MAIA, P. H. C.; http://lattes.cnpq.br/5093882516719820; MAIA, Pedro Henrique Costa.
Resumo:
With the increased availability of georeferenced databases, the interest
in analyzing them in researches that require understanding
mobility patterns of people, specially in urban centers, has expanded.
This knowledge may help researches in different fields, as
well as being useful for improving the infrastructure of large cities.
Given this range of applications, several metrics were proposed in
the literature to infer patterns of user movement, however, they are
not frequently available for use in other studies. The algorithms
used may be unavailable, incompatible, or in need of adjustments,
making it hard to be reused. In this paper, we present Mobipy, a
Python library that brings together metrics and functions frequently
used to calculate user mobility patterns. It was developed with
a focus on usability and compatibility with multiple data sets, facilitating
the tasks of research and data analysis. For validation,
the library was tested with real-world data. We hope Mobipy will