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How does pedestrian permeability vary in and across cities? A fine-grained assessment for all large cities in Germany Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-04-17 Ariane Droin, Michael Wurm, Matthias Weigand, Carsten Gawlas, Manuel Köberl, Hannes Taubenböck
Pedestrian permeability is a key aspect of the accessibility of urban environments. In particular, high permeability increases the walkability of cities, which is advocated by sustainable urban design practices. Previous research on pedestrian permeability has predominantly focused only on single and very specific, characteristic, and homogenous urban morphologies but investigations at a broader scale
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How far will you go? From empirical findings to formalization of walking route distances Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-04-16 Jonatan Almagor, Itzhak Omer, Noam Omer, Amit Birenboim
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Exploring emergent soundscape profiles from crowdsourced audio data Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-04-08 Aura Kaarivuo, Jonas Oppenländer, Tommi Kärkkäinen, Tommi Mikkonen
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A self-supervised detection method for mixed urban functions based on trajectory temporal image Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-04-05 Zhixing Chen, Luliang Tang, Xiaogang Guo, Guizhou Zheng
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Navigating the post-pandemic urban landscape: Disparities in transportation recovery & regional insights from New York City Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-04-03 Dan Qiang, Grant McKenzie
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Fire and smoke digital twin – A computational framework for modeling fire incident outcomes Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-04-03 Ryan Hardesty Lewis, Junfeng Jiao, Kijin Seong, Arya Farahi, Paul Navrátil, Nate Casebeer, Dev Niyogi
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Who has access to cycling infrastructure in Canada? A social equity analysis Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-27 Qiao Zhao, Meghan Winters, Trisalyn Nelson, Karen Laberee, Colin Ferster, Kevin Manaugh
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Community resilience to wildfires: A network analysis approach by utilizing human mobility data Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-26 Qingqing Chen, Boyu Wang, Andrew Crooks
Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event
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Building footprint data for countries in Africa: To what extent are existing data products comparable? Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-22 Heather R. Chamberlain, Edith Darin, Wole Ademola Adewole, Warren C. Jochem, Attila N. Lazar, Andrew J. Tatem
Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data
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A data-driven framework for agent-based modeling of vehicular travel using publicly available data Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-19 Yirong Zhou, Xiaoyue Cathy Liu, Bingkun Chen, Tony Grubesic, Ran Wei, Danielle Wallace
This study presents a methodology for creating a synthetic travel demand, encompassing households and individuals and their daily activities, to support agent-based modeling (ABM) in urban planning and travel analysis. Unlike previous studies, which often rely on proprietary data, our approach is entirely based on open data, ensuring replicability by the broader research community. The research is
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When spatial interpolation matters: Seeking an appropriate data transformation from the mobile network for population estimates Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-15 Martin Šveda, Pavol Hurbánek, Michala Sládeková Madajová, Konštantín Rosina, Filip Förstl, Petr Záboj, Ján Výbošťok
Analyses utilizing mobile positioning data rarely provide an exact method of data transformation to target spatial units. A common reason is likely the fact that researchers have already worked with spatially aggregated data prepared by the mobile operator or processing company. The article demonstrates the critical importance of employing an appropriate method to transform data from the mobile network
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A graph-based neural network approach to integrate multi-source data for urban building function classification Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-15 Bo Kong, Tinghua Ai, Xinyan Zou, Xiongfeng Yan, Min Yang
Accurately understanding the functions of buildings is crucial for urban monitoring, analysis of urban economic structures, and effectively allocating resources. Previous studies have investigated building function classification using single or dual data sources. However, the complexity of building functions cannot be fully reflected by a limited number of data sources. In addition, the functions
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Examining the relationship between active transport and exposure to streetscape diversity during travel: A study using GPS data and street view imagery Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-14 Hanlin Zhou, Jue Wang, Michael Widener, Kathi Wilson
Active transport (AT)—physical activity (PA) during travel—can promote human health. Among built environment factors related to travel research, design refers to the street network features encouraging AT. The advent of street view images (SVIs) presents the potential to measure design during travel by capturing the eye-level built environments. Benefited by SVIs, this study innovatively introduces
