geolife 笔记：将所有轨迹放入一个DataFrame

news2024/9/20 1:09:36

单条轨迹的处理：geolife笔记：整理处理单条轨迹-CSDN博客

1 加载数据

import pandas as pd
import numpy as np
import datetime as dt
import os

data_dir = 'Geolife Trajectories 1.3/Data/'

1.1 列出所有文件夹

dirlist = os.listdir(data_dir)
dirlist
'''
['133',
 '079',
 '173',
 '020',
 '003',
 '004',
 '014',
 '074',
...
'''

1.2 拼接出所有绝对路径

folder_dirs = []
for dir in dirlist:  
  folder_dirs.append(data_dir + '/' + dir+'/'+'Trajectory')
folder_dirs
'''
['data/Geolife Trajectories 1.3/Data//133/Trajectory',
 'data/Geolife Trajectories 1.3/Data//079/Trajectory',
 'data/Geolife Trajectories 1.3/Data//173/Trajectory',
 'data/Geolife Trajectories 1.3/Data//020/Trajectory',
 'data/Geolife Trajectories 1.3/Data//003/Trajectory',
...
'''

1.3 列出所有文件

file_dirs=[]
for dir in folder_dirs:
    for file in os.listdir(dir):
        file_dirs.append(dir+'/'+file)
len(file_dirs),file_dirs
'''
(18670,
 ['data/Geolife Trajectories 1.3/Data//133/Trajectory/20110130143621.plt',
  'data/Geolife Trajectories 1.3/Data//133/Trajectory/20110419143237.plt',
  'data/Geolife Trajectories 1.3/Data//133/Trajectory/20110421082008.plt',
  'data/Geolife Trajectories 1.3/Data//133/Trajectory/20110420024807.plt',
...
'''

2 读取所有文件，并拼接到一个DataFrame中

2.1 计算haversine距离的函数

def haversine_distance(lat1, lon1, lat2, lon2):
    R = 6371  # Earth radius in kilometers
    dlat = np.radians(lat2 - lat1)
    dlon = np.radians(lon2 - lon1)
    a = np.sin(dlat/2) * np.sin(dlat/2) + np.cos(np.radians(lat1)) * np.cos(np.radians(lat2)) * np.sin(dlon/2) * np.sin(dlon/2)
    c = 2 * np.arctan2(np.sqrt(a), np.sqrt(1-a))
    return R * c

2.2 读取文件

所有对应的操作都在单条轨迹处理中已经说明

import pandas as pd
import numpy as np
traj=pd.DataFrame()
traj
num=0
for file in file_dirs:
    #read data:
    data = pd.read_csv(file,
                   header=None, 
                   skiprows=6,
                   names=['Latitude', 'Longitude', 'Not_Important1', 'Altitude', 'Not_Important2', 'Date', 'Time'])
    '''
    merge date and time
    '''
    data['Datetime'] = pd.to_datetime(data['Date'] + ' ' + data['Time'])
    data=data[['Latitude', 'Longitude', 'Altitude', 'Datetime']]

    '''
    retain positions in Beijing city
    '''
    data=data[(data['Latitude']>B1[0]) & (data['Latitude']<B2[0]) & (data['Longitude']>B1[1]) & (data['Longitude']<B2[1])] 

    '''
    time gap to 5s, and remain first record every 5s
    '''
    data['Datetime_5s']=data['Datetime'].dt.floor('5s')
    data=data.drop_duplicates(subset=['Datetime_5s'],keep='first')

    '''
    remove stopping point
    '''
    data['is_moving'] = (data['Latitude'] != data['Latitude'].shift()) | (data['Longitude'] != data['Longitude'].shift())
    data=data[data['is_moving']==True]
    data=data[['Latitude','Longitude','Datetime_5s']]


    '''
    split trajs without records in 10min into 2 trajs （and update id）
    '''
    data['time_diff']=data['Datetime_5s'].diff()
    data['split_id']=0
    mask=data['time_diff']>pd.Timedelta(minutes=10)
    data.loc[mask,'split_id']=1
    data['split_id']=data['split_id'].cumsum()

    
    data['id']=str(num)
    num+=1
    data['id']=data['id']+'_'+data['split_id'].astype(str)


    '''
    calc each traj's length, filter out short trajs and truncate long ones
    '''
    #calculate nearby location's lon and lat gap
    lat_lon_diff = data.groupby('id',group_keys=False).apply(lambda group: group[['Latitude', 'Longitude']].diff())
    #calc nearby locationn's distance
    distance = lat_lon_diff.apply(lambda row: haversine_distance(row['Latitude'], row['Longitude'], 0, 0), axis=1)
    data['distance']=distance
    #calculate each id's accumulated distance
    data['accum_dis']=data.groupby('id')['distance'].cumsum()
    #split those trajs longer than 10km into 2 trajs
    data['split_traj_id']=data['accum_dis']//10
    data['split_traj_id']=data['split_traj_id'].fillna(0)
    data['split_traj_id']=data['split_traj_id'].astype(int).astype(str)
    #get new id
    data['id']=data['id']+'_'+data['split_traj_id']
    #remove those shorter than 1km
    iid=data.groupby('id')['accum_dis'].max()
    iid=iid.reset_index(name='distance')
    iid=iid[iid['distance']>1]
    data=data[data['id'].isin(iid['id'])]


    '''
    filter trajs shorter than 10  records
    '''
    iid=data.groupby('id').size()
    iid=iid.reset_index(name='count')
    iid=iid[iid['count']>=10]
    data=data[data['id'].isin(iid['id'])]

    '''
    remove 'staypoints'
    '''
    latlon=pd.DataFrame()
    latlon['max_lat']=data.groupby('id')['Latitude'].max()
    latlon['min_lat']=data.groupby('id')['Latitude'].min()
    latlon['max_lon']=data.groupby('id')['Longitude'].max()
    latlon['min_lon']=data.groupby('id')['Longitude'].min()
    latlon['max_dis']=latlon.apply(lambda row: haversine_distance(row['max_lat'],row['max_lon'],row['min_lat'],row['min_lon']),axis=1)
    latlon=latlon[latlon['max_dis']>=1]


    data=data[data['id'].isin(latlon.index)]
    data=data[['Latitude','Longitude','Datetime_5s','id']]
    #print(data)
    traj=pd.concat([traj,data])