| Title |
Portraying Citizens' Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework |
| ID_Doc |
44124 |
| Authors |
Zhang, XM; Gao, F; Liao, SY; Zhou, F; Cai, GF; Li, SY |
| Title |
Portraying Citizens' Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework |
| Year |
2021 |
| Published |
Isprs International Journal Of Geo-Information, 10, 6 |
| 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. |
| Author Keywords |
mobile phone data; employed population; citizens' occupations; spatiotemporal mobility; APPs |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000665889200001 |
| WoS Category |
Computer Science, Information Systems; Geography, Physical; Remote Sensing |
| Research Area |
Computer Science; Physical Geography; Remote Sensing |
| PDF |
https://www.mdpi.com/2220-9964/10/6/392/pdf?version=1622973461
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