Big Data For the Big Data for the
Big Data For the Big Data for the Common Good Virginia Carlson, CEO Metro Chicago Info Center @VL_Carlson • • Jake Porway, Data Without Borders @Data. No. Borders • •
The Data-Driven World
But what are we really driving?
The Independent Sector
Useful Government Stats are Disappearing
Open. Gov Side Bar
Gov 1. 75
Bright. Scope
Internal and Project Data
Data Exhaust Opportunities
Lack of Capacity
Data Scientists!
How Can We Get These Two Together? ?
• • Metro Chicago Information Center Data Without Borders Data Science for the Social Good
Power Outages
YEAR PCT SER. NUM DATESTOP TIMESTOP RECSTAT CRIMSUSP 1 2010 78 81 1012010 340 2 2010 26 21 1042010 1548 1 3 2010 18 34 1092010 1550 1 4 2010 108 102 1112010 1120 A 5 2010 23 2437 1222010 1620 1 6 2010 26 299 1282010 1342 1 7 2010 100 283 2012010 1603 A 8 2010 30 834 2032010 1645 1 9 2010 73 1686 2042010 1420 A 10 2010 100 493 2132010 2354 1 11 2010 107 1228 2202010 1350 A 12 2010 24 470 2272010 1949 A 13 2010 28 1149 3012010 410 1 14 2010 28 1150 3012010 1232 1 15 2010 17 267 3022010 1835 A 16 2010 25 1954 3042010 1745 A 17 2010 110 3912 3072010 1550 A 18 2010 44 1658 3072010 1930 A 19 2010 71 1551 3112010 15 A 20 2010 26 918 3232010 5 1 21 2010 75 5151 3252010 1608 A 22 2010 42 7 1012010 1 1 23 2010 41 71 1012010 5 A 24 2010 114 105 1012010 10 1 25 2010 79 44 1012010 10 A 26 2010 32 393 1012010 15 A 27 2010 46 11 1012010 15 A 28 2010 32 475 1012010 15 A 29 2010 120 798 1012010 15 A 30 2010 23 530 1012010 15 1 31 2010 23 529 1012010 15 1 32 2010 114 66 1012010 15 A 33 2010 62 32 1012010 15 A INOUT 1 O I O O O I I O I O O O O O TRHSLOC PEROBS O P 1 MISD P 2 ROBBERY T 1 MISD P 5 BURGLARY H 1 CPM T 5 PETIT LARCENY P 1 CPW/ CRIMINAL TRESPASS T 2 220. 39 / 195. 05 P 2 GLA T 10 FEL/ GRAND LARCENY P 3 ASSAULT 1 P 1 MISD T 5 ROBBERY T 4 FORGERY P 1 ROBBERY P 1 CPW/ROBBERY P 1 FEL P 1 MISD P 2 CPW T 5 PETIT LARCENY P 1 FELONY P 2 CPW P 1 CPW H 5 CPW P 2 FELONY P 2 FEL P 1 CPW P 2 FELONY P 5 CPW P 1 ROBBERY P 5 BURGLARY P 1 ROBBERY
mid gender 29030 8 ki 6 x 9 18107 5 b 6 g 4 d 29433 8 ob 0 p 6 2081 0 od 2 qt 5153 1 h 5 fyz 39485 bs 6 coy 40952 dre 34 b 26004 7 ogmpv 43017 lmht 1 f 10311 2 wv 1 pz 32166 9 fyl 8 d 25899 7 ncoeq 44990 t 5 frms 4365 1 bpskh 26754 7 x 31 d 4 34717 abl 3 qw 22425 6 k 1 lc 5 14977 4 dzpct 39882 bwv 914 8219 2 bw 76 y 15985 4 m 23 c 6 39547 bt 0 hop 33135 9 sbyhq 37722 b 7 j 0 fs 21410 69 fxhg 18661 5 gk 4 ft 9230 2 m 91 yl 11201 37 eqry 19980 5 tefrb 28653 8 h 1 dfa Female Male Male Female Male Female Male Male Male city year. school Kochin baranquxcc_lla tarakeswar Santa Clara mirpur Port Moresby San ferndando La Union santa cruz de la sierra batangas Malang kingston Lima Multan Sonsonate noida Abuja bursa Mumbai Osun SAO ROQUE habra enugu escuintla Puerto Princesa City cebu city Santo Domingo jaipur San Miguel palayan city Giza latitude longitude 2009 NA NA 2011 26. 8205530 30. 802498 2011 -4. 0500000 39. 666667 2008 5. 5324624 5. 898714 2011 7. 1908544 5. 157920 2011 19. 4500000 -70. 700000 2009 NA NA 2009 10. 3156992 123. 885437 2011 NA NA 1997 17. 6868159 83. 218481 2006 NA NA 2006 -6. 2115440 106. 845172 2000 NA NA 2007 31. 0886523 77. 179780 2009 61. 5240100 105. 318756 2006 NA NA 2011 6. 2359250 -75. 575137 2008 5. 5557170 -0. 196306 2010 0. 3136111 32. 581111 1997 14. 5547290 121. 02445 2005 -28. 7500000 31. 90000 2004 14. 6133333 -90. 53528 2010 -0. 0951600 34. 74733 2008 10. 9222560 108. 10953 1999 0. 3136111 32. 58111 2010 -16. 4990100 -68. 14625 2008 -33. 9248685 18. 42406 2005 31. 5450500 74. 34068 2009 -17. 3841400 -66. 16670 2004 -7. 5666667 110. 81667
Why Stop There?
Commercial Data
First Responders
Target + Health Centers
Google + Aging Vision
The Siloed World
So What Next? www. mcic. org Virginia Carlson @VL_Carlson DC Datadive: http: //dwb. cc/dcdatadive Join us! http: //dwb. cc/join-dwb @Data. No. Borders
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