Doorda UK Population Data | Geodemographic Data | Linked to 2.2M+ Postcodes from 173 Data Sources | Location Intelligence and Analytics
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urn
|
change_date
|
msoa
|
period
|
total_annual_income_average
|
total_annual_income_upper_confidence_limit
|
total_annual_income_lower_confidence_limit
|
total_annual_income_confidence_interval
|
net_annual_income_average
|
net_annual_income_upper_confidence_limit
|
net_annual_income_lower_confidence_limit
|
net_annual_income_confidence_interval
|
net_annual_income_before_housing_average
|
net_annual_income_before_housing_upper_confidence_limit
|
net_annual_income_before_housing_lower_confidence_limit
|
net_annual_income_before_housing_confidence_interval
|
net_annual_income_after_housing_average
|
net_annual_income_after_housing_upper_confidence_limit
|
net_annual_income_after_housing_lower_confidence_limit
|
net_annual_income_after_housing_confidence_interval
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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| 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 | 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 | 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 | 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 |
| Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx |
Volume
2.2M
Postcodes
Data Quality
100%
UK Coverage
Avail. Format
.csv
File
Coverage
1
Country
Data Dictionary
[Sample] Household Income
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 7300 | |
|
change_date
|
DateTime | 2020-03-05T00:00:00+00:00 | |
|
msoa
|
String | E02002501 | |
|
period
|
DateTime | 2018-01-01T00:00:00+00:00 | |
|
total_annual_income_average
|
Float | 29700.0 | |
|
total_annual_income_upper_confidence_limit
|
Float | 35600.0 | |
|
total_annual_income_lower_confidence_limit
|
Float | 24800.0 | |
|
total_annual_income_confidence_interval
|
Float | 10800.0 | |
|
net_annual_income_average
|
Float | 27400.0 | |
|
net_annual_income_upper_confidence_limit
|
Float | 32300.0 | |
|
net_annual_income_lower_confidence_limit
|
Float | 23300.0 | |
|
net_annual_income_confidence_interval
|
Float | 9000.0 | |
|
net_annual_income_before_housing_average
|
Float | 24200.0 | |
|
net_annual_income_before_housing_upper_confidence_limit
|
Float | 27500.0 | |
|
net_annual_income_before_housing_lower_confidence_limit
|
Float | 21300.0 | |
|
net_annual_income_before_housing_confidence_interval
|
Float | 6200.0 | |
|
net_annual_income_after_housing_average
|
Float | 19100.0 | |
|
net_annual_income_after_housing_upper_confidence_limit
|
Float | 20900.0 | |
|
net_annual_income_after_housing_lower_confidence_limit
|
Float | 17500.0 | |
|
net_annual_income_after_housing_confidence_interval
|
Float | 3400.0 |
[Sample] Postcode Index
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 2109820 | |
|
change_date
|
DateTime | 2023-11-29T00:00:00+00:00 | |
| String | SN11 8UY | Postal Code | |
|
postcode_introduce
|
DateTime | 1980-01-01T00:00:00+00:00 | |
|
postcode_terminated
|
DateTime | ||
|
oa_2001
|
String | E00162886 | |
|
oa_classification_2001
|
String | 4B1 | |
|
oa_classification_2001_description
|
String | Prospering older families | |
|
oa_2011
|
String | E00162886 | |
|
oa_classification_2011
|
String | 6A3 | |
|
oa_classification_2011_description
