The code source of this blog
http://code.google.com/p/the-criminality-usa/
http://code.google.com/p/the-criminality-usa/
Predict Violent Crimes on the USA. Modeling, classify, clustering, dbscan, neural networks, svm : all methods are used to build a good model of violent crimes on the USA
Real value for 2012 | Predict for 2013 | