This paper presents a Cultural, Ethnic and Linguistic Output Area Classification for England and Wales built from clustering census variables which pertain to cultural identity. The study provides a quick insight into the broad patterns in ethnic segregation based on the residential geography recorded from the 2011 Census and is therefore a useful tool for supermarket planners seeking to identify areas where to target particular ethnic origin foods. To confirm this association, the classification has also been compared with the total sales of a selection of ethnic origin foods using supermarket customer loyalty data.
The CDRC Masters Research Dissertation ProgrammeGuy Lansley
The CDRC Masters Research Dissertation Programme gives Masters students from across the UK the opportunity to undertake their dissertation on a predefined topic researching consumer data, with the sponsorship of a major retailer or large UK business.
Previously undertaken projects have covered a wide range of themes including segmenting households by their average daily temporal profiles of gas usage as recorded by smart reader readings (sponsored by British Gas), evaluating the utility of geotagged social media to high street retailers (sponsored by Marks and Spenser), estimating the impact of click and collect services on existing retail networks (sponsored by Tesco) and topic modelling (Easyjet & Argos).
For more details please visit: https://www.cdrc.ac.uk/retail-masters/
Exploring the geography of the registered addresses of car models through a b...Guy Lansley
This document outlines a study exploring the geography of registered car models in the UK through a custom car classification system. The author created a classification of 10 car segments based on vehicle size, purpose, and marketing. Using DVLA registration data linked to census data at the small area level, the author analyzed trends in car segments by age, socioeconomic status, and geography. K-means clustering was used to group areas based on car characteristics and demographics, identifying 7 distinctive clusters across England and Wales. The analysis found car model composition provides useful information about societies and life stages at local levels.
The CDRC Masters Research Dissertation Programme - Call for SponsorsGuy Lansley
This document calls for industry sponsors for a masters student research program run through University College London and the CDRC. It provides information on the program, including that students conduct research projects for sponsor companies using retail or consumer data, and present their findings at an academic conference where cash prizes are awarded. Sponsor responsibilities and benefits are outlined, and examples of previous successful industry-student partnerships and sponsor feedback are provided. Industry representatives are invited to propose research topics and select a student.
The geography of topics from geo-referenced social media data in London - Guy...Guy Lansley
Recent years have seen an increased use of social media data as a cheaper alternative to more traditional methods of market research. Social media services generate a large quantity of data everyday and some of the data is available through their Application Programming Interfaces (APIs). This paper explores the data recorded through the Twitter social media service. In particular we are interested in the analysis of the content of Tweet messages. At the finest geographical level, this type of analysis can gauge very useful information to local planners in general and retail planners in particular. Whilst much past research on the Tweets' content has emphasized on exploring the sentiments users express in their messages, there has been limited attempts to link the geography of user generated topics across space to land use and activity. In this paper, we explore a hierarchy of popular themes of Tweet messages generated across Greater London, using a sample of over 9 million Tweets recorded in 2013. This paper will investigate how such themes are associated with particular land use categories and associated activities with an emphasis on high street locations. We first develop an appropriate aggregate spatial geography for the analysis of geo-referenced Tweets in order to obtain an accurate picture of user generated topics across space from a wider range of individuals. Unsupervised modelling techniques such as Latent Dirichlet Allocation (LDA) are explored to classify topics. Finally, we present the temporal variations in topic formulation and popularity, both daily temporally and seasonally
Presented by Guy Lansley, 2015
New analytical methods for geocomputation - Guy Lansley, UCLGuy Lansley
This document summarizes a presentation on new analytical methods for geocomputation using big data. It discusses how software has adapted to larger, more complex data and challenges in representing real-world phenomena. Specific techniques covered include using R for spatial analysis, text mining, topic modeling on tweets, identifying patterns in data, and creating interactive maps. The overall message is that coding skills are necessary to fully leverage big data, but converting data to insights remains challenging.
