4 三角形和六边形栅格的生成与旋转

在这个例子中，我们将介绍更多用于“TransBigData”栅格处理的选项，包括： - 添加旋转角度。- 三角形和六边形栅格。

旋转栅格

#Read taxi gps data
import transbigdata as tbd
import pandas as pd
data = pd.read_csv('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'])

“TransBigData”提供的栅格坐标系也支持为栅格添加旋转角度。

您还可以通过将“theta”添加到栅格参数中来指定栅格的旋转角度：

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

{'slon': 113.75, 'slat': 22.4, 'deltalon': 0.00974336289289822, 'deltalat': 0.008993210412845813, 'theta': 5, 'method': 'rect', 'gridsize': 1000}

#Map the GPS data to grids
data['LONCOL'],data['LATCOL'] = tbd.GPS_to_grid(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.grid_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))

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三角形和六边形栅格

#Triangle grids
params['method'] = 'tri'

#Map the GPS data to grids
data['loncol_1'],data['loncol_2'],data['loncol_3'] = tbd.GPS_to_grid(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.grid_to_polygon([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))

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#Hexagon grids
params['method'] = 'hexa'

#Map the GPS data to grids
data['loncol_1'],data['loncol_2'],data['loncol_3'] = tbd.GPS_to_grid(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.grid_to_polygon([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))

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