Abstract
Geodemographics is a tool to summarize the characteristics of spatial units based on socio-economic data. It has been used over several decades to classify the characteristics of areas based on the similarities in such data, generally working by identifying groups or clusters of similar areas. It has seen use in the academic and private sectors but mostly became popular by 1980s for market research purposes – this rise in use drew attention to many issues that needed to be addressed in the literature. The purpose of this paper is to provide a geodemographic classification for the smallest scale administrative units in Ireland (Small Areas) based on the latest 2016 census population data. A further aim is to support reproducible research by following the methodology of an earlier study for the 2011 Census and to bring up the opportunity to compare the cluster results between the 2011 and 2016 census data. The comparison output provides useful insights for policy discussions in the Irish context. One of the other focal points of this study is to bring some clarity regarding some criticisms of geodemographics from an academic perspective. The paper also argues for the use of open data, open source software and supports open analysis.
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Adnan, M., Longley, P. A., Singleton, A. D., & Brunsdon, C. (2010). Towards real-time geodemographics: Clustering algorithm performance for large multidimensional spatial databases. Transactions in GIS, 14(3), 283–297.
Batey, P., Brown, P., & Corver, M. (1999). Participation in higher education: A geodemographic perspective on the potential for further expansion in student numbers. Journal of Geographical Systems, 1(3), 277–303.
Booth, C. (1902). London poverty map legend: 1898-1899. https://booth.lse.ac.uk/map/12/-0.0934/51.4859/100/0. Accessed on 18.10.2018.
Brown, P. J., Hirschfield, A., & Batey, P. W. (1991). Applications of geodemographic methods in the analysis of health condition incidence data. Papers in Regional Science, 70(3), 329–344.
Brunsdon, C., Longley, P., Singleton, A., & Ashby, D. (2011a). Predicting participation in higher education: A comparative evaluation of the performance of geodemographic classifications. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(1), 17–30.
Brunsdon, C., Rigby, J., & Charlton, M. (2011b). Ireland census of population 2011: a classification of small areas. https://rpubs.com/chrisbrunsdon/14998.
Brunsdon, C., Charlton, M., Rigby, J., & E. (2016). An open source geodemographic classification of small areas in the republic of Ireland. Applied Spatial Analysis and Policy, 11, 183–204. https://doi.org/10.1007/s12061-016-9212-4.
Charlton, M., Openshaw, S., & Wymer, C. (1985). Some new classifications of census enumeration districts in Britain: A poor man’s ACORN. Journal of Economic and Social Measurement, 13, 69–98.
CSO, Central Statistics Office Ireland, (2016). Ireland Population Census Results, Small Areas, Accessed in September 2017. https://www.cso.ie/en/census/census2016reports/census2016smallareapopulationstatistics/
Farr, M., Wardlaw, J., & Jones, C. (2008). Tackling health inequalities using geodemographics: A social marketing approach. International Journal of Market Research, 50(4), 449–467.
Feng, Z., & Flowerdew, R. (1998). Fuzzy geodemographics: A contribution from fuzzy clustering methods. In S. Carver (Ed.), Innovations in GIS 5 (pp. 119–127). London: Taylor and Francis.
Feng, Z., & Flowerdew, R. (1999). The use of fuzzy classification to improve geodemographic targeting. In B. Gittings (Ed.), Innovations in GIS 6 (pp. 133–143). London: Taylor & Francis.
Fisher, P., Tate, N., & Slingsby, A. (2014). Type-2 Fuzzy Sets Applied to Geodemographic Classification. Paper presented at the Eighth International Conference on Geographic Information Science (GIScience 2014), 23-09-2014 - 26-09-2014, Vienna, Austria.
Fisher, P., & Tate, N. J. (2015). Modelling class uncertainty in the geodemographic output area classification. Environment and Planning. B, Planning & Design, 42, 541–563.
Goss, J. (1995). “We know who you are and we know where you live”: The instrumental rationality of geodemographic systems. Economic Geography, 71(2), 171–198.
Grekousis, G., & Hatzichristos, T. (2012). Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The fuzzy C-means and Gustafson–Kessel methods. Applied Geography, 34, 125–136.
Harris, R., Sleight, P., & Webber, R. (2005). Geodemographics, GIS and neighbourhood targeting. John Wiley and Sons, ISBN: 978-0-470-86413-5.
Hartigan, J. A. (1975). Clustering algorithms. London: Wiley.
Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Journal of the Royal Statistical Society: Series C: Applied Statistics, 28(1), 100–108.
Herbert, D. T. (1967). Social areal analysis: A British study. Urban Studies, 4(1), 41–60.
