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Generating Grid area

Dataset

Load dataset

First, we load a dataset containing accident record with high detail about location, times, roads intercross, detail rural area around that accident.

accident_data = pd.read_csv(r'..\map\Accidents_2015.csv')
accident_data = accident_data.sample(n=1000, random_state=None)
accident_data.dropna(subset=["Latitude", "Longitude"], inplace=True)
accident_data = accident_data.reset_index(drop=True)

geometry = [Point(xy) for xy in zip(accident_data.Latitude, accident_data.Longitude)]
accident_data = gpd.GeoDataFrame(accident_data, geometry=geometry, crs="EPSG:4326")
  Accident_Index Location_Easting_OSGR Location_Northing_OSGR Longitude Latitude Police_Force Accident_Severity Number_of_Vehicles Number_of_Casualties Date Day_of_Week Time Local_Authority_(District) Local_Authority_(Highway) 1st_Road_Class 1st_Road_Number Road_Type Speed_limit Junction_Detail Junction_Control 2nd_Road_Class 2nd_Road_Number Pedestrian_Crossing-Human_Control Pedestrian_Crossing-Physical_Facilities Light_Conditions Weather_Conditions Road_Surface_Conditions Special_Conditions_at_Site Carriageway_Hazards Urban_or_Rural_Area Did_Police_Officer_Attend_Scene_of_Accident LSOA_of_Accident_Location geometry
0 201501HT20994 535370 182030 -0.050251 51.521 1 3 1 1 07/11/2015 7 21:00 5 E09000030 3 11 6 30 3 2 4 121 0 5 4 1 2 0 0 1 1 E01004290 POINT (51.520973 -0.050251)
1 201504WA15499 334010 433730 -3.00329 53.7957 4 1 2 1 09/12/2015 4 23:11 71 E06000009 3 5230 3 70 1 4 3 583 0 0 4 2 2 0 0 1 1 E01012688 POINT (53.795674 -3.003287)
2 2015132A80678 423223 410602 -1.65063 53.5915 13 3 1 1 08/10/2015 5 09:10 203 E08000034 4 6116 6 30 8 4 6 0 0 1 1 1 1 0 0 2 2 E01011113 POINT (53.591487 -1.650627)
3 201506K003369 373360 406620 -2.4036 53.5555 6 3 1 1 27/06/2015 7 19:30 100 E08000001 5 0 6 30 0 -1 -1 0 0 0 1 1 1 0 0 1 2 E01004857 POINT (53.555523 -2.403599)
4 201531C234515 456392 345659 -1.16108 53.0053 31 3 1 1 07/11/2015 7 11:48 346 E06000018 6 0 6 30 0 -1 -1 0 0 1 1 2 2 0 0 1 1 E01013849 POINT (53.005285 -1.161076)

The Code

By calling maps.get_grid, we can generate grid with 2 types of outputs, geojson or array format. Both of this output needed for difference condition and analysis.

result = maps.get_grid(main_data, upper_right=None, lower_left=None, n=6, col_list=['Latitude','Longitude'], type_='geojson')

This function requires the following parameters:

  • main_data (geodataframe): Data location and value
  • upper_right (float): upper right on boundary
  • lower_left (float): lower left on boundary
  • n (int): interval
  • col_list (list): longitude and latitude columns name
  • type_ (string): output type

The result for Json output 

[{'type': 'FeatureCollection',
  'properties': {'lower_left': [-2.461976, 55.132204],
   'upper_right': [-1.2882623333333332, 56.3643015]},
  'features': [{'type': 'Feature',
    'geometry': {'type': 'Polygon',
     'coordinates': [[[-2.461976, 56.3643015],
       [-1.2882623333333332, 56.3643015],
       [-1.2882623333333332, 55.132204],
       [-2.461976, 55.132204],
       [-2.461976, 56.3643015]]]}}]},
 {'type': 'FeatureCollection',
  'properties': {'lower_left': [-1.2882623333333332, 55.132204],
   'upper_right': [-0.11454866666666641, 56.3643015]},
  'features': [{'type': 'Feature',
    'geometry': {'type': 'Polygon',
     'coordinates': [[[-1.2882623333333332, 56.3643015],
       [-0.11454866666666641, 56.3643015],
       [-0.11454866666666641, 55.132204],
       [-1.2882623333333332, 55.132204],
       [-1.2882623333333332, 56.3643015]]]}}]},
 {'type': 'FeatureCollection',
  'properties': {'lower_left': [-0.11454866666666641, 55.132204],
   'upper_right': [1.0591650000000004, 56.3643015]},
  'features': [{'type': 'Feature',
    'geometry': {'type': 'Polygon',
     'coordinates': [[[-0.11454866666666641, 56.3643015],
       [1.0591650000000004, 56.3643015],
       [1.0591650000000004, 55.132204],
       [-0.11454866666666641, 55.132204],
       [-0.11454866666666641, 56.3643015]]]}}]}]

drawing

The result for Array output 

array([[50.09284 , -7.371592],
       [51.377747, -7.371592],
       [52.662654, -7.371592],
       [53.947561, -7.371592],
       [55.232468, -7.371592],
       [56.517375, -7.371592],
       [57.802282, -7.371592],
       [50.09284 , -5.856329],
       [51.377747, -5.856329],
       [52.662654, -5.856329],
])