Linking Socio-economic and Demographic Characteristics to Twitter Topics - Gu...Guy Lansley
Social media data is now widely considered a viable source for market and social research. Everyday Twitter’s users generate large quantities of data through Tweet messages which express the users’ thoughts and opinions, and may also describe their activity, plans and location. In its raw form, textual data at this volume is hard to process and understand, however, it is possible to model the Tweets into a small number of topics using generative probabilistic algorithms. This paper aims to research how the content of Tweets may vary by socio-economics and demographic characteristics using Tweets from Inner London sourced from the Twitter application programming interface.
Earlier research has successfully allocated over 1 million geo-located Tweets from Inner London in 2013 into a hierarchical classification of 20 groups and 100 subgroups created using a latent dirichlet allocation algorithm. The 20 groups consist of distinctive topics and uses of language, and they all demonstrate unique spatial and temporal patterns across Inner London. The next stage of the analysis explores how the Twitter classification varies across the residential geography of Inner London. Assuming that most Tweets sourced from residential buildings are likely to be sourced by residents, the classification can be compared to socio-economic and other demographic characteristics from open data sources. In addition, some characteristics such as gender and ethnicity can also be inferred from the names of Twitter users.
Using R to Visualize Spatial Data: R as GIS - Guy LansleyGuy Lansley
This talk demonstrates some of the benefits of using R to visualize spatial data efficiently and clearly.
It was originally presented by Guy Lansley (UCL and the Consumer Data Research Centre) to the GIS for Social Data and Crisis Mapping Workshop at the University of Kent.
Evaluating the Utility of Geo-referenced Twitter Data as a Source of Reliable...Guy Lansley
Evaluating the Utility of Geo-referenced Twitter Data as a Source of Reliable Footfall Insight
Guy Lansley, UCL
Presented at the American Association of Geographers Annual Meeting, Tampa, FL. (10/04/2014)
The CDRC Masters Research Dissertation ProgrammeGuy Lansley
The CDRC Masters Research Dissertation Programme gives Masters students from across the UK the opportunity to undertake their dissertation on a predefined topic researching consumer data, with the sponsorship of a major retailer or large UK business.
Previously undertaken projects have covered a wide range of themes including segmenting households by their average daily temporal profiles of gas usage as recorded by smart reader readings (sponsored by British Gas), evaluating the utility of geotagged social media to high street retailers (sponsored by Marks and Spenser), estimating the impact of click and collect services on existing retail networks (sponsored by Tesco) and topic modelling (Easyjet & Argos).
For more details please visit: https://www.cdrc.ac.uk/retail-masters/
Exploring the geography of the registered addresses of car models through a b...Guy Lansley
This document outlines a study exploring the geography of registered car models in the UK through a custom car classification system. The author created a classification of 10 car segments based on vehicle size, purpose, and marketing. Using DVLA registration data linked to census data at the small area level, the author analyzed trends in car segments by age, socioeconomic status, and geography. K-means clustering was used to group areas based on car characteristics and demographics, identifying 7 distinctive clusters across England and Wales. The analysis found car model composition provides useful information about societies and life stages at local levels.
The CDRC Masters Research Dissertation Programme - Call for SponsorsGuy Lansley
This document calls for industry sponsors for a masters student research program run through University College London and the CDRC. It provides information on the program, including that students conduct research projects for sponsor companies using retail or consumer data, and present their findings at an academic conference where cash prizes are awarded. Sponsor responsibilities and benefits are outlined, and examples of previous successful industry-student partnerships and sponsor feedback are provided. Industry representatives are invited to propose research topics and select a student.
The geography of topics from geo-referenced social media data in London - Guy...Guy Lansley
Recent years have seen an increased use of social media data as a cheaper alternative to more traditional methods of market research. Social media services generate a large quantity of data everyday and some of the data is available through their Application Programming Interfaces (APIs). This paper explores the data recorded through the Twitter social media service. In particular we are interested in the analysis of the content of Tweet messages. At the finest geographical level, this type of analysis can gauge very useful information to local planners in general and retail planners in particular. Whilst much past research on the Tweets' content has emphasized on exploring the sentiments users express in their messages, there has been limited attempts to link the geography of user generated topics across space to land use and activity. In this paper, we explore a hierarchy of popular themes of Tweet messages generated across Greater London, using a sample of over 9 million Tweets recorded in 2013. This paper will investigate how such themes are associated with particular land use categories and associated activities with an emphasis on high street locations. We first develop an appropriate aggregate spatial geography for the analysis of geo-referenced Tweets in order to obtain an accurate picture of user generated topics across space from a wider range of individuals. Unsupervised modelling techniques such as Latent Dirichlet Allocation (LDA) are explored to classify topics. Finally, we present the temporal variations in topic formulation and popularity, both daily temporally and seasonally
Presented by Guy Lansley, 2015
New analytical methods for geocomputation - Guy Lansley, UCLGuy Lansley
This document summarizes a presentation on new analytical methods for geocomputation using big data. It discusses how software has adapted to larger, more complex data and challenges in representing real-world phenomena. Specific techniques covered include using R for spatial analysis, text mining, topic modeling on tweets, identifying patterns in data, and creating interactive maps. The overall message is that coding skills are necessary to fully leverage big data, but converting data to insights remains challenging.
Linking Socio-economic and Demographic Characteristics to Twitter Topics - Gu...Guy Lansley
Social media data is now widely considered a viable source for market and social research. Everyday Twitter’s users generate large quantities of data through Tweet messages which express the users’ thoughts and opinions, and may also describe their activity, plans and location. In its raw form, textual data at this volume is hard to process and understand, however, it is possible to model the Tweets into a small number of topics using generative probabilistic algorithms. This paper aims to research how the content of Tweets may vary by socio-economics and demographic characteristics using Tweets from Inner London sourced from the Twitter application programming interface.
Earlier research has successfully allocated over 1 million geo-located Tweets from Inner London in 2013 into a hierarchical classification of 20 groups and 100 subgroups created using a latent dirichlet allocation algorithm. The 20 groups consist of distinctive topics and uses of language, and they all demonstrate unique spatial and temporal patterns across Inner London. The next stage of the analysis explores how the Twitter classification varies across the residential geography of Inner London. Assuming that most Tweets sourced from residential buildings are likely to be sourced by residents, the classification can be compared to socio-economic and other demographic characteristics from open data sources. In addition, some characteristics such as gender and ethnicity can also be inferred from the names of Twitter users.
Using R to Visualize Spatial Data: R as GIS - Guy LansleyGuy Lansley
This talk demonstrates some of the benefits of using R to visualize spatial data efficiently and clearly.
It was originally presented by Guy Lansley (UCL and the Consumer Data Research Centre) to the GIS for Social Data and Crisis Mapping Workshop at the University of Kent.
Evaluating the Utility of Geo-referenced Twitter Data as a Source of Reliable...Guy Lansley
Evaluating the Utility of Geo-referenced Twitter Data as a Source of Reliable Footfall Insight
Guy Lansley, UCL
Presented at the American Association of Geographers Annual Meeting, Tampa, FL. (10/04/2014)
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- London was the most populated urban area with over 8 million residents and a density over 5,200 per square km.
- The population is aging, with the median age projected to rise to 42.8 years by 2037 from 39.7 in 2012.
- Christianity is still the largest religious group at 59.5% in 2011, but the non-religious now outnumber Christians at 32.8% versus 32.
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The document provides data and information for an afterschool agency to use in deciding where to expand their programs. They currently serve over 1,100 students ages 5-13 across 3 sites in Queens Community Board 1. The data includes demographics of current participants, school data and testing scores for three local school districts, census data on population changes of children in the areas, and guidance questions to structure their decision making process. They will use the "Ask, Assess, Decide" framework and data to determine the best location for their expansion.
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Secrets of a Successful Sale: Optimizing Your Checkout ProcessAggregage
https://www.onlineretailtoday.com/frs/26905197/secrets-of-a-successful-sale--optimizing-your-checkout-process
Once upon a time, in the vast realm of online commerce, there lived a humble checkout button overlooked by many. Yet, within its humble click lay the power to transform a mere visitor into a loyal customer. 🧐 💡
Getting checkout right can mark the difference between a successful sale and an abandoned cart, yet many businesses fail to make payments a part of their commerce strategy even when it has a direct impact on revenue. But payments are just one part of a chain. What’s the next touch point? How do you use the data sitting behind a payment to find the next loyal customer?
