Methodology for Mining, Discovering and Analyzing Semantic Human Mobility Behaviors

TL;DR

This paper introduces a novel methodological pipeline called simba for mining and analyzing semantic human mobility sequences, enabling better understanding of daily activities and behaviors from large datasets.

Contribution

The paper presents a new framework and pipeline for analyzing semantic mobility data, integrating statistical indicators and visual tools for explicability, validated on real survey data.

Findings

Automatically discovers complementary knowledge in mobility data

Provides a framework for semantic sequence clustering and analysis

Validated on large real-world mobility datasets

Abstract

Various institutes produce large semantic datasets containing information regarding daily activities and human mobility. The analysis and understanding of such data are crucial for urban planning, socio-psychology, political sciences, and epidemiology. However, none of the typical data mining processes have been customized for the thorough analysis of semantic mobility sequences to translate data into understandable behaviors. Based on an extended literature review, we propose a novel methodological pipeline called simba (Semantic Indicators for Mobility and Behavior Analysis), for mining and analyzing semantic mobility sequences to identify coherent information and human behaviors. A framework for semantic sequence mobility analysis and clustering explicability based on integrating different complementary statistical indicators and visual tools is implemented. To validate this methodology, we used a large set of real daily mobility sequences obtained from a household travel survey. Complementary knowledge is automatically discovered in the proposed method.