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

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
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Request Sample Access

Attribute	Type	Example
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

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
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Request Sample Access

Attribute	Type	Example
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

file	column_id	classification_label	label	Base / Premium
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Request Sample Access

Attribute	Type	Example
file	String	india_base
column_id	String	p2022
classification_label	String	Population Summary
label	String	Resident Population 2022
Base / Premium	String	Base

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