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Understanding the protection of privacy when counting subway travelers through anonymization Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-14 Nadia Shafaeipour, Valeriu-Daniel Stanciu, Maarten van Steen, Mingshu Wang
Public transportation, especially in large cities, is critical for livability. Counting passengers as they travel between stations is crucial to establishing and maintaining effective transportation systems. Various information and communication technologies, such as GPS, Bluetooth, and Wi-Fi, have been used to measure people's movements automatically. Regarding public transportation applications,
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Interpretable machine learning for predicting urban flash flood hotspots using intertwined land and built-environment features Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-13 Zhewei Liu, Tyler Felton, Ali Mostafavi
Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally
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Intercity connectivity and urban innovation Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-03-01 Xiaofan Liang, César A. Hidalgo, Pierre-Alexandre Balland, Siqi Zheng, Jianghao Wang
Urban outputs, from economy to innovation, are known to grow as a power of a city's population. But, since large cities tend to be central in transportation and communication networks, the effects attributed to city size may be confounded with those of intercity connectivity. Here, we map intercity networks for the world's two largest economies (the United States and China) to explore whether a city's
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From intangible to tangible: The role of big data and machine learning in walkability studies Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-26 Jun Yang, Pia Fricker, Alexander Jung
Walkability reflects the well-being of a city, and its measurement is evolving rapidly due to advancements of big data and machine learning technologies. The study examines the transformative impact of these technological interventions on the evaluation of walkability trends over the period 2015 to 2022. We create a framework consisting of big data sources, machine learning methods, and research purposes
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Rating places and crime prevention: Exploring user-generated ratings to assess place management Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-23 Thom Snaphaan, Wim Hardyns, Lieven J.R. Pauwels, Kate Bowers
This study assesses how the quality of place management (measured with user-generated ratings from Google Places) is related to crime occurrences at specific settings and whether specific crime types are related to specific types of places. In 50 randomly sampled neighborhoods in Ghent (Belgium) and London (United Kingdom), we analyzed Google Places data as a proxy measure for the quality of place
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Applicability and sensitivity analysis of vector cellular automata model for land cover change Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-17 Yao Yao, Ying Jiang, Zhenhui Sun, Linlong Li, Dongsheng Chen, Kailu Xiong, Anning Dong, Tao Cheng, Haoyan Zhang, Xun Liang, Qingfeng Guan
Urbanization-induced land cover changes significantly impact ecological environments and socioeconomic growth. Vector-based cellular automata (VCA) models are an advanced cellular automata (CA) method that use irregular cells and perform well in simulating land use changes within urban areas. However, the applicability and parameter setting of VCA models for land cover change simulation are still challenging
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A deep multi-scale neural networks for crime hotspot mapping prediction Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-17 Changfeng Jing, Xinxin Lv, Yi Wang, Mengjiao Qin, Shiyuan Jin, Sensen Wu, Gaoran Xu
Prediction of high-risk areas for urban crime is of great significance for maintaining public safety and sustainable development. However, existing approaches are deficient in spatiotemporal sensitivity and perceptivity, which make it difficult to extract the spatiotemporal dependency from uneven and sparsely distributed data. To address this problem, the novel multi-scale neural network models, namely
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Machine learning-based characterisation of urban morphology with the street pattern Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-15 Cai Wu, Jiong Wang, Mingshu Wang, Menno-Jan Kraak
Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the and of street networks. To
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Application of the local colocation quotient method in jobs-housing balance measurement based on mobile phone data: A case study of Nanjing City Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-08 Hao Liu, Mei-Po Kwan, Mingxing Hu, Hui Wang, Jiemin Zheng
The issue of jobs-housing balance concerns the sustainable development of cities and the well-being of residents. Conventional measurement approaches, however, often fall short due to the zoning problem (as a subproblem of the modifiable areal unit problem), leading to inconsistent and inaccurate results depending on the spatial partitioning scheme applied. This paper discusses the application and
