AMDA: Anchor Mobility Data Analytic for Determining Home-Work Location from Mobile Positioning Data

Amanda Pratama Putra, Wa Ode Zuhayeni Madjida, Ignatius Aditya Setyadi, Amin R. S. Nugroho, A. R. M. Munaf
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Abstract

In conducting a mobility analysis using Mobile Positioning Data, the most critical step is to define each customer's usual environment. The initial concept of mobility used is the movement that occurs from and to every usual environment, so errors in determining the usual environment will cause incorrect mobility statistics. Therefore, Anchor Mobility Data Analytic (AMDA) is proposed for Home-Work Location Determination from Mobile Positioning Data. This algorithm uses clockwise reversal to make it easier to classify someone in their usual environment. Unfortunately, only about 80% of the raw data can be used to establish usual environments. The remaining 20% do not have sufficient data history. This study found that the accuracy of AMDA in determining monthly home location was 98.8% at the provincial level and 88.7% at the regency level. As for the determination of monthly work locations, 98.9% at the provincial level and 70.4% at the regency level.
AMDA:从移动定位数据确定家庭工作位置的锚移动数据分析
在使用移动定位数据进行移动分析时,最关键的一步是定义每个客户的日常环境。所使用的移动性的初始概念是在每个通常环境之间发生的运动,因此在确定通常环境时的错误将导致不正确的移动性统计。为此,提出了基于移动定位数据的锚点移动数据分析方法(AMDA)。该算法使用顺时针反转,使其更容易在通常的环境中对某人进行分类。不幸的是,只有大约80%的原始数据可用于建立常规环境。剩下的20%没有足够的历史数据。本研究发现,AMDA在确定省级家庭月住址方面的准确率为98.8%,在摄政一级为88.7%。至于每月工作地点的确定,98.9%在省级,70.4%在县级。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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