![Geodemographics
• “Analysis of people by where they live [places]”
(Sleight, 1993:3)
• Social similarity, not locational proximity
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Person Address
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Spatio-temporal linkage of real and virtual identity
- 1. Spatio-temporal linkage of real and virtual identity Muhammad Adnan (and Paul Longley) University College London
- 2. Geodemographics • “Analysis of people by where they live [places]” (Sleight, 1993:3) • Social similarity, not locational proximity Home Person Address Area
- 4. Identity of individuals in the real world • Name (Forename & Surname) • Surnames have geographic concentrations • Prospects for linkage with socio-economic data • E.g. Analysing the socio-economic circumstances of different ethnic groups
- 5. An example – gbnames.publicprofiler.org Longley Cheshire
- 6. An example – Output Area Classification Kingston upon Hull Hereford
- 7. A socio-economic and ethnic classification
- 8. A socio-economic and ethnic classification
- 10. Wu
- 11. Source: Cheshire and Longley (2011)
- 12. Courtesy: James Cheshire 12
- 13. Wordle.net
- 14. The European scale 16 countries. 400 million people. 5.95 million unique surnames Courtesy: James Cheshire
- 15. Onomap classification Forename-Surname clustering (based on Hanks and Tucker, 2000) UK Electoral Roll Mateos Pablo Garcia Juan Pérez Forenames Surnames Rosa ... Marta Sánchez ... Rodríguez ... – Several iterations until self-contained cluster is exhausted – Cluster assigned a cultural, ethnic & linguistic Onomap type – Probability of ethnicity assigned to each name Mateos et al (2007) CASA Working Paper 116
- 16. WorldNames CEL clusters Source: Mateos et al (2011)
- 19. Uncertainty and virtual identity • Identity increasingly shaped by online activities – => value may be leveraged from the fusion of physical and virtual data sources • Data fusion and generalisation to relate physical and virtual properties • Use of residence alongside activity patterns and social network information
- 20. Most of us have virtual identities • Email address; social media accounts • People use different procedures and providers to establish virtual identities • Harvesting these data has interesting potential applications • Cyber crime • Cyber geodemographics (Facebook has already started this)
- 21. Most of us have virtual identities • Facebook data mining engine • Analyses the words you use and tailors advertisement accordingly
- 22. Starting Point http://worldnames.publicprofiler.org • Worldnames holds data for approximately 1 billion population around 28 countries of the world • Approximately 1.6 million unique users have visited the website since 2008
- 23. Starting Point http://worldnames.publicprofiler.org • Worldnames has been archiving „Surname search‟, „Email Address‟, „Gender‟, and „IP Address‟ for searches over the past 6 months • c. 175,000 records: email validation • 150,000 usable „IP Address‟ entries
- 24. IP Address to Latitude/Longitude conversion http://quova.com An API to convert “IP addresses” to their corresponding latitude / longitude values
- 25. IP Address to Latitude/Longitude conversion http://quova.com A search for an IP Address in UCL (128.40.214.196)
- 26. Top Countries Website was searched from 155 countries over the past 6 months UNITED STATES UNITED KINGDOM 76708 21892 CANADA 8154 GERMANY 7158 ITALY 4058 90000 AUSTRALIA 2978 BRAZIL 2440 80000 FRANCE 2028 ARGENTINA 1958 70000 SPAIN 1830 NEW ZEALAND 1236 60000 NETHERLANDS 1074 50000 GREECE 1040 SWITZERLAND 992 40000 BELGIUM 940 POLAND 880 30000 AUSTRIA 874 MEXICO 834 20000 IRELAND 710 SWEDEN 630 10000 0
- 27. UK and Ireland
- 28. Europe
- 29. North America
- 30. South America
- 31. India, China, Japan, Singapore
- 32. Popular Surname Searches SMITH 708 JONES 306 JOHNSON 258 ANDERSON 224 WILLIAMS 222 800 MILLER 218 MARTIN 202 700 WILSON 194 BROWN 194 MOORE 188 600 THOMAS 178 TAYLOR 170 500 CLARK 164 LEE 160 ROBERTS 156 400 DAVIS 152 CAMPBELL 144 300 LEWIS 138 HARRIS 138 MITCHELL 136 200 100 0
