Geolocet | Demographic Data | Europe | Population, Age, Gender, Marital Status and more | GDPR Compliant | Fully customizable format
# | Commune_code |
Commune_Name |
Polygon_id |
Total_ Population |
Age_0_14 |
Age_15_29 |
Age_30_44 |
Age_45_59 |
Age_60_74 |
Age_75+ |
Females |
Males |
Single |
Married |
Widowed |
Divorced |
Austrian |
Other_European_Countries |
Africa |
America |
Asia |
Other_unkown_countries |
Employed |
Unemployed |
Inactive |
Comp_military_community_serv |
Males_Age_0_14 |
Males_Age_15_29 |
Males_Age_30_44 |
Males_Age_45_59 |
Males_Age_60_74 |
Males_Age_75+ |
Females_Age_0_14 |
Females_Age_15_29 |
Females_Age_30_44 |
Females_Age_45_59 |
Females_Age_60_74 |
Females_Age_75+ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 |
2 | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxxx | Xxxxx | 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 |
3 | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx |
4 | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx |
5 | 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 | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx |
6 | 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 | Xxxxxxxxx | xxxxxxx | Xxxxxxxx |
7 | 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 |
8 | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | 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 |
9 | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | 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 |
10 | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | 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 | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
Commune_code
|
String | 10101 | |
Commune_Name
|
String | Eisenstadt | |
Polygon_id
|
String | 159495 | |
Total_ Population
|
Integer | 15,729 | |
Age_0_14
|
Integer | ||
Age_15_29
|
Integer | ||
Age_30_44
|
Integer | ||
Age_45_59
|
Integer | ||
Age_60_74
|
Integer | ||
Age_75+
|
Integer | ||
Females
|
Integer | 8,129 | |
Males
|
Integer | 7,600 | |
Single
|
Integer | 4,741 | |
Married
|
Integer | 6,830 | |
Widowed
|
Integer | 1,849 | |
Divorced
|
Integer | 2,309 | |
Austrian
|
Integer | 12,869 | |
Other_European_Countries
|
Integer | 2,271 | |
Africa
|
Integer | 57 | |
America
|
Integer | 41 | |
Asia
|
Integer | 482 | |
Other_unkown_countries
|
Integer | 9 | |
Employed
|
Integer | 7,857 | |
Unemployed
|
Integer | 306 | |
Inactive
|
Integer | 5,275 | |
Comp_military_community_serv
|
Integer | 50 | |
Males_Age_0_14
|
Integer | 1,083 | |
Males_Age_15_29
|
Integer | 1,213 | |
Males_Age_30_44
|
Integer | 1,612 | |
Males_Age_45_59
|
Integer | 1,614 | |
Males_Age_60_74
|
Integer | 1,345 | |
Males_Age_75+
|
Integer | 733 | |
Females_Age_0_14
|
Integer | 1,158 | |
Females_Age_15_29
|
Integer | 1,298 | |
Females_Age_30_44
|
Integer | 1,725 | |
Females_Age_45_59
|
Integer | 1,726 | |
Females_Age_60_74
|
Integer | 1,439 | |
Females_Age_75+
|
Integer | 783 |
Attribute | Type | Example | Mapping |
---|---|---|---|
Area_Code
|
String | 10101 | |
Area_Name
|
String | Eisenstadt | |
Polygon ID
|
String | 45556 | |
Total_Polulation
|
Integer | 15729 | |
Age_0_14
|
Integer | ||
Age_15_29
|
Integer | ||
Age_30_44
|
Integer | ||
Age_45_59
|
Integer | ||
Age_60_74
|
Integer | ||
Age_75+
|
Integer | ||
Females
|
Integer | 8129 | |
Males
|
Integer | 7600 | |
Single
|
Integer | 4741 | |
Married
|
Integer | 6830 | |
Widowed
|
Integer | 1849 | |
Divorced
|
Integer | 2309 | |
Austrian
|
Integer | 12869 | |
Other_European_Countries
|
Integer | 2271 | |
Africa
|
Integer | 57 | |
America
|
Integer | 41 | |
Asia
|
Integer | 482 | |
Other_unkown_countries
|
Integer | 9 | |
Employed
|
Integer | 7857 | |
Unemployed
|
Integer | 306 | |
Inactive
|
Integer | 5275 | |
Comp_military_community_serv
|
Integer | 50 | |
Males_0_14
|
Integer | 1083 | |
Males_15_29
|
Integer | 1213 | |
Males_30_44
|
Integer | 1612 | |
Males_45_59
|
Integer | 1614 | |
Males_60_74
|
Integer | 1345 | |
Males_74+
|
Integer | 733 | |
Females_0_14
|
Integer | 1158 | |
Females_15_29
|
Integer | 1298 | |
Females_30_44
|
Integer | 1725 | |
Females_45_59
|
Integer | 1726 | |
Females_60_74
|
Integer | 1439 | |
Females_75+
|
Integer | 783 |
Description
Country Coverage
Volume
200 | datasets |
2,000 | attributes |
Pricing
License | Starts at |
---|---|
One-off purchase |
€150 / purchase |
Monthly License | Not available |
Yearly License |
€130 / year |
Usage-based | Not available |
Suitable Company Sizes
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format?
Geolocet’s data spans across Europe, offering access to over 200 datasets and more than 2000 attributes - age distributions, gender demographics, marital status, housing and more. Our data is 100% customizable to suit your specific needs. Available in CSV, Excel, JSON or other formats.
What is Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format used for?
This product has 5 key use cases. Geolocet recommends using the data for Location Intelligence, Demographic Segmentation, Market Intelligence, Market Potential Analysis, and Data Driven Marketing. Global businesses and organizations buy Demographic Data from Geolocet to fuel their analytics and enrichment.
Who can use Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Demographic Data. Get in touch with Geolocet to see what their data can do for your business and find out which integrations they provide.
Which countries does Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format cover?
This product includes data covering 42 countries like Germany, United Kingdom, France, Italy, and Spain. Geolocet is headquartered in Bulgaria.
How much does Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format cost?
Pricing for Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format starts at EUR130 per year. Connect with Geolocet to get a quote and arrange custom pricing models based on your data requirements.
How can I get Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format?
Businesses can buy Demographic Data from Geolocet and get the data via S3 Bucket, SFTP, and Email. Depending on your data requirements and subscription budget, Geolocet can deliver this product in .json, .xml, .csv, .xls, .sql, and .txt format.
What is the data quality of Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format?
You can compare and assess the data quality of Geolocet using Datarade’s data marketplace.
What are similar products to Geolocet Demographic Data Europe Population, Age, Gender, Marital Status and more GDPR Compliant Fully customizable format?
This product has 3 related products. These alternatives include GapMaps Crime Risk Insurance Data by AGS USA and Canada Census Block Level, GeoPostcodes Population Data Demographic data Geodemographic data Consumer data Audience targeting data 55 year span Global coverage, and Versium REACH - Consumer Basic Demographic (Age, Gender, Marital Status, etc) Append API, USA, CCPA Compliant. You can compare the best Demographic Data providers and products via Datarade’s data marketplace and get the right data for your use case.