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Detecting permanent and intermittent purchase hotspots via computational stigmergy
Name
1902.01601.pdf
Description
Accepted version
Size
718.24 KB
Format
Adobe PDF
Checksum (MD5)
ed4d7371d272f3c20bbc45fb56eedfe0
Author(s)
Alfeo, Antonio
Cimino, Mario
Lepri, Bruno
Pentland, Alex
Vaglini, Gigliola
Date Issued
2019
Publisher
SCITEPRESS - Science and Technology Publications
Citation
Alfeo, Antonio, Cimino, Mario, Lepri, Bruno, Pentland, Alex and Vaglini, Gigliola. 2019. "Detecting permanent and intermittent purchase hotspots via computational stigmergy."
Version
Author's final manuscript
Abstract
Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational stigmergy. By using computational stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015.
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
10.5220/0007581308220829