- 33. Popular Email Domains GMAIL.COM 31842 HOTMAIL.COM 22098 YAHOO.COM 15542 35000 AOL.COM 5550 COMCAST.NET 2696 30000 HOTMAIL.CO.UK 1948 MSN.COM 1624 WEB.DE 1522 25000 YAHOO.CO.UK 1290 GMX.DE 1260 SBCGLOBAL.NET 1246 20000 BTINTERNET.COM 860 HOTMAIL.IT 844 15000 VERIZON.NET 798 GOOGLEMAIL.COM 742 LIVE.COM 742 10000 COX.NET 708 ATT.NET 632 5000 MAILINATOR.COM 616 LIBERO.IT 616 0
- 34. Popular Email Domains by Surnames Smith (English) Jones (Welsh) Johnson (English) GMAIL.COM GMAIL.COM GMAIL.COM YAHOO.COM HOTMAIL.COM HOTMAIL.COM HOTMAIL.COM YAHOO.COM YAHOO.COM AOL.COM COMCAST.NET MSN.COM MAILINATOR.COM GOOGLEMAIL.COM VERIZON.NET Perez (Spanish) Gupta (Indian) Meyer (German) GMAIL.COM GMAIL.COM GMAIL.COM HOTMAIL.COM HOTMAIL.COM HOTMAIL.COM YAHOO.ES YAHOO.COM YAHOO.COM CHARTER.NET GOOGLAMAIL.COM AOL.COM GRANDECOM.NET INDIATIMES.COM GMX.DE
- 35. Popular Email Domains by Country UK USA France GMAIL.COM GMAIL.COM HOTMAIL.FR HOTMAIL.COM YAHOO.COM GMAIL.COM HOTMAIL.CO.UK HOTMAIL.COM HOTMAIL.COM YAHOO.CO.UK AOL.COM YAHOO.FR YAHOO.COM COMCAST.NET LAPOSTE.NET Germany Brazil Japan WEB.DE HOTMAIL.COM YAHOO.COM GMX.DE GMAIL.COM YAHOO.CO.JP T-ONLINE.DE YAHOO.COM.BR GMAIL.COM YAHOO.DE IG.COM.BR HOTMAIL.COM GMAIL.COM BOL.COM.BR MSN.COM
- 36. Top GoogleMail.com users Top Surnames BINDER WATKINS WHITE WOODS ROBINSON SLEEMAN BENNETT RITCHIE SHARP ROLLINGS
- 37. GoogleMail.com users • Surname „Binder‟ Germany Switzerland
- 38. GoogleMail.com users • Surname „Binder‟ Germany Switzerland
- 39. GoogleMail.com users • Surname „Blackbourn‟ New Zealand
- 40. Who use their surnames as part of their email address • Approximately 40% of the users have their surname as part of their email address • abbie.harper@hotmail.com (Surname: Harper) • helmut.kempe@inode.at (Surname: Kempe) • Top Countries 50 45 40 35 30 25 20 15 10 5 0
- 41. Who use long email addresses ? • Grand mean average email length of 8 characters • Number of characters on the left side of „@‟ • United Kingdom, USA, Canada, and other European countries • People from South American countries and India have long email addresses (Average length: 13 characters) BRAZIL ANA.ARAUJO3909@CREASP.ORG.BR (14 characters) CHILE BYRON.DELGADO.INOSTROZA@HOTMAIL.COM (25 characters) URUGUAY DIEGOJAVIERZEBALLOS@GMAIL.COM (17 characters) INDIA GANGULYDEEPANJAN@HOTMAIL.COM (18 characters) ARGENTINA AGUSTINAREYNOZO@GMAIL.COM (13 characters) • South Indians have longer email address than North Indians
- 42. What else we can infer from email addresses • Internet service provider • A.GOODEVE@AOL. COM • BERRYMANL@BTINTERNET.COM • CARL@VALLEYWISP.NET (Person lives in a rural area of northeast Oregon) • Country of origin • A.HAKIM26@YAHOO.FR • CBARNES@MEDIAWORKS.CO.NZ • Probable temporal aspects • ABBY527@OPTONLINE.NET • BERZINSKY102@YAHOO.COM • C.JOHNSTON2@BTINTERNET.COM
- 43. What else we can infer from email addresses • Probable forename of a person • BEVERLY.RICHARDS@YAHOO.COM • BJORN.SOBRY@HOTMAIL.COM • BRANDAN.HOLMES@HOTMAIL.COM • How up to date someone is with technology • ALEXANDER.BREUSCH@GMAIL.COM • WILLIAM.NEALON@GOOGLEMAIL.COM • Professional Affiliations • CHRIS@IEEE.ORG
- 44. What else we can infer from email addresses • Work Locations • DOUG.GOODMAN@FOUNDATION.ORG.UK • GRL@KCS.ORG.UK • ERM43@CAM.AC.UK • Studying • RTRIPOLI@STUDENT.UMASS.EDU • CBALIN01@STUDENTS.BBK.AC.UK • KATHERINE.LITTEN@STUDENT.KIRKWOOD.EDU
- 45. Conclusion and future work • There are some interesting patterns found in the study of email addresses • some problems (accuracy of geocoding techniques) • Prospect of data linkage of data coded to unit postcode level • cluster analysis and data mining techniques • Future work may involve the data mining of Facebook and Twitter data • issues of generalisation • Visualisation of the data
- 46. Thanks for Listening Any Questions ?
- 47. A research agenda 1 Acquire relevant real and virtual data sources and devise DBMS 2 Devise GB-wide classification of NICT usage at neighbourhood scale 3 Devise GB-wide classification of social network traffic 4 Develop enhanced worldnames site to harvest real and virtual user data 5 Undertake text analysis of worldnames user data and use to link classifications (2) and (3) 6 Devise, implement and analyse social networking application and cybergeodemographic classification
Editor's Notes
- #11: wu