Year: 2020
Type: article
Abstract: With the increase in the availability of georeferenced databases, in- terest in analyzing them in research that requires an understanding of peoples mobility patterns, especially in urban centers, has increased. Given this range of applications, several metrics have been proposed in the literature to infer patterns of peoples movement, however, they are often not available for use in other studies. In this article, we introduce Mobipy, a Python library that brings together metrics and functions frequently used to calculate peoples mobility patterns. It was developed with a focus on usability and compatibility with multiple datasets, facilitating research and data analysis tasks. We hope that Mobipy will provide researchers with new possibilities, enriching their analysis and generating new knowledge.
Authors: Pedro H. C. Maia, Cláudio E. C. Campelo
Institution: Universidade Federal de Campina Grande
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W3114867597