GapMaps Premium Demographic Data | Asia/ MENA | 150m x 150m Grids| Current Estimates
# | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx |
2 | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx |
3 | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx |
4 | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx |
5 | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx |
6 | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx |
7 | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx |
8 | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx |
9 | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx |
10 | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx |
... | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx |
# | 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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx |
2 | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx |
3 | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx |
4 | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx |
5 | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx |
6 | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx |
7 | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx |
8 | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx |
9 | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx |
10 | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxx |
... | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx |
# | file |
column_id |
classification_label |
label |
Base / Premium |
---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx |
2 | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx |
3 | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx |
4 | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx |
5 | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx |
6 | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx |
7 | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx |
8 | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx |
9 | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx |
10 | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx |
... | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
area_id
|
Integer | 1801913 | |
w2022
|
Float | 0.0 | |
uc2022
|
Float | 44.0 | |
mc2022
|
Float | 59.0 | |
nc2022
|
Float | 453.0 | |
p5_2022
|
Float | 44.0 | |
p4_2022
|
Float | 59.0 | |
p3_2022
|
Float | 106.0 | |
p2_2022
|
Float | 201.0 | |
p1_2022
|
Float | 146.0 | |
sfb356
|
Float | 13181648.0 | |
sg356
|
Float | 28838533.0 | |
sap356
|
Float | 4686003.0 | |
sonf356
|
Float | 12171617.0 | |
s356
|
Float | 58877801.0 | |
sfb356pc
|
Float | 42.64208633093525 | |
sg356pc
|
Float | 93.2931654676259 | |
sap356pc
|
Float | 15.160071942446043 | |
sonf356pc
|
Float | 39.37589928057554 | |
s356pc
|
Float | 190.46942446043167 |
Attribute | Type | Example | Mapping |
---|---|---|---|
area_id
|
Integer | 1801913 | |
p2022
|
Float | 556.0 | |
p_06
|
Float | 57.0 | |
p_7_
|
Float | 494.0 | |
p_sc
|
Float | 127.0 | |
p_st
|
Float | 2.0 | |
p_s_
|
Float | 422.0 | |
p_lit
|
Float | 393.0 | |
p_ill
|
Float | 150.0 | |
mainwork_p
|
Float | 65.0 | |
mgwk_0_3_p
|
Float | 12.0 | |
mgwk_3_6_p
|
Float | 52.0 | |
non_work_p
|
Float | 316.0 | |
main_cl_p
|
Float | 21.0 | |
main_al_p
|
Float | 35.0 | |
main_hh_p
|
Float | 11.0 | |
main_ot_p
|
Float | 93.0 |
Attribute | Type | Example | Mapping |
---|---|---|---|
file
|
String | india_base | |
column_id
|
String | p2022 | |
classification_label
|
String | Population Summary | |
label
|
String | Resident Population 2022 | |
Base / Premium
|
String | Base |
Attribute | Type | Example | Mapping |
---|---|---|---|
Population
|
String | ||
Worker Population
|
String | ||
Consuming Class
|
String | ||
Premium Consuming Class
|
String | ||
Demographics
|
String | Age Profile | |
Resident Retail Spending
|
String | Food & Beverage |
Description
Country Coverage
Volume
3.7 million | Small Area regions assessed in India |
46,000 | Small Area Regions assessed in Singapore |
3.5 million | Small Area Regions Assessed in Saudi Arabia |
1.49 million | Small Area Regions Assessed in Indonesia |
Pricing
License | Starts at |
---|---|
One-off purchase | Not available |
Monthly License | Not available |
Yearly License | Available |
Usage-based | Not available |
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates?
GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent demographic datasets at 150m grid levels across Asia and MENA. Understand who lives in a catchment, where they work and their spending potential.
What is GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates used for?
This product has 5 key use cases. GapMaps recommends using the data for Location Intelligence, Location-based Analytics, Retail Site Selection, Location Planning, and Location Insights. Global businesses and organizations buy Demographic Data from GapMaps to fuel their analytics and enrichment.
Who can use GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates?
This product is best suited if you’re a Medium-sized Business or Enterprise looking for Demographic Data. Get in touch with GapMaps to see what their data can do for your business and find out which integrations they provide.
Which countries does GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates cover?
This product includes data covering 6 countries like India, Indonesia, Saudi Arabia, Singapore, and Malaysia. GapMaps is headquartered in Australia.
How much does GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates cost?
Pricing information for GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates is available by getting in contact with GapMaps. Connect with GapMaps to get a quote and arrange custom pricing models based on your data requirements.
How can I get GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates?
Businesses can buy Demographic Data from GapMaps and get the data via S3 Bucket and SFTP. Depending on your data requirements and subscription budget, GapMaps can deliver this product in .json and .csv format.
What is the data quality of GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates?
You can compare and assess the data quality of GapMaps using Datarade’s data marketplace. GapMaps has received 1 review from clients.
What are similar products to GapMaps Premium Demographic Data Asia/ MENA 150m x 150m Grids Current Estimates?
This product has 3 related products. These alternatives include GapMaps Premium Demographic Data Saudi Arabia 150m Grids Current Estimates, Geospatial Data: Places Data Global Location Data on 52M+ Places, and The Data Appeal Global Geographic Data API, Dataset 251M POI Data Mapped Coverage from 2019. You can compare the best Demographic Data providers and products via Datarade’s data marketplace and get the right data for your use case.