GapMaps Premium Store Location Data | Asia/ MENA Demographics | 150m x 150m Grids| Current Estimates
# | area_id |
p2022 |
p1_2022 |
p2_2022 |
p3_2022 |
p4_2022 |
p5_2022 |
p6_2022 |
a04 |
a59 |
a1014 |
a1519 |
a2024 |
a2529 |
a3034 |
a3539 |
a4044 |
a4549 |
a5054 |
a5559 |
a6064 |
a6569 |
a7074 |
a7579 |
a8084 |
a8589 |
a90 |
chinese |
malays |
indians |
others |
w2022 |
uc2022 |
mc2022 |
nc2022 |
fb |
fr |
ap |
onf |
tr |
fbpc |
frpc |
appc |
onfpc |
trpc |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx |
2 | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx |
3 | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx |
4 | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | 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 |
5 | 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 | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx |
6 | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx |
7 | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx |
8 | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx |
9 | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx |
10 | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx |
... | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxx |
# | 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 | 27587 | |
p2022
|
Integer | 0 | |
p1_2022
|
Integer | 0 | |
p2_2022
|
Integer | 0 | |
p3_2022
|
Integer | 0 | |
p4_2022
|
Integer | 0 | |
p5_2022
|
Integer | 0 | |
p6_2022
|
Integer | 0 | |
a04
|
Integer | 0 | |
a59
|
Integer | 0 | |
a1014
|
Integer | 0 | |
a1519
|
Integer | 0 | |
a2024
|
Integer | 0 | |
a2529
|
Integer | 0 | |
a3034
|
Integer | 0 | |
a3539
|
Integer | 0 | |
a4044
|
Integer | 0 | |
a4549
|
Integer | 0 | |
a5054
|
Integer | 0 | |
a5559
|
Integer | 0 | |
a6064
|
Integer | 0 | |
a6569
|
Integer | 0 | |
a7074
|
Integer | 0 | |
a7579
|
Integer | 0 | |
a8084
|
Integer | 0 | |
a8589
|
Integer | 0 | |
a90
|
Integer | 0 | |
chinese
|
Integer | 0 | |
malays
|
Integer | 0 | |
indians
|
Integer | 0 | |
others
|
Integer | 0 | |
w2022
|
Integer | 0 | |
uc2022
|
Integer | 0 | |
mc2022
|
Integer | 0 | |
nc2022
|
Integer | 0 | |
fb
|
Integer | 0 | |
fr
|
Integer | 0 | |
ap
|
Integer | 0 | |
onf
|
Integer | 0 | |
tr
|
Integer | 0 | |
fbpc
|
Integer | 0 | |
frpc
|
Integer | 0 | |
appc
|
Integer | 0 | |
onfpc
|
Integer | 0 | |
trpc
|
Integer | 0 |
Attribute | Type | Example | Mapping |
---|---|---|---|
file
|
String | singapore | |
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
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Quality
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Frequently asked questions
What is GapMaps Premium Store Location Data Asia/ MENA Demographics 150m x 150m Grids Current Estimates?
GapMaps Store Location Data uses known population data combined with billions of mobile device location points to provide highly accurate demographics insights 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 Store Location Data Asia/ MENA Demographics 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 Store Location Data Asia/ MENA Demographics 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 Store Location Data Asia/ MENA Demographics 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 Store Location Data Asia/ MENA Demographics 150m x 150m Grids Current Estimates cost?
Pricing information for GapMaps Premium Store Location Data Asia/ MENA Demographics 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 Store Location Data Asia/ MENA Demographics 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 Store Location Data Asia/ MENA Demographics 150m x 150m Grids Current Estimates?
GapMaps has reported that this product has the following quality and accuracy assurances: 100% Coverage of Major cities (150m grids), 100% Coverage in Non-Urban Areas (1km grids). 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 Store Location Data Asia/ MENA Demographics 150m x 150m Grids Current Estimates?
This product has 3 related products. These alternatives include GapMaps Premium Demographics GIS Data Asia/ MENA 150m x 150m Grids Current Estimates, Xtract.io - Store Location Data Polygon Data Park and Ride Locations in US and Canada, and Geospatial Data Places Data Polygon Data GIS Data Store Location Data Global Coverage. You can compare the best Demographic Data providers and products via Datarade’s data marketplace and get the right data for your use case.