geospatial analysis of consumer datakukto.snu.ac.kr/nfupload/nfupload_down.php?tmp...a: intermediate...
TRANSCRIPT
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Geospatial Analysis of Consumer Data
Paul Longley and colleagues, CDRC and University College London
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Groups
A1: Struggling suburbs
A2: Suburban localities
B1: Disadvantaged diaspora
B2: Bangladeshi enclaves
B3: Students and minority mix
C1: Asian owner occupiers
C2: Transport service workers
C3: East End Asians
C4: Elderly Asians
D1: Educational advantage
D2: City central
E1: City and student fringe
E2: Graduation occupation
F1: City enclaves
F2: Affluent suburbs
G1: Affordable transitions
G2: Public sector and service
employees
H1: Detached retirement
H2: Not quite Home Counties
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A: Intermediate Lifestyles
E00007298; Perry Mead, Enfield
Groups
A1: Struggling suburbs
A2: Suburban localities
• Later stages in life-cycle
• White and born in the UK
• Few dependent children
• Most live in single family terraced
or semi detached properties.
• Higher social rented.
• Average employment in full and
part time intermediate
occupations.
• Lower levels of highest
qualifications
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B: High Density and High Rise Flats
E00009768; Lancaster Court, Fulham
Groups
B1 Disadvantaged diaspora
B2 Bangladeshi enclaves
• Densely populated areas of flats.
• Families have children of school age
• Many residents Bangladeshi origins
• High Black residents or Mixed or
Other ethnic groups.
• Higher spoken language is not
English.
• Qualifications are below the London
average
• Some residents are full-time students
living in shared accommodation.
• Levels of unemployment and part-
time working high
• Employment more typically in
administration, or in accommodation
and food services industries.
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C: Settled Asians
E00013190; Catherine Gardens, Hounslow
Groups
C1 Asian owner occupiers
C2 Transport service workers
C3 East End Asians
C4 Elderly Asians
• Traditional single-family
houses
• Above average numbers of
which are owner-occupied.
• Full age range
• Main language spoken in
many households is not
English.
• Occupations drawn from a
wide range of non-
professional sectors. Many of
Asian origins, although many
are second or subsequent
generation British residents.
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D: Urban Elites
E00009327; Stoner Road, West Kensington
Groups
D1 Educational advantage
D2 City central
• Young professionals
• Working in the science,
technology, finance and
insurance sectors. Large
numbers of students
• Many privately owned flats
• Residents are
disproportionately drawn
from pre 2001 EU countries,
• High of Chinese, Arab and
other minority backgrounds.
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E: City Vibe
E00009228; Netherwood Road, Shepherds Bush
Groups
E1 City and student fringe
E2 Graduation occupation
• Many young, single professionals
• Mostly living in Zone 2
• Few individuals originate from the
Indian sub-continent
• Mixed ethnic groups are well
represented, as are migrants from
pre 2001 EU countries.
• Large number student
households
• Individuals rent within the private
sector
• Well qualified
• Employed in a range of
professional, scientific and
technical occupations.
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F: London Life-Cycle
E00017504; Trinity Road, Wimbledon
Groups
F1 City enclaves
F2 Affluent suburbs
• Predominantly White in ethnic
composition (including
individuals from other pre 2001
EU countries)
• Households cover the full
family life-cycle
• Residents are highly qualified
• Employment rates are high
• Employment is concentrated in
the technical, scientific,
finance, insurance and real
estate industries.
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G: Multi-Ethnic Suburbs
E00022299; Crescent Road, Leyton
Groups
G1 Affordable transitions
G2 Public sector and service employees
• Wide range of non-White
ethnic groups
• EU post 2001 are well
represented.
• Young children or children of
school age,
• Low over 65s
• Family housing in
overcrowded terraces,
• social housing sector.
• unemployment are high.
• Employment blue collar
occupations.
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H: Ageing City Fringe
E00005585; Peacock Gardens, South Croydon
Groups
H1 Detached retirement
H2 Not quite Home Counties
• Many residents 45+
• Many above state pensionable
age.
