Geodemographic Profiling, Knowledge Workers and Networks
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Transcript of Geodemographic Profiling, Knowledge Workers and Networks
Geodemographic Profiling,Knowledge Workers and
Networks
Dr Tom Williamson
Visiting Professor
Institute of Criminal Justice Studies
University of Portsmouth
Geodemographic Profiling• Charles Booth: 19th Century industrialist turned
social scientist• Profiled all homes in London: 5 broad groups• 30.7% below the poverty line• Descriptive map of London Poverty 1889• http://booth.lse.ac.uk/• Chicago School. Park Burgess and McKenzie
1925. Lost until rediscovered by the commercial sector in 1980s.
• Cf. Harris, R., Sleight, P., Webber, R. (2005) Geodemographics, GIS and Neighbourhood Targeting. Wiley.
Importance of Neighbourhood context
• Geodemographic software MOSAIC. 11 broad neighbourhood groups 61 smaller types.
• Built from census, commercial transaction and survey data to provide the neighbourhood profile.
• Massive amount of data or is it ‘knowledge’.
Knowledge
• ‘Knowledge is the sum of what is known to mankind’ OED
Progression towards knowledge.
• Data.• Information. Analysis of data provides
information• Intelligence is information prepared for action• Intelligence acted upon provides experience• Experience contributes to our knowledge and
understanding and allows us to test hypotheses
Networks and the digital divide• Between traditional manual systems and the ever-
widening influence of ICT networks.• The ‘Future is Digital’. • Wrong!, digital will be a given in the 21st Century.
We will live in a networked society. Transactions and travel captured digitally.
• ICT automatically captures data. The Future is Data. Combine harvesters. Data aggregators.
• Google type technologies together with analytical tools means we are all becoming ‘knowledge workers’ processing the digital harvest.
Widely used in the commercial sector
Site location
Segmenting customers: Targeting of communications
Perceptions of local crime rate
A B C D E F G H I J K
Walton North ward (NE corner)
Low level of social cohesion
Most prevalent Index profiles
Community coding of Electoral Register
• 46,330,000 records on file• 99.1% coded by community of origin• 130 Cultural, Ethnic, Linguistic CEL types• 13 Cultural, Ethnic, Linguistic CEL groups
CoverageNUMBER AND % RECORDS BY CEL GROUP
CEL GROUP RECORDS %
AFRICAN 139,920 0.302
CELTIC 10,238,813 22.097
EAST ASIAN 176,886 0.382
ENGLISH 32,735,358 70.648
EUROPEAN 582,716 1.258
GREEK ORTHODOX 103,043 0.222
HISPANIC 143,246 0.309
JAPANESE 5,740 0.012
JEWISH AND ARMENIAN 47,404 0.102
MUSLIM 1,018,107 2.197
NORDIC 36,277 0.078
SIKH 285,036 0.615
SOUTH ASIAN 491,126 1.060
INTERNATIONAL 29,088 0.063
UNRECOGNISED 154,247 0.333
DATA ERROR 149,080 0.322
TOTAL 46,336,087 100.000
The following maps are created in a novel way
• 1 : We have examined a UK database containing 46 million records. Each contains personal name + family name + postcode
• 2 : We have classified 180,000 family names and 100,000 personal names on the basis of ethnicity, loosely defined
• 3 : Using these tables we have coded 99.3% of the 46 million records according to their most likely ‘cultural/ethnic/linguistic group’
• 4 : We have then selected the 60,000 UK postcodes containing 7 or more individuals identified as belonging to a group which is neither British nor Irish
• 5 : The postcodes have then been coloured according to the group with the highest number of names in the postcode.
• 6 : One of the maps shows the distribution of all major groups within Greater London.• 7 : The other map features the largest of just three groups in Birmingham and the Black
Country• 8 : In the Black Country map there is a green background behind each postcode. The strength
of the green colouring indicates the proportion of the population in the postcode with a South Asian name. Thus the map shows both the level of concentration of South Asian names in a postcode and which of the minority groups is most strongly represented.
Black Country : dominant names by postcodeBlue = Sikh, Yellow = Pakistani, Red = Hindu
The ethnic map of London
R. Webber
Hackney
Brent
Health communications : Camden PCT
Camden PCT : Recognisable Adults by 'CEL' based on personal name and family name
CEL 'Imported from overseas' CEL from British Isles
CEL Count CEL Count CEL Count
BANGLADESH 2,826 INDIA NORTH 476 ENGLAND 49,753
PAKISTAN 2,001 GREECE 406 IRELAND 8,265
ITALY 1,741 TURKEY 347 SCOTLAND 6,036
PORTUGAL 1,210 SOMALIA 313 WALES 3,166
CYPRUS 1,137 INDIA SIKH 304
GERMANY 1,104 YUGOSLAVIA 292
HONG KONG 1,101 MUSLIM INDIAN 258
INDIA HINDI 924 GHANA 242
NIGERIA 907 IRAN 211
JEWISH 891 DENMARK 208
MUSLIM OTHER 839 CHINA 199
FRANCE 794 SWEDEN 185
POLAND 772 JAPAN 180
SPAIN 713 SRI LANKA 178
PAKISTANI KASHMIR 533 HINDU NOT INDIAN 166
VIETNAM 142
Residential segregation : selected Local Authorities
Ethnic and religious profiling
• Legal in the UK
• Ethnic marketing is a growing business
• Public sector applications?
Conclusions
• ICT Networks will become increasingly pervasive in 21st Century
• Knowledge no longer in the hands of the ‘police’ or ‘police officers’. Consumers of knowledge.
• Geodemographic, cultural, ethnic and language profiling will become easily accessible.
• Challenge is whether we buy into this ‘knowledge’ as a new way of doing business or continue ignoring it.