Data Quality Assessment and Associated Characteristic Mining of Point Line Polygon Features from Volunteered Geographic Information

LIN Anqi; LUO Wenting; WU Hao

doi:10.13203/j.whugis20230271

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LIN Anqi, LUO Wenting, WU Hao. Data Quality Assessment and Associated Characteristic Mining of Point Line Polygon Features from Volunteered Geographic Information[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230271

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Data Quality Assessment and Associated Characteristic Mining of Point Line Polygon Features from Volunteered Geographic Information

LIN Anqi^1,2,
LUO Wenting^1,2,
WU Hao^1,2, ,

1 School of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079;
2 Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430079

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Received Date: July 23, 2023
Available Online: November 01, 2023

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Objectives: With the characteristics of large amount, high update frequency and low collection cost, Volunteered Geographic Information (VGI) has become the useful supplement to classic geographic information data and plays an important role in many fields. However, due to the lack of strict and unified data production standards and quality control process, the data quality of VGI is uneven and the spatial distribution is not equal. Therefore, this study proposed the quality assessment index system compose of the evaluation object, quality element and quality index for VGI point line polygon features. Methods: According to different spatial data structure and application characteristics of point line polygon features, a comprehensive evaluation was conducted from different dimensions such as geometry, topology and semantic quality, and further the spatial and semantic characters of data quality were discussed. Results: The results show that (1) The new evaluation indexes is more sensitive than the traditional ones, and the evaluation results of each quality element are more differentiated after the index synthesis. (2) The spatial aggregation of POI semantic similarity is the strongest, while the spatial aggregation of road and building quality is weak. (3) Category attributes have significant correlation with POI interest points and road element quality, but have no significant correlation with building quality. Conclusions: The comprehensive quality assessment can effectively reduce the result uncertainty caused by using any single index. The spatial aggregation characteristics of VGI point, line and polygon quality are significantly different, and it changes significantly along the direction of urban ring. Category attributes have the potential to be the quality indicator of VGI data.

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Data Quality Assessment and Associated Characteristic Mining of Point Line Polygon Features from Volunteered Geographic Information

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