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

Abstract

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 ($0.509 \pm 0.039$) to the jobs variable.

Keywords

• street vending
• public space
• accessibility tool
• machine learning
• spatial categories