Mobility of Indonesian during Early Pandemic: Insights from Mobile Positioning Data

Widyawan, Muhammad Syarif, A. R. Pratama
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Abstract

Mobile Positioning Data (MPD) contains information on the location of the mobile phone by approximating mobile phones’ location relative to fixed infrastructures (e.g., telecommunication towers that transmit signals). While the data query is technically straightforward, obtaining this dataset requires particular permission to protect customers’ privacy. Additionally, the dataset has large volumes of data (i.e, up to 300GB per day), resulting in not many researchers holding this data source to analyze the mobility of people. In this work, we collaborate with one of the biggest telecommunication service providers in Indonesia to collect MPD and prepare the big data infrastructure. We thus analyze mobility patterns during the early phase of COVID-19 in 2020 using actual Mobile Positioning Data in five provinces in Java. We use three metrics, namely, the number of visits, averaged travel distance, and Origin-Destination matrix. The findings indicate that the social restriction in the corresponding provinces has reduced the average traveled distance of the people, but not their number of visits. That is, while the traveled distance has declined more than eight times compared to the baseline, the number of visits may rocket up, up to nine times. It indicates that people are still having shorter trips even though their regular activities (working, schooling, etc.) have been restricted. The data also show that during Ramadhan month, the government has a successful intervention in restricting people for mudik Lebaran, The number of visits dropped to below 30 visits during Ramadhan and only small spikes exist during ‘libur lebaran’.
流行病早期印度尼西亚的流动性:来自移动定位数据的见解
移动定位数据(MPD)通过近似移动电话相对于固定基础设施(例如,传输信号的电信塔)的位置包含关于移动电话位置的信息。虽然数据查询在技术上很简单,但获取此数据集需要获得保护客户隐私的特定许可。此外,数据集具有大量数据(即每天高达300GB),导致没有多少研究人员持有该数据源来分析人员的流动性。在这项工作中,我们与印度尼西亚最大的电信服务提供商之一合作,收集MPD并准备大数据基础设施。因此,我们利用爪哇五个省的实际移动定位数据,分析了2020年COVID-19早期阶段的流动模式。我们使用三个指标,即访问次数、平均旅行距离和出发地-目的地矩阵。研究结果表明,相应省份的社会限制减少了人们的平均出行距离,但没有减少他们的访问次数。也就是说,虽然旅行距离比基线下降了八倍多,但访问次数可能会飙升,最高可达九倍。这表明,尽管人们的日常活动(工作、上学等)受到限制,但他们的旅行时间仍然较短。数据还显示,在斋月期间,政府在限制人们参加mudik Lebaran方面进行了成功的干预,斋月期间的访问量降至30次以下,而在“liblebaran”期间只有小幅飙升。
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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