Classification of Urban Districts based on Road-centric Crowd Movements

Abstract

Movements of urban citizens largely take part in geo-social urban dynamics, since they exploit diverse urban districts as necessary. However, it is a non-trivial task to measure city-wide crowd movements and analyze them to understand how we exploit a city space for our daily lives. In this paper, we attempt to capture and take advantages of urban crowd movements by exploiting taxis as a sensor capable of monitoring city-wide, continuous, natural and crowd-sourced movements indirectly. Significantly, we propose a road-centric data space model, with which a variety of heterogenous sensor data can be represented in a common form along roads, enabling to hide heterogenous types of primitive movement logs and to support for convenient data integration in terms of roads, time and sensed urban phenomena. Based on the road-centric integration of taxi-based crowd movements, we classify urban districts according to latent temporal road utilization patterns extracted by a Non-negative Matrix Factorization method.

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