Computers Environment and Urban Systems最新文献

{"title":"Machine learning to model gentrification: A synthesis of emerging forms","authors":"Mueller Maya ,&nbsp;Hoque Simi ,&nbsp;Hamil Pearsall","doi":"10.1016/j.compenvurbsys.2024.102119","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102119","url":null,"abstract":"<div><p>Gentrification is a complex and context-specific process that involves changes in the built environment and social fabric of neighborhoods, often resulting in the displacement of vulnerable communities. Machine Learning (ML) has emerged as a powerful predictive tool that is capable of circumventing the methodological challenges that historically held back researchers from producing reliable forecasts of gentrification. Additionally, computer vision ML algorithms for landscape character assessment, or deep mapping, can now capture a wider range of built metrics related to gentrification-induced redevelopment. These novel ML applications promise to rapidly progress our understandings of gentrification and our capacity to translate academic findings into more productive direction for communities and stakeholders, but with this sudden development comes a steep learning curve. The current paper aims to bridge this divide by providing an overview of recent progress and an actionable template of use that is accessible for researchers across a wide array of academic fields. As a secondary point of emphasis, the review goes over Explainable Artificial Intelligence (XAI) tools for gentrification models and opens up discussion on the nuanced challenges that arise when applying black-box models to human systems. Abstract: Gentrification is a complex and context-specific process that involves changes in the built environment and social fabric of neighborhoods, often resulting in the displacement of vulnerable communities. Machine Learning (ML) has emerged as a powerful predictive tool that is capable of circumventing the methodological challenges that historically held back researchers from producing reliable forecasts of gentrification. Additionally, computer vision ML algorithms for landscape character assessment, or deep mapping, can now capture a wider range of built metrics related to gentrification-induced redevelopment. These novel ML applications promise to rapidly progress our understandings of gentrification and our capacity to translate academic findings into more productive direction for communities and stakeholders, but with this sudden development comes a steep learning curve. The current paper aims to bridge this divide by providing an overview of recent progress and an actionable template of use that is accessible for researchers across a wide array of academic fields. As a secondary point of emphasis, the review goes over Explainable Artificial Intelligence (XAI) tools for gentrification models and opens up discussion on the nuanced challenges that arise when applying black-box models to human systems.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"111 ","pages":"Article 102119"},"PeriodicalIF":6.8,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894742","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}

{"title":"From hearing to seeing: Linking auditory and visual place perceptions with soundscape-to-image generative artificial intelligence","authors":"Yonggai Zhuang ,&nbsp;Yuhao Kang ,&nbsp;Teng Fei ,&nbsp;Meng Bian ,&nbsp;Yunyan Du","doi":"10.1016/j.compenvurbsys.2024.102122","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102122","url":null,"abstract":"<div><p>People experience the world through multiple senses simultaneously, contributing to our sense of place. Prior quantitative geography studies have mostly emphasized human visual perceptions, neglecting human auditory perceptions at place due to the challenges in characterizing the acoustic environment vividly. Also, few studies have synthesized the two-dimensional (auditory and visual) perceptions in understanding human sense of place. To bridge these gaps, we propose a Soundscape-to-Image Diffusion model, a generative Artificial Intelligence (AI) model supported by Large Language Models (LLMs), aiming to visualize soundscapes through the generation of street view images. By creating audio-image pairs, acoustic environments are first represented as high-dimensional semantic audio vectors. Our proposed Soundscape-to-Image Diffusion model, which contains a Low-Resolution Diffusion Model and a Super-Resolution Diffusion Model, can then translate those semantic audio vectors into visual representations of place effectively. We evaluated our proposed model by using both machine-based and human-centered approaches. We proved that the generated street view images align with our common perceptions, and accurately create several key street elements of the original soundscapes. It also demonstrates that soundscapes provide sufficient visual information places. This study stands at the forefront of the intersection between generative AI and human geography, demonstrating how human multi-sensory experiences can be linked. We aim to enrich geospatial data science and AI studies with human experiences. It has the potential to inform multiple domains such as human geography, environmental psychology, and urban design and planning, as well as advancing our knowledge of human-environment relationships.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102122"},"PeriodicalIF":6.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140816009","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}