|
String | Suburban achievers | |
|
lsoa_2001
|
String | E01031938 | |
|
lsoa_2001_description
|
String | North Wiltshire 012D | |
|
lsoa_2011
|
String | E01031938 | |
|
lsoa_2011_description
|
String | Wiltshire 013D | |
|
msoa_2001
|
String | E02006655 | |
|
msoa_2001_description
|
String | North Wiltshire 012 | |
|
msoa_2011
|
String | E02006655 | |
|
msoa_2011_description
|
String | Wiltshire 013 | |
|
rural_urban_classification_2001
|
Integer | 7 | |
|
rural_urban_classification_2001_description
|
String | (England/Wales) Village – less sparse: OA falls within th... | |
|
rural_urban_classification_2011
|
String | E1 | |
|
rural_urban_classification_2011_description
|
String | (England/Wales) Rural village | |
|
easting
|
Integer | 404040 | |
|
northing
|
Integer | 170084 | |
|
easting_northing_quality
|
Integer | 1 | |
| Float | 51.4297 | Latitude | |
| Float | -1.943773 | Longitude | |
|
local_authority_district
|
String | E06000054 | |
|
local_authority_district_description
|
String | Wiltshire | |
|
country
|
String | E92000001 | |
|
country_description
|
String | England | |
|
region
|
String | E12000009 | |
|
region_description
|
String | South West | |
|
county
|
String | E99999999 | |
|
county_description
|
String | (pseudo) England (UA/MD/LB) | |
|
county_electoral_division
|
String | E99999999 | |
|
county_electoral_division_description
|
String | ||
|
ward
|
String | E05013414 | |
|
ward_description
|
String | Calne Rural | |
|
statistical_ward
|
String | 46UCHY | |
|
statistical_ward_description
|
String | Hilmarton | |
|
census_area_statistics_ward
|
String | 46UCHY | |
|
census_area_statistics_ward_description
|
String | Hilmarton | |
|
parish
|
String | E04011666 | |
|
parish_description
|
String | Cherhill | |
|
national_park
|
String | E65000001 | |
|
national_park_description
|
String | ||
|
postcode_user_type
|
Integer | 0 | |
|
workplace_zone
|
String | E33050606 | |
|
bua
|
String | E34001349 | |
|
bua_description
|
String | Cherhill BUA | |
|
bua_sub_division
|
String | E35999999 | |
|
bua_sub_division_description
|
String | England (not covered) | |
|
ccg_ons_code
|
String | E38000231 | |
|
ccg_nhs_code
|
String | 92G | |
|
ccg_description
|
String | NHS Bath and North East Somerset, Swindon and Wiltshire CCG | |
|
strategic_health_authority
|
String | E18000010 | |
|
strategic_health_authority_description
|
String | South West | |
|
nhs_region
|
String | E40000006 | |
|
nhs_region_description
|
String | South West | |
|
primary_care_trust
|
String | E16000140 | |
|
primary_care_trust_description
|
String | Wiltshire | |
|
national_cancer_vanguard
|
String | E56000033 | |
|
national_cancer_vanguard_description
|
String | Somerset, Wiltshire, Avon and Gloucestershire | |
|
standard_statistical_region
|
Integer | 6 | |
|
standard_statistical_region_description
|
String | South West | |
|
westminster_parliamentary_constituency
|
String | E14000860 | |
|
westminster_parliamentary_constituency_description
|
String | North Wiltshire | |
|
european_electoral_region
|
String | E15000009 | |
|
european_electoral_region_description
|
String | South West | |
|
local_learning_and_skills_council
|
String | E24000036 | |
|
local_learning_and_skills_council_description
|
String | Wiltshire and Swindon | |
|
travel_to_work_area
|
String | E30000276 | |
|
