Dr Raphael Susewind

India's general elections are coming up, and many data folks are looking forward to analyze and map results spatially (assuming, as I also argued last time, that all politics are local). Until very recently, only few could do this, however, because the basic prerequisite - GIS shapefiles of India's post-delimitation constituencies and polling station localities - were only available commercially (and could easily cost several thousand US dollars). Today, I wish to present a set of draft shapefiles comprising current polling booth localities, assembly constituencies and parliamentary constituencies under an open license, shared in the hope that they enable more visualizations and better spatial analyses of the ongoing elections.

Unlike the only other set of openly licensed shapefiles I am aware of - the handcrafted parliamentary constituency shapefiles recently published by DataMeet after their Bangalore hackathon (which does not yet contain assembly constituencies or polling station localities) - I chose an automated, algorithm-driven approach, working off draft polling station locality data published online by the Election Commission. I processed this data in multiple steps to derive assembly and later parliamentary constituency shapefiles:

Last week at the AAS in Philly, I had an interesting discussion of votebank politics in India and the importance of spatial variation. My contention was that most politics are local, and that electoral dynamics such as Muslim votebanks (i.e. Muslims voting for certain parties) and the extent of ethnic coordination (i.e. Muslims voting for Muslim candidates) depend on largely local factors. Some people disagreed, many agreed - but it remained a gut feeling. Until, on the flight back, I got an idea how to prove my point. This brief post thus explains at which level votebanks form and operate in India (well, in one instance at least)...

I am back in Lucknow since a week or so, and this time concentrate on economic questions - both those relating to real estate (see here and here) and to the scandal of poverty. Both issues are frequently linked, of course, since many people claim that Muslims could not find housing in new Lucknow because they are by and large poor (see my earlier discussion of residential segregation). As a quantifiable basis to discuss this proposition, I had analysed data from the Public Distribution System (PDS) before my departure. The key finding: Muslims in Lucknow are poor - but not poorer than their non-Muslim counterparts.

In last week's post, I began to introduce Lucknow's real estate market in an attempt to unearth the story behind the city's residential segregation along religious lines. I have shown where Muslims achieve higher and where lower prices than non-Muslims for comparable property (importantly, as one commentator pointed out by email, adopting a seller's perspective -- for sellers, higher prices are good, while for buyers, lower ones would be. I am currently wrapping my head around how this impacts my findings). Today, I want to volunteer one explanation for why this price difference (still firmly from a sellers perspective) might be as it is: it has to do with social networks and proximity - and with the uneven opportunities in colluding with the "actually existing state".

Pretty much a year ago, I blogged about residential segregation of Lucknow's Muslims. There were three prominent explanations for why Muslims tend not to live in newer parts of the city, and if so, then more segregated: a) they do not want to because they prefer old-city conviviality, b) they cannot afford to because they are poor, and c) they are not allowed to, i.e. discriminated against in the housing market. All three explanations have important implications for my overall interest in Muslim belonging. But which is the most likely?

As one comment back then pointed out, data on real estate would be key to sort this out. I now have that data, and will attempt to solve the riddle in two posts. Today, I will give an overview of Lucknow's real estate market, while taking a closer look at the local state's involvement next week. This is all quite experimental still, and I would be very interested in your comments (if you are interested in a more extensive analysis, please drop me a line)!

The basis for my analysis is data from Lucknow's Property Index Register, which records all registered property sales since 2006, more than 250.000 transactions. As a first step, have a look at the following map (larger version), which shows the average sales prices per square meter over this period: