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Discretized Random Walk Models for Efficient Movement Interpolation

Published: 22 November 2024 Publication History

Abstract

Datasets containing human or animal movement are often sparse, for instance, due to poor GPS reception during recording or because of considerations concerning the battery life of the tracking device. Still, it may be desirable to estimate which walk the observed subject could have taken. A practical solution to this problem is to interpolate between measurements using random walk models. It is however intrinsically difficult to generate walks according to such models that also match the measurements, which in turn makes it difficult to compute metrics like visit probabilities.
Recently, bridgelets were introduced by Krumm to address this issue. They correspond to a simple random walk model on a grid, in which the subject may only move a single step in the grid per time step. In our paper, we show how such a grid-based approach can be extended to a much wider class of models including Brownian bridges, biased and correlated random walks, and location-dependent movement models. To efficiently generate the random walks we use dynamic programming and utilize transition matrices. We implemented and experimentally evaluated our algorithms in a framework written in Rust that allows to efficiently generate random walks on large datasets.

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cover image ACM Conferences
SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
October 2024
743 pages
This work is licensed under a Creative Commons Attribution-NoDerivatives International 4.0 License.

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Published: 22 November 2024

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Author Tags

  1. Bridgelets
  2. Dynamic Programming
  3. Grids
  4. Movement interpolation
  5. Random walks

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SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
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