Provides pan-India variation in prosperity at pincode level.
Derived from household asset penetration in following categories:
1. Electricity Connection
2. LPG/PNG Connection
3. Bank Account
4. Television
5. Telephone
6. Computer/Laptop
7. Computer/Laptop with internet
8. Two Wheeler
9. Four wheeler
10. Ownership of TV, Computer/Laptop, Landline/Mobile Phone and Scooter/Car
Provides intra-city variation in prosperity at ward level for top cities of India.
Derived from household asset penetration in following categories:
1. Electricity Connection
2. LPG/PNG Connection
3. Bank Account
4. Television
5. Telephone
6. Computer/Laptop
7. Computer/Laptop with internet
8. Two Wheeler
9. Four wheeler
10. Ownership of TV, Computer/Laptop, Landline/Mobile Phone and Scooter/Car
Provides boothwise results for last few elections for micro level planning.
Which way did the votes of a particular locality go? Which locality came out strongly in support of a particular candidate? Cluster of which polling booths was lost by a Big margin. Which areas demonstrated unstinting support? Which polling booths were ambivalent?
Creating products based on insights derived from massive datasets.
Today data is becoming easily available from multiple sources. Government is keen to publish their datasets for public consumption. Companies are putting information about their products, sales/service network on their websites. Companies are capturing information about every transaction and making it available for analysis inside the organization. In short cost of data is plummeting.
At the same time it is becoming harder and harder to make sense of these mammoth datasets. While cost of data is plummeting, price of insights derived from data is increasing rapidly. Companies want to match their internal data with data available outside the organization. We think map is the perfect platform to bring these datasets together, visualize and make sense of it.