In this session you’ll learn:
• The integral relationship between payment experience and customer satisfaction
• Proven methods for optimizing the checkout journey
• Leveraging payments data for personalized marketing and enhanced customer loyalty
• Gain invaluable insights into consumer behavior across online and offline channels through data
The document summarizes population statistics from the 2011 UK census. Some key points:
- The total UK population was 63.2 million, with England having the largest at 53 million (84% of UK population). Scotland had 5.3 million and Wales 3.1 million.
- London was the most populated urban area with over 8 million residents and a density over 5,200 per square km.
- The population is aging, with the median age projected to rise to 42.8 years by 2037 from 39.7 in 2012.
- Christianity is still the largest religious group at 59.5% in 2011, but the non-religious now outnumber Christians at 32.8% versus 32.
Migration's effects on sending communities: Zimbabwe case study 7-8-15 - by E...Migrating out of Poverty
This document summarizes a quantitative research study on the effects of migration on communities in Zimbabwe. It provides background on the study which examines how migration relates to poverty reduction. It then describes the Zimbabwe case study in more detail, including the districts studied, definitions used, questionnaire details, and preliminary results. Key results include statistics on migrant demographics and characteristics, common destinations, reasons for migrating, remittance amounts, perceived changes in living standards and women's situations, and main uses of remittances.
The document provides data and information for an afterschool agency to use in deciding where to expand their programs. They currently serve over 1,100 students ages 5-13 across 3 sites in Queens Community Board 1. The data includes demographics of current participants, school data and testing scores for three local school districts, census data on population changes of children in the areas, and guidance questions to structure their decision making process. They will use the "Ask, Assess, Decide" framework and data to determine the best location for their expansion.
Writing in the homePlan a parent education session that helps pa.docxodiliagilby
The document provides guidance for planning a parent education session to promote writing at home for children in pre-kindergarten through 2nd grade. It recommends including information on the stages of writing development, ways to promote writing at each stage, specific activities parents can use immediately, and additional resources for parents.
Housing and Health: Working in PartnershipMark Reading
The document discusses the link between housing conditions, health, and ethnicity in the UK. Some key points:
- Minority ethnic groups experience worse housing conditions like hazards and excess cold more than white households. This contributes to higher rates of fuel poverty.
- Fixing housing conditions for ethnic minorities could save costs for the healthcare system and improve health outcomes.
- Conditions like sickle cell disease are exacerbated by cold homes, disproportionately impacting some ethnic groups.
- As trends show greater numbers of ethnic minorities in private renting and poorer housing, these disparities may persist or worsen without interventions targeted at improving housing for these communities.
The document summarizes the findings of a needs assessment conducted with Black and Minority Ethnic (BME) communities in Brighton and Hove. It found that BME residents face issues with housing, employment, and educational attainment. Specifically, BME residents were more likely to rent privately and less likely to own homes, and certain ethnic groups had unemployment rates twice the city average. BME children and young people also tended not to reach their full potential compared to their White British peers. The steering group conducting the assessment is prioritizing work on these issues of housing, employment, and education, and engaging the community for input.
Geography Skills Aberdeen City CPD event 3rd decceliamac58
This document provides an introduction and overview of Scottish government statistics, census data, and practical applications. It discusses key findings from the 2011 Scottish census including population demographics, household composition, ethnicity, and place of birth. It then demonstrates how to access and use census data through the Census Data Explorer tool to analyze and compare census information for different geographic areas in Scotland. Finally, it briefly outlines some of the major social surveys and statistics produced in Scotland.
By Safaraz Ali, founder of the Asian Apprenticeship Awards, an event celebrating the growing involvement of British Asians in apprenticeships and workplace training.
Includes statistics of government targets and targets for BAME (black and ethnic minority) representation, as apprentices.
AIRO & ICLRD All Island Atlas Seminar 1: DemographyJustin Gleeson
The document provides population statistics for the island of Ireland, including:
- The total population is 6.4 million as of 2011, with 72% in the Republic of Ireland and 28% in Northern Ireland.