• High levels of marriage
• Mainly white
• Much of the dwelling stock semi-
detached and detached houses
• Levels of qualifications are low
• Private vehicle ownership is
high
• Levels of unemployment are
very low and drawn from a
range of sectors
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London Output Area Classification
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2011 Output Area Classification
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http://www.google.co.uk/intl/en_uk/earth/
52: POORER FAMILIES,
MANY CHILDREN,
TERRACED HOUSING
51: YOUNG PEOPLE IN SMALL, LOW COST TERRACES
59: DEPRIVED AREAS AND HIGH-RISE FLATS
11: SETTLED SUBURBIA, OLDER PEOPLE
Urban Adversity
Affluent Achievers
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Research Dissertation ProgrammeOutputs from 2015
www.cdrc.ac.uk/retail-masters/2015-projects/
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CDRC Maps Geodemographic
Classification: OAC
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CDRC Maps Modal Dwelling
Age Group
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Internet User Classification
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Towards the ‘Smart Census’
• Context of better use of existing data resources, e.g. workplace statistics
• Activity patterns associated with consumption
• Big Data as ‘exhaust’ (Harford): no research ‘design’
• ‘Horses for courses’ approach to data creation, maintenance and linkage
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Data available through the Twitter API
• User Creation Date
• Followers
• Friends
• User ID
• Language
• Location
• Name
• Screen Name
• Time Zone
• Geo Enabled
• Latitude
• Longitude
• Tweet date and time
• Tweet text
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Twitter estimated footfall in Soho
Time (hours) Time (hours)Time (hours)Time (hours)
Fre
qu
en
cy
Fre
qu
en
cy
Fre
qu
en
cy
Fre
qu
en
cy
The average weekday activity in 2013
• The frequency of
geotagged Tweets
across space and
time can tell us a lot
about the dynamics
of a city
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Forenames – Age (Males)
5 clusters of forenames based on their
age distributions
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Inferred demographic structure of Tweeters
The O2 Arena The Emirates stadium Canary Wharf Westfield Stratford
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Twitter vs the Census (courtesy: Guy Lansley)
Day NightTwitter
Census
• Lower Super Output Area level
• Census work day statistics vs Tweets from 10:00 – 16:00
• Census residential population vs Tweets from 19:00 – 7:00
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Customers most frequently visited store outside MSOA of residence[Courtesy: Alyson Lloyd)
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Smart Meter Data4 clusters of smart meters based on typical daily energy profiles
From: Samson, N., Lansley, G. and Simpson, A. (2014) Using smart meter data to determine energy efficiency of customers’ homes. PopFest 2014. 4th – 6th August. University College London, UK.
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Smart Sensors
• Co-production of Big Data
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total
67.5k businesses
17.5m residents
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total
17.5k businesses
3.4m residents
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Size of the data
• 1000 sensors generate approximately 1.5 GB of data every day. This equals approximately 5-10m records every day.
• Compared to this, tracking 100,000 properties with 50 parameters updated every month generate 2 GB of data in 10 years.
100,0
00 p
roperty
data
base
1000 s
ensor data
base
Comparison of data generated by the sensors to a retail property database in a year
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Complexity of visualisation
• Footfall counts
• Trends
• Hourly footfall
• Hourly trends
• Relationship between the sensors
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01 Jan 2015, Fri
05 Jan 2015, Tue
04 Jan 2015, Mon
03 Jan 2015, Sun
02 Jan 2015, Sat
* from sensors at Market Harborough
Classification
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Classification
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Classification
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Some prospects
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44
Clapham Junction
Victoria
Waterloo
London Bridge
Liverpool Street
Fenchurch Street
St Pancras
Kings Cross
Euston
Paddington
Marylebone
Lewisham
Topic 6 Subgroup B – Trains and Delays
Underrepresented Overrepresented
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Topic 13 Subgroup D – Education
UCL
University of
Westminster
Imperial College
LondonLondon South
Bank University
Kings College
London
Queen Mary
London Metropolitan
University
University of
Greenwich
City University
Goldsmiths
Birkbeck
SOAS
LSE
Various
Various
Underrepresented Overrepresented
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Some prospects• Rethinking ‘place’ as the
measurable accumulated effects of slow and fast dynamics
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Courtesy:
James
Cheshire
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Isonymy groups (left) and the geographic distribution of genotypes (right)
[courtesy Jens Kandt]
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Socio-economics• There is an association between names and socio-economics
and geodemographics
• E.g. Top 5 forenames for each 2011 OAC Supergroup
Rural Residents Cosmopolitans Ethnicity CentralMulticultural
Metropolitans
PENELOPE TOM MOHAMED MOHAMMED
HUGH NICK AHMED MUHAMMAD
ALASTAIR HARRIET ALI MOHAMMAD
ROSEMARY MAX JOSE ABDUL
PHILIPPA ALEX ABDUL AHMED
Urbanites SuburbanitesConstrained City
Dwellers
Hard-Pressed
Living
TOBY HILARY LILLIAN KAYLEIGH
PHILIPPA GEOFFREY MAY LEANNE
JEREMY KATHRYN ETHEL LYNDSEY
KATHERINE JILL KAYLEIGH STACEY
DUNCAN GILLIAN ELSIE KYLE
Data: 2011 Enhanced Electoral Roll (CACI UK Ltd)
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Holborn10:30 – 11:10
Holborn Underground station pace
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DemographicsCommute
Energy. . .
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Some prospects• Rethinking ‘place’ as the
measurable accumulated effects of slow and fast dynamics
• New ways of framing research questions, e.g. segregation, health outcomes
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The local geodemography of Glasgow, showing the 7.8 mile route that links
communities with life expectancies of 54 and 82 [courtesy Alex Singleton]