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Towards a scalable and transferable approach to map deprived areas using Sentinel-2 images and machine learning Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-07 Maxwell Owusu, Arathi Nair, Amir Jafari, Dana Thomson, Monika Kuffer, Ryan Engstrom
African cities are growing rapidly and more than half of their populations live in deprived areas. Local stakeholders urgently need accurate, granular, and routine maps to plan, upgrade, and monitor dynamic neighborhood-level changes. Satellite imagery provides a promising solution for consistent, accurate high-resolution maps globally. However, most studies use very high spatial resolution images
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How do contributions of organizations impact data inequality in OpenStreetMap? Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-06 Anran Yang, Hongchao Fan, Qingren Jia, Mengyu Ma, Zhinong Zhong, Jun Li, Ning Jing
Despite the rapid advancement and extensive applications of online Volunteered Geographical Information (VGI) projects such as OpenStreetMap (OSM), the persistence of data inequality remains a significant challenge, compromising the global reliability of their data products. This study examines the influence of contributions made by organizations, which have notably risen within the OSM community,
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Learning visual features from figure-ground maps for urban morphology discovery Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-02-03 Jing Wang, Weiming Huang, Filip Biljecki
Most studies of urban morphology rely on morphometrics, such as building area and street length. However, these methods often fall short in capturing visual patterns that carry abundant information about the configuration of urban elements and how they interact spatially. In this study, we introduce a novel method for learning morphology features based on figure-ground maps, which leverages recent
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Intelligent coverage and cost-effective monitoring: Bus-based mobile sensing for city air quality Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-01-20 Meng Huang, Xinchi Li, Mingchuan Yang, Xi Kuai
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Urban tree failure probability prediction based on dendrometric aspects and machine learning models Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-01-19 Danilo Samuel Jodas, Sérgio Brazolin, Giuliana Del Nero Velasco, Reinaldo Araújo de Lima, Takashi Yojo, João Paulo Papa
Urban forests provide many benefits for municipalities and their residents, including air quality improvement, urban atmosphere cooling, and pluvial flooding reduction. Monitoring the trees is one of the tasks among the several urban forest assessment procedures. Trees with a risk of falling may threaten the locals and the infrastructure of the cities, thereby being an immediate concern for forestry
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An ANN-based method C population Dasymetric mapping to avoid the scale heterogeneity: A case study in Hong Kong, 2016–2021 Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-01-16 Weipeng Lu, Qihao Weng
A comprehensive understanding of population distribution is critical for assessing socio-economic issues. However, the widely used dasymetric mapping method relies on models built at a coarse administrative scale and estimates population at a fine-gridded scale. This difference in scale between the training and estimating domains results in significant heterogeneity in data distribution. To address
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Inferring storefront vacancy using mobile sensing images and computer vision approaches Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2024-01-09 Yan Li, Ying Long
Storefront vacancy has been a widespread and worldwide phenomenon, raising concerns about the changing characteristic of the retail landscape, loss of community vitality, and hollowing out of cities. Although the causes leading to this phenomenon have been extensively debated, little granular data are available to evaluate the issue in a timely manner. Therefore, this study aims to develop a data-driven
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Measuring pedestrians' movement and building a visual-based attractiveness map of public spaces using smartphones Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-12-27 Deng Ai, Haofeng Wang, Da Kuang, Xiuqi Zhang, Xiaojun Rao
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Towards healthcare access equality: Understanding spatial accessibility to healthcare services for wheelchair users Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-12-23 Kun Chen, Pengxiang Zhao, Kun Qin, Mei-Po Kwan, Niman Wang
Considering that the number of wheelchair users is on the rise at the global level due to population aging, it is crucial to secure their rights to have adequate access to healthcare services. Spatial accessibility to healthcare services has been well recognized to influence people's health. However, research on healthcare accessibility of wheelchair users is scarce. This study proposes a barrier-free
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A novel framework for road vectorization and classification from historical maps based on deep learning and symbol painting Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-12-14 Chenjing Jiao, Magnus Heitzler, Lorenz Hurni
Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical
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A simple agent-based model for planning for bicycling: Simulation of bicyclists' movements in urban environments Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-12-15 Parisa Zare, Simone Leao, Ori Gudes, Christopher Pettit
Bicycling can improve the sustainability and liveability of cities, many of which desperately require better active transport infrastructure. Urban and transport planners need to examine how improvements in infrastructure change bicyclists' behaviour. With this knowledge, investment in bicycling networks can be more efficient and encourage the use of bicycling for transportation. This study developed
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Enhancing geospatial retail analysis by integrating synthetic human mobility simulations Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-12-08 Santiago Garcia-Gabilondo, Yuya Shibuya, Yoshihide Sekimoto
The accuracy of retail location models depends on their precise calibration, but the data necessary for such a key task is seldom available. In this research, we use synthetic human mobility data, which introduces commuting dynamics, to improve the reliability of such models. We use the origin-destination flows to distribute households' potential expenditures in their home and commuting locations with
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Advances in estimating pedestrian measures through artificial intelligence: From data sources, computer vision, video analytics to the prediction of crash frequency Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-11-25 Ting Lian, Becky P.Y. Loo, Zhuangyuan Fan
Data are essential for planning walkable cities that are comfortable, convenient and safe to pedestrians. Yet, in contrast to massive vehicular traffic data, data on pedestrian traffic have not been systematically collected by municipal governments. Nowadays, geospatial big data provide rich information related to human activities and, hence, can capture street scenes in an innovative way. Using bus
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Exploiting geospatial data of connectivity and urban infrastructure for efficient positioning of emergency detection units in smart cities Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-11-21 João Paulo Just Peixoto, João Carlos N. Bittencourt, Thiago C. Jesus, Daniel G. Costa, Paulo Portugal, Francisco Vasques
The detection of critical situations through the adoption of multi-sensor Emergency Detection Units (EDUs) can significantly reduce the time between the initial stages of urban emergencies and the actual responses to relieve its negative effects, usually through the rescuing of endangered people, the attending to eventual victims, and the mitigating of its causes. However, although the benefits of
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Impacts of COVID-19 on urban networks: Evidence from a novel approach of flow measurement based on nighttime light data Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-11-20 Congxiao Wang, Zuoqi Chen, Bailang Yu, Bin Wu, Ye Wei, Yuan Yuan, Shaoyang Liu, Yue Tu, Yangguang Li, Jianping Wu
The coronavirus disease 2019 (COVID-19) has caused significant changes in urban networks due to epidemic prevention policies (e.g., social distancing strategies) and personal concerns. Previous measurements of urban networks were mainly based on flow data or were simulated from statistical data using models (e.g., Gravity model). However, these measurements are not directly applicable to the mapping
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Spatial (in)accuracy of cell broadcast alerts in urban context: Feedback from the April 2023 Cannes tsunami trial Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-11-16 Esteban Bopp, Johnny Douvinet, Noé Carles, Pierre Foulquier, Matthieu Péroche
Since June 2022, France is equipped with cell broadcast technology which alerts individuals within a predefined area. Despite the proven effectiveness of this technology, few studies take a spatial view of cell broadcast alert at a local level. Trials carried out in France were assessed only on their technical success, without verifying the rate of reception of the message by individuals in the official
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The detection of residential developments in urban areas: Exploring the potentials of deep-learning algorithms Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-11-02 Ji-hwan Kim, Dohyung Kim, Hee-Jung Jun, Jae-Pil Heo
A rich volume of research has detected urban growth by quantifying the land use/land cover (LU/LC) changes based on remote sensing technologies. However, the research has limitations in identifying various formats of urban growth, particularly small-scale urban growth, such as infill development or redevelopment in urban areas, prompted by smart growth and sustainable urban development. This paper
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Do human mobility network analyses produced from different location-based data sources yield similar results across scales? Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-10-27 Chia-Wei Hsu, Chenyue Liu, Kiet Minh Nguyen, Yu-Heng Chien, Ali Mostafavi
The burgeoning availability of sensing technology and location-based data is driving the expansion of analysis of human mobility networks in science and engineering research, as well as in epidemic forecasting and mitigation, urban planning, traffic engineering, emergency response, and business development. However, studies employ datasets provided by different location-based data providers, and the
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A topology-based approach to identifying urban centers in America using multi-source geospatial big data Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-10-20 Zheng Ren, Stefan Seipel, Bin Jiang