- The population has decreased 27% since 1841, with a 42% decrease in the Republic and a 9% increase in Northern Ireland.
- Between 2001/2002 and 2011, the population increased 14.25% overall, with a 17.2% increase in the Republic and 7.45% in Northern Ireland.
- The age profiles differ significantly between the two jurisdictions, with the Republic having higher rates of young people and lower rates of elderly.
Similar to Creating an Output Area Classification of Cultural and Ethnic Heritage (12)
Secrets of a Successful Sale: Optimizing Your Checkout ProcessAggregage
https://www.onlineretailtoday.com/frs/26905197/secrets-of-a-successful-sale--optimizing-your-checkout-process
Once upon a time, in the vast realm of online commerce, there lived a humble checkout button overlooked by many. Yet, within its humble click lay the power to transform a mere visitor into a loyal customer. 🧐 💡
Getting checkout right can mark the difference between a successful sale and an abandoned cart, yet many businesses fail to make payments a part of their commerce strategy even when it has a direct impact on revenue. But payments are just one part of a chain. What’s the next touch point? How do you use the data sitting behind a payment to find the next loyal customer?
In this session you’ll learn:
• The integral relationship between payment experience and customer satisfaction
• Proven methods for optimizing the checkout journey
• Leveraging payments data for personalized marketing and enhanced customer loyalty
• Gain invaluable insights into consumer behavior across online and offline channels through data
Creating an Output Area Classification of Cultural and Ethnic Heritage
1. Creating an Output Area Classification of
Cultural and Ethnic Heritage
to Assist the Planning of Ethnic Origin Foods in
Supermarkets in England and Wales
Guy Lansley
UCL
Yiran Wei
UCL
Tim Rains
J Sainsbury’s
GISRUK 2015
The University of Leeds@GuyLansley
2. Context
• Many minority ethnic and cultural groups in Britain
have distinctive food consumption habits which
emanate from their cultural origins
• The UK is becoming ethnically more diverse due to
migration and variations in fertility rates between
different cultural groups
• Minority groups still have a tendency to residentially
cluster within urban areas
• Understanding where minority groups cluster could be
beneficial for grocery store planners
3. Aim
• Using data from the 2011 Census, this research aims to
identify the major spatial traits in ethnic identity across
the residential geography of England and Wales by
producing a Cultural, Ethnic and Linguistic Output Area
Classification (CELOAC)
4. Defining Ethnicity
• Ethnicity can be an intangible concept
• Definitions can derive from:
• Primordialist theories
• Ethnicity as a physicality from ancestry
• Constructivist theories
• Ethnicity as a social construction
• Instrumental theories
• Ethnicity based on historical & symbolic memory
5. Defining Ethnicity
• Ethnic groups can be considered as distinct
groups of individuals who share a common
identity through:
• kinship
• Religion
• Language
• Location
• Nationality
• Physical similarities from ancestry
Bulmer (1996)
6. What is Ethnicity?
Defining Consistent Ethnic Groups
The Modifiable Ethnic Unit Problem (MEUP)!
OR?
Self-identities spectrum Ethnic Groups
(Courtesy Pablo Mateos, 2011)
7. Uncertainty
• There are various different
measures of CEL groups from
the 2011 Census
• E.g. Ethnicity, Country of Birth,
Language, passports held,
nationality…
• And also from other sources
too
Extract from Longley et al (2015)
9. Data
Census Table Name
QS203EW Country of birth (detailed)
QS204EW Main language (detailed)
QS205EW Proficiency in English
QS208EW Religion
QS211EW Ethnic group (detailed)
QS802EW Age of arrival in the UK
QS803EW Length of residence in the UK
• Variables from 7 ONS Census Quick Statistics tables were
selected for the analysis
10. Methods
• Variables with total populations below
10,000 were aggregated into broader groups
based on their global regions of origin or
removed altogether if they were considered
too distinctive to merge.