{"title":"How shareable is your trip? A path-based analysis of ridesplitting trip shareability","authors":"Guan Huang ,&nbsp;Zhan Zhao ,&nbsp;A.G.O. Yeh","doi":"10.1016/j.compenvurbsys.2024.102120","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102120","url":null,"abstract":"<div><p>As an emerging sustainable mobility solution, ridesplitting services match passengers in a similar direction with a single vehicle to reduce fleet size, vehicle kilometers traveled and traffic emissions. However, these benefits can only be achieved with successful matching (sharing) between passengers, which emphasizes the importance of a comprehensive understanding of the matching success rate, i.e., shareability. Despite extensive research into the determinants of shareability, existing literature either relies on simulations and theoretical models with limited empirical validation, or focuses on system-level shareability for the whole market, overlooking the significant spatiotemporal variability of shareability across trips. This study aims to fill these gaps by proposing a path-based model that leverages real-world ridesplitting data to quantify the determinants of shareability at a finer spatiotemporal granularity. Utilizing data from New York City, our results show that: (1) shareability is spatiotemporally heterogeneous; (2) high demand intensity, especially the intensity of medium−/short-distance trips, contributes to greater shareability; (3) the positive contribution of demand intensity diminishes as it increases; (4) a higher road speed improves shareability; (5) excessive one-way street and over-dense street network are related to low shareability. These findings validate and enrich prior findings, which can be used to inform the future development of ridesplitting services.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102120"},"PeriodicalIF":6.8,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807526","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}

{"title":"Mapping Great Britain's semantic footprints through a large language model analysis of Reddit comments","authors":"Cillian Berragan ,&nbsp;Alex Singleton ,&nbsp;Alessia Calafiore ,&nbsp;Jeremy Morley","doi":"10.1016/j.compenvurbsys.2024.102121","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102121","url":null,"abstract":"<div><p>Observed regional variation in geotagged social media text is often attributed to dialects, where features in language are assumed to exhibit region-specific properties. While dialects are seen as a key component in defining the identity of regions, there are a multitude of other geographic properties that may be captured within natural language text. In our work, we consider locational mentions that are directly embedded within comments on the social media website Reddit, providing a range of associated semantic information, and enabling deeper representations between locations to be captured. Using a large corpus of geoparsed Reddit comments from UK-related local discussion subreddits, we first extract embedded semantic information using a large language model, aggregated into local authority districts, representing the semantic footprint of these regions. These footprints broadly exhibit spatial autocorrelation, with clusters that conform with the national borders of Wales and Scotland. London, Wales, and Scotland also demonstrate notably different semantic footprints compared with the rest of Great Britain.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102121"},"PeriodicalIF":6.8,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0198971524000504/pdfft?md5=ea3c1ade10d7db227e51de2d2551f34b&pid=1-s2.0-S0198971524000504-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140649792","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}

{"title":"Indoor mobility data encoding with TSTM-in: A topological-semantic trajectory model","authors":"Jianxin Qin ,&nbsp;Lu Wang ,&nbsp;Tao Wu ,&nbsp;Ye Li ,&nbsp;Longgang Xiang ,&nbsp;Yuanyuan Zhu","doi":"10.1016/j.compenvurbsys.2024.102114","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102114","url":null,"abstract":"<div><p>The growing ubiquity of location/activity sensing technologies has created unprecedented opportunities for research on human spatiotemporal interaction behavior in mobile environments. However, existing studies of human mobility need to sufficiently account for the association of indoor scenes with the semantics of human behavior. This paper introduces TSTM-in, a trajectory model that combines trajectory data and indoor scenes using topological semantic modeling, semantic trajectory reconstruction, and trajectory queries. The model effectively manages indoor semantic trajectory data and extracts topological behavioral semantics by incorporating important points across a trajectory to reflect the semantics of key points connected to indoor corridors and regions. These topological semantics facilitate the creation of a flexible intersection-based indoor semantic trajectory reconstruction. Reconstructed semantic trajectories represent human mobility by integrating semantic data sets along the time axis. A case study with real-world trajectory queries from travelers demonstrates the model's effectiveness. TSTM-in realizes the association of indoor scenes with human behavior semantics, supporting the construction of mobile object management applications for indoor scenes and providing scientific and reasonable spatiotemporal semantic information description for location service-based intelligent cities.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102114"},"PeriodicalIF":6.8,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140647373","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}

