Hi Nishant,

I'll share mainly about maps related issues. I don't come from anywhere
near a formal data science qualification and am not sure if this fits into
the accepted range of data science topics, but for me in the absence of
these basic layers I don't think we can reach a stage to be able to talk
about applying the more popular topics like ML, AI etc.

We practically do have free, granular enough data with village-level
boundaries, most coded by census codes. If this is not true for some states
then it is true for others and we can start from there. With precise
village-level mapping of areas as basis, a lot of opportunities open up:

1. Ownership of land by non-persons can be mapped. We ought to have a
one-stop website where each and every prior and new *land acquisition* by
the government has been mapped, where *land holdings of major corporate
entities* is mapped, *declared no-habitation zones* are mapped. And
obviously where the interchange of land ownership between these entities
can be tracked. This is not violation of privacy rights as it's not
personal holdings, and can have significant impact in matters like
communities vs entities disputes, locating the factory that is polluting
the region's groundwater or the bottling plant that's sucking it out, etc.


2. For Maharashtra at least, I don't think we have *village-level
association of state assembly and parliamentary constituencies*. (Some
folks may disagree but I beg you to prove me wrong by attaching in your
reply a simple excel/CSV list of 2011 census codes grouped by constituency
rather than sending me off on another wild goose chase). At best there are
official PDFs that say "half of X taluka is under Y MLA constituency and
half of it is under Z constituency, and then another half is under Q
constituency". The shapefiles I've seen are cutting straight lines across
all the villages. Since at election time everbody DOES figure which
constituency they're voting for (hopefully), it would be nice to have this
little wrinkle ironed out once and for all. We can then start mapping
political accountability at the MLA and MP level rather than sticking to
full state-wide statistics of drought, suicides etc that look very big but
don't really help in filtering down to root causes.

-----
Other topic: transport

3. With ways of digitizing public transport agencies' schedules data becoming
more accessible
<http://datameet.org/2018/04/13/a-tool-for-composing-transit-schedules-data-in-static-gtfs-standard/>,
(sorry for tooting my own horn there) Indian cities' public transport
agencies' schedules data ought to be digitized and brought at par with
global standards like GTFS. When the real-time streaming data starts to
come, it will need a base layer of routes, stops, and timetables data in
order to make sense. Else we'll be staring at a map with floating markers
and will need considerable work in figuring out what the depot managers
already know. Again, the more popular data science topics become applicable
to this sector only after the initial groundwork is done.


I'll write in again if more things come up. Also, I came across another
link by GoI taking inputs similar to what you asked. Here's the link:
https://www.aitf.org.in



--
Cheers,
Nikhil VJ
+91-966-583-1250
Pune, India
Website <http://nikhilvj.co.in>
DataMeet Pune chapter <https://datameet-pune.github.io/>
Self-designed learner at Swaraj University <http://www.swarajuniversity.org>
Contribute <https://www.instamojo.com/@nikhilvj/>

On Sun, Apr 22, 2018 at 3:16 PM, Palash Kulshrestha <[email protected]>
wrote:

> +1
>
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