travel_to_work_area_description
|
String | Swindon | |
|
lau2
|
String | E06000054 | |
|
lau2_description
|
String | ||
|
local_enterprise_partnership_primary
|
String | E37000033 | |
|
local_enterprise_partnership_primary_description
|
String | Swindon and Wiltshire | |
|
local_enterprise_partnership_secondary
|
String | ||
|
local_enterprise_partnership_secondary_description
|
String | ||
|
police_force_area
|
String | E23000038 | |
|
police_force_area_description
|
String | Wiltshire | |
|
sustainability_transformation_partnership
|
String | E54000040 | |
|
sustainability_transformation_partnership_description
|
String | Bath and North East Somerset, Swindon and Wiltshire | |
|
index_multiple_deprivation
|
Integer | 21300 |
[Sample] Population Estimates
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 1812483 | |
|
change_date
|
DateTime | 2021-10-12T00:00:00+00:00 | |
|
oa
|
String | W00008592 | |
|
period
|
DateTime | 2020-01-01T00:00:00+00:00 | |
|
total_male
|
Float | 140.0 | |
|
male_age_0
|
Float | 2.0 | |
|
male_age_1
|
Float | 0.0 | |
|
male_age_2
|
Float | 0.0 | |
|
male_age_3
|
Float | 3.0 | |
|
male_age_4
|
Float | 3.0 | |
|
male_age_5
|
Float | 0.0 | |
|
male_age_6
|
Float | 0.0 | |
|
male_age_7
|
Float | 4.0 | |
|
male_age_8
|
Float | 2.0 | |
|
male_age_9
|
Float | 0.0 | |
|
male_age_10
|
Float | 0.0 | |
|
male_age_11
|
Float | 3.0 | |
|
male_age_12
|
Float | 0.0 | |
|
male_age_13
|
Float | 2.0 | |
|
male_age_14
|
Float | 1.0 | |
|
male_age_15
|
Float | 0.0 | |
|
male_age_16
|
Float | 0.0 | |
|
male_age_17
|
Float | 1.0 | |
|
male_age_18
|
Float | 2.0 | |
|
male_age_19
|
Float | 1.0 | |
|
male_age_20
|
Float | 3.0 | |
|
male_age_21
|
Float | 1.0 | |
|
male_age_22
|
Float | 2.0 | |
|
male_age_23
|
Float | 2.0 | |
|
male_age_24
|
Float | 0.0 | |
|
male_age_25
|
Float | 1.0 | |
|
male_age_26
|
Float | 3.0 | |
|
male_age_27
|
Float | 1.0 | |
|
male_age_28
|
Float | 1.0 | |
|
male_age_29
|
Float | 2.0 | |
|
male_age_30
|
Float | 0.0 | |
|
male_age_31
|
Float | 2.0 | |
|
male_age_32
|
Float | 1.0 | |
|
male_age_33
|
Float | 1.0 | |
|
male_age_34
|
Float | 2.0 | |
|
male_age_35
|
Float | 0.0 | |
|
male_age_36
|
Float | 2.0 | |
|
male_age_37
|
Float | 2.0 | |
|
male_age_38
|
Float | 3.0 | |
|
male_age_39
|
Float | 0.0 | |
|
male_age_40
|
Float | 0.0 | |
|
male_age_41
|
Float | 0.0 | |
|
male_age_42
|
Float | 1.0 | |
|
male_age_43
|
Float | 0.0 | |
|
male_age_44
|
Float | 1.0 | |
|
male_age_45
|
Float | 2.0 | |
|
male_age_46
|
Float | 4.0 | |
|
male_age_47
|
Float | 2.0 | |
|
male_age_48
|
Float | 2.0 | |
|
male_age_49
|
Float | 1.0 | |
|
male_age_50
|
Float | 2.0 | |
|
male_age_51
|
Float | 3.0 | |
|
male_age_52
|
Float | 3.0 | |
|
male_age_53
|
Float | 2.0 | |
|
male_age_54
|
Float | 1.0 | |
|
male_age_55
|
Float | 0.0 | |
|
male_age_56
|
Float | 5.0 | |
|
male_age_57
|
Float | 4.0 | |
|
male_age_58
|
Float | 2.0 | |
|
male_age_59
|
Float | 1.0 | |
|
male_age_60
|
Float | 2.0 | |
|
male_age_61
|
Float | 3.0 | |
|
male_age_62
|
Float | 2.0 | |
|
male_age_63
|
Float | 2.0 | |
|
male_age_64
|
Float | 2.0 | |
|
male_age_65
|
Float | 0.0 | |
|
male_age_66
|
Float | 2.0 | |
|
male_age_67
|
Float | 4.0 | |
|
male_age_68
|
Float | 0.0 | |
|
male_age_69
|
Float | 1.0 | |
|
male_age_70
|
Float | 1.0 | |
|
male_age_71
|
Float | 2.0 | |
|
male_age_72
|
Float | 2.0 | |
|
male_age_73
