Pincodewise Prosperity Index
Pincodewise Prosperity Index
Wardwise Prosperity Index
Wardwise Prosperity Index
Tehsilwise Prosperity Index
Tehsilwise Prosperity Index
UA/Town/Village wise Prosperity Index
UA, Town and Village wise Prosperity Index
Competition Index
Competition Index
Custom Datasets
Custom Datasets

Wardwise Prosperity Index 

Provides intra-city variation in affluence at ward level

Often, Ward level data is what the enterprise may be comfortable working with.

Which are the rich or wealthy areas of the city? How fast they are growing? Who are the prospective customers? These are the basic questions that every marketer wants to answer. Until now, most of the analyses used to be limited to state, district or town levels. Strategist is now providing a platform to answer these questions at the next level of granularity i.e. ward, tehsil and village. Ward maps with attributes as listed below are available in most of the industry standard formats.

Strategist has created a Prosperity Index for each geography upto ward level. For the first time one can see the intracity variation in prosperity. Strategist’s Prosperity Index is derived from household ownership of assets and average penetration level of these assets.

The Ward Maps are geo-referenced using high resolution satellite imagery. Every attempt is made for high positional accuracy of map.

India missed its regular census in 2021, making the latest available census data more than a decade old (from 2011). Even the standard ten-year gap between censuses is too long given how rapidly the country is changing. To bridge this gap, we have developed predictive models using historical census data, yearly built-up area trends since 1991, and school enrollment figures from 2012 onward. This allows us to accurately estimate annual populations at ward level.

We have also created a model to predict per capita income by using district-level GDP data from 2012 onward, local bank credit growth trends since 2005, and monthly nightlight indices since 2012 derived from VIIRS nightlight imagery. These advanced models enable precise annual estimates of GDP and per capita income at ward level.

Our models provide detailed annual estimates of population and per capita income from 2012 onward. Estimates for 2021 are now ready, with updates provided each year. Subscribe today for continuous access to the latest insights.

Strategist’s Prosperity Index is derived from household ownership of assets and average penetration level of these assets. Using household asset penetration numbers at ward-level provided by census bureau, Strategist has created a Prosperity Index at Ward granularity.

Our models generate annual population and per capita income estimates starting from 2012. The population model incorporates census data, built-up area trends since 1991, and recent school enrollment data. The GDP model combines district GDP data, local bank credit growth, and nightlight indices from satellite imagery to deliver accurate and timely insights.

Prosperity Index uses penetration of following assets in households:

  • 1. Percentage Households Using Electricity
  • 2. Percentage Households Using LPG/PNG fuel for Cooking
  • 3. Percentage Households Using Banking Services
  • 4. Percentage Households Using Television
  • 5. Percentage Teledensity
  • 6. Percentage Households Using Computer/Laptop
  • 7. Percentage Households Using Computer/Laptop with internet
  • 8. Percentage Households Using Scooter/Motorcycle/Moped
  • 9. Percentage Households Using Car/Jeep/Van
  • 10. Percentage Households with TV, Computer/Laptop, Landline/Mobile Phone and Scooter/Car

Weightage for each asset is (100/national level household penetration of asset) i.e. if asset penetration is 20% then weightage for it is 5 while if asset penetration is 100% then weightage for it is 1.

Prosperity_index is the sum of all above 10 assets multiplied by their weightage factor.

