In this example, we will introduce more options for TransBigData grid processing, including:

Adding rotation angle.
Triangle and hexagon grids.

Rotate the grids¶

In [1]:

#Read taxi gps data
import transbigdata as tbd
import pandas as pd
data = pd.read_csv('../example/data/TaxiData-Sample.csv',header = None) 
data.columns = ['VehicleNum','time','lon','lat','OpenStatus','Speed'] 
#Define the study area
bounds = [113.75, 22.4, 114.62, 22.86]
#Delete the data out of the study area
data = tbd.clean_outofbounds(data,bounds = bounds,col = ['lon','lat'])

The grid coordinates system offer by TransBigData also support adding rotation angle for the grids.
you can also specify a rotation angle for the grids by adding the theta into gridding params:

In [7]:

#Obtain the gridding parameters
params = tbd.grid_params(bounds,accuracy = 1000)
#Add a rotation angle
theta = 5
params = [*params[:4],theta]
print(params)

[113.75, 22.4, 0.00974336289289822, 0.008993210412845813, 5]

In [8]:

#Map the GPS data to grids
data['LONCOL'],data['LATCOL'] = tbd.GPS_to_grids(data['lon'],data['lat'],params)
#Aggregate data into grids
grid_agg = data.groupby(['LONCOL','LATCOL'])['VehicleNum'].count().reset_index()
#Generate grid geometry
grid_agg['geometry'] = tbd.gridid_to_polygon(grid_agg['LONCOL'],grid_agg['LATCOL'],params)
#Change the type into GeoDataFrame
import geopandas as gpd
grid_agg = gpd.GeoDataFrame(grid_agg)
#Plot the grids
grid_agg.plot(column = 'VehicleNum',cmap = 'autumn_r',figsize=(10,5))

Out[8]:

<AxesSubplot:>

Triangle and Hexagon grids¶

In [9]:

#Map the GPS data to grids
data['loncol_1'],data['loncol_2'],data['loncol_3'] = tbd.GPS_to_grids_tri(data['lon'],data['lat'],params)
#Aggregate data into grids
grid_agg = data.groupby(['loncol_1','loncol_2','loncol_3'])['VehicleNum'].count().reset_index()
#Generate grid geometry
grid_agg['geometry'] = tbd.gridid_to_polygon_tri(grid_agg['loncol_1'],grid_agg['loncol_2'],grid_agg['loncol_3'],params)
#Change the type into GeoDataFrame
import geopandas as gpd
grid_agg = gpd.GeoDataFrame(grid_agg)
#Plot the grids
grid_agg.plot(column = 'VehicleNum',cmap = 'autumn_r',figsize=(10,5))

Out[9]:

<AxesSubplot:>

In [10]:

#Map the GPS data to grids
data['loncol_1'],data['loncol_2'],data['loncol_3'] = tbd.GPS_to_grids_hexa(data['lon'],data['lat'],params)
#Aggregate data into grids
grid_agg = data.groupby(['loncol_1','loncol_2','loncol_3'])['VehicleNum'].count().reset_index()
#Generate grid geometry
grid_agg['geometry'] = tbd.gridid_to_polygon_hexa(grid_agg['loncol_1'],grid_agg['loncol_2'],grid_agg['loncol_3'],params)
#Change the type into GeoDataFrame
import geopandas as gpd
grid_agg = gpd.GeoDataFrame(grid_agg)
#Plot the grids
grid_agg.plot(column = 'VehicleNum',cmap = 'autumn_r',figsize=(10,5))

Out[10]:

<AxesSubplot:>

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