Indigenization of Urban Mobility

TL;DR

This paper analyzes urban mobility patterns using social media check-in data to improve location prediction accuracy, introducing indigenization coefficients that quantify native-like behavior without demographic data.

Contribution

It presents a novel indigenization coefficient and a hybrid prediction algorithm that outperform demographic-based methods in urban mobility prediction.

Findings

Indigenization coefficients effectively quantify native-like mobility behavior.

Hybrid algorithms with indigenization weights outperform demographic-based models.

Behavioral data can replace demographic info for accurate mobility prediction.

Abstract

The identification of urban mobility patterns is very important for predicting and controlling spatial events. In this study, we analyzed millions of geographical check-ins crawled from a leading Chinese location-based social networking service (Jiepang.com), which contains demographic information that facilitates group-specific studies. We determined the distinct mobility patterns of natives and non-natives in all five large cities that we considered. We used a mixed method to assign different algorithms to natives and non-natives, which greatly improved the accuracy of location prediction compared with the basic algorithms. We also propose so-called indigenization coefficients to quantify the extent to which an individual behaves like a native, which depends only on their check-in behavior, rather than requiring demographic information. Surprisingly, the hybrid algorithm weighted using the indigenization coefficients outperformed a mixed algorithm that used additional demographic information, suggesting the advantage of behavioral data in characterizing individual mobility compared with the demographic information. The present location prediction algorithms can find applications in urban planning, traffic forecasting, mobile recommendation, and so on.