{"title":"Measuring two decades of urban spatial structure: The evolution of agglomeration economies in American metros","authors":"Elijah Knaap,&nbsp;Sergio Rey","doi":"10.1016/j.compenvurbsys.2024.102116","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102116","url":null,"abstract":"<div><p>In this paper we examine the evolution of urban spatial structure in U.S. metropolitan areas over nearly two decades. Using annual block-level data from the Longitudinal Employment Household Dynamics database, we introduce a technique for identifying regional employment centers that both adheres to urban economic theory and pays homage to classic contributions in local spatial statistics. Centers are defined as local spatial statistical outliers on the network-based job accessibility surface. We proceed by identifying the location and employment makeup of centers for each metropolitan region in the USA from 2002 to 2019 and discuss emergent trends across time and space. Critically, we not only explore empirical patterns, but we discuss the relationship between polycentricity, the evolution of urbanization and localization economies, and regional specialization. We confirm again the pattern of polycentricity in U.S. metros and show that the structure of metropolitan employment is largely stable over time. We also document a continuing trend away from urbanization economies into more specialized subcenters.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102116"},"PeriodicalIF":6.8,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0198971524000450/pdfft?md5=9fd6287b175eb18342b8ee2c1892ab5d&pid=1-s2.0-S0198971524000450-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643636","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}

{"title":"Spatial constraints in cellular automata-based urban growth models: A systematic comparison of classifiers and input urban maps","authors":"Cassiano Bastos Moroz,&nbsp;Tobias Sieg,&nbsp;Annegret H. Thieken","doi":"10.1016/j.compenvurbsys.2024.102118","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102118","url":null,"abstract":"<div><p>Spatial constraints are fundamental to integrating the spatial suitability to urbanization into Cellular Automata-based (CA) urban growth models, but there is a lack of consensus on the optimal methods for this purpose. This study compared the performance of three probabilistic classifiers to generate suitability surfaces for CA-based urban growth models: Logistic Regression using Generalized Linear Model (LR-GLM), Logistic Regression using Generalized Additive Model (LR-GAM), and Random Forest (RF). The study also evaluated the sensitivity of these classifiers to the input urban map adopted as a dependent variable. For this analysis, seven maps were tested: the historical urban map containing the entire extent of the urban footprint, and six additional maps containing only the recently urbanized areas over timeframes ranging from one year up to two decades. The comparison evaluated the goodness of fit of the suitability surfaces and the spatial accuracy of the urban growth simulations, using five large Brazilian cities as case study areas. The results revealed that the RF classifier significantly outperformed the LR-based classifiers. However, this overperformance was more prominent when incorporating the new urban cells over the last one to two decades of growth as input urban maps. In addition, the sensitivity analysis of the input urban maps emphasized the benefits of calibrating the classifier using the recently urbanized cells rather than the historical urban extent. We consistently observed these results concerning classifiers and input urban maps across all five case study areas. Thus, the RF classifier combined with a training dataset containing the newly urbanized areas over at least the last 10 years systematically resulted in the suitability surfaces with the highest predictability among all tested scenarios.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102118"},"PeriodicalIF":6.8,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140632947","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}

