| Title |
City Intelligence Quotient Evaluation System Using Crowdsourced Social Media Data: A Case Study of the Yangtze River Delta Region, China |
| ID_Doc |
39555 |
| Authors |
Wu, ZQ; Li, X; Zhou, XG; Yang, TR; Lu, RY |
| Title |
City Intelligence Quotient Evaluation System Using Crowdsourced Social Media Data: A Case Study of the Yangtze River Delta Region, China |
| Year |
2021 |
| Published |
Isprs International Journal Of Geo-Information, 10, 10 |
| DOI |
10.3390/ijgi10100702 |
| Abstract |
Despite the trending studies on smart city development, how to evaluate the smartness of a city remains unclear. This research aimed to design a smart city evaluation system, named the City Intelligence Quotient (CityIQ) evaluation system, which considers both the hard (e.g., physical infrastructure) and soft sides (e.g., citizens' perspectives) of smart city development. Based on the two-level structure of the CityIQ evaluation system (i.e., five dimensions and twenty indicators), a list of keywords was defined for automated information scraping in leading social media platforms to obtain volunteered geographic information. Semantic analysis was then used to update the CityIQ evaluations in a timely manner. Fifteen major cities in the Yangtze River Delta region, China, were selected for the empirical study, in which their smartness indices were calculated, traced and compared. Finally, suggestions for collaborative smart agglomerations were put forward. With the CityIQ evaluation system, policy makers can be informed of up-to-date changes in urban smartness levels and, thus, design context-specific collaborative policies to promote smart agglomerations. |
| Author Keywords |
smart city; evaluation system; social media data; crowdsourcing; citizens |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000747255800001 |
| WoS Category |
Computer Science, Information Systems; Geography, Physical; Remote Sensing |
| Research Area |
Computer Science; Physical Geography; Remote Sensing |
| PDF |
https://www.mdpi.com/2220-9964/10/10/702/pdf?version=1634520998
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