Spatial-Textal Correlation Analysis Based on Crowdsource Geospatial Data

PAN Xiao; ZHANG Cuijuan; WU Lei; YAN Xiaoqian

doi:10.13203/j.whugis20200185

Volume 45 Issue 12

Dec. 2020

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Geomatics and Information Science of Wuhan University > 2020 > 45(12): 1910-1918. > DOI: 10.13203/j.whugis20200185

PAN Xiao, ZHANG Cuijuan, WU Lei, YAN Xiaoqian. Spatial-Textal Correlation Analysis Based on Crowdsource Geospatial Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1910-1918. DOI: 10.13203/j.whugis20200185

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Spatial-Textal Correlation Analysis Based on Crowdsource Geospatial Data

1.
School of Economic and Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

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The Natural Science Foundation Project of Hebei Province F2018210109

Hebei Provincial Department of Education Key Project ZD2018040

The 4th Outstanding Youth Science Fund Project of Shijiazhuang Tiedao University Z661250444

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PAN Xiao, PhD, associate professor, specializes in data management, mobile computing, privacy protection. E-mail: smallpx@stdu.edu.cn
Received Date: April 25, 2020
Published Date: December 04, 2020

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Abstract

Crowdsource geospatial data is one kind of open geographic data collected by the public. A wealth of spatial information and knowledge are hidden in crowdsource geospatial data. Check-in data is one of the representative crowdsource geospatial data. Most existing work on spatial-textual objects, such as evaluating the similarity of two spatial-textual objects in spatial keyword query, considers that spatial similarity and text similarity are independent of each other. According to the first law of geography: Everything is connected; the closer two objects are, the stronger their connection is. We explore the correlation between spatial information and textual information in the real check-in data scrawled from the location based social networks. After data preprocess and geographic mapping, we computed the textual attribute values in each region. Then, we use the exploratory spatial analysis to analyze the global spatial autocorrelation and local spatial autocorrelation in different the spatial scales, that is different states in United States and the two cities such as New York and Los Angeles, respectively. The results show that different textual attributes in different regions have different global spatial autocorrelation; the results obtained from the local autocorrelation analysis show the phenomenon that the textual attributes get together. Both the above results provide the basis for research on the assumption that "the texts are similar in the near space". Furthermore, the conclusion can help departments or enterprises to make reasonable decisions.

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