• The remaining variables were standardised
and transformed so each variable could be
fairly compared
• Highly correlated or unstable cases were then
identified and removed or aggregated
• 52 variables were clustered using a K-means
clustering algorithm
11. Final Variable Selection
2011 Census Table No of original
variables
No of aggregated
variables
No of final
variables
Country of birth (detailed) 57 49 15
Ethnic group (write-in
responses)
94 40 18
Main language (detailed) 92 20 7
Proficiency in English 5 5 1
Religion 9 8 7
Age of arrival in the UK 17 7 2
Length of residence in the
UK 5
5 2
Total 435 134 52
.
12. K-means Clustering
• K-means is an iterative allocation-reallocation method
where the number of cluster groups (k) is predefined
by the user
• The approach creates distinctive cluster groups by
attempting to minimise the sum of the distances from
each case to their cluster centre based on the variable
distributions.
• An 8 cluster solution was found to hold the best fit
overall
• However, for this analysis two groups which were
predominately White British were merged as the focus
of this paper is on the minority population
• Their main distinction was differences in the percent of
those who identified themselves as Christian
13. Results
A B C D E F G A B C D E F G A B C D E F G
Ethnic Group Country of Birth Proficiency in English
White British Romania Cannot speak English
Irish Other EU accession countries Main language
Pakistani & British Pakistani Ghana French
Bangladeshi & British Bangladeshi Nigeria Russian
Arab Other Central & Western Africa Turkish
Afghan Kenya West or Central Asian Language
Australian & New Zealander Somalia South Asian Language
Baltic Other South & Eastern Africa East Asian Language
Polish Middle East African Language
Sri Lankan China Religion
Other Eastern European Hong Kong Christian
Other Western European Other Southern Asia Buddhist
Greek or Greek Cypriot Philippines Hindu
Black British Other South-East Asia Jewish
Caribbean United States Muslim
South East Asian Length of residence in UK Sikh
African Less than 2 years No religion or not stated
Indian or British Indian 10 years or more
Age of arrival in UK
0 to 4
45 - 64
Below Average Above Average
Label
A Pakistani & Bangladeshi
B Indian & South Asian mix
C Black African & Caribbean
D Non-British White
E Middle Eastern & East Asian
F Mixed
G White British
14. CELOAC and Ethnic Groups
Ethnic Group A B C D E F G
White British 43.29 22.50 33.14 53.73 42.93 72.15 93.01
White Irish 1.21 1.70 1.80 2.82 1.71 1.47 0.64
Other White 4.69 8.62 12.40 20.63 14.24 7.96 2.06
Mixed & multiple 3.43 3.81 6.64 4.88 4.53 3.49 1.30
Indian 9.84 25.14 3.01 2.53 5.55 3.06 0.73
Pakistani 20.23 9.36 2.85 0.78 3.46 1.42 0.34
Bangladeshi 5.87 2.83 3.10 1.10 1.96 0.66 0.14
Chinese 0.66 0.90 1.35 2.09 5.89 1.30 0.30
Other Asian 3.11 10.24 3.92 2.88 5.66 2.49 0.43
Black ethnicities 5.49 10.81 27.80 5.30 7.72 4.60 0.70
Arab 0.84 1.59 1.14 1.35 3.67 0.49 0.10
Other 1.25 2.38 2.69 1.85 2.60 0.77 0.17
The average percentage of ethnic groups by each CELOAC group
15. Mapping CELOAC
• England and Wales A Pakistani & Bangladeshi
B Indian & South Asian Mix
C Black African & Caribbean
D Non-British White
E Middle Eastern & East Asian
F Mixed
G White British
NorthEast
NorthWest
Yorkshire&Humber
WestMidlands
EastMidland
East
SouthEast
SouthWest
London
Wales
EnglandandWales
A 1.27 5.69 7.54 4.61 13.66 2.97 2.59 0.47 3.61 0.49 4.51
B 0.05 0.36 0.14 2.69 1.76 0.45 0.85 0.05 13.55 0.02 2.51
C 0.03 1.15 0.71 0.69 1.45 0.58 0.52 0.57 27.71 0.16 4.45
D 0.01 0.15 0.05 0.02 0.05 0.99 1.30 0.22 19.26 0.03 3.02
E 1.89 1.73 2.07 1.60 1.49 0.83 1.24 0.59 8.51 1.16 2.36
F 3.77 5.84 6.85 10.55 6.62 16.92 18.78 8.07 19.00 4.36 11.38
G 92.98 85.08 82.64 79.83 74.97 77.25 74.72 90.04 8.36 93.78 71.78
Regional Variations
