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Geodemographic Profiling, Knowledge Workers and Networks. Dr Tom Williamson Visiting Professor Institute of Criminal Justice Studies University of Portsmouth. Geodemographic Profiling. Charles Booth: 19 th Century industrialist turned social scientist
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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.
Perceptions of local crime rate A B C D E F G H I J K
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
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
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.