How to reduce Geojson size for repeated geometries (like timestamped data) in Pandas

Question

I have a geopandas data frame which contain respective geometries as follows:

Date      ,    value, Region Name, Geometry
2022-01-01  10      , ABC        , Point((194 34),(121,23))
2022-02-01, 12      , ABC        , Point((194 34),(121,23))
2022-02-01, 13      , DEF        , Point((195 35),(123,24))

Almost equivalent Py code

import pandas as pd
import geopandas
import matplotlib.pyplot as plt
from shapely.geometry import Point
import geopandas
d = pd.DataFrame({'RegionName': ['ABC', 'ABC','DEF'],'Date': ['2021-01-01', '2021-02-01','2021-01-01'], 'Values': [10,11,12], 'Latitude': [-34.58, -34.58, -33.45], 'Longitude': [-58.66, -58.66, -70.66]})
gdf = geopandas.GeoDataFrame(d, geometry=geopandas.points_from_xy(d.Longitude, d.Latitude))
gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")

How can I save this data into a json/geojson file by reducing the size of the file and appending non-repetitive data (e.g. date and value) to the repetitive value (e.g. geometry)

Sth like this:

[
---Region name:
-----ABC
-----Date:
--------2022-01-01
--------2022-02-01
-----Value:
--------10
--------12
-----Geometry
--------Polygon((194 34),(121,23))


---Region name:
-----DEF
-----Date:
--------2022-02-01
-----Value:
--------13
-----Geometry
--------Polygon((194 34),(121,23))
]

Requirement:

This file needs to be consumed by mapbox/leaflet/or any other similar tool

Moh-Spark · Answer

Was able to solve this, first we need the distinct of repetitive columns (lets call it A,e.g. geometry), then form a list of non repetetive ones (lets call this B, e.g. date and value) and then merge B and A and then do the Json conversion.

Py code:

import pandas as pd
import geopandas
import matplotlib.pyplot as plt
from shapely.geometry import Point
import geopandas
d = pd.DataFrame({'RegionName': ['ABC', 'ABC','DEF'],'Date': ['2021-01-01', '2021-02-01','2021-01-01'], 'Values': [10,11,12], 'Latitude': [-34.58, -34.58, -33.45], 'Longitude': [-58.66, -58.66, -70.66]})
gdf = geopandas.GeoDataFrame(d, geometry=geopandas.points_from_xy(d.Longitude, d.Latitude))
gdf = geopandas.GeoDataFrame(d, crs="EPSG:4326")


#create a unique list of static data
df_dis_test= pd.DataFrame({'RegionName': ['ABC', 'DEF'],'Latitude': [-34.58, -33.45], 'Longitude': [-58.66, -70.66]})

gdfdf_dis_test = geopandas.GeoDataFrame(df_dis_test, geometry=geopandas.points_from_xy(df_dis_test['Longitude'], df_dis_test['Latitude']))
gdfdf_dis_test = geopandas.GeoDataFrame(df_dis_test, crs="EPSG:4326")
dgrp=d.groupby(['RegionName']). agg({ 'Date': lambda x: ','.join(x) }      )
result = dgrp.merge( gdfdf_dis_test,  how="inner", on="RegionName")

dgrpval=d.groupby(['RegionName']). agg({ 'Values': lambda x: list(x) }      )
result2 = result.merge( dgrpval,  how="inner", on="RegionName")
result2=result2.rename(columns={'geometry_x':'geometry'})
result2Gpd = geopandas.GeoDataFrame(result2, crs="EPSG:4326")#.drop(['geometry_y'],axis=1)
with open('Result2.geojson', 'w') as f:
    f.write(result2Gpd.to_json (sort_keys=True, default=str))

and the output

How to reduce Geojson size for repeated geometries (like timestamped data) in Pandas

1 Answers