|
Float | 2.0 | |
|
male_age_74
|
Float | 1.0 | |
|
male_age_75
|
Float | 5.0 | |
|
male_age_76
|
Float | 4.0 | |
|
male_age_77
|
Float | 0.0 | |
|
male_age_78
|
Float | 2.0 | |
|
male_age_79
|
Float | 1.0 | |
|
male_age_80
|
Float | 2.0 | |
|
male_age_81
|
Float | 4.0 | |
|
male_age_82
|
Float | 4.0 | |
|
male_age_83
|
Float | 1.0 | |
|
male_age_84
|
Float | 1.0 | |
|
male_age_85
|
Float | 0.0 | |
|
male_age_86
|
Float | 0.0 | |
|
male_age_87
|
Float | 0.0 | |
|
male_age_88
|
Float | 1.0 | |
|
male_age_89
|
Float | 0.0 | |
|
male_age_90_plus
|
Float | 0.0 | |
|
total_female
|
Float | 126.0 | |
|
female_age_0
|
Float | 1.0 | |
|
female_age_1
|
Float | 0.0 | |
|
female_age_2
|
Float | 0.0 | |
|
female_age_3
|
Float | 1.0 | |
|
female_age_4
|
Float | 0.0 | |
|
female_age_5
|
Float | 0.0 | |
|
female_age_6
|
Float | 0.0 | |
|
female_age_7
|
Float | 0.0 | |
|
female_age_8
|
Float | 0.0 | |
|
female_age_9
|
Float | 1.0 | |
|
female_age_10
|
Float | 0.0 | |
|
female_age_11
|
Float | 0.0 | |
|
female_age_12
|
Float | 1.0 | |
|
female_age_13
|
Float | 0.0 | |
|
female_age_14
|
Float | 1.0 | |
|
female_age_15
|
Float | 0.0 | |
|
female_age_16
|
Float | 2.0 | |
|
female_age_17
|
Float | 0.0 | |
|
female_age_18
|
Float | 0.0 | |
|
female_age_19
|
Float | 3.0 | |
|
female_age_20
|
Float | 1.0 | |
|
female_age_21
|
Float | 1.0 | |
|
female_age_22
|
Float | 0.0 | |
|
female_age_23
|
Float | 1.0 | |
|
female_age_24
|
Float | 2.0 | |
|
female_age_25
|
Float | 0.0 | |
|
female_age_26
|
Float | 1.0 | |
|
female_age_27
|
Float | 3.0 | |
|
female_age_28
|
Float | 0.0 | |
|
female_age_29
|
Float | 0.0 | |
|
female_age_30
|
Float | 1.0 | |
|
female_age_31
|
Float | 1.0 | |
|
female_age_32
|
Float | 1.0 | |
|
female_age_33
|
Float | 0.0 | |
|
female_age_34
|
Float | 0.0 | |
|
female_age_35
|
Float | 0.0 | |
|
female_age_36
|
Float | 2.0 | |
|
female_age_37
|
Float | 1.0 | |
|
female_age_38
|
Float | 1.0 | |
|
female_age_39
|
Float | 0.0 | |
|
female_age_40
|
Float | 1.0 | |
|
female_age_41
|
Float | 3.0 | |
|
female_age_42
|
Float | 1.0 | |
|
female_age_43
|
Float | 0.0 | |
|
female_age_44
|
Float | 0.0 | |
|
female_age_45
|
Float | 0.0 | |
|
female_age_46
|
Float | 1.0 | |
|
female_age_47
|
Float | 2.0 | |
|
female_age_48
|
Float | 1.0 | |
|
female_age_49
|
Float | 2.0 | |
|
female_age_50
|
Float | 1.0 | |
|
female_age_51
|
Float | 5.0 | |
|
female_age_52
|
Float | 3.0 | |
|
female_age_53
|
Float | 3.0 | |
|
female_age_54
|
Float | 1.0 | |
|
female_age_55
|
Float | 3.0 | |
|
female_age_56
|
Float | 2.0 | |
|
female_age_57
|
Float | 1.0 | |
|
female_age_58
|
Float | 3.0 | |
|
female_age_59
|
Float | 0.0 | |
|
female_age_60
|
Float | 2.0 | |
|
female_age_61
|
Float | 1.0 | |
|
female_age_62
|
Float | 3.0 | |
|
female_age_63
|
Float | 2.0 | |
|
female_age_64
|
Float | 2.0 | |
|
female_age_65
|
Float | 0.0 | |
|
female_age_66
|
Float | 2.0 | |
|
female_age_67
|
Float | 0.0 | |
|
female_age_68
|
Float | 2.0 | |
|
female_age_69
|
Float | 0.0 | |
|
female_age_70
|
Float | 1.0 | |
|
female_age_71
|
Float | 9.0 | |
|
female_age_72
|
Float | 2.0 | |
|
female_age_73
|
Float | 3.0 | |
|
female_age_74
|
Float | 2.0 | |
|
female_age_75
|
Float | 2.0 | |
|
female_age_76
|
Float | 0.0 | |
|
female_age_77
|
Float | 3.0 | |
|
female_age_78
|
Float | 3.0 | |
|
female_age_79
|
Float | 2.0 | |
|
female_age_80
|