{"title":"How does pedestrian permeability vary in and across cities? A fine-grained assessment for all large cities in Germany","authors":"Ariane Droin ,&nbsp;Michael Wurm ,&nbsp;Matthias Weigand ,&nbsp;Carsten Gawlas ,&nbsp;Manuel Köberl ,&nbsp;Hannes Taubenböck","doi":"10.1016/j.compenvurbsys.2024.102115","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102115","url":null,"abstract":"<div><p>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 have not been conducted up to now. In this paper, we apply the concept of Individual Walkable Neighbourhoods (IWN) to measure local urban pedestrian permeability for all large cities in Germany with more than 100,000 inhabitants. Our results reveal great differences in intra- and inter-urban pedestrian permeability, and based on examples, we explore various factors that influence local permeability, such as topography or structural types. Furthermore, the large-scale analysis is used to identify characteristic patterns of high (e.g., urban centers) or low (e.g., neighbourhoods of single-family detached houses) permeability for German cities.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102115"},"PeriodicalIF":6.8,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0198971524000449/pdfft?md5=be7fc3429e9d7abaa14ed36540f9d82f&pid=1-s2.0-S0198971524000449-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140607063","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}

{"title":"How far will you go? From empirical findings to formalization of walking route distances","authors":"Jonatan Almagor ,&nbsp;Itzhak Omer ,&nbsp;Noam Omer ,&nbsp;Amit Birenboim","doi":"10.1016/j.compenvurbsys.2024.102117","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102117","url":null,"abstract":"<div><p>Empirically based theorization of walking range patterns is rather limited, leading researchers and planners to rely on simplistic assumptions as to the typical distance and duration that pedestrians may walk. Using high-resolution GPS data collected from over 11,000 participants in the Tel-Aviv metropolitan area, we provide an empirical estimate for the distribution of walking route distance and duration, while examining potential factors that may affect it. In addition, we develop a general analytical framework that describes walking route patterns. Our results show that the average route distance and duration in Tel-Aviv metropolitan is 630 m and 7.9 min. Factors associated with walking range include socio-demographic characteristics of walkers (age-group, socioeconomic status and number of cars in a household) and city characteristics (longer routes in cities with a larger population and in areas with high density of street intersections). Our main finding is that walking route distance distribution can be best described using the theoretical log-normal distribution and can be characterized using its mean-log and SD-log parameters. The log-normal parameters make an analytical framework that enables the evaluation of differences in walking patterns between places and identification of where interventions are required to promote active travel. We explain why the log-normal distribution is likely to be suitable to other cases worldwide.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102117"},"PeriodicalIF":6.8,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140558414","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}

{"title":"Exploring emergent soundscape profiles from crowdsourced audio data","authors":"Aura Kaarivuo ,&nbsp;Jonas Oppenländer ,&nbsp;Tommi Kärkkäinen ,&nbsp;Tommi Mikkonen","doi":"10.1016/j.compenvurbsys.2024.102112","DOIUrl":"https://doi.org/10.1016/j.compenvurbsys.2024.102112","url":null,"abstract":"<div><p>The key component of designing sustainable, enriching, and inclusive cities is public participation. The soundscape is an integral part of an immersive environment in cities, and it should be considered as a resource that creates the acoustic image for an urban environment. For urban planning professionals, this requires an understanding of the constituents of citizens' emergent soundscape experience. The goal of this study is to present a systematic method for analyzing crowdsensed soundscape data with unsupervised machine learning methods. This study applies a crowdsensed sound- scape experience data collection method with low threshold for participation. The aim is to analyze the data using unsupervised machine learning methods to give insights into soundscape perception and quality.</p><p>For this purpose, qualitative and raw audio data were collected from 111 participants in Helsinki, Finland, and then clustered and further analyzed. We conclude that a machine learning analysis combined with accessible, mobile crowdsensing methods enable results that can be applied to track hidden experiential phenomena in the urban soundscape.</p></div>","PeriodicalId":48241,"journal":{"name":"Computers Environment and Urban Systems","volume":"110 ","pages":"Article 102112"},"PeriodicalIF":6.8,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0198971524000413/pdfft?md5=cd512c7aaeca07125b7aafa5779034ba&pid=1-s2.0-S0198971524000413-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140536120","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}