Public space accessibility and machine learning tools for street vending spatial categorization

Bibliographic Data

ID16840486
AuthorsAntonio Alfonso Barreda Luna (0000-0002-2850-4644, Autonomous University of Queretaro), Gonzalo Hatch Kuri (0000-0002-5189-4708, Universidad Nacional Autonoma de Mexico), Juvenal Rodríguez‐Reséndiz (0000-0001-8598-5600, Autonomous University of Queretaro, corresponding author), Marco Antonio Zamora Antuñano (0000-0002-9865-3944, Universidad del Valle de México), José Antonio Altamirano Corro (0000-0003-0061-091X, Autonomous University of Queretaro), Wilfrido Jacobo Paredes-García (0000-0001-9740-0358, Autonomous University of Queretaro)
Year2022
Volume18
Issue1
Pages43-52
Publication date2022-01-01
Open AccessYes
TypeARTICLE
VenueJournal of Maps (JOURNAL)
Journal identifiersISSN: 1744-5647 • E-ISSN: 1744-5647
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/17445647.2022.2035836
OpenAlexW4211017615
LanguageEN
Citations received3
References cited18

Street vending is a complex systemic phenomenon in most cities worldwide, with different intensities and features. In the Mexican case, it is an activity with remnants of a precolonial logic in its spatial distribution. Thus, a low correlation exists between the street vending government regulations and the actual day by day organization of the activity. Certain authors have suggested and compiled an econometric model that considers some variables to comprehend the phenomenon better. All the variables came with the detailed information except for the territorial aspect. Thus, an accessibility tool was created to provide a robust location profile, using official variables related to socioeconomic topics recommended by the World Bank. The resulting database was then analyzed by Machine Learning prediction models. The results provided a map with spatial categorization of the street vending activity, with a solid correlation (0.509±0.039) to the jobs variable

Cartography · Categorization · Data science · Econometric model · Econometrics · Economics · Geography · Machine learning · Phenomenon · Regional science · Socioeconomic status · Sociology · Variables · Computer Science · Demography · Latin American Urban Studies · Mathematics · Urban Transport and Accessibility · Artificial Intelligence

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Open DOIOpen Access