Hoyt, H. (1939). The structure and growth of residential neighborhoods in American cities. Washington DC: Federal Housing Administration.
Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: John Wiley and Sons.
Kitchin, R., & McArdle, G. (2016). Urban data and city dashboards: Six key issues, the Programmable City working paper 21, Maynooth University.
Longley, P. (2005). Geographical information systems: A renaissance of geodemographics for public service delivery. Progress in Human Geography, 29(1), 57–63.
Longley, P. (2012). Geodemographics and the practices of geographic information science. IJGIS, 26(12), 2227–2237.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Mitchell, V. W., & McGoldrick, P. J. (1994). The role of geodemographics in segmenting and targeting consumer markets: A Delphi study. European Journal of Marketing, 28(5), 54–72.
Openshaw, S. (1983). Multivariate analysis of census data: The classification of areas. In D. W. Rhind (Ed.), A census users handbook (pp. 243–264). London: Methuen.
Openshaw, S., & Blake, M. (1995). Geodemographic segmentation systems for screening health data. Journal of Epidemiology & Community Health, 49(Suppl. 2), S34–S38.
Openshaw, S., Cullingford, D., & Gillard, A. (1980). A critique of the national classifications of OPCS/PRAG. Town Planning Review, 51(4), 421–439.
Park, R. E., Burgess, E., & McKenzie, R. (1925). The City: Suggestions for investigation of human behaviour in the urban environment. Chicago: Chicago University Press.
Petersen, J., Gibbin, M., Longley, P., Mateos, P., Atkinson, P., & Ashby, D. (2011). Geodemographics as a tool for targeting neighbourhoods in public health campaigns. Journal of Geographical Systems, 13(2), 173–192.
Shevky, E., & Bell, W. (1955). Social area analysis. Stanford: Stanford University Press.
Shevky, E., & Williams, M. (1949). The social areas of Los Angeles. Berkeley: University of California Press.
Singleton, A. D. (2010). The geodemographics of educational progression and their implications for widening participation in higher education. Environment and Planning A, 42(11), 2560–2580.
Singleton, A. D., & Longley, P. (2009). Geodemographics, visualisation and social networks. Applied Geography, 29, 289–298.
Singleton, A. D., & Spielman, S. (2014). The past, present, and future of Geodemographic research in the United States and United Kingdom. The Professional Geographer, 66(4), 558–567.
Son, L. H., Cuong, B. C., Lanzi, P. L., & Thong, N. T. (2012). A novel intuitionistic fuzzy clustering method for geo-demographic analysis. Expert Systems with Applications, 39(10), 9848–9859.
Stodden, V., Bailey, D. H., Borwein, J., LeVeque, R. J., Rider, W., & Stein, W., 2013. Setting the default to reproducible reproducibility in computational and experimental mathematics, ICERM workshop report.
Tonks, D. G., & Farr, M. (1995). Market segments for higher education: Using geodemographics. Marketing Intelligence & Planning, 13(4), 24–33.
Tryon, R. C. (1955). Identification of social areas by cluster analysis: General method with application to the San Francisco Bay area. Berkeley: University of California Press.
Webber, R. J. (1975). Liverpool Social Area Study, 1971 Data, PRAG technical paper 14. London: Centre for Environmental Studies.
Webber, R. J. (1977). An Introduction to the National Classification of Wards and Parishes, PRAG technical paper 23. London: Centre for Environmental Studies.
Webber, R., & Burrows, R. (2018). The predictive postcode: The Geodemographic classification of British society. London: SAGE Publications.
Webber, R., & Craig, J. (1978). Socio-economic classifications of local authority areas. Studies on medical and population subjects (p. 35). London: OPCS.
Yazgi Walsh, B., Brunsdon, C., & Charlton, M. (2017). Ireland Census 2016: a Classification of Small Areas, https://www.rpubs.com/burcinwalsh/343141.
Acknowledgements
This paper has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 15/IA/3090. We would also like to thank the two anonymous referees for the helpful comments on the earlier version of this paper.
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Appendices
Appendix 1 – Cluster characteristic cards
Group 1
The main economic activities of this group are construction and agriculture. The high numbers of car ownership rate, septic tank usage and number of rooms per household are some of the main characteristics of this group and they are much higher than Dublin average. This group has the highest number of small areas and second highest population percentage (18.3%). From a spatial perspective, this group will be highly distributed in the counties and none in cities.
