Machine Learning to Assess Urbanistic Development in the South Pole of Lima City

Abstract

We employ Machine Learning through the Mitchell’s criteria to carry out an assessment on the potential spatial configurations at the south pole of Lima city, at Perú. Based at both qualitative and quantitative facts, an model has been proposed that targets to measure the success of spatial expansion of districts based at distances and number of habitants. In this manner Machine Learning appears as a robust tool with capabilities to anticipate the possible achievements as well as issues along the time the city is under spatial growth. The efficiency of sustained growth is measured in terms of success probability. Therefore, we can claim that the ongoing growth of Villa el Salvador engages to some extent the philosophy of Mitchell’s criteria.