Page 22 - Case study: Crime prediction for more agile policing in cities – Rio de Janeiro, Brazil
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Endnotes
1 Section 1.1 is based on Wikipedia data on Rio de Janeiro.
2 http:// www .forumseguranca .org .br/ wp -content/ uploads/ 2018/ 08/ FBSP _Anuario _Brasileiro _Seguranca
_Publica _Infogr %C3 %A1fico _2018 .pdf
3 https:// www .nytimes .com/ 2018/ 08/ 10/ world/ americas/ brazil -murder -rate -record .html
4 https:// en .wikipedia .org/ wiki/ Rio _de _Janeiro
5 Braga, Papachristos, Hureau, 2012.
6 Johnson, Guerette, Bowers, 2014.
7 Muggah, 2018.
8 FAT/ML (n.d.). Fairness, Accountability, and Transparency in Machine Learning. Retrieved
8 February 07, 2019, from http:// www .fatml .org/ resources/ principles -for -accountable -algorithms
9 Knight, 2017.
10 Lum, Isaac, 2016.
11 Oram, 2016.
12 Cohen, Felson, 1979.
13 Cornish, Clarke, 1987.
14 Cullen, Wilcox, (n.d.).
15 Lum, Isaac, 2016.
16 Ensign, Friedler, Neville, Scheidegger, Venkatasubramanian, 2017.
17 Winston, Burrington, 2018.
18 Winston, 2018.
19 Mortimer, 2017.
20 Olligschlaeger, (n.d.).
21 Muggah, 2018.
16 Crime prediction for more agile policing in cities – Rio de Janeiro, Brazil