Group 2
The composition of the households is around the middle age groups with two or more cars. Living in slightly bigger houses probably with septic tank facility. The tendency of the households in this group are more working from home. Group 2 consists the highest population compared to other groups (20.8%). Cork County, Galway County and Donegal County are the locations with the highest spatial distribution of this group.
Group 3
Household composition in this group is mostly young families with dependent children. Employment rates are high. From a population perspective 16.4% of Ireland are residents of this group which is the third highest in rank. Fingal, South Dublin, Kildare and Cork County are the areas where this group is highly concentrated.
Group 4
The composition of the households is mostly a combination of separated people, people living on their own and people over the age of 65. Unemployment rate is quite high, and the health conditions of the households are particularly bad. This group’s spatial concentration is high in Cork county, Tipperary and Donegal and very low in Dun Laoghaire. 10.4% of population of Ireland is in this cluster.
Group 5
The composition of the population in this group is mostly elderly households; either families with nondependent children or people who are living alone. The percentage of the pensioners in this group is higher than the other groups and Dublin. Based on the households’ declarations who are part of this group, their health conditions are not good. 14% of inhabitants are part of this group in Ireland. Dublin with its four local authorities (Dublin City, Dun Laoghaire, South Dublin and Fingal) has the 64.5% of the small areas assigned to this group.
Group 6
This group is consisting separated people and single parents. The unemployment rate is high, and the health conditions of people are low. In this group households are mainly renting their houses from local authorities. This group holds the 9% of the total population. Dublin city has the highest spatial concentration of this group followed by South Dublin.
Group 7
Households in this group are mostly pre-families. Young couples both employed and with no children is one of the main characteristics of this group. The general tendency among the households in this group is to rent from private landlords and specially to rent flats. The composition of the population is based on international households mostly born out of Europe and Ireland. This group has 5.9% of population which spatially gathered in Dublin (63.3%).
Group 8
One of the noticeable characteristics of this group is higher education levels. The mean value of the higher education quality is over the Dublin average. The households who are part of this group are highly participating in using public transport to travel to work. The house type that the households are living in are mostly flats and rental. Double income households with no kids are the other property of this group that emerges. Compared to other groups small part of the population is residing in this group (5.2%) but high part of the small areas assigned to this group are in Dublin (78.1%) – Dublin city (52.3%), Dun Laoghaire (15.2%), Fingal (5.6%). Cork city and Galway city are the other spots that this group is highly concentrated. On the other hand, counties like Cavan, Longford, Monaghan, Tipperary and Leitrim do not show any characteristics related to this group.
Appendix 2 – Variables
Variable | Census Theme | Description | Analysis Theme |
|---|---|---|---|
Age0_4 | Sex, Age and Marital Status | Age 0 to 4 | Demographic |
Age5_14 | Sex, Age and Marital Status | Age 5 to 14 | Demographic |
Age25_44 | Sex, Age and Marital Status | Age 25 to 44 | Demographic |
Age45_64 | Sex, Age and Marital Status | Age 45 to 64 | Demographic |
Age65over | Sex, Age and Marital Status | Age 65 and over | Demographic |
EU_National | Migration, Ethnicity, Religion and Foreign Languages | EU Nationality | Demographic |
ROW_National | Migration, Ethnicity, Religion and Foreign Languages | Nationality - Rest of world | Demographic |
Born_outside_Ireland | Migration, Ethnicity, Religion and Foreign Languages | Birthplace out of Ireland | Demographic |
Separated | Sex, Age and Marital Status | Separated and Divorced | Household Composition |
SinglePerson | Sex, Age and Marital Status | 1 person households | Household Composition |
Pensioner | Families | Retired households | Household Composition |
LoneParent | Families | One parent family with children | Household Composition |
DINK | Families | Pre-family | Household Composition |
NonDependentKids | Families | Families with youngest child aged 20 and over | Household Composition |
RentPublic | Housing | Rented from private landlord | Housing |
RentPrivate | Housing | Rented from Local Authority | Housing |
Flats | Housing | Flat/apartment | Housing |
NoCenHeat | Housing | No central heating | Housing |
RoomsHH | Housing | Average no. of rooms for household | Housing |
PeopleRoom | Housing | Average no. of people for room | Housing |
SepticTank | Housing | Individual septic tank | Housing |
HEQual | Education | Higher education - including bachelor, postgraduate, doctorate | Socio Economic |
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Yazgi Walsh, B., Brunsdon, C. & Charlton, M. Open Geodemographics: Classification of Small Areas, Ireland 2016. Appl. Spatial Analysis 14, 51–79 (2021). https://doi.org/10.1007/s12061-020-09343-6
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DOI: https://doi.org/10.1007/s12061-020-09343-6