  • 1. State name
  • 2. District name
  • 3. Tehsil name
  • 4. City name
  • 5. Ward No
  • 6. Total Household
  • 7. Total Population
  • 8. Total Male
  • 9. Total Female
  • 10. Literacy
  • 11. Male Literacy
  • 12. Female Literacy
  • 13. Sex ratio
  • 14. Percentage Households Using Electricity
  • 15. Percentage Households Using LPG/PNG fuel for Cooking
  • 16. Percentage Households Using Banking Services
  • 17. Percentage Households Using Radio/Transistor
  • 18. Percentage Households Using Television
  • 19. Percentage Households Using Computer/Laptop
  • 20. Percentage Households Using Computer/Laptop with internet
  • 21. Percentage Teledensity
  • 22. Percentage Households Using Scooter/Motorcycle/Moped
  • 23. Percentage Households Using Car/Jeep/Van
  • 24. Percentage Households with TV, Computer/Laptop, Landline/Mobile Phone and Scooter/Car
  • 25. Percentage Households with None of the assets
  • 26. Prosperity Index
  • 27. Population estimated 2021
  • 28. Per Capita Income estimated 2021
Sr No City Name State District Population Census 2011 Wards
1 Greater Mumbai Maharashtra Mumbai 12442373 88
2 Delhi Delhi Delhi 11402709 272
3 Bengaluru Karnataka Bengaluru 8495492 198
4 Hyderabad Telangana Hyderabad 6993262 150
5 Ahmedabad Gujarat Ahmedabad 5633927 57
6 Chennai Tamil Nadu Chennai 4646732 155
7 Surat Gujarat Surat 4501610 101
8 Kolkata West Bengal Kolkata 4496694 141
9 Pune Maharashtra Pune 3196239 144
10 Jaipur Rajasthan Jaipur 3046163 77
11 Lucknow Uttar Pradesh Lucknow 2817105 110
12 Kanpur Uttar Pradesh Kanpur Nagar 2768057 110
13 Nagpur Maharashtra Nagpur 2405665 136
14 Indore Madhya Pradesh Indore 1994397 69
15 Thane Maharashtra Thane 1841488 116
16 Bhopal Madhya Pradesh Bhopal 1798218 70
17 Vadodara Gujarat Vadodara 1752371 13
18 Visakhapatnam Andhra Pradesh Visakhapatnam 1728128 72
19 Pimpri Chinchwad Maharashtra Pune 1727692 106
20 Patna Bihar Patna 1684297 72
21 Ghaziabad Uttar Pradesh Ghaziabad 1648643 80
22 Ludhiana Punjab Ludhiana 1618879 75
23 Agra Uttar Pradesh Agra 1585704 90
24 Nashik Maharashtra Nashik 1486053 108
25 Faridabad Haryana Faridabad 1414050 35
26 Rajkot Gujarat Rajkot 1323363 23
27 Meerut Uttar Pradesh Meerut 1305429 80
28 Kalyan-Dombivli Maharashtra Thane 1247327 107
29 Srinagar Jammu & Kashmir Srinagar 1206419 74
30 Varanasi Uttar Pradesh Varanasi 1198491 90
31 Aurangabad Maharashtra Aurangabad 1175116 99
32 Allahabad Uttar Pradesh Allahabad 1168385 97
33 Dhanbad Jharkhand Dhanbad 1162472 55
34 Amritsar Punjab Amritsar 1159227 88
35 Vijayawada Andhra Pradesh Krishna 1143232 89
36 Navi Mumbai Maharashtra Thane 1120547 89
37 Jabalpur Madhya Pradesh Jabalpur 1081677 79
38 Haora West Bengal Haora 1077075 50
39 Ranchi Jharkhand Ranchi 1073427 55
40 Gwalior Madhya Pradesh Gwalior 1069276 61
41 Jodhpur Rajasthan Jodhpur 1056191 67
42 Coimbatore Tamil Nadu Coimbatore 1050721 72
43 Raipur Chhattisgarh Raipur 1027264 72
44 Madurai Tamil Nadu Madurai 1017865 72
45 Kota Rajasthan Kota 1001694 60
46 Chandigarh Chandigarh Chandigarh 970602 28
47 Guwahati Assam Kamrup Metropolitan 962334 61
48 Solapur Maharashtra Solapur 951558 98
49 Hubli-Dharwad Karnataka Dharwad 943788 67
50 Mysuru Karnataka Mysore 920550 72
51 Bareilly Uttar Pradesh Bareilly 904797 71
52 Moradabad Uttar Pradesh Moradabad 887871 70
53 Gurgaon Haryana Gurgaon 886519 36
54 Bhubaneswar Orissa Khordha 885363 81
55 Aligarh Uttar Pradesh Aligarh 874408 70
56 Tiruchirappalli Tamil Nadu Tiruchirappalli 847387 60
57 Salem Tamil Nadu Salem 829267 60
58 Mira-Bhayandar Maharashtra Thane 809378 79
59 Thiruvananthapuram Kerala Thiruvananthapuram 788271 88
60 Bhiwandi Nizampur Maharashtra Thane 709665 84
61 Warangal Telangana Warangal 704570 38
62 Gorakhpur Uttar Pradesh Gorakhpur 673446 70
63 Amravati Maharashtra Amravati 647057 81
64 Bikaner Rajasthan Bikaner 644406 60
65 Kochi Kerala Ernakulam 633553 73
66 Bhilai Nagar Chhattisgarh Durg 627734 69
67 Cuttack Orissa Cuttack 610189 54
68 Bhavnagar Gujarat Bhavnagar 605882 19
69 Jamnagar Gujarat Jamnagar 600943 21
70 Jammu Jammu & Kashmir Jammu 576198 96
71 Dehradun Uttarakhand Dehradun 574840 61
72 Durgapur West Bengal Barddhaman 566517 43
73 Asansol West Bengal Barddhaman 563917 50
74 Kozhikode Kerala Kozhkode 550440 59
75 Kolhapur Maharashtra Kolhapur 549236 77
76 Nellore Andhra Pradesh Sri Potti Sriramulu Nellore 547621 56
77 Gulbarga Karnataka Gulbarga 543147 58
78 Ajmer Rajasthan Ajmer 542321 55
79 Raurkela Orissa Sundargarh 536450 35
80 Loni Uttar Pradesh Ghaziabad 516082 45
81 Ujjain Madhya Pradesh Ujjain 515215 54
82 Siliguri West Bengal Darjiling 513264 47
83 Jhansi Uttar Pradesh Jhansi 505693 63
84 Sangli Miraj Kupwad Maharashtra Sangli 502793 74
85 Mangaluru Karnataka Dakshina Kannada 499487 64
86 Belgaum Karnataka Belgaum 490045 60
87 Malegaon Maharashtra Nashik 481228 74
88 Gaya Bihar Gaya 474093 54
89 Tirunelveli Tamil Nadu Tirunelveli 473637 55
90 Jalgaon Maharashtra Jalgaon 460228 69
91 Udaipur Rajasthan Udaipur 451100 55
92 Maheshtala West Bengal South Twenty Four Parganas 448317 35
93 Patiala Punjab Patiala 446246 57
94 Davanagere Karnataka Davanagere 434971 41
95 Akola Maharashtra Akola 425817 71
96 Rajpur Sonarpur West Bengal South Twenty Four Parganas 424368 35
97 Bellary Karnataka Bellary 410445 35
98 South DumDum West Bengal North Twenty Four Parganas 403316 35
99 Rajarhat Gopalpur West Bengal North Twenty Four Parganas 402844 35
100 Bhagalpur Bihar Bhagalpur 400146 51
101 Agartala Tripura West Tripura 400004 35
102 Bhatpara West Bengal North Twenty Four Parganas 386019 36
103 Latur Maharashtra Latur 382940 62
104 Panihati West Bengal North Twenty Four Parganas 377347 35
105 Rohtak Haryana Rohtak 374292 31
106 Kollam Kerala Kollam 367107 54
107 Bilaspur Chhattisgarh Bilaspur 365579 61
108 Korba Chhattisgarh Korba 365253 59
109 Brahmapur Orissa Ganjam 356598 37
110 Muzaffarpur Bihar Muzaffarpur 354462 49
111 Ahmadnagar Maharashtra Ahmadnagar 350859 65
112 Kamarhati West Bengal North Twenty Four Parganas 330211 35
113 Bijapur Karnataka Bijapur 327427 35
114 Shimoga Karnataka Shimoga 322650 35
115 Junagadh Gujarat Junagadh 319462 19
116 Thrissur Kerala Thrissur 315957 52
117 Barddhaman West Bengal Barddhaman 314265 35
118 Parbhani Maharashtra Parbhani 307170 57
119 Hisar Haryana Hisar 307024 32
120 Tumkur Karnataka Tumkur 302143 35
121 Ozhukarai Puducherry Puducherry 300104 37

