CITATION.cff

cff-version: 1.2.0
title: Infostop
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Ulf
    family-names: Aslak
    email: ulfaslak@gmail.com
  - given-names: Laura
    family-names: Alessandretti
    email: lauale@dtu.dk
    affiliation: Technical University of Denmark
identifiers:
  - type: doi
    value: 10.48550/arXiv.2003.14370
repository-code: 'https://github.com/ulfaslak/infostop'
abstract: >-
  Data-driven research in mobility has prospered in recent
  years, providing solutions to real-world challenges
  including forecasting epidemics and planning
  transportation. These advancements were facilitated by
  computational tools enabling the analysis of large-scale
  data-sets of digital traces. One of the challenges when
  pre-processing spatial trajectories is the so-called stop
  location detection, that entails the reduction of raw time
  series to sequences of destinations where an individual
  was stationary. The most widely adopted solution to this
  problem was proposed by Hariharan and Toyama (2004) and
  involves filtering out non-stationary measurements, then
  applying agglomerative clustering on the stationary
  points. This state-of-the-art solution, however, suffers
  of two limitations: (i) frequently visited places located
  very close (such as adjacent buildings) are likely to be
  merged into a unique location, due to inherent measurement
  noise, (ii) traces for multiple users can not be analysed
  simultaneously, thus the definition of destination is not
  shared across users. In this paper, we describe the
  Infostop algorithm that overcomes the limitations of the
  state-of-the-art solution by leveraging the flow-based
  network community detection algorithm Infomap. We test
  Infostop for a population of ∼1000 individuals with highly
  overlapping mobility. We show that the size of locations
  detected by Infostop saturates for increasing number of
  users and that time complexity grows slower than for
  previous solutions. We demonstrate that Infostop can be
  used to easily infer social meetings. Finally, we provide
  an open-source implementation of Infostop, written in
  Python and C++, that has a simple API and can be used both
  for labeling time-ordered coordinate sequences (GPS or
  otherwise), and unordered sets of spatial points.
preferred-citation:
  type: article
  authors:
  - family-names: "Aslak"
    given-names: "Ulf"
    orcid: "https://orcid.org/0000-0003-4704-3609"
  - family-names: "Alessandretti"
    given-names: "Laura"
    orcid: "https://orcid.org/0000-0001-6003-1165"
  journal: "arXiv preprint"
  doi: 10.48550/arXiv.2003.14370
  title: "Infostop: scalable stop-location detection in multi-user mobility data"
  year: 2020
  arxiv: "2003.14370"