{"title":"A city is not a static tree: understanding urban areas through the lens of real-time behavioral data","authors":"E. Moro","doi":"10.26754/ojs_zarch/zarch.2022197407","DOIUrl":null,"url":null,"abstract":"Cities are the main ground on which our society and culture develop today and will develop in the future. Against the traditional understanding of cities as physical spaces mostly around our neighborhoods, recent use of large-scale mobility datasets has enabled the study of our behavior at unprecedented spatial and temporal scales, much beyond our static residential spaces. Here we show how it is possible to use these datasets to investigate the role that human behavior plays in traditional urban problems like segregation, public health, or epidemics. Apart from measuring or monitoring such problems in a more comprehensive way, the analysis of those large datasets using modern machine learning techniques or causality detection permits to unveil of the behavioral roots behind them. As a result, only by incorporating real-time behavioral data can we design more efficient policies or interventions to improve such critical societal issues in our urban areas.","PeriodicalId":37382,"journal":{"name":"ZARCH","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ZARCH","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26754/ojs_zarch/zarch.2022197407","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Cities are the main ground on which our society and culture develop today and will develop in the future. Against the traditional understanding of cities as physical spaces mostly around our neighborhoods, recent use of large-scale mobility datasets has enabled the study of our behavior at unprecedented spatial and temporal scales, much beyond our static residential spaces. Here we show how it is possible to use these datasets to investigate the role that human behavior plays in traditional urban problems like segregation, public health, or epidemics. Apart from measuring or monitoring such problems in a more comprehensive way, the analysis of those large datasets using modern machine learning techniques or causality detection permits to unveil of the behavioral roots behind them. As a result, only by incorporating real-time behavioral data can we design more efficient policies or interventions to improve such critical societal issues in our urban areas.
期刊介绍:
ZARCH adopts a double perspective. Firstly, a global vision, that is international, although with its headquarters in our university and in the Spanish and European sphere, which implies coming to terms that most of the contributions are published in English, even though it seems compatible with a special attention to the Latin languages, not only in Spanish but also in French, Italian, Portuguese and others. Secondly, an interdisciplinary, transversal approximation with integrating visions, starting from the architectural field but open to other disciplines according with the changing limits and situations that today characterize the architecture field and urban studies. This leads us to the acceptance of close disciplines, from social sciences to technical visions, with logic condition of the scientific quality of contributions, previously evaluated by a rigorous system of arbitration. In any case, the Scientific Council''s advice to the magazine, guarantees the rigour and the attention to the standpoints and methodologies more innovative in our fields.