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Article

Deep Understanding of Urban Dynamics from Imprint Urban Toponymic Data Using a Spatial–Temporal–Semantic Analysis Approach

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School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
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Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China
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Department of Geography, University of Connecticut, Storrs, CT 06269, USA
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Key Laboratory of Big Data in Geosciences and Deep Earth Resources of Zhejiang Province, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Academic Editor: Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2021, 10(5), 278; https://doi.org/10.3390/ijgi10050278
Received: 11 March 2021 / Revised: 9 April 2021 / Accepted: 26 April 2021 / Published: 28 April 2021
Urban land use is constantly changing via human activities. These changes are recorded by imprint data. Traditionally, urban dynamics studies focus on two-dimensional spatiotemporal analysis. Based on our best knowledge, there is no study in the literature that uses imprint data for better understanding urban dynamics. In this research, we propose a spatial–temporal–semantic triple analytical framework to better understand urban dynamics by making full use of the imprint data, toponyms. The framework includes a text classification method and geographical analysis methods to understand urban dynamics in depth. Based on the inherent temporal and spatial information, we enrich semantic information with street names to explain urban dynamics in multiple dimensions. Taking Hangzhou city as an example, we used street names to reproduce the city changes over the past century. The results obtained through analysis of street names may accurately reflect the real development process of Hangzhou. This research demonstrates that imprint data left by urban development may play a pivotal role in better understanding urban dynamics. View Full-Text
Keywords: urban dynamics; spatial–temporal–semantic analytics; urban development characteristic; toponyms; street names urban dynamics; spatial–temporal–semantic analytics; urban development characteristic; toponyms; street names
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MDPI and ACS Style

Chen, Y.; Zhang, F.; Li, X.; Zhang, C.; Chen, N.; Du, Z.; Liu, R.; Wang, B. Deep Understanding of Urban Dynamics from Imprint Urban Toponymic Data Using a Spatial–Temporal–Semantic Analysis Approach. ISPRS Int. J. Geo-Inf. 2021, 10, 278. https://doi.org/10.3390/ijgi10050278

AMA Style

Chen Y, Zhang F, Li X, Zhang C, Chen N, Du Z, Liu R, Wang B. Deep Understanding of Urban Dynamics from Imprint Urban Toponymic Data Using a Spatial–Temporal–Semantic Analysis Approach. ISPRS International Journal of Geo-Information. 2021; 10(5):278. https://doi.org/10.3390/ijgi10050278

Chicago/Turabian Style

Chen, Yurong, Feng Zhang, Xinba Li, Chuanrong Zhang, Ninghua Chen, Zhenhong Du, Renyi Liu, and Bo Wang. 2021. "Deep Understanding of Urban Dynamics from Imprint Urban Toponymic Data Using a Spatial–Temporal–Semantic Analysis Approach" ISPRS International Journal of Geo-Information 10, no. 5: 278. https://doi.org/10.3390/ijgi10050278

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