east massachusetts geodemographic classification

EAST MASSACHUSETTS GEODEMOGRAPHIC CLASSIFICATION

Stas Sushkov

Maria Sushkova

Introduction

Geodemographics is the analysis of people by where they live (Sleight, 1996)

Geodemographic classification categories neighborhoods based on their socio-economic and lifestyle characteristics.

Geodemographic segmentation is a multivariate statistical classification technique for discovering whether the individuals of a population fall into different groups by making quantitative comparisons of multiple characteristics with the assumption that the differences within any group should be less than the differences between groups. (Wikipedia)

Introduction

Market Research• Survey analysis• Concept testing• Product positioning • New product information• Image and awareness

Market Analysis• Site Selection• Market share analysis• Trade area analysis• New product rollout• Market entry planning• Test marketing

Advertising Media• Media analysis• Ad/Product positioning• Creative development• Media scheduling• Ad budget allocation• Advertising sales

Direct Marketing• Mailing list selection• Freestanding inserts• Response analysis• Program evaluation

Geodemographic classification applications

Source: adapted from Harris et al. 2005

Research Objectives

To build the geodemographic classification of East Massachusetts based on results of 2000 US Census

To create the multipurpose basis for further research analysis

To develop and test methodology for more comprehensive geodemographic classifications to be elaborated

Research Methodologies

The methodology applied for creation of East Massachusetts classification was developed by Dr. Dan Vickers (University of Sheffield) and described in his work “Multi-level Integrated

Classification Based on the 2001 Census”. Dr. Vickers has created The National Classification of Census Output Areas for UK, this classification was officially endorsed by the Office for National Statistics (UK). http://www.sasi.group.shef.ac.uk/area_classification/index.html

Clustering Process

Selection of cluster objects (operational

taxonomic units) Variables selection

Variables standardisation

Clustering method selection

Identification of cluster number

Interpretation, testing and mapping

of clusters

Classification Inputs

The 4,340 block groups of 9 Massachusetts counties were used: Essex, Middlesex, Suffolk, Norfolk, Bristol, Plymouth, Barnstable, Worchester

Total number of households: 2,117,000

Overall population: 5,510,000

Project Results

Various approaches to the classification design were evaluated and more appropriate ones were selected

The geodemographic classification was visually represented and tested in ArcGis, Google Earth and Maptube

5 super clusters were identified, narratively described and mapped.

Geographic database of cluster locations has been created

Mapping Clusters

Mapping clusters in ArcGis using full boundaries – each block group belongs to a certain cluster. Although the population clustering is obvious, uneven census sizes gives wrong perception of the population density. Thus low populated areas dominate on the map

Mapping Clusters

Mapping clusters in Arcgis using block group centroids represents cluster distribution more accurately and corresponds with population density.

Mapping Clusters

Mapping the classification visualizes the geodemographic clusterisation and shows that the population is clustered. For example brown dots are grouped representing one of the cluster areas.

The created clusters can also be exported to Google Earth…

Mapping Clusters

or Maptube.org – the online resource which allows to create public maps for free. East Massachusetts Geodemographic Classification Map can be easily accessed there.

http://www.maptube.org/map.aspx?s=DBHEKjJ1hctxoRRQLGBMCnwcCnU8ChVd



Naming and Describing Clusters

5 clusters of the classification were named:

1. Common City Dwellers

2. City Strugglers

3. Wealthy Suburbs

4. University Students and Downtown Residents

5. Suburban Middle Class

#1 Common City Dwellers

White populationBlack populationAsian population

Workers

Transportation to work; Car

Transportation to work; Transit

Transportation to work; Walk or Bicycle

Working from home

Population with high school degree

Pop with bachelor of higher degree

Medium hh income

Median number of rooms

Owner occupied dwellings

Medium house value

Hh without mortgage

Population paying high mortgage and owner cost

Foreign born population

Hh with Social Security income

Hh with Supplemental Security Income (SSI) and/or public assistance income

Poor population

Medium rentAge 0-9

Age 10-17Age 18-24Age 25-29Age 45-64Age 65+

1 person hh

2 person non family hh

2 person family hh

5+ person family hh

Disabled pop

Urban pop

Work 35+ h per week

Work 1-14 h per week

Unemployed pop

1 parent hh

Hh with 2+ vehicles

Self employed pop

Housing lacking kitch and plumb facilities

Hh with no cars

Own children U18 pop

Unempl male over16 pop

Single pop

Married popPop Employee of Private Comp

Vehicles per 100 residents

-50

0

50

#1 Common City Dwellers

Represented by the “average” middle class population residing mainly in high populated metropolitan areas.

