Data Quality Assessment and Associated Characteristic Mining of Point Line Polygon Features from Volunteered Geographic Information
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摘要: 志愿者地理信息具有数据量大、更新频率高和采集成本低等特点,已经成为专业地理信息数据的有益补充,在众多领域发挥积极作用。然而,志愿者地理信息由非专业人士生产,缺乏严格、统一的数据生产标准和质量控制流程,导致数据质量参差不齐和空间分布不均等问题。为此,针对志愿者地理信息点线面三类要素的几何结构和应用特点,从数据完整性、重复性和精确性等不同维度设计质量评价指标,构建了由评价对象-评价元素-评价指标三层结构组成的数据质量评价框架,并深入挖掘数据质量的空间和语义关联特征。研究结果表明:(1)相比传统评价指标,所提出的指标对数据质量问题的反馈更加灵敏,使评价结果更加有区分度,有效降低了传统指标造成的评价结果不确定性。(2)志愿者地理信息数据质量的空间聚集特征差异性显著,兴趣点等点要素质量的空间聚集性最强,道路和建筑物等线、面要素质量的空间聚集性较弱,并且沿城市交通环线方向上变化明显。(3)兴趣点、道路等点和线要素质量与类别属性的关联性较为显著,而建筑物等面要素质量与其类别没有明显关联。研究结果可为志愿者地理信息的数据质量控制策略提供有益参考。Abstract: 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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