# Stigmergy-based modeling to discover urban activity patterns from positioning data

## TL;DR

This paper introduces a stigmergy-based computational approach to analyze urban activity patterns from positioning data, enabling the discovery of unexpected crowd behaviors and temporal dynamics in city environments.

## Contribution

It presents a novel stigmergy-inspired method for modeling urban crowd behavior from positioning data, facilitating pattern discovery and day clustering.

## Key findings

- Identified high-density hotspots and their activity over time.
- Clustered days based on activity patterns, revealing unexpected urban behaviors.
- Analyzed NYC taxi traces to validate the approach.

## Abstract

Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015.