Modifiable Areal Unit Problem on Grided Mobile Crowd Sensing: Analysis and Restoration

Abstract

Aggregating crowd density in grids from big mobile datasets is a basic but critical work in urban computing and mobile computing. The error of position estimation in raw mobile data, including spatial deviation and temporal deviation, is inevitable and directly impacts the accuracy of aggregated crowd density results. In this case, a key modifiable areal unit problem is raised to understand the relationship among the crowd density accuracy, raw mobile data error, grid shape, and size, but few studies focused on it. This paper analyzes this modifiable areal unit problem of the error in crowd density estimation from big mobility data. By regarding the error as the result of a convolution operation, an optimization model based restoration method was proposed to fix the error of the estimated result, and we analyzed the restoration effect under different circumstances by several simulation experiments. A real application for grided population distribution map construction and restoration from Call Detail Record was conducted to prove the reliability of the whole analysis, which demonstrates the restoration method can reduce the error by nearly 40% under certain conditions.