Float | 6.0 | |
|
female_age_81
|
Float | 3.0 | |
|
female_age_82
|
Float | 2.0 | |
|
female_age_83
|
Float | 3.0 | |
|
female_age_84
|
Float | 5.0 | |
|
female_age_85
|
Float | 0.0 | |
|
female_age_86
|
Float | 2.0 | |
|
female_age_87
|
Float | 2.0 | |
|
female_age_88
|
Float | 0.0 | |
|
female_age_89
|
Float | 0.0 | |
|
female_age_90_plus
|
Float | 3.0 |
[Sample] Household Spend Food
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 69 | |
|
change_date
|
DateTime | 2023-05-31T00:00:00+00:00 | |
|
income_decile
|
Float | 9.0 | |
|
period
|
DateTime | 2022-01-01T00:00:00+00:00 | |
|
bread_rice_and_cereal
|
Float | 7.4 | |
|
pasta
|
Float | 0.6 | |
|
buns_cakes_and_biscuits
|
Float | 5.8 | |
|
savoury_pastry
|
Float | 1.3 | |
|
beef
|
Float | 2.3 | |
|
pork
|
Float | 0.6 | |
|
lamb
|
Float | 0.6 | |
|
poultry
|
Float | 2.9 | |
|
bacon_and_ham
|
Float | 1.0 | |
|
other_meat
|
Float | 8.4 | |
|
fish
|
Float | 4.1 | |
|
milk
|
Float | 2.1 | |
|
cheese
|
Float | 3.2 | |
|
eggs
|
Float | 1.0 | |
|
other_dairy_products
|
Float | 3.3 | |
|
butter
|
Float | 0.6 | |
|
margarine_veg_fats_and_peanut_butter
|
Float | 0.7 | |
|
cooking_oil_and_fats
|
Float | 0.5 | |
|
fresh_fruit
|
Float | 5.2 | |
|
chilled_or_frozen_fruits
|
Float | 0.7 | |
|
fried_fruit_and_nuts
|
Float | 1.2 | |
|
preserved_fruit_and_fruit_based_products
|
Float | 0.2 | |
|
fresh_vegetables
|
Float | 6.0 | |
|
fried_vegetables
|
|||
|
other_preserved_processed_vegetables
|
Float | 3.1 | |
|
potatoes
|
Float | 0.7 | |
|
other_tuber_vegetables
|
Float | 2.5 | |
|
sugar
|
Float | 0.6 | |
|
jams_and_marmalades
|
Float | 0.5 | |
|
chocolate
|
Float | 2.9 | |
|
confectionery
|
Float | 1.1 | |
|
edible_ice_and_ice_cream
|
Float | 1.2 | |
|
other_food
|
Float | 4.1 | |
|
coffee
|
Float | 1.3 | |
|
tea
|
Float | 0.6 | |
|
cocoa_and_powdered_chocolate
|
Float | 0.2 | |
|
fruit_and_vegetable_juices
|
Float | 1.6 | |
|
mineral_spring_water
|
Float | 0.5 | |
|
soft_drinks
|
Float | 3.0 |
[Sample] Deprivation Indices
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 3 | |
|
change_date
|
DateTime | 2019-09-27T00:00:00+00:00 | |
|
lsoa
|
String | E01031351 | |
|
period
|
DateTime | 2019-01-01T00:00:00+00:00 | |
|
imd_rank
|
Integer | 21496 | |
|
imd_decile
|
Integer | 6 | |
|
income_rank
|
Integer | 22492 | |
|
income_decile
|
Integer | 6 | |
|
employment_rank
|
Integer | 20663 | |
|
employment_decile
|
Integer | 6 | |
|
health_disability_rank
|
Integer | 21271 | |
|
health_disability_decile
|
Integer | 6 | |
|
education_rank
|
Integer | 10364 | |
|
education_decile
|
Integer | 3 | |
|
access_to_service_rank
|
Integer | 27442 | |
|
access_to_service_decile
|
Integer | 8 | |
|
living_environment_rank
|
Integer | 19181 | |
|
living_environment_decile
|
Integer | 5 | |
|
crime_rank
|
Integer | 12742 | |
|
crime_decile
|
Integer | 3 |
[Sample] Reported Crime
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 80329326 | |
|
change_date
|
DateTime | 2024-01-25T00:00:00+00:00 | |
|
lsoa
|
String | E01004661 | |
|
period
|
DateTime | 2023-12-01T00:00:00+00:00 | |
|
crime_id
|
String | ||
|
police_force_reporting
|
String | Metropolitan Police Service | |
|
police_force_area
|
String | Metropolitan Police Service | |
| Float | -0.169111 | Longitude | |
| Float | 51.519247 | Longitude | |
|
crime_location_approx
|
String | On or near Praed Street | |