Close to average household income ($48,000*; mean - $55,600) and educational level (20% with bachelor or higher degree). House value is around $185,000 which is 13% lower than the mean.

High percentage (49%) of Common City Dwellers rent their primary residence, in comparison with 38% mean.

Within this cluster the share of households with 2+ cars is 30% while the mean is 42%.

*Note: Here and further medium household income, medium house value and medium rent are shown based on the values of 2000 year when the US 2000 Census took place.

See this cluster 3D on Google Maps

http://maps.google.com/maps?f=q&source=s_q&hl=eng&geocode=&q=55.354135,40.297852&sspn=22.48198,56.513672&ie=UTF8&layer=c&cbll=42.412849,-71.061236&panoid=Atd53t8Y1FRVTyIQ9IE1sQ&ll=42.412938,-71.061287&spn=0,359.956098&z=15&iwloc=A&cbp=12,52.45,,0,-8.24

#2 City Strugglers

White populationBlack populationAsian populationWorkers




Working from home



Medium hh income



Medium house value

Hh without mortgage





Poor populationMedium rent

Age 0-9Age 10-17Age 18-24Age 25-29Age 45-64

Age 65+1 person hh


2 person family hh

5+ person family hh

Disabled pop

Urban pop

Work 35+ h per week


Unemployed pop

1 parent hh

Hh with 2+ vehicles

Self employed pop


Hh with no cars



Single popMarried pop

Pop Employee of Private CompVehicles per 100 residents

-50

0

50

#2 City Strugglers Mostly concentrated in areas close to the city center. 26% of this cluster are people for whom “poor” status

is determined. High level of black population, and population born

outside the US. High share of one parent households (41%, mean -

14%). High percentage of households with no cars (33%,

mean -12%), and who travel to work by transit. Almost ¾ of City Strugglers rent their residence.

Median household income – $28,400, which is almost 2 times lower than the average. Median house value – $136,000.

Only 7% of this population have bachelor or higher degree (mean-23%).

See this cluster area 3D on Google Maps

http://maps.google.com/maps?f=q&source=s_q&hl=ru&geocode=&q=42.322001,-71.066394&sspn=0.009313,0.01929&ie=UTF8&ll=42.32254,-71.066372&spn=0.009313,0.01929&t=h&z=16&iwloc=A&layer=c&cbll=42.321995,-71.066388&panoid=WQr5FMYUc6_NXeXIPwtjlQ&cbp=12,30.7,,0,-3.21

#3 Wealthy Suburbs





Working from home



Medium hh income



Medium house value

Hh without mortgage







Age 65+1 person hh


2 person family hh

5+ person family hh

Disabled pop

Urban pop

Work 35+ h per week


Unemployed pop

1 parent hh

Hh with 2+ vehicles

Self employed pop


Hh with no cars





-50

0

50

#3 Wealthy Suburbs Represented by people with high medium

household income ($82,000; mean - $55,600) and high medium house value ($307,000; mean - $211,000) , who mostly reside in suburban areas.

Low share of black (1%; mean - 6%) and foreign born (8%; mean - 3%) population.

High proportion of households with 2+ cars (64%; mean 49%) and low percentage of households with no cars.

High share of population with bachelor or higher degree (35%; mean - 23%).

Low proportion of one parent households (6%; mean – 14%).

