Evaluate the Nepal Rastra Bank's data for potential inclusion in NepalMap

Nepal Rastra Bank publishes data on its "Financial Inclusion Dashboard" at http://www.emap.nrb.org.np/. Evaluate those data sets to see if any of them might be useful for inclusion in NepalMap. Some basic rules for useful data:

- If we publish data for a given level, every geographic unit at that level must have data in the data set. For example, if we want to publish data at the district level, we must have data for each of the 77 districts. If we publish data at the local level, we must have data for each of the 753 localities.
- There must be a good way to visualize the data. The two major ways in which the Wazimap framework can present data are in histograms, bar charts and pie charts. For examples, see the pre-federal [NepalMap](https://nepalmap.org/profiles/country-NP-nepal/), [Counting India](https://www.countingindia.com/profiles/country-IN-india/) and South Africa's [Wazimap](https://wazimap.co.za/profiles/country-ZA-south-africa/).
- They are data that someone might be reasonably interested in seeing.

To fulfill this issue, you can evaluate the NRB data by

- trying to determine the smallest level at which a data set has data for every geographical unit
- trying to imagine if there is a good way to visualize those data in the Wazimap framework
- trying to determine who might be interested or why.

The full list of geographical areas that we have can be found in the SQL file at https://github.com/Code4Nepal/nepalmap_federal/blob/dev/sql/geography.sql. You may find it useful to see them in a map. To do so, follow the readme directions to run a local instance of the federal version of NepalMap.

Start with the NRB Financial Inclusion Dashboard and read sql/geography.sql to compare its geographic coverage with NepalMap's available units. Follow the README directions to run a local federal NepalMap instance and assess possible histogram, bar-chart, or pie-chart presentations. Done means documenting candidate datasets, their smallest complete geographic level, useful visualizations, and likely audience.

Written by the indexing model from the issue text.