ORTEGA: An open-source Python package for context-aware analysis and visualization of individuals’ interaction based on their movement

Keywords: interaction analysis, movement data, time geography, human movement
Abstract Type: Paper Abstract

Abstract

Interaction analysis for moving individuals in space and time contributes to understanding social relationships and dynamics among individuals. Recent advancements in trajectory analytics have created methods to identify and extract spatiotemporal patterns of interaction using movement tracking data. Existing methods of quantifying interactions mostly rely on the spatial proximity between two individuals and many of them require user-defined spatial and temporal thresholds. However, these proximity-based approaches are limited when interacting individuals' paths are not tracked simultaneously due to signal loss or imperfect tracking, or when the interactions are delayed. In contrast, time-geography or Potential Path Area (PPA) based approaches provide a more robust framework to identify both concurrent and delayed interactions between individuals. This is mainly because the PPA incorporates the uncertainty of positioning and gaps in movement data by considering the locations accessible to moving individuals between consecutive tracking points. This paper presents and evaluates a new extension of ORTEGA, as an open-source Python package, for analyzing and visualizing interactions between entities based on their movement observations (i.e., movement tracking data) within geographic context. The developed package ORTEGA contributes three significant capabilities: 1) the function of detecting potential interactions (e.g., encounters, concurrent interactions, delayed interactions) in probabilistic or deterministic manners from movement data of two or more entities and the contextual correlates using both the time-geographic and traditional proximity-based approaches; 2) the ability to calculate attributes of potential interaction events including start time, end time, and duration; and 3) visualization functions to map the detected interactions between the entities.