transdata笔记：手机数据处理

1 mobile_stay_duration 每个停留点白天和夜间的持续时间

transbigdata.mobile_stay_duration(
    staydata, 
    col=['stime', 'etime'], 
    start_hour=8, 
    end_hour=20)

1.1 主要参数

 1.2 返回内容

1.3 举例

import pandas as pd
from transbigdata import mobile_stay_duration

# 示例数据
data = {
    'stime': ['2024-01-01 07:00', '2024-01-01 22:00', '2024-01-02 11:00', '2024-01-02 03:00'],
    'etime': ['2024-01-01 10:00', '2024-01-02 02:00', '2024-01-02 15:00', '2024-01-02 05:00']
}
staydata = pd.DataFrame(data)
staydata['stime'] = pd.to_datetime(staydata['stime'])
staydata['etime'] = pd.to_datetime(staydata['etime'])
staydata

每一行表示在一个staypoint的开始-结束时间

result = mobile_stay_duration(staydata, col=['stime', 'etime'], start_hour=8, end_hour=20)
result
'''
(0     3600.0
 1    14400.0
 2        0.0
 3     7200.0
 dtype: float64,
 0     7200.0
 1        0.0
 2    14400.0
 3        0.0
 dtype: float64)
'''

两个pandas.Series，分别表示夜间/白天持续时间

2 mobile_identify_work 识别工作地点

transbigdata.mobile_identify_work(
    staydata, 
    col=['uid', 'stime', 'etime', 'LONCOL', 'LATCOL'], 
    minhour=3, 
    start_hour=8, 
    end_hour=20, 
    workdaystart=0, 
    workdayend=4)

从手机停留数据中识别工作地点。规则是确定工作日白天持续时间最长的位置（平均持续时间应超过“minhour”）

2.1 参数

2.2 举例

import pandas as pd
from transbigdata import mobile_identify_work

# 示例数据
data = {
    'uid': [1, 1, 1, 2, 2],
    'stime': ['2024-01-01 09:00', '2024-01-02 09:30', '2024-01-03 08:45', '2024-01-06 09:15', '2024-01-07 09:45'],
    'etime': ['2024-01-01 17:00', '2024-01-02 16:30', '2024-01-03 18:00', '2024-01-06 17:15', '2024-01-07 16:45'],
    'LONCOL': [116.38, 116.39, 116.38, 116.40, 116.40],
    'LATCOL': [39.90, 39.91, 39.90, 39.92, 39.92]
}
staydata = pd.DataFrame(data)
staydata['stime'] = pd.to_datetime(staydata['stime'])
staydata['etime'] = pd.to_datetime(staydata['etime'])
staydata

work_locations = mobile_identify_work(
    staydata, 
    col=['uid', 'stime', 'etime', 'LONCOL', 'LATCOL'], 
    minhour=3, 
    start_hour=8, 
    end_hour=20, 
    workdaystart=0, 
    workdayend=4
)
work_locations

对于uid=2，他的两个停留记录（1月6日 & 1月7日是周六周日，不是工作日，所以不记录用户2的工作位置）

3 mobile_identify_home 识别居住地位置

从手机停留数据中识别居住地位置。规则是确定夜间持续时间最长的位置

3.1 举例

import pandas as pd
from transbigdata import mobile_identify_home

# 示例数据
data = {
    'uid': [1, 1, 2, 2],
    'stime': ['2024-01-01 22:00', '2024-01-02 23:00', '2024-01-01 22:30', '2024-01-02 21:00'],
    'etime': ['2024-01-02 06:00', '2024-01-03 07:00', '2024-01-02 05:30', '2024-01-03 06:00'],
    'LONCOL': [116.38, 116.39, 116.40, 116.41],
    'LATCOL': [39.90, 39.91, 39.92, 39.93]
}
staydata = pd.DataFrame(data)
staydata['stime'] = pd.to_datetime(staydata['stime'])
staydata['etime'] = pd.to_datetime(staydata['etime'])
staydata

home_locations = mobile_identify_home(staydata, 
                                      col=['uid', 'stime', 'etime', 'LONCOL', 'LATCOL'], 
                                      start_hour=8, 
                                      end_hour=20)
home_locations