|
crime_type
|
String | Anti-social behaviour | |
|
crime_last_outcome
|
String | ||
|
easting
|
Integer | 527129 | |
|
northing
|
Integer | 181625 |
[Sample] Universal Credit Claimants
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 28697607 | |
|
change_date
|
DateTime | 2024-01-01T00:00:00+00:00 | |
|
oa
|
String | E00066187 | |
|
period
|
DateTime | 2024-01-01T00:00:00+00:00 | |
|
total_claimants
|
Float | 0.0 | |
|
total_male
|
Float | 0.0 | |
|
male_16_to_19
|
Float | 0.0 | |
|
male_20_to_24
|
Float | 0.0 | |
|
male_25_to_29
|
Float | 0.0 | |
|
male_30_to_34
|
Float | 0.0 | |
|
male_35_to_39
|
Float | 0.0 | |
|
male_40_to_44
|
Float | 0.0 | |
|
male_45_to_49
|
Float | 0.0 | |
|
male_50_to_54
|
Float | 0.0 | |
|
male_55_to_59
|
Float | 0.0 | |
|
male_60_to_65
|
Float | 0.0 | |
|
male_over_65
|
Float | 0.0 | |
|
total_female
|
Float | 0.0 | |
|
female_16_to_19
|
Float | 0.0 | |
|
female_20_to_24
|
Float | 0.0 | |
|
female_25_to_29
|
Float | 0.0 | |
|
female_30_to_34
|
Float | 0.0 | |
|
female_35_to_39
|
Float | 0.0 | |
|
female_40_to_44
|
Float | 0.0 | |
|
female_45_to_49
|
Float | 0.0 | |
|
female_50_to_54
|
Float | 0.0 | |
|
female_55_to_59
|
Float | 0.0 | |
|
female_60_to_65
|
Float | 0.0 | |
|
female_over_65
|
Float | 0.0 |
[Sample] Household Tenure
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 232395 | |
|
change_date
|
DateTime | 2023-03-21T00:00:00+00:00 | |
|
oa
|
String | E00000122 | |
|
period
|
DateTime | 2021-03-21T00:00:00+00:00 | |
|
total_household
|
Integer | 117 | |
|
total_household_owned
|
Integer | 59 | |
|
household_tenure_owned_outright
|
Integer | 23 | |
|
household_tenure_owned_with_mortgage_loan
|
Integer | 36 | |
|
household_tenure_shared_ownership
|
Integer | 10 | |
|
total_household_social_rented
|
Integer | 19 | |
|
household_tenure_rented_from_council
|
Integer | 18 | |
|
household_tenure_other_social_rented
|
Integer | 1 | |
|
total_household_private_rented
|
Integer | 29 | |
|
household_tenure_private_landlord_or_agency
|
Integer | 17 | |
|
household_tenure_employer_of_household_member
|
Integer | ||
|
household_tenure_relative_or_friend_of_household_member
|
Integer | ||
|
household_tenure_other_private_rental
|
Integer | 12 | |
|
household_tenure_living_rent_free
|
Integer | 0 |
[Sample] Pension Credit
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
urn
|
Integer | 4327501 | |
|
change_date
|
DateTime | 2023-08-01T00:00:00+00:00 | |
|
oa
|
String | E00066187 | |
|
period
|
DateTime | 2023-08-01T00:00:00+00:00 | |
|
total_claimants
|
Float | 0.0 | |
|
total_male
|
Float | 0.0 | |
|
male_under_65
|
Float | 0.0 | |
|
male_65_to_69
|
Float | 0.0 | |
|
male_70_to_74
|
Float | 0.0 | |
|
male_75_to_79
|
Float | 0.0 | |
|
male_80_to_84
|
Float | 0.0 | |
|
male_85_to_89
|
Float | 0.0 | |
|
male_90_and_over
|
Float | 0.0 | |
|
total_female
|
Float | 0.0 | |
|
female_under_65
|
Float | 0.0 | |
|
female_65_to_69
|
Float | 0.0 | |
|
female_70_to_74
|
Float | 0.0 | |
|
female_75_to_79
|
Float | 0.0 | |
|
female_80_to_84
|
Float | 0.0 | |
|
female_85_to_89
|
Float | 0.0 | |
|
female_90_and_over
|
Float | 0.0 |
Description
Explore Doorda's UK Geodemographic Data, offering insights into 2.2M+ Postcodes, linked to 230k Output Areas sourced from 173 data sources. Unlock location intelligence and targeted marketing capabilities.
