Characterization of behavioral patterns exploiting description of geographical areas

TL;DR

This paper presents a novel method to classify city areas based on human activity patterns derived from mobile phone data, improving the prediction of communication activity over traditional land use classifications.

Contribution

It introduces an advanced area classification approach based on human activity categories from mobile data, enhancing behavioral pattern recognition and prediction accuracy.

Findings

The new classification aligns better with temporal communication patterns.

It outperforms official land use classifications in predicting communication activity.

Machine learning effectively predicts location-based activity types.

Abstract

The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location's contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification.