area_id | w2022 | uc2022 | mc2022 | nc2022 | p5_2022 | p4_2022 | p3_2022 | p2_2022 | p1_2022 | sfb356 | sg356 | sap356 | sonf356 | s356 | sfb356pc | sg356pc | sap356pc | sonf356pc | s356pc |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1801913 | 0.0 | 44.0 | 59.0 | 453.0 | 44.0 | 59.0 | 106.0 | 201.0 | 146.0 | 13181648.0 | 28838533.0 | 4686003.0 | 12171617.0 | 58877801.0 | 42.64208633093525 | 93.2931654676259 | 15.160071942446043 | 39.37589928057554 | 190.46942446043167 |
1801914 | 0.0 | 49.0 | 66.0 | 499.0 | 49.0 | 66.0 | 117.0 | 221.0 | 161.0 | 14552143.0 | 31836873.0 | 5173207.0 | 13437100.0 | 64999323.0 | 38.61400651465798 | 84.48045602605863 | 13.72801302931596 | 35.656351791530945 | 172.47719869706842 |
1801916 | 0.0 | 17.0 | 29.0 | 227.0 | 17.0 | 29.0 | 58.0 | 100.0 | 69.0 | 6078482.0 | 13397909.0 | 2121885.0 | 5443679.0 | 27041955.0 | 81.57142857142857 | 179.7985347985348 | 28.476190476190474 | 73.05494505494505 | 362.8974358974359 |
1801917 | 0.0 | 56.0 | 96.0 | 739.0 | 56.0 | 96.0 | 189.0 | 328.0 | 222.0 | 19848667.0 | 43749514.0 | 6928802.0 | 17775781.0 | 88302764.0 | 24.993265993265993 | 55.08978675645342 | 8.725028058361392 | 22.383838383838384 | 111.19079685746352 |
1801920 | 0.0 | 40.0 | 54.0 | 408.0 | 40.0 | 54.0 | 96.0 | 181.0 | 131.0 | 11913114.0 | 26063260.0 | 4235046.0 | 11000283.0 | 53211703.0 | 47.22908366533864 | 103.32868525896414 | 16.790836653386453 | 43.61155378486056 | 210.9581673306773 |
1801921 | 0.0 | 66.0 | 112.0 | 867.0 | 66.0 | 112.0 | 222.0 | 385.0 | 260.0 | 23276622.0 | 51305253.0 | 8125437.0 | 20845739.0 | 103553051.0 | 21.31004784688995 | 46.97129186602871 | 7.439234449760765 | 19.085167464114832 | 94.80478468899521 |
1801959 | 0.0 | 93.0 | 152.0 | 929.0 | 93.0 | 152.0 | 256.0 | 420.0 | 253.0 | 29968024.0 | 64944218.0 | 10902549.0 | 28749379.0 | 134564170.0 | 21.7427597955707 | 47.120102214650764 | 7.910562180579216 | 20.858603066439525 | 97.63202725724021 |
1801960 | 0.0 | 26.0 | 43.0 | 268.0 | 26.0 | 43.0 | 73.0 | 120.0 | 75.0 | 8598924.0 | 18634876.0 | 3128340.0 | 8249250.0 | 38611390.0 | 75.74480712166172 | 164.1513353115727 | 27.55786350148368 | 72.66468842729971 | 340.1186943620178 |
1801962 | 250.0 | 149.0 | 244.0 | 1490.0 | 149.0 | 244.0 | 411.0 | 674.0 | 405.0 | 48058582.0 | 104148576.0 | 17484004.0 | 46104287.0 | 215795449.0 | 13.556027615507169 | 29.378120021242697 | 4.932023366967605 | 13.004779607010091 | 60.87095061072756 |
1801963 | 2246.0 | 120.0 | 196.0 | 1196.0 | 120.0 | 196.0 | 330.0 | 541.0 | 325.0 | 38587414.0 | 83623447.0 | 14038336.0 | 37018263.0 | 173267460.0 | 16.882275132275133 | 36.58664021164021 | 6.142195767195767 | 16.195767195767196 | 75.8068783068783 |
area_id | p2022 | p_06 | p_7_ | p_sc | p_st | p_s_ | p_lit | p_ill | mainwork_p | mgwk_0_3_p | mgwk_3_6_p | non_work_p | main_cl_p | main_al_p | main_hh_p | main_ot_p |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1801913 | 556.0 | 57.0 | 494.0 | 127.0 | 2.0 | 422.0 | 393.0 | 150.0 | 65.0 | 12.0 | 52.0 | 316.0 | 21.0 | 35.0 | 11.0 | 93.0 |
1801914 | 614.0 | 63.0 | 545.0 | 141.0 | 2.0 | 465.0 | 434.0 | 166.0 | 72.0 | 14.0 | 58.0 | 349.0 | 24.0 | 39.0 | 12.0 | 102.0 |
1801916 | 273.0 | 29.0 | 241.0 | 58.0 | 4.0 | 208.0 | 187.0 | 79.0 | 22.0 | 4.0 | 18.0 | 152.0 | 10.0 | 17.0 | 7.0 | 56.0 |
1801917 | 891.0 | 95.0 | 788.0 | 191.0 | 16.0 | 676.0 | 613.0 | 259.0 | 73.0 | 13.0 | 60.0 | 496.0 | 35.0 | 58.0 | 23.0 | 185.0 |
1801920 | 502.0 | 51.0 | 447.0 | 115.0 | 2.0 | 381.0 | 355.0 | 136.0 | 59.0 | 11.0 | 47.0 | 285.0 | 19.0 | 32.0 | 10.0 | 84.0 |
1801921 | 1045.0 | 112.0 | 924.0 | 224.0 | 19.0 | 793.0 | 719.0 | 304.0 | 86.0 | 15.0 | 71.0 | 582.0 | 41.0 | 68.0 | 27.0 | 217.0 |
1801959 | 1174.0 | 118.0 | 1046.0 | 273.0 | 3.0 | 888.0 | 860.0 | 288.0 | 68.0 | 15.0 | 52.0 | 653.0 | 17.0 | 37.0 | 64.0 | 307.0 |
1801960 | 337.0 | 33.0 | 301.0 | 78.0 | 0.0 | 256.0 | 246.0 | 82.0 | 19.0 | 4.0 | 15.0 | 187.0 | 5.0 | 10.0 | 18.0 | 88.0 |
1801962 | 1883.0 | 189.0 | 1678.0 | 439.0 | 5.0 | 1423.0 | 1379.0 | 463.0 | 109.0 | 25.0 | 84.0 | 1047.0 | 28.0 | 60.0 | 102.0 | 493.0 |
1801963 | 1512.0 | 152.0 | 1347.0 | 352.0 | 4.0 | 1143.0 | 1107.0 | 371.0 | 88.0 | 20.0 | 67.0 | 841.0 | 23.0 | 48.0 | 82.0 | 395.0 |
file | column_id | classification_label | label | Base / Premium |
---|---|---|---|---|
india_base | p2022 | Population Summary | Resident Population 2022 | Base |
india_premium | w2022 | Population Summary | Workers 2022 (working within catchment, major cities only) | Premium |
india_premium | uc2022 | Consuming Class (2022, No of Residents) | Premium Consuming Class (SEC A) | Premium |
india_premium | mc2022 | Consuming Class (2022, No of Residents) | Other Consuming Class (SEC B) | Premium |
india_premium | nc2022 | Consuming Class (2022, No of Residents) | Non Consuming Class (SEC C/D/E) | Premium |
india_premium | p5_2022 | Prosperity Index (2022, No of Residents) | A. High (% of residents) | Premium |
india_premium | p4_2022 | Prosperity Index (2022, No of Residents) | B. Mid-High (% of residents) | Premium |
india_premium | p3_2022 | Prosperity Index (2022, No of Residents) | C. Mid (% of residents) | Premium |
india_premium | p2_2022 | Prosperity Index (2022, No of Residents) | D. Low-Mid (% of residents) | Premium |
india_premium | p1_2022 | Prosperity Index (2022, No of Residents) | E. Low (% of residents) | Premium |
Description
Sourcing accurate and up-to-date demographic data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly. GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographic datasets across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities. With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to: - Better understand your customers - Identify optimal locations to expand your retail footprint - Define sales territories for franchisees - Run targeted marketing campaigns. Premium demographics data for Asia and MENA includes the latest estimates (updated annually) on: 1. Population (how many people live in your local catchment) 2. Demographics (who lives within your local catchment) 3. Worker population (how many people work within your local catchment) 4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers) 5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category. Primary Use Cases for GapMaps Demographic Data: 1. Retail (eg. Fast Food/ QSR, Cafe, Fitness, Supermarket/Grocery) - Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential - Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics - Site Selection: Identify optimal locations for future expansion and benchmark performance across existing locations. - Target Marketing: Develop effective marketing strategies to acquire more customers. - Integrate GapMaps demographic data with your existing GIS or BI platform to generate powerful visualizations. 2. Commercial Real-Estate (Brokers, Developers, Investors, Single & Multi-tenant O/O) - Tenant Recruitment - Target Marketing - Market Potential / Gap Analysis 3. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens) - Customer Profiling - Target Marketing - Market Share Analysis
Country Coverage
(6 countries)Data Categories
- Demographic Data
- Map Data
- GIS Data
- Geodemographic Data
- Spending Data
Pricing
One-off purchase |
Not available |
Monthly License |
Not available |
Yearly License |
Available |
Usage-based |
Not available |
Volumes
- Small Area regions assessed in India
- 3.7M
- Small Area Regions assessed in Singapore
- 46K
- Small Area Regions Assessed in Saudi Arabia
- 3.5M
- Small Area Regions Assessed in Indonesia
- 1.49M
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