Measuring heterogeneous perception of urban space with massive data and machine learning: An application to safety

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dc.catalogadorgrr
dc.contributor.authorRamírez Sarmiento, Tomás Ignacio
dc.contributor.authorHurtubia, Ricardo
dc.contributor.authorLöbel Díaz, Hans-Albert
dc.contributor.authorRossetti, T.
dc.contributor.otherCEDEUS (Chile)
dc.date.accessioned2024-03-27T13:32:11Z
dc.date.available2024-03-27T13:32:11Z
dc.date.issued2021
dc.description.abstractUrban space safety Machine learning Heterogeneous perception Built environment In the last decade, large street imagery data sets and machine learning developments have allowed increasing scalability of methodologies to understand the effects of landscape attributes on the way they are perceived. However, these new methodologies have not incorporated individual heterogeneity in their analysis, even though differences by gender and other sociodemographic characteristics in the perception of safety and other aspects of landscapes and public spaces have been widely studied in social sciences and urban planning in lower scale studies. In the present study, we combine computational and statistical tools to develop a methodological proposal with high scalability and low implementation cost, which helps to identify and measure heterogeneous perception and its correlation to the presence of elements in the landscape. To achieve this, we implement a survey of perception of public spaces, collecting sociodemographic information of respondents. Then, we fit a discrete choice model to quantify perceptions of these spaces using a parametrization of images that jointly considers semantic segmentation and object detection as input. Our results show heterogeneity in the perception of safety in public spaces according to gender and the observer’s habitual mobility choices. The model is then applied to the city of Santiago, Chile. This produces a map of safety perception for different types of users. The proposed method and the obtained results can be a relevant input for the design of public spaces and decision making in the urban planning process.
dc.fechaingreso.objetodigital2024-04-01
dc.format.extent13 páginas
dc.fuente.origenORCID
dc.identifier.doi10.1016/j.landurbplan.2020.104002
dc.identifier.eissn1872-6062
dc.identifier.issn0169-2046
dc.identifier.scopusidSCOPUS_ID:85098708944
dc.identifier.urihttps://doi.org/10.1016/j.landurbplan.2020.104002
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/84826
dc.identifier.wosidWOS:000614249100006
dc.information.autorucEscuela de Ingeniería; Hurtubia Gonzalez, Ricardo Daniel; 0000-0002-3553-610X; 1015343
dc.information.autorucEscuela de Ingeniería; Ramírez Sarmiento, Tomás Ignacio; S/I; 223235
dc.information.autorucEscuela de Ingeniería; Löbel Díaz, Hans-Albert; 0000-0003-3514-9414; 131278
dc.language.isoen
dc.nota.accesocontenido parcial
dc.revistaLandscape and Urban Planning
dc.rightsacceso restringido
dc.subjectUrban space safety
dc.subjectMachine learning
dc.subjectHeterogeneous perception
dc.subjectBuilt environment
dc.subject.ddc620
dc.subject.deweyIngenieríaes_ES
dc.subject.ods11 Sustainable Cities and Communities
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleMeasuring heterogeneous perception of urban space with massive data and machine learning: An application to safety
dc.typeartículo
dc.volumen208
sipa.codpersvinculados1015343
sipa.codpersvinculados223235
sipa.codpersvinculados131278
sipa.trazabilidadORCID;2024-03-25
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