Doorda's UK Geodemographic Data provides a comprehensive database of over 2.2 million Postcodes, linked to 230 thousand Output Areas sourced from 173 data sources, offering unparalleled insights for location intelligence and modelling purposes.
Volume and stats:
- 2.2M Postcodes locations
- 230K Comparable output areas linked to postcodes
- Consolidated UK census from England & Wales, Scotland and Northern Ireland
Our Geodemographic Data offers a multitude of use cases:
- Market Analysis
- Competitor Analysis
- Targeted Marketing
- Customer Segmentation
- Location Planning
The key benefits of leveraging our Geodemographic Data include:
- Data Accuracy
- Informed Decision-Making
- Competitive Advantage
- Efficiency
- Single Source
Covering a wide range of industries and sectors, our data empowers organisations to make informed decisions, uncover market trends, and gain a competitive edge in the UK market.
Country Coverage
Europe
(1)
United Kingdom
Volume
| 2.2 million | Postcodes |
| 173 | Data Sources |
| 23,000 | Comparable Areas |
Pricing
Free sample available
| License | Starts at |
|---|---|
| One-off purchase | Available |
| Monthly License | Available |
| Yearly License | Available |
| Usage-based | Available |
Suitable Company Sizes
Small Business
Medium-sized Business
Enterprise
Quality
Self-reported by the provider
UK Coverage
Delivery
Methods
UI Export
REST API
Frequency
monthly
quarterly
Format
.csv
Use Cases
Categories
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Frequently asked questions
What is Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics?
Explore Doorda’s UK Geodemographic Data, offering insights into 2.2M+ Postcodes, linked to 230k Output Areas sourced from 173 data sources. Unlock location intelligence and targeted marketing capabilities.
What is Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics used for?
This product has 5 key use cases. Doorda recommends using the data for Data Enrichment, Data Modeling, Location Planning, Data Augmentation, and Data Driven Marketing. Global businesses and organizations buy Demographic Data from Doorda to fuel their analytics and enrichment.
Who can use Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Demographic Data. Get in touch with Doorda to see what their data can do for your business and find out which integrations they provide.
Which countries does Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics cover?
This product includes data covering 1 country like UK. Doorda is headquartered in United Kingdom.
How much does Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics cost?
Pricing information for Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics is available by getting in contact with Doorda. Connect with Doorda to get a quote and arrange custom pricing models based on your data requirements.
How can I get Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics?
Businesses can buy Demographic Data from Doorda and get the data via UI Export and REST API. Depending on your data requirements and subscription budget, Doorda can deliver this product in .csv format.
What is the data quality of Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics?
Doorda has reported that this product has the following quality and accuracy assurances: 100% UK Coverage. You can compare and assess the data quality of Doorda using Datarade’s data marketplace.
What are similar products to Doorda UK Population Data Geodemographic Data Linked to 2.2M+ Postcodes from 173 Data Sources Location Intelligence and Analytics?
This product has 3 related products. These alternatives include Global Demographic data Geodemographic data Consumer data Audience targeting data, Doorda UK Vulnerability Data Location Data 1.8M Postcodes from 30 Data Sources Location Intelligence and Analytics, and Demographic Data Append (Income, Home Value, Financial Data, etc) Enrichment, 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.
