bihar pre delimitation AC shapefiles

[Chandrashekhar Raman]

 does anyone have access to pre-delimitation assembly constituency shapefiles of Bihar.. i.e 2005 backwards. much appreciate if can be shared.

thanks

cs

[Devdatta Tengshe]

Hi Chandrashekar,

There is some data available at: http://www.dartmouth.edu/~sandip/data.html#maps. This link was shred several years ago on this group.

I've extracted the ACs belonging to Bihar, and am sharing them in Shapefile format.

Regards,

Dev

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[Chandrashekhar Raman]

thank you Dev, this is looking good!! 

regards

cs

[Dilip Damle]

The predetermination data was available fro download from the Election commission website itself.
I had kept it safely.

Here it is. There is a known problem about the placement of Uttarakhand in one of the sets.

[Dilip Damle]

Sorry for the typo 

It is Pre Delimitation

[Dilip Damle]

Here is PC data

On Friday, October 30, 2015 at 12:17:20 PM UTC+5:30, Dilip Damle wrote:

[Chandrashekhar Raman]

Thank you dileep.. Will check this out as well. 

Btw tried the one that Dev shared and that seems to be pretty good as well. Just one AC missing - i think govindpur

Regards

Cs

Sent from my iPhone

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<GIS_AC_Data.zip>

[David Sorge]

Thanks so much for sharing this! Three quick questions:

1) Do you have the link within the ECI website?

I'd love to be able to link back to it, check for additional metadata, etc. Unfortunately, ECI is a pretty difficult website to navigate (for me at least -- maybe I just need to learn how to use it better.)

2) Do you know what projection the files are in?

I'd like to transform it so it fits with data in a Kalianpur 1975/India Zone IIa Projection, but to get it there, I need to figure out what projection the data starts in. Were there any clues about this on the site where the files originated?

3) Is there a well-known fix for the Uttarakhand issue?
I was able to do a quick and imprecise fix in python using geopandas and shapely:

```

import pandas as pd

import geopandas as gpd

from shapely.affinity import translate

AC_path = Path('geocoding/AC_Data/States')

gdfs = []

for file in AC_path.glob('*/*.shp'):

    gdf = gpd.read_file(file)

    gdfs.append(gdf)

AC_data = pd.concat(gdfs)

mask = AC_data.ST_CODE == 'S28'

AC_data.loc[mask, 'geometry'] = \

    AC_data[AC_data.ST_CODE == 'S28'].geometry \

    .apply(lambda geom:

    translate(geom, xoff=-14.3468, yoff=0.1394))

```

However, this results in only an approximate fix, with some boundary problems as shown in the plot here. The fact that Uttarkhand looks just a bit small for the space probably has to do with issues with the projection.

This fix will work well enough for my purposes, I think, but I'd be interested to know about another solution if one exists.

Thanks so much!

David

[Nikhil VJ]

Hi David,

This is in response to your second question about the projection the shapes are in.

This might help to find out: http://projfinder.com/ 

Have to start with identifying a spot in the shapefile that you can recognise and locate on a real map. Center the map in the website there, and key in the co-ords for that spot in the shapefile. The website then gives you a listing of most likely projections.

We used that tool here: https://github.com/draftmpd41/layers_draft_delhi_master_plan_2041

--
Cheers,
Nikhil VJ
https://nikhilvj.co.in

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[David Sorge]

Fantastic! Thanks so much!