based and routing-based metrics derived from low-stress networks. Using a negative binomial regression model, we examine the association between bike crashes and connectivity metrics across 125 block groups in Santa Barbara and Goleta, California. We find that increased density-based connectivity in both bike infrastructure and low-stress networks correlates with fewer crashes. In contrast, routing-based connectivity measures, which reflect bike access to key destinations, are positively associated with crashes. We conclude that different connectivity metrics can alter the direction of connectivity-safety associations. Our proposed metrics, which incorporate low-stress networks and routing algorithms, provide a more nuanced understanding of how connectivity is related to bicycling safety.</div></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"119 ","pages":"Article 102271"},"PeriodicalIF":7.1,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592326","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}
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