Jake porway data_viz_meetup_sf_talk

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Jake porway data_viz_meetup_sf_talk

  1. 1. Data Visualization for a Better World Jake Porway Data Scientist, New York Times R&D Founder, Data Without Borders
  2. 2. What will the future How do we connect How do we make the worldof information look data with the social a better place? like? sector?
  3. 3. Project Cascade: Social Sharing of NYT Content •  Discussions have always taken place around the news we publish and the stories we tell•  Increasingly, those discussions are taking place within the 4 realms of social media
  4. 4. 6
  5. 5. Hiring data scientists and developers! 8http://bit.ly/nyt_rnd_jobs
  6. 6. The Data-Driven World
  7. 7. Data Scientists
  8. 8. The Results? http://bestparking.com http://salelocator.com http://sportaneous.com
  9. 9. “Hacking” Change
  10. 10. Social Organizations Are Flooded With Data Photo by Amy Sun 2010
  11. 11. Can’t We Get These Two Together?
  12. 12. Expert Data Fellowships,Scientists + Short-Term,Social Orgs Weekends Work on Merging, Analytics, Visualization, and more
  13. 13. YEAR PCT SER.NUM DATESTOP TIMESTOP RECSTAT INOUT TRHSLOC PEROBSCRIMSUSP1 2010 78 81 1012010 340 1 O P 1 MISD2 2010 26 21 1042010 1548 1 O P 2 ROBBERY3 2010 18 34 1092010 1550 1 I T 1 MISD4 2010 108 102 1112010 1120 A O P 5 BURGLARY5 2010 23 2437 1222010 1620 1 O H 1 CPM6 2010 26 299 1282010 1342 1 I T 5 PETIT LARCENY7 2010 100 283 2012010 1603 A O P 1 CPW/ CRIMINAL TRESPASS8 2010 30 834 2032010 1645 1 O T 2 220.39 / 195.059 2010 73 1686 2042010 1420 A O P 2 GLA10 2010 100 493 2132010 2354 1 I T 10 FEL/ GRAND LARCENY11 2010 107 1228 2202010 1350 A O P 3 ASSAULT 112 2010 24 470 2272010 1949 A O P 1 MISD13 2010 28 1149 3012010 410 1 I T 5 ROBBERY14 2010 28 1150 3012010 1232 1 I T 4 FORGERY15 2010 17 267 3022010 1835 A O P 1 ROBBERY16 2010 25 1954 3042010 1745 A I P 1 CPW/ROBBERY17 2010 110 3912 3072010 1550 A O P 1 FEL18 2010 44 1658 3072010 1930 A I P 1 MISD19 2010 71 1551 3112010 15 A O P 2 CPW20 2010 26 918 3232010 5 1 I T 5 PETIT LARCENY21 2010 75 5151 3252010 1608 A O P 1 FELONY22 2010 42 7 1012010 1 1 O P 2 CPW23 2010 41 71 1012010 5 A I P 1 CPW24 2010 114 105 1012010 10 1 O H 5 CPW25 2010 79 44 1012010 10 A O P 2 FELONY26 2010 32 393 1012010 15 A O P 2 FEL27 2010 46 11 1012010 15 A O P 1 CPW28 2010 32 475 1012010 15 A O P 2 FELONY29 2010 120 798 1012010 15 A O P 5 CPW30 2010 23 530 1012010 15 1 O P 1 ROBBERY31 2010 23 529 1012010 15 1 O P 1 ROBBERY32 2010 114 66 1012010 15 A O P 5 BURGLARY33 2010 62 32 1012010 15 A O P 1 ROBBERY
  14. 14. Person.ID Gender GPS.Location.E GPS.Location.N District3234 Person-026941 Female 29.59990 -0.64902085 Kasese3235 Person-029886 Female 29.66908 -0.66138100 Kasese3236 Person-023784 Female 29.66908 -0.66138100 Kasese3237 Person-027782 Male 29.66908 -0.66138100 Kasese3238 Person-024140 Male 29.66908 -0.66138100 Kasese3239 Person-029825 Male 29.66908 -0.66138100 Kasese3240 Person-029797 Male 29.66908 -0.66138100 Kasese3241 Person-029826 Male 29.66908 -0.66138100 Kasese3242 Person-023801 Male 29.66908 -0.66138100 Kasese3243 Person-029798 Male 29.66908 -0.66138100 Kasese3244 Person-029827 Male 29.66908 -0.66138100 Kasese3245 Person-029796 Male 29.66908 -0.66138100 Kasese3246 Person-026972 Female 29.67850 -0.65206900 Kasese3247 Person-025369 Male 29.67850 -0.65206900 Kasese3248 Person-026736 Male 29.67850 -0.65206900 Kasese3249 Person-026731 Male 29.67850 -0.65206900 Kasese3250 Person-082788 Male 29.72264 0.05016537 Kasese3251 Person-041658 Male 29.72266 0.05002727 Kasese3252 Person-023773 Male 29.72436 0.04000193 Kasese3253 Person-056707 Female 29.72675 0.10446988 Luwero Nakaseke3254 Person-065053 Female 29.72676 0.10446263 Kasese3255 Person-075908 Male 29.72735 0.04980069 Kasese3256 Person-065518 Male 29.72800 0.03944763 Kasese3257 Person-030251 Male 29.72802 0.03960702 Kasese3258 Person-062942 Male 29.72802 0.03942526 Kasese3259 Person-030248 Male 29.72806 0.03947782 Kasese3260 Person-040118 Male 29.72806 0.03952481 Kasese3261 Person-026184 Male 29.72808 0.03960091 Kasese
  15. 15. mid gender city year.school latitude longitude29030 8ki6x9 Female Kochin 2009 NA NA18107 5b6g4d Male baranquxcc_lla 2011 26.8205530 30.80249829433 8ob0p6 Male tarakeswar 2011 -4.0500000 39.6666672081 0od2qt Male Santa Clara 2008 5.5324624 5.8987145153 1h5fyz Male mirpur 2011 7.1908544 5.15792039485 bs6coy Male Port Moresby 2011 19.4500000 -70.70000040952 dre34b Male San ferndando La Union 2009 NA NA26004 7ogmpv Male santa cruz de la sierra 2009 10.3156992 123.88543743017 lmht1f Male batangas 2011 NA NA10311 2wv1pz Female Malang 1997 17.6868159 83.21848132166 9fyl8d Female kingston 2006 NA NA25899 7ncoeq Male Lima 2006 -6.2115440 106.84517244990 t5frms Female Multan 2000 NA NA4365 1bpskh Male Sonsonate 2007 31.0886523 77.17978026754 7x31d4 Male noida 2009 61.5240100 105.31875634717 abl3qw Female Abuja 2006 NA NA22425 6k1lc5 Male bursa 2011 6.2359250 -75.57513714977 4dzpct Male Mumbai 2008 5.5557170 -0.19630639882 bwv914 Male Osun 2010 0.3136111 32.5811118219 2bw76y Female SAO ROQUE 1997 14.5547290 121.0244515985 4m23c6 Male habra 2005 -28.7500000 31.9000039547 bt0hop Male enugu 2004 14.6133333 -90.5352833135 9sbyhq Female escuintla 2010 -0.0951600 34.7473337722 b7j0fs Male Puerto Princesa City 2008 10.9222560 108.1095321410 69fxhg Female cebu city 1999 0.3136111 32.5811118661 5gk4ft Male Santo Domingo 2010 -16.4990100 -68.146259230 2m91yl Male jaipur 2008 -33.9248685 18.4240611201 37eqry Male San Miguel 2005 31.5450500 74.3406819980 5tefrb Male palayan city 2009 -17.3841400 -66.1667028653 8h1dfa Male Giza 2004 -7.5666667 110.81667
  16. 16. The Top-Down World Data Skills Stories Social Good
  17. 17. The Bottom-Up World “As we have moved from a world of states to a world of governments and social actors, we’ve come to a networked world, and in that networked world, those entities we used to think of as billiard balls, are just nodes in a much greater network that forms our society.” Anne Marie Slaughter
  18. 18. So What Next? DC Datadive: dwb.cc/dcdatadive Volunteer: dwb.cc/join-dwb Join us: dwb.cc/dwb-careers

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