I was employed as a spatial data and cartographic consultant on a project to analyse specific agricultural commodities and Agricultural Produce Marketing Committees (APMCs) in the Indian states of Karnataka and Madhya Pradesh. The final product was a set of maps for various publications, as well as the clean datasets themselves.

Agricultural market datasets for the states of Karnataka and Madhya Pradesh were obtained for the purposes of spatial visualisation; these contained information on wheat procurement in Madhya Pradesh (2008 – 2012), tuar production in Karnataka (2007 – 2009) and the locations and categories of APMCs in both these states. Some of the data was linked to district names, while the rest was geocoded using a free online geocoding service. I used Quantum GIS, TextEdit and Microsoft Excel extensively for this project; Excel and TextEdit are invaluable when processing CSV files, and QGIS is where all the actual mapping itself takes place.

The actual process itself involved lots of data-cleaning and a little bit of mapping. First, for the geocoding, I ran the column containing the village names through the geocoder thrice; at each repetition, I tweaked the names a little more to get more accurate coordinate results. I then had to similarly tweak the district names to get them to match up with my source shapefiles; fixing bad spellings can be a LOT of work. In its entirity, this was a tedious process that involved organising, cleaning and validating four distinct datasets with both automated and manual operations. However, the final products were datasets that were clean, had accurate spatial locations and could easily be used to produce analytically valuable maps.

CASI _ Five years of wheat procurement in Madhya Pradesh _ Animated


I had like to say that processing agricultural data obtained from official Indian sources is a difficult task and involves manual intervention at various stages. However, these are rich datasets which can help create and implement national level agricultural policies. The creation of maps, or cartographic visualisations of these datasets, has a small but influential role to play in informing such policy creation. At a glance, complex spatial data can be digested and used as evidence to follow a certain course of action.

Once these datasets are clean and useable, it is possible to analyse them in multiple ways. For example, these same datasets can be used in association with web resources, such as Google Charts or JavaScript libraries (such as d3.js) to create interactive online maps that can be shared with a far larger audience than was traditionally possible.

EPW _ Five years of wheat procurement in Madhya Pradesh

Part of this work was published in an EPW article earlier this year, available at this link.

2 thoughts on “Mapping Mandis

  1. This is so nice.. Can you do more of these maps when you are free? or create portal so that people can contribute their free time do this for the country?
    There is so much data here.


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