Urban structure can be better comprehended through analyzing its cores. Geospatial big data facilitate the identification of urban centers in terms of high accuracy and accessibility. However, previous studies seldom leverage multi-source geospatial big data to identify urban centers from a topological perspective. This study attempts to identify urban centers through the spatial integration of multi-source
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Comprehensive urban space representation with varying numbers of street-level images Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-10-11 Yingjing Huang, Fan Zhang, Yong Gao, Wei Tu, Fabio Duarte, Carlo Ratti, Diansheng Guo, Yu Liu
Street-level imagery has emerged as a valuable tool for observing large-scale urban spaces with unprecedented detail. However, previous studies have been limited to analyzing individual street-level images. This approach falls short in representing the characteristics of a spatial unit, such as a street or grid, which may contain varying numbers of street-level images ranging from several to hundreds
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360-degree video for virtual place-based research: A review and research agenda Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-10-05 Jonathan Cinnamon, Lindi Jahiu
360-degree video is an immersive technology used in research across academic disciplines. This paper provides the first comprehensive review on the use of 360-degree video for virtual place-based research, highlighting its use in experimental, experiential, and environmental observation studies. Five key research domains for 360-degree video are described: tourism and cultural heritage; built environment
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A space-time flow LISA approach for panel flow data Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-09-22 Ran Tao, Yuzhou Chen, Jean-Claude Thill
Spatial flow data represent meaningful spatial interaction (SI) phenomena between geographic regions that are often highly dynamic. However, most existing flow analytical methods are cross-sectional, and there is a lack of methods to measure spatiotemporal autocorrelation of flow data. To fill this gap, we proposed a new localized spatial statistical method called Space-Time Flow LISA. The method design
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Does partition matter? A new approach to modeling land use change Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-09-18 Fei He, Jun Yang, Yuqing Zhang, Wenbo Yu, Xiangming Xiao, Jianhong Xia
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Sidewalk networks: Review and outlook Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-09-15 Daniel Rhoads, Clément Rames, Albert Solé-Ribalta, Marta C. González, Michael Szell, Javier Borge-Holthoefer
From a transport perspective, increasing active travel –and walking in particular– is crucial for the future of sustainable cities, as reflected in global decarbonisation policies and agendas. Further, walking is much more than a mere mode of transport: it provides a fundamental social function, fostering vibrant cohesive communities. Arguably, walking and its associated infrastructure –sidewalks–
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Streetscapes as part of servicescapes: Can walkable streetscapes make local businesses more attractive? Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-09-12 Bon Woo Koo, Uijeong Hwang, Subhrajit Guhathakurta
Attractive local businesses can make cities more walkable by providing desirable destinations to walk to. The term servicescape has been used to describe the physical settings and environments that affect customers' inference of the service quality of businesses at that location. This study extends the concept of servicescapes to include walkable streetscapes and examines whether features that make
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OMOD: An open-source tool for creating disaggregated mobility demand based on OpenStreetMap Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-09-15 Leo Strobel, Marco Pruckner
This paper introduces OMOD (OpenStreetMap Mobility Demand Generator), a new open-source activity-based mobility demand generation tool. OMOD uses a data-driven approach, calibrated with household travel survey data, to generate a population of agents with detailed daily activity schedules that state what activities each agent plans to conduct, where, and for how long. The temporal aspect of the output
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Rooftop segmentation and optimization of photovoltaic panel layouts in digital surface models Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-09-01 Mohammad Aslani, Stefan Seipel
Rooftop photovoltaic panels (RPVs) are being increasingly used in urban areas as a promising means of achieving energy sustainability. Determining proper layouts of RPVs that make the best use of rooftop areas is of importance as they have a considerable impact on the RPVs performance in efficiently producing energy. In this study, a new spatial methodology for automatically determining the proper
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Smart water metering as a non-invasive tool to infer dwelling type and occupancy – Implications for the collection of neighbourhood-level housing and tourism statistics Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-29 A. Newing, O. Hibbert, J. Van-Alwon, S. Ellaway, A. Smith
The international rollout of advanced metering infrastructure (AMI) in the residential water supply sector affords tremendous benefits in driving water-use efficiencies, accurate billing and network management (e.g. leak detection). AMI, using ‘smart meters’ fitted at a dwelling level, record water consumption at high temporal resolution. Since water is typically only consumed when householders are
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Detecting older pedestrians and aging-friendly walkability using computer vision technology and street view imagery Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-19 Dongwei Liu, Ruoyu Wang, George Grekousis, Ye Liu, Yi Lu
As an emerging and freely available urban big data, Street View Imagery (SVI) has proven to be a useful resource to examine various urban phenomena in human behavior, the built environment and their interactions. However, due to technical limitations, previous studies often focused on general pedestrians and ignored certain population subgroups such as older adults. In this study, we develop an innovative
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Implementing Deep Learning algorithms for urban tree detection and geolocation with high-resolution aerial, satellite, and ground-level images Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-14 Luisa Velasquez-Camacho, Maddi Etxegarai, Sergio de-Miguel
Urban forests are becoming increasingly important for human well-being as they provide ecosystem services that contribute to improving well-being of city dwellers and to addressing climate change. However, despite their importance, there is an information gap in most of the world's urban forests due to the high cost and complexity of conducting standard forest inventories in urban environments. New
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Augmenting the Social Vulnerability Index using an agent-based simulation of Hurricane Harvey Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-08 Anna E. Brower, Balaji Ramesh, Kazi Ashik Islam, Henning S. Mortveit, Stefan Hoops, Anil Vullikanti, Madhav V. Marathe, Benjamin Zaitchik, Julia M. Gohlke, Samarth Swarup
In this work, an agent-based model (ABM) of population evacuation during Hurricane Harvey is developed. The ABM integrates data from several sources, including data about physical conditions, population demographics, and geography. This simulation is then used to compute multiple measures of exposures to hazards for the population. Outputs from the ABM are then evaluated by adding computed measures
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Spatial stratified heterogeneity and driving mechanism of urban development level in China under different urban growth patterns with optimal parameter-based geographic detector model mining Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-04 Qingsong He, Miao Yan, Linzi Zheng, Bo Wang
The rapid urbanization leads to the dynamic changes of the urban external landscape and forms different urban growth patterns (UGP), which in turn affects the development level of the urban internal functions as well. However, few studies have quantitatively examined the spatial stratified heterogeneity (SSH) and driving mechanism of the urban development level (UDL) under different UGPs. Based on
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Large-scale agent-based simulation model of pedestrian traffic flows Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-04 Dana Kaziyeva, Petra Stutz, Gudrun Wallentin, Martin Loidl
Mobility patterns of pedestrians at a very high spatial and temporal resolution support urban planning strategies and facilitate a better understanding of the transport system. In this study, we develop an agent-based model that simulates disaggregated pedestrian traffic flows at a regional scale. This model is designed to overcome limitations of existing approaches in pedestrian traffic modelling
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Areal interpolation of population projections consistent with different SSPs from 1-km resolution to block level based on USA Structures dataset Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-03 Heng Wan, Sumitrra Ganguli, Milan Jain, David Anderson, Narmadha Meenu Mohankumar, Kyle Wilson
Population data are normally collected at various census administrative levels, and areal interpolation of population is often required to transform population to the desired spatial resolution. Building footprint datasets, such as Microsoft building footprints, have proven to be useful in estimating population distribution and can therefore be used for areal interpolation of population. In addition
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Predicting building age from urban form at large scale Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-08-02 Florian Nachtigall, Nikola Milojevic-Dupont, Felix Wagner, Felix Creutzig
To stay within 1.5 °C of global warming, reducing energy-related emissions in the building sector is essential. Rather than generic climate recommendations, this requires tailored, low-carbon urban planning solutions and spatially explicit methods that can inform policy measures at urban, street and building scale. Here, we propose a scalable method that is able to predict building age information
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Evolution of residents' cooperative behavior in neighborhood renewal: An agent-based computational approach Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-07-31 Ruopeng Huang, Guiwen Liu, Kaijian Li, Zhengxuan Liu, Xinyue Fu, Jun Wen
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Editorial Board Comput. Environ. Urban Syst. (IF 6.454) Pub Date : 2023-07-26
Abstract not available