Evaluation and enhancement methods of POI data quality in the context of geographic big data

Abstract

Geographic big data enables a fine-grained depiction of regional human-terrestrial systems and provides new data for the study of human- terrestrial relations and regional development. At present, geographic big data research has entered the stage of widespread application, but the examination of its quality and the corresponding evaluation methods have been lacking to guarantee the widespread and efficient application of the data. POI is an important part of geographic big data and plays an important role in location-based services and an understanding of regional scenarios. This paper proposes a method to assess and enhance POI-type big data, and realize quality evaluation based on site research, GIS and other methods from three dimensions: feature identification completeness, data redundancy rate and spatial location accuracy; discover and summarize possible influencing factors of data quality based on data production process, and prove that multi-source data fusion is an effective means to enhance POI data quality. We found that: the volume of Amap data acquired based on API interface is slightly higher than that of Baidu, the accuracy rate of spatial location is comparable and the redundancy rate is lower; Amap focuses on identifying the entrance of features, which is suitable for analysis such as accessibility; Baidu focuses on discovering non-significant features, which is suitable for analysis such as spatial planning; the discovery, acquisition and processing stages are possible links to reduce data quality, which is influenced by data protection mechanism, and the data quality is inversely proportional to the acquisition volume and area. The quality assessment, enhancement and integration of multi- source heterogeneous geographic data is one of the key ways to enhance the "emergent value" of data, promote trans- and cross-multidisciplinary and solve geographic problems in the new era.