Market Segmentation ▴

Wards have been segregated into ten classes using clustering algorithm. Table below shows asset penetration rates increasing consistently as prosperity increases. This can be used for Pareto 80-20 marketing i.e. target 80% market by only covering 20% areas. You can also decide your market segments for effective targeting e.g. microfinance ideal target is people just above sustenance but not effectively covered by banking.

Prosperity Class No of Wards % Households Prosperity Range % Car Ownership % Car Market % Bike Ownership % Bike Market % TV Ownership % TV Market % Comp. Ownership % Comp. Market
1 7299 3.77 0 - 551 0.85 0.33 7.16 0.77 27.14 1.33 3.37 0.68
2 12554 8.15 552 - 821 1.66 1.39 14.21 3.29 52.44 5.58 4.90 2.14
3 12764 10.43 822 - 1047 2.43 2.60 20.14 5.96 65.49 8.91 6.80 3.79
4 11603 11.71 1048 - 1267 3.50 4.21 25.91 8.61 72.90 11.14 8.90 5.58
5 10455 12.63 1268 - 1513 4.91 6.37 31.92 11.45 77.75 12.82 11.62 7.86
6 9247 13.47 1514 - 1827 6.75 9.33 36.24 13.85 81.75 14.37 15.46 11.14
7 7595 12.52 1828 - 2275 9.71 12.48 42.89 15.24 85.15 13.91 20.71 13.87
8 5787 13.19 2276 - 2996 14.55 19.70 48.33 18.10 87.85 15.12 28.62 20.21
9 3305 9.93 2997 - 4305 24.09 24.56 55.26 15.58 90.34 11.71 40.65 21.61
10 1143 4.21 4306 - 9724 44.10 19.04 59.85 7.15 92.91 5.10 58.27 13.12

Intra-City Prosperity Estimates ▴

Based on the above methodology we have calculated prosperity index for all wards of top cities. Chart below shows wardwise intra-city prosperity variation for top ten cities.

Based on the above methodology we have calculated per capita income for all wards of top cities. Chart below shows wardwise intra-city per capita income variation for top ten cities.