Next Article in Journal
Unfolding Events in Space and Time: Geospatial Insights into COVID-19 Diffusion in Washington State during the Initial Stage of the Outbreak
Previous Article in Journal
Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression
Open AccessArticle

Uncorrelated Geo-Text Inhibition Method Based on Voronoi K-Order and Spatial Correlations in Web Maps

by Yufeng He 1,2,3, Yehua Sheng 1,2,3,*, Yunqing Jing 1,2,3, Yue Yin 1,2,3 and Ahmad Hasnain 1,2,3
1
School of Geography, Nanjing Normal University, Nanjing 210023, China
2
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
3
Jiangsu Center for Collaborative Innovation in Geographical Information, Resource Development and Application, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 381; https://doi.org/10.3390/ijgi9060381
Received: 29 April 2020 / Revised: 27 May 2020 / Accepted: 5 June 2020 / Published: 9 June 2020
Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations based on Voronoi k-order neighborhood partition and auto-correlation statistical models. On the basis of the geo-text classification and semantic vector transformation, a quantitative description method for spatial autocorrelation was established by the Voronoi weighting method of inverse vicinity distance. The Voronoi k-order neighborhood self-growth strategy was used to detect the minimum convergence neighborhood for spatial autocorrelation. The Pearson method was used to calculate the correlation degree of the geo-text in the convergence region and then deduce the type of geo-text to be filtered. Experimental results showed that for given geo-text types in the study region, the proposed method effectively calculated the correlation between new geo-texts and the convergence region, providing an effective suggestion for preventing uncorrelated geo-text from uploading to the web map environment. View Full-Text
Keywords: geo-text; spatial autocorrelation; Voronoi k-order; volunteered geographic information; semantic analysis; text auto-classification geo-text; spatial autocorrelation; Voronoi k-order; volunteered geographic information; semantic analysis; text auto-classification
Show Figures

Graphical abstract

MDPI and ACS Style

He, Y.; Sheng, Y.; Jing, Y.; Yin, Y.; Hasnain, A. Uncorrelated Geo-Text Inhibition Method Based on Voronoi K-Order and Spatial Correlations in Web Maps. ISPRS Int. J. Geo-Inf. 2020, 9, 381.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop