GeodemCreator created by Muhammad Adnan for his PhD – allows the creation of bespoke geodemographic classification.Does not use the same methodology as the 2001 OAC.
2001 OAC 94 Variables initially considered.Reduced to 41 Variables.Variables removed due to high correlation (and hence high redundancy) and other factors.Difficult to report on all reasons why variables were selected.Variable selection the only “black box” element of the 2001 OAC.Similar variables also used in Ward Level Classification to allow comparability with the 2001 OAC.Variables not weighted.
Beyond 2011The 2011 Census may have been the last traditional Census so alternatives to using a decennial Census dataset need to be considered.How important a role will Open Data play in the current and future development of geodemographic classifications?
Open Geodemographics: Open Tools and the 2011 OAC
UCL DEPARTMENT OF GEOGRAPHY Open Geodemographics: Open Tools and the 2011 OAC Chris Gale* Muhammad Adnan Paul Longley mapblog.in gis-tech.co.uk paul-longley.com @geogale @gisandtech * Conference attendance kindly supported by RGS-IBG funded QMRG bursary UCL Department of Geography, Gower Street, London, WC1E 6BT
UCL DEPARTMENT OF GEOGRAPHY Outline • What is Geodemographics? • Need for Open Geodemographics • GeodemCreator • The 2011 Output Area Classification • Summary
UCL DEPARTMENT OF GEOGRAPHY Geodemographics • The analysis of people by where they live • Areas can be described by the characteristics and attitudes of those people who live in them • Based on the concept that similar people with similar characteristics are more likely to live within the same locality and that such area types will be distributed in different locations across a geographical space • Commercial (MOSAIC, ACORN) and free (OAC) classifications available
UCL DEPARTMENT OF GEOGRAPHY Commercial Geodemographic Classifications • Created as ‘black box’ systems (Longley and Singleton, 2009) • Closed methods are used with little documentation • Little information is given regarding the data inputs, normalisation and weighting procedures, and clustering methods employed
UCL DEPARTMENT OF GEOGRAPHY Need of Open, Transparent, and Flexible Classifications • Increased amount of data sources due to ‘open data’ initiatives – ONS NeSS data exchange, London data store, Crime data API • Need of open methods – Open method of Estimation, Normalisation, and Clustering procedures • Open public consultation
UCL DEPARTMENT OF GEOGRAPHY Need of Open, Transparent, and Flexible Classifications • A number of statistical packages could be used for building geodemographic classifications – R, SPSS, Microsoft Excel • No unified software utility exists that could be used for building open, transparent, and flexible classifications • ‘GeodemCreator’ is a unified software utility for building geodemographic classifications
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator • A cross platform java software utility for building geodemographic classifications • Requires ‘Java’ and ‘R’ installed on user’s machine • Geodemographic classifications could be created for any geographical level and by using any data set • Users can combine census data with their own data sources
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator • Operates in ‘Basic’ and ‘Advanced’ modes – Basic Mode is for inexperienced and new users – Advanced modes is for experienced users • Clusters the data by using k-means clustering algorithm
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator Case Study • A Socio-economic and Ethnic classification of Greater London • Created by using 41 OAC variables and 12 ethnicity variables (created from ethnicity data source http://worldnames.publicprofiler.org) • GeodemCreator was used for building the final classification
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator Case Study Data Sources • Variables V1 to V41 from the 2001 OAC • Variables V42 to V53 ethnicity V42: ‘European’ ethnic group V43: ‘East Asian & Pacific’ ethnic group V44: ‘Muslim’ ethnic group V45: ‘Greek’ ethnic group V46: ‘English’ ethnic group V47: ‘Nordic’ ethnic group V48: ‘African’ ethnic group V49: ‘Japanese’ ethnic group V50: ‘Hispanic’ ethnic group V51: ‘Celtic’ ethnic group V52: ‘Jewish’ ethnic group V53: ‘South Asian’ ethnic group
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator Case Study Results • A Socio-economic and Ethnic classification of Greater London:
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator Case Study Results • GeodemCreator also produces radial charts for each cluster solution English and European ethnic groups Well off and educated Asian families living in suburban areas
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator Case Study Results English, European, and Celtic fringe Poor Asian Families city commuters
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator Case Study Results Childless European city dwellers Native blue collar communities
UCL DEPARTMENT OF GEOGRAPHY GeodemCreator Case Study Results English and European ethnic groups living in council properties
UCL DEPARTMENT OF GEOGRAPHY The 2001 Output Area Classification (OAC) • Groups the UK population into: – 7 Supergroups – 21 Groups – 52 Subgroups • Only data source used is the 2001 Census – 41 Variables • Variety of organisations use it including local government and commercial companies
UCL DEPARTMENT OF GEOGRAPHY The 2011 Output Area Classification • Building on the success of the 2001 OAC • The 2001 OAC’s real achievement was showing that open-source geodemographic classifications were possible • Can utilise developments in computing over the past 6 years, since the 2001 OAC’s publication, to make improvements • Can be produced using open-source software (if required) with a fully open and transparent methodology
UCL DEPARTMENT OF GEOGRAPHY The 2011 Output Area Classification • Not just a repeat of the 2001 Output Area Classification • Methodology that will possibly not rely on 100% Census data • Enhanced outputs to cater for different potential users • Designed to allow easy creation of bespoke variants – Variables and/or Geography – Automated variable selection depending on user criteria • e.g. variables used for a national classification not necessarily being suitable for a regional classification
UCL DEPARTMENT OF GEOGRAPHY 2011 OAC Variables • Code used to auto-select best variables for desired purpose • Allows for a fully transparent and repeatable methodology – Variable selection the only “black box” element of the 2001 OAC • Allows for wider scale bespoke geodemographics – A user with no geodemographics experience can produce their own classification by selecting the variables, standardisation method, number of clusters. – Removes any technical barriers that could prevent wider adoption of bespoke geodemographic classifications.
UCL DEPARTMENT OF GEOGRAPHY Bespoke Geodemographic Classifications • Categorised into 3 main types: – Using the same data already provided in classification. – Changing the number of variables used to create a classification. – Uploading other data that was not originally included into a pre-existing classification or creating a new classification from scratch. • In the case of OAC this could resolve a problem when used at a regional level – London is an example of one such region that OAC does not classify very well.
UCL DEPARTMENT OF GEOGRAPHY The Hull City Council Classification • Bespoke free area classification of Hull • 45 Census Variables used • 10 Groups in 3 hierarchies
UCL DEPARTMENT OF GEOGRAPHY 2011 OAC and Open Data • Would it be better to use potentially “newer” Open Data (when compared with the 2011 Census)? • How much of a problem is the lack of data currently available at OA level? • Using Open Data raises a lot of questions: – What sources of Open Data should be used? – What should the coverage of the Open Data be? – Does the integrity of the Open Data matter? – How often should the Open Data sources be updated? • Beyond 2011
UCL DEPARTMENT OF GEOGRAPHY On-The-Fly Clustering • To meet the changing and varying needs of users a dynamic classification environment needs to be created • Ability to create bespoke classifications a requirement – both for different geographies (e.g. London or UK) and the range and number of variables utilised (e.g. Census and/or non-Census) with an additional weighting capacity • Will require clustering to happen in real-time • Research of users specific has been undertaken – 2011 OAC User Engagement (run in partnership with the ONS) – Results to be published by ONS by late April
UCL DEPARTMENT OF GEOGRAPHY On-The-Fly Clustering Objectives • Find optimum real-time clustering solution – Using mean Within-Cluster Sum of Squares (WCSS) value to determine optimum cluster solution using K-Means. – Number of cluster algorithm iterations to use to create a good clustering solution that does not result in poor functionality. • Create repeatability – Overcome inherent random seeding of K-Means that results in an OA remaining in the same cluster group but being given a random cluster assignment (e.g. a number from 1 to 7) for every iteration. • Incorporate different data sources – Both Census and non-Census data
UCL DEPARTMENT OF GEOGRAPHY What the Within-Cluster Sum of Squares Value means • Lower the mean value the more homogenous (i.e. better) the final cluster groupings are – Clustering using the lowest WCSS value can therefore be considered to create the optimum cluster groupings. • Using anything other than optimum cluster solution can have differing results depending on the dataset and level of geography
UCL DEPARTMENT OF GEOGRAPHY Summary • The 2001 OAC was an important first step for open source geodemographics • The 2011 OAC can build on the successes of the 2001 OAC • Tools like GeodemCreator can be used to create bespoke geodemographic classifications easily and without any “expert” knowledge • The 2011 OAC is still in the planning phase but should be released in some form by late 2012/early 2013