High average rent price - $800 (mean - $680), and higher than average share of population who pays high mortgage and housing costs (8%; mean – 3%).


http://maps.google.ca/maps?f=q&source=s_q&hl=en&geocode=42.339117,-71.199582&sspn=0.00931,0.01929&ie=UTF8&ll=42.338705,-71.199667&spn=0.00931,0.01929&t=h&z=16&iwloc=A&layer=c&cbll=42.3381,-71.199691&panoid=Pm0ajYmMLumnYuo_I43A1Q&cbp=12,3.7,,0,5

#4 University Students and Downtown Residents





Working from home



Medium hh income



Medium house value

Hh without mortgage







Age 65+1 person hh


2 person family hh

5+ person family hh

Disabled pop

Urban pop

Work 35+ h per week


Unemployed pop

1 parent hh

Hh with 2+ vehicles

Self employed pop


Hh with no cars





-50

0

50

#4 Downtown Residents and University Students

This cluster members mostly resides in vicinity to universities, or in downtown of metropolitan cities.

Extremely high proportion of people do not use a car as a means of transportation to get to work (64%; mean - 29%), instead they take public transit (29%; mean - 10%), use a bicycle or walk (23%; mean - 5%). Low share of households with 2+ cars (12%; mean – 42%).

High percentage of population with bachelor or higher degree (41%; mean 23%). Medium income ($49,000; mean – $55,600), medium house value ($276,000; mean – $211,000).

High average rent - $910 (mean - $680) and low proportion of residence owners (28%; mean 61%).


http://maps.google.ca/maps?f=q&source=s_q&hl=en&geocode=&q=42.349791,-71.079247&sspn=0.008912,0.01929&ie=UTF8&ll=42.350393,-71.079526&spn=0,359.972534&t=h&z=16&iwloc=near&layer=c&cbll=42.349898,-71.079675&panoid=hRPj2voHAC26RfEgJz8UAg&cbp=12,9.91,,0,-1.47

#5 Suburban Middle Class

White populationBlack populationAsian population

WorkersTransportation to work; Car



Working from home



Medium hh income



Medium house value

Hh without mortgage





Poor population

Medium rentAge 0-9

Age 10-17Age 18-24Age 25-29Age 45-64Age 65+

1 person hh


2 person family hh

5+ person family hh

Disabled pop

Urban pop

Work 35+ h per week


Unemployed pop

1 parent hh

Hh with 2+ vehicles

Self employed pop


Hh with no cars





-50

0

50

#5 Suburban Middle Class

The cluster which groups middle class households located in suburban areas.

Mostly white population (96%; mean - 83%).

84% (mean - 72%) use car to go to work, 55% (mean- 42%) of households have 2+ cars. Only 5% of households have no car.

Household median income is almost the same as the mean - $56,000, but average house value ($175,000) is lower than the mean ($211,000).

Large proportion of owner occupied households (78%; mean – 62%).


http://maps.google.ca/maps?f=q&source=s_q&hl=en&geocode=&q=42.479852,-71.994267&sspn=0.009289,0.01929&ie=UTF8&ll=42.474851,-72.00285&spn=0.00929,0.027466&t=h&z=16&layer=c&cbll=42.474745,-72.002076&panoid=Vec3h4sSRMPvYkgnOU0I6g&cbp=12,173.13,,0,8.52

References Callingham, M. (2005), From areal classification to geodemographics, paper

presented at the Demographic User Group Conference, Royal Society, London 10th November 2005.

Debenham, J. E. (2002), Understanding Geodemographic Classification: Creating The Building Blocks For An Extension, Working Paper 02/1 School of Geography, University of Leeds [online] http://www.geog.leeds.ac.uk/wpapers/02-1.pdf

Harris, R., Sleight, P. and Webber, R. (2005), Geodemographics, GIS and Neighbourhood Targeting, London, Wiley

Longley, P. A. (2005), Geographical Information Systems: a renaissance of geodemographics for public service delivery, Progress in Human Geography, 29(1)

Sleight, P. (2004) Targeting customers: How to Use Geodemographic and Lifestyle Data in Your Business, Henley-on –Thames, World Advertising Research Centre

Vickers, D. (2006) , Multi-level Integrated Classification Based on the 2001 Census, The University of Leeds

Webber, R. and Farr, M. (2001) , MOSAIC-From an area classification system to household classification, Journal of Targeting, Measurement and Analysis for Marketing,10(1).

http://www.geog.leeds.ac.uk/wpapers/02-1.pdf