Activity behavior of residents of Paraisópolis slum: Analysis of multiday activity patterns using data collected with smartphones

Abstract

This paper investigates the activity behavior of residents of Paraisópolis, the second largest slum of São Paulo (Brazil). The study used data from a survey and one week of GPS traces of a sample of residents. Location data was the basis to infer individual stays and points of interest. Stays were clustered into 6 classes, based on spatial, temporal, repetition and sequence variables characterizing each stay. These stays classes were used to describe individual weekly activity patterns. Individuals were then clustered into 7 categories, based on the similarity of their activity patterns, as described by measures of intensity, variation and repetition. Finally, each group was analyzed in terms of its demographic and socioeconomic composition. Results reveal considerable coherence, confirming expected relationships between the weekly activity patterns and individuals’ attributes. It should be highlighted that more than half of sampled residents were classified into groups with diversified behavior. This result, considering the high density and mixed land use of the Paraisópolis area, reinforces the idea that modelling efforts, even in poorer areas, need to consider activity patterns beyond the more usual simple commute. The article also demonstrates how new multiday data collection methods can contribute to improving the access to hard-to-reach groups, like slums residents.