Thesis Mapeo de las zonas rojas: identificación y visualización de la cobertura del delivery alimenticio en la región Metropolitana de Chile
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Date
2025-12
Authors
Rodríguez Rodríguez, Felipe Williams Francisco
Journal Title
Journal ISSN
Volume Title
Program
Ingeniería Civil Telemática
Departament
Campus
Campus Santiago San Joaquín
Abstract
Este trabajo aborda la problemática de la exclusión territorial digital en el acceso a servicios de reparto de alimentos, un fenómeno que se manifiesta en la creación de zonas rojas de delivery, entendidas como sectores urbanos excluidos de estos servicios debido a factores como la inseguridad, la baja rentabilidad o las limitaciones logísticas. El propósito central de esta memoria es desarrollar e implementar una herramienta georreferenciada que permita identificar y visualizar con precisión estas zonas de exclusión de los servicios de delivery alimenticio en la Región Metropolitana de Chile. El estudio busca responder a la pregunta de cuáles territorios de la Región Metropolitana presentan cobertura de delivery alimenticio y cuáles se encuentran excluidos. La metodología implementada se basó en el desarrollo de un sistema de muestreo territorial que integra múltiples fuentes de datos. Para ello, se utilizaron datos geográficos correspondientes a las 1.865 zonas censales de la Región Metropolitana de Chile del Censo 2017. Se empleó la geocodificación inversa, usando la API de Google Maps, para transformar las coordenadas de puntos representativos de cada zona censal en direcciones físicas comprensibles. Posteriormente, se utilizó la técnica de web scraping automatizado, implementada con Selenium y Python, para consultar y extraer la disponibilidad de cobertura del servicio de la aplicación PedidosYa en cada dirección. Finalmente, los resultados fueron clasificados y visualizados en una plataforma web interactiva, desarrollada con FastAPI y React.js, utilizando Deck.GL para la visualización geoespacial, que mapea las zonas con cobertura y las zonas excluidas. El proceso de verificación demostró una alta confiabilidad del sistema, con una precisión del 97,4 % en la clasificación de la cobertura de las zonas censales. Los resultados del análisis geoespacial evidenciaron un patrón de desigualdad espacial en la distribución del servicio de PedidosYa en la Región Metropolitana. Se confirmó la concentración(...).
This work addresses the problem of digital territorial exclusion in access to food delivery services, a phenomenon manifested in the creation of delivery red zones, understood as urban areas excluded from these services due to factors such as insecurity, low profitability, or logistical limitations. The central purpose of this thesis is to develop and implement a georeferenced tool that allows for the precise identification and visualization of these exclusion zones from food delivery services in the Metropolitan Region of Chile. The study seeks to answer the question of which areas of the Metropolitan Region have food delivery coverage and which are excluded. The implemented methodology was based on the development of a territorial sampling system that integrates multiple data sources. For this purpose, geographic data corresponding to the 1,865 census tracts of the Metropolitan Region of Chile from the 2017 Census were used. Reverse geocoding, using the Google Maps API, was employed to transform the coordinates of representative points in each census tract into understandable physical addresses. Subsequently, automated web scraping, implemented with Selenium and Python, was used to query and extract the availability of PedidosYa service coverage at each address. Finally, the results were classified and visualized on an interactive web platform, developed with FastAPI and React.js, using Deck.GL for geospatial visualization, which maps areas with coverage and areas without coverage. The verification process demonstrated high system reliability, with 97.4% accuracy in classifying census tract coverage. The results of the geospatial analysis revealed a pattern of spatial inequality in the distribution of PedidosYa’s service in the Metropolitan Region. The study confirmed the concentration(...).
This work addresses the problem of digital territorial exclusion in access to food delivery services, a phenomenon manifested in the creation of delivery red zones, understood as urban areas excluded from these services due to factors such as insecurity, low profitability, or logistical limitations. The central purpose of this thesis is to develop and implement a georeferenced tool that allows for the precise identification and visualization of these exclusion zones from food delivery services in the Metropolitan Region of Chile. The study seeks to answer the question of which areas of the Metropolitan Region have food delivery coverage and which are excluded. The implemented methodology was based on the development of a territorial sampling system that integrates multiple data sources. For this purpose, geographic data corresponding to the 1,865 census tracts of the Metropolitan Region of Chile from the 2017 Census were used. Reverse geocoding, using the Google Maps API, was employed to transform the coordinates of representative points in each census tract into understandable physical addresses. Subsequently, automated web scraping, implemented with Selenium and Python, was used to query and extract the availability of PedidosYa service coverage at each address. Finally, the results were classified and visualized on an interactive web platform, developed with FastAPI and React.js, using Deck.GL for geospatial visualization, which maps areas with coverage and areas without coverage. The verification process demonstrated high system reliability, with 97.4% accuracy in classifying census tract coverage. The results of the geospatial analysis revealed a pattern of spatial inequality in the distribution of PedidosYa’s service in the Metropolitan Region. The study confirmed the concentration(...).
Description
Keywords
Zonas rojas, Cobertura de delivery alimenticio, Geocodificación inversa, Web scraping, PedidosYa
