Store Location Data | Asia/ MENA | Latest Estimates on Population, Demographics, Consuming Class, Retail Spend | GIS Data | Demographic Data
# | 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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Frequently asked questions
What is Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data?
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 Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data 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 Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data?
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 Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data cover?
This product includes data covering 6 countries like India, Indonesia, Saudi Arabia, Singapore, and Malaysia. GapMaps is headquartered in Australia.
How much does Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data cost?
Pricing information for Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data 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 Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data?
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 Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data?
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 Store Location Data Asia/ MENA Latest Estimates on Population, Demographics, Consuming Class, Retail Spend GIS Data Demographic Data?
This product has 3 related products. These alternatives include Premium GIS Data Asia/ MENA Latest Estimates on Population, Consuming Class, Retail Spend, Demographics Map Data Demographic Data, Retail Store Data: Accurate Places Data Global Location Data on 52M+ Places, and Store Location Data Park & Ride Locations in US and Canada Polygon Data Location Insights. You can compare the best Demographic Data providers and products via Datarade’s data marketplace and get the right data for your use case.