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Demographic Data | Asia & MENA | Make Informed Business Decisions w...
Demographic Data | Asia & MENA | Make Informed Business Decisions with High Q...
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Demographic Data | Asia & MENA | Make Informed Business Decisions with High Quality and Granular Insights | GIS Data | Map Data product image in hero

Demographic Data | Asia & MENA | Make Informed Business Decisions with High Quality and Granular Insights | GIS Data | Map Data

GapMaps
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Data Samples
GapMaps_india_premium_sample (1).csv GapMaps_india_base_sample.csv GapMaps_India_DataDictionary (7).csv
#
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
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#
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
Sign In To Preview Data
#
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
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GapMaps_india_premium_sample (1).csv GapMaps_india_base_sample.csv GapMaps_India_DataDictionary (7).csv
Volume
3.7M
Small Area regions assessed in India
Avail. Formats
.json and .csv
File
Coverage
6
Countries

Data Dictionary

[Sample] GapMaps_india_premium_sample (1).csv
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
[Sample] GapMaps_india_base_sample.csv
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
[Sample] GapMaps_India_DataDictionary (7).csv
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
Product Attributes
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

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate demographic datasets at 150m grid levels across Asia and MENA. Understand who lives in a catchment, where they work and their spending potential to make informed business decisions.
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

Asia (6)
India
Indonesia
Malaysia
Philippines
Saudi Arabia
Singapore

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

Free sample available
License Starts at
One-off purchase Not available
Monthly License Not available
Yearly License Available
Usage-based Not available
Request detailed pricing

Suitable Company Sizes

Small Business
Medium-sized Business
Enterprise

Delivery

Methods
S3 Bucket
SFTP
Frequency
yearly
Format
.json
.csv

Use Cases

Categories

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Frequently asked questions

What is Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map Data?

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate demographic datasets at 150m grid levels across Asia and MENA. Understand who lives in a catchment, where they work and their spending potential to make informed business decisions.

What is Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map 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 Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map 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 Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map 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 Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map Data cost?

Pricing information for Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map 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 Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map 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 Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map Data?

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 Demographic Data Asia & MENA Make Informed Business Decisions with High Quality and Granular Insights GIS Data Map Data?

This product has 3 related products. These alternatives include Map Data Asia & MENA Premium Demographics & Point-of-Interest Data To Optimise Business Decisions GIS Data Demographic Data, Polygon Data Marinas in US and Canada Map & Geospatial Insights, and Geospatial Data: Places Data Global Location Data on 52M+ Places. You can compare the best Demographic Data providers and products via Datarade’s data marketplace and get the right data for your use case.

Pricing available upon request
License Starts at
One-off purchase Not available
Monthly License Not available
Yearly License Available
Usage-based Not available

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