Positioning Error in Mobile Phone Tracking Data with Consideration of Geographic Environment Factors

Song, Xiaoqing; Long, Yi; Zhang, Ling

Summary. This research contributes to both theory and application. This is an important supplement to the research on the quality and uncertainty of spatial big data and it will help mobile phone data play a more effective application value in future research. In recent years, the rapid development of data acquisition means has led to the rapid accumulation of spatial data and generated spatial big data with geographic location information. However, the uncertainty in spatial big data leads to the quality problem of spatial big data, which is one of the main bottlenecks restricting the effective utilization of spatial big data. As an important part of spatial big data, it is necessary to study the quality and uncertainty of mobile phone data. In view of this, we delve into the key geographical environment factors that affect the positioning error of mobile phone data, extract error evaluation indexes and construct the spatial distribution model of positioning error with machine learning algorithms. To explore the distribution law of mobile phone data positioning error under different geographical scenarios. In addition, considering the high accuracy of personal travel GPS positioning, this study takes personal travel GPS positioning data as the comparative object of mobile phone data for analysis.

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Song, Xiaoqing / Long, Yi / Zhang, Ling: Positioning Error in Mobile Phone Tracking Data with Consideration of Geographic Environment Factors. 2019. Copernicus Publications.

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Rechteinhaber: Xiaoqing Song et al.

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