16. Mapping CELOAC
• London
A Pakistani & Bangladeshi
B Indian & South Asian Mix
C Black African & Caribbean
D Non-British White
E Middle Eastern & East Asian
F Mixed
G White British
17. A London Only Classification
A Bangladeshi, African and South-East European
B Indian & South Asian
C Black African & Caribbean
D Non-British White & East Asian
E Middle Eastern
F Mixed
G White British
K = 7
18. Ethnic Origin Food Consumption
• Sainsbury’s provided the number of sales for six pre-
selected grocery products by OA as recorded from their
customer loyalty database
• The data represented the total sales within a 52 week
period commencing in 2011
• Each of the foods were chosen due to their distinctive
cultural heritage with minority groups
• The data was standardised by the total number of food
items sold in each OA from that time period
19. Food Consumption Data
Group
Black Eye
Beans
Chickpeas
Chinese
Leaf
Ghee Halal Ogorki
A: Pakistani and Bangladeshi 215.9 80.99 81.49 250.5 163.2 122
B: Indian and South Asian Mix 472.7 111.2 137.8 601 711.5 312.4
C: Black African and Caribbean 305.8 120.2 131.6 277.2 598.5 286.3
D: Non-British White 218.7 230.3 229.7 216.1 413.4 356.1
E: Middle Eastern & East Asian 202.5 122.7 260.9 229.7 402.4 286
F: Mixed 151.7 136.3 151.4 151.4 109.6 184.5
G: White British 50.4 87.58 78.99 44.77 19.14 49.37
100 = average representation
20. Conclusions
• The CELOAC successfully segmented 2011 Census
Output Areas by cultural, ethnic and linguistic
characteristics
• London, and other major metropolitan areas still contain
higher proportion of ethnic minorities and there are
distinctive segregations between CELOAC groups within
towns and cities
• However, the classification still conceals more intricate
differences within each CELOAC group
• Ethnic origin food consumption was found to vary
between different major cultural groups
21. References
• Bulmer, M. (1996) ‘The ethnic group question in the 1991 Census of
population’ in Coleman, D. and Salt J. (des.) Ethnicity in the 1991 Census.
vol.1 Demographic characteristics of the ethnic minority populations
London: HMSO
• Harris R, Sleight P, Webber R. (2005) Geodemographics: neighbourhood
targeting and GIS. Chichester, UK: John Wiley and Sons.
• Longley, P.A., M.F. Goodchild, D.J. Maguire, and D.W. Rhind (2015)
Geographic Information Systems and Science. Fourth Edition. New York:
Wiley.
• Mateos, P. (2011) Uncertain segregation: the challenge of defining and
measuring ethnicity in segregation studies, Built Environment, 37 (2) 226-
238
• The Office for National Statistics (2014) 2011 Area Classifications
http://www.ons.gov.uk/ons/guide-method/geography/products/area-
classifications/national-statistics-area-classifications/national-statistics-
2011-area-classifications/index.html
Editor's Notes
Sainsburys
this research aims to identify the major spatial traits in ethnic identity across the residential geography of England and Wales by producing a Cultural, Ethnic and Linguistic Output Area Classification (CELOAC)
How you do you create a definitive classification from an intangible concept?
Country of birth – loads of clusters,
3 south Asian
African
Caribean
East european
Turkish
Greek
Other E Asia (Korea)
South Africa
7 tables which can indicate cultural identity in some form (435 variables)
The variable with the lowest membership had a population of 70,000 persons
Not showing analysis on segregation
OAC – variable reduction then k-means
8 groups – k= tested
2 English
The variable with the lowest membership had a population of 70,000 persons
71% white – but shows there is segregation
Pakistani
Indian – and south Asian (Kenya)
Black (& Bangladeshi)
Europe
Middle East
Urban
Regions
Completely new groups
Not allowed to show any other data – (i.e. correlations)
Muslim v Pakistan