# Detecting Permanent and Intermittent Purchase Hotspots via Computational Stigmergy

## TL;DR

This paper introduces a novel computational stigmergy method to detect and analyze permanent and intermittent purchase hotspots in credit card transaction data, revealing spatiotemporal patterns and community behaviors.

## Contribution

The paper presents a new computational technique, stigmergy, for modeling and identifying dynamic purchase hotspots in large-scale transaction data.

## Key findings

- Identified both permanent and intermittent hotspots in real-world transaction data.
- Demonstrated the effectiveness of stigmergy in capturing spatiotemporal patterns.
- Analyzed community-level behaviors through hotspot dynamics.

## Abstract

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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Source: https://tomesphere.com/paper/1902.01601