1. Python codes can be used for converting those json files into shape
files. Using geopandas library the task is simple for such a large number
of files, the code goes as follows,
import geopandas as gpd
import glob
from geopandas import GeoDataFrame
jsn=glob.glob("/home/me/raw-json-102-241/*.json")
#getting the list of json file in the folder
df1=pd.DataFrame()
for js in jsn:
dd = gpd.read_file(js)
dd1=dd[[u'DIST_NAME', 'NAME', 'OBJECTID','STAT_NAME', u'TEHS_NAME', u
'geometry']]
df1=df1.append(dd1)
#looping over each json file and opening it with geopandas cool tool
read_file(json)
db1 = GeoDataFrame(df1, columns=[u'DIST_NAME', 'NAME', 'OBJECTID',
'STAT_NAME', u'TEHS_NAME', u'geometry'], index=df1.index)
#organizing the looped shape formats and converting into geodataframe
#now saving the shape file!
db1.to_file('v102-241.shp',driver='ESRI Shapefile')
2. There is a web map service(WMS) for this same data (I am little sure!)
2001 census map upto village/town level. See this link for more
info http://gis.stackexchange.com/a/115876 to use that WMS in QGIS. Here
also the data is not available in readily usable format, it is available in
raster format (aka pdf format!).
3. I think the RTI can be stress it out for shape files or form of data
which can be "used" not only for simple "view". Moreover the RTI can be
more emphasis to the demand on already published data as pdf files/WMS such
as village or town level map, as the ward level map is not published, it
can raise the denial of RTI.
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