A project to bring data analytics and machine learning to policy making.
- What are the natural limits of a city?
- Is the city growing, stagnant or deflating?
- How is the population changing? Is it organic through birth and death or through migration?
- Is the change in demographic uniform? or Is it region bound? If it is region bound which region?
- What are the current available services and delivery mechanism?
- What are the additional service requirements?
- Where are they required?
- What are the future service requirements?
- By when are they required?
Lack of proper data on which to base the answer would lead to the decision maker deciding on the basis of his gut instinct. This invariably results in building facilities in those areas personally known to the decision maker, or those areas which agitate for them, while the rest of the domain suffers from neglect.
Data driven policy is an initiative to explore public data analysis to facilitate informed decision making.
As a part of this project we will look at macro level data sets and use it to arrive at decisions related to improvement of the city.
This project tries to identify various sources of data, their quality, and the prediction models that can be built using them.
Some experimental prediction models are available at http://rpubs.com/vijayvithal
Also check the wiki page at https://github.com/jahagirdar/Data-Driven-Policy/wiki for topic specific analysis and potential data sources.