Households Household composition and surnames It is possible to glean further information from the nature of the household entry , by analysing the possible combinations of gender and surnames Possible household categories Family 1 male: 1 female, sharing the same surname Extended Family Family with at least one other adult of the same surname Pseudo Family 1 male: 1 female, but with different surnames Single male Male homesharers 2 or more males with 2 or more surnames Multi-occupancy dwelling More than 5 surnames at one address This is a simplified version of the household analysis in Mosaic. The use of such a simplified system does have drawbacks e.g. a brother living with a widowed sister would appear to be a pseudo family. Given name frequencies could be used to help decide if extended families comprise of parents or offspring. (Mosaic classifies your forename into 50 clusters each with a similar age distribution.) “… It would seem that type of neighbourhood, age and gender represent three items of information which are ‘orthogonal’, ie complementary to each other in that they operate in three quite independent domains. Given that both gender and age can be inferred from a person’s first name with a fair degree of reliability (especially when also using public information such as years at their current address on the electoral roll and the presence and name (if present) of a partner) then it would seem that most behaviours could be predicted for any consumer from their name and address with a fairly high degree of success” R Webber, “father of UK Geodemographics” and in the USA, “The development process also uncovered a correlation between cluster membership and given names. In the 35 million-name database, there were many names that appeared with unusual frequency in only one cluster. For that reason, all of the clusters were given high-indexing first names, resulting in titles like “Jules & Roz” (affluent and physically active urbanites with children), “Denise” (single mothers on a tight budget), and”Elmer” (very sedentary older men)… [Certain] people defy categorization and have been lumped into a potpourri group known as the “Omegas.” Nearly 9 percent of all U.S. households are Omegas.” Source: J Bickert, 1995. Names ‘typical’ of their age groups: Core age f m 38-44 Michelle, Sharon Kevin, Gary 44-64 Pamela, Janet Philip, Brian 65-84 Sylvia, Brenda Kenneth, Raymond 85+ Hilda, Ethel Percy, Herbert Source: ‘Geographics,GIS and neighbourhood targeting’ Wiley, 2005 p 72 Female names are more fashion-driven than male names. If they are combined with a male partner name, then geodemographers are pretty confident in their estimates of that couple’s age-range. This is re-inforced by the length of residency – a statistic that is consistently lower where lower-age groups are involved. As for surnames: “…in Scotland the percentages of electors with self-evidently Scottish names is significantly higher among consumers in highland and island communities than among consumers in student areas, defence establishments and areas of high-incomes singles and families in inner areas of Glasgow. Indeed the percentage with Scottish names has proved a more effective indicator than the Census indicator ‘speaking Gaelic’ in identifying areas with the most traditionally Scottish way of life.” Source: R Webber Designing geodemographic classifications to meet contemporary business needs, Interactive Marketing, 5(3), 2004, p 233-234. Example: I have just played around and collected household data for name D in the PO postcode area Postcode a b c d e f g h i Households 1 2 3 1 1 4 5 3 2 Main types 1 2 1 1 1 2 2 3 2 1 mod means 1 mod means1 hard- pressed 3 mod- means 1 comf-off 1 mod means 3 wealthy- achievers1 comf-off 4 wealthy- achievers1 comf-off 1 wealthy- achiever1 comf-off1 hard- pressed 1 comf-off1 hard- pressed sub- types 1 2 3 2 1 4 3 3 2 Postcode j k l m n o p q r Household 1 1 2 2 1 9 1 1 1 Main types 1 1 2 1 1 3 1 1 1 1 mod- means 1 comf-off 1 urban prosperty1 hard- pressed 2 comf-off 1 wealthy- achiever 6 comf-off1 moderate means2 hard- pressed 1 hard- pressed 1 urban- prosperity 1 wealthy achiever sub- types 1 1 1 1 3 1 1 1 Number % NationalAverage Postcodesinvolved Households Types Numbers Wealthy achievers 11 25.6 25.1 5 10 Singles(Young) 6 Urban prosperity 2 4.7 10.7 2 2 Singles (Mature) 17 Comfortably Off 14 32.6 26.6 8 14 Doubles 17 Moderate means 8 18.6 14.5 6 8 3+ 2 Hard-Pressed 8 18.6 22.4 6 7 This name is a broad mid-southern name. In this Postal area, it is associated with the suburban sprawl, rather than urban heartlands. Mature residents are to the north of Portsmouth, or Bognor. There is negligible movement to retire to the IOW. Judging from the forenames, and residential areas, the age-profile is quite high. Many of the 2 person households would appear to be pensioners. The young singles are in a minority. It might be a fruitful exercise to correlate geodemographic status against household composition for a name/class of names Another possible broader-brush classification scheme at local authority level is A new classification of UK Local Authorities using 2001 Census key statistics [link no longer available] by Daniel Vickers, Phil Rees, Mark Birkin. Does the above have implications for the philosophy of identity? The standard position is that a name has reference but no meaning i.e. it is a label that refers to one object. If it refers to more than one, then its usage is that of a common noun. For example, if I talk about a polar bear, the mind conjures up a class of bear with all its associations, snow, whiteness, polar region. If I say ‘John’ there is no equivalent class of ‘Johns’ sharing all the same attributes, into which my one John neatly fits. But with geodemographic clusters, someone with a distinctive name might group into defined socio-economic, lifestyle groups – not everyone, but a significant number. So are Geodemographic clusters “common nouns”? Or does the lifestyle communality imply meaning??? This is in all probability early-morning tosh- I certainly have not thought it through Is a Victorian Neighbourhood Classification possible? based on Census data, property values, rentals, occupation, hosehold size, composition, age structure? Now there’s an ESRC funding idea! Other Spatial Analysis Tools Index of concentration Location quotient Cluster Analysis Still to be written (sometime):- Comparative distribution maps in Colin Rogers and David Hey. Distribution maps from :Census, GRO Indexes, National Burial Index, Hearth tax, Subsidy Rolls. John Titterton’s Median Radius Technique. Rex Bottle’s Diffusion of English Surnames.