Portraying Citizens’ Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework

Xiaoming Zhang,Fan Zhou,Shunyi Liao,Guanfang Cai,Shaoying Li,Feng Gao

doi:10.3390/ijgi10060392

Abstract

Mobile phone data is a typical type of big data with great potential to explore human mobility and individual portrait identification. Previous studies in population classifications with mobile phone data only focused on spatiotemporal mobility patterns and their clusters. In this study, a novel spatiotemporal analytical framework with an integration of spatial mobility patterns and non-spatial behavior, through smart phone APP (applications) usage preference, was proposed to portray citizens’ occupations in Guangzhou center through mobile phone data. An occupation mixture index (OMI) was proposed to assess the spatial patterns of occupation diversity. The results showed that (1) six types of typical urban occupations were identified: financial practitioners, wholesalers and sole traders, IT (information technology) practitioners, express staff, teachers, and medical staff. (2) Tianhe and Yuexiu district accounted for most employed population. Wholesalers and sole traders were found to be highly dependent on location with the most obvious industrial cluster. (3) Two centers of high OMI were identified: Zhujiang New Town CBD and Tianhe Smart City (High-Tech Development Zone). It was noted that CBD has a more profound effect on local as well as nearby OMI, while the scope of influence Tianhe Smart City has on OMI is limited and isolated. This study firstly integrated both spatial mobility and non-spatial behavior into individual portrait identification with mobile phone data, which provides new perspectives and methods for the management and development of smart city in the era of big data.

Highlights

The main purpose of this study is to identify the typical occupation types of the employed population
To testaccuracy the accuracy of employed the employed population identified in this study, study of the of the population identified in this study, the the study areaarea of 79
The scope of influence zens’ occupations and assess urban occupation mixture with mobile phone data. In this of the High-tech Development Zone in the suburbs on occupation mixture was relatively study, employed population were first extracted from the full samples based on users’

Summary

Introduction

With the development of communication technology and big data, the trajectory of big data with both temporal and spatial information has become the focus of urban and spatial research, especially such topics as mobile phone data [1,2,3,4,5,6,7], social platform user density data [8,9], ridesourcing trajectory data [10,11], Baidu mobility data [12], and bike sharing riding record data [13,14,15] as well as rail transit ridership data [16,17]. Mobile phone data is the one that can capture users’ spatiotemporal mobility patterns and users’ non-spatial attribute information such as age, gender, and so on. Mobile phone data is of great potential to mine citizens’ mobility patterns and to classify them

Objectives

Discussion

Conclusion