Discovering functional zones using bus smart card data and points of interest in Beijing

TL;DR

This study develops a data-driven model using bus smart card data and points of interest to identify and analyze urban functional zones in Beijing, aiding urban planning and geographic understanding.

Contribution

Introduces the DZoF model combining smart card data and POIs with clustering techniques to accurately discover urban functional zones.

Findings

DZoF model effectively matches actual land use zones.

Clustering with dimensionality reduction improves zone identification.

Method supports urban planning and geographic research.

Abstract

Cities comprise various functional zones, including residential, educational, commercial zones, etc. It is important for urban planners to identify different functional zones and understand their spatial structure within the city in order to make better urban plans. In this research, we used 77976010 bus smart card records of Beijing City in one week in April 2008 and converted them into two-dimensional time series data of each bus platform, Then, through data mining in the big database system and previous studies on citizens' trip behavior, we established the DZoF (discovering zones of different functions) model based on SCD (smart card Data) and POIs (points of interest), and pooled the results at the TAZ (traffic analysis zone) level. The results suggested that DzoF model and cluster analysis based on dimensionality reduction and EM (expectation-maximization) algorithm can identify functional zones that well match the actual land uses in Beijing. The methodology in the present research can help urban planners and the public understand the complex urban spatial structure and contribute to the academia of urban geography and urban planning.