Synthetic mobile device data

This repository contains two synthetic mobile device datasets, one for GPS location records ("input_case1_v2.csv") and the other for cellular location records ("input_case2_v2.csv"). The two datasets are stored in CSV files. In each CSV file, there are 12 data fields, explained in the "Dictionary.docx" file.

The two datasets contain the location records for 582 individual mobile devices for a month. The GPS dataset ("input_case1_v2.csv") contains 668,939 location records, and the cellular dataset ("input_case2_v2.csv") contains 61,390 location records.

Using the synthetic mobile data generation method developed by Chen et al. (2014), the two datasets are generated based on two real-world data sources. The first one is mobile app data, which comes from people using location-aware mobile apps. The mobile app data encompasses both GPS and cellular data, and covers the month of March in 2019 in the central Puget Sound region. It includes 582 individual mobile device users. The second data source is household travel survey data. It covers the month of March in 2017 in the central Puget Sound region, and includes 582 survey respondents.

The 582 (mobile device) users and the 582 survey respondents are randomly linked. The visited locations in the household travel survey are viewed as the ground-truth stays. Four fields of information from the mobile app data are preserved in the synthetic location records: the number of location records, and the user ID (anonymized), timestamp of each location records, and location accuracy associated with a record. If the timestamp of a location record falls within the duration of a ground-truth stay, the location record will be associated to the stay.

The latitudes and longitudes of synthetic location records are generated such that their spatial distribution is the same as that from the mobile app data for a given user on a given day. The spatial distribution is measured by the distance and angle from a location record to the corresponding stay. Methods to infer stays from the mobile data is described in Wang et al., (2019), which was developed using the method developed in (Chen et al. 2014). For synthetic location records not associated to any (ground-truth) stay, their locations are random deviates from locations evenly distributed on the straight line connecting the last and the next stays, as described in Chen et al. (2014).

本仓库包含两个合成移动设备数据集，其一为GPS位置记录数据集（文件名为`input_case1_v2.csv`），其二为蜂窝网络位置记录数据集（文件名为`input_case2_v2.csv`），两个数据集均以CSV格式存储。每份CSV文件内含12个数据字段，字段说明详见`Dictionary.docx`文档。

两个数据集均涵盖582台独立移动设备为期一个月的位置记录。其中GPS数据集（`input_case1_v2.csv`）包含668,939条位置记录，蜂窝数据集（`input_case2_v2.csv`）包含61,390条位置记录。

本数据集采用Chen等人（2014年）提出的合成移动数据生成方法，基于两个真实世界数据源构建。第一个数据源为移动应用数据，来自使用位置感知类移动应用的用户，该数据同时包含GPS与蜂窝网络位置信息，覆盖2019年3月的普吉特海湾中部地区，涉及582名独立移动设备用户。第二个数据源为家庭出行调查数据，覆盖2017年3月的普吉特海湾中部地区，共包含582名调查受访者。

582名移动设备用户与582名调查受访者通过随机方式完成匹配。家庭出行调查中记录的到访地点被视为真实基准停留点（ground-truth stays）。合成位置记录保留了移动应用数据中的四类信息：位置记录总数、匿名化用户ID、每条位置记录的时间戳，以及对应记录的位置精度。若某条位置记录的时间戳落在某一真实基准停留点的持续时段内，则将该位置记录关联至该停留点。

合成位置记录的经纬度生成方式保证，其空间分布与特定用户在特定日期的移动应用数据的空间分布一致。该空间分布通过位置记录到对应停留点的距离与角度进行衡量。从移动数据中推断停留点的方法详见Wang等人（2019年）的研究，该方法基于Chen等人（2014年）提出的技术开发。对于未关联至任何真实基准停留点的合成位置记录，其位置为从连接上一个与下一个停留点的直线上均匀分布的位置中随机抽取的偏差值，具体实现方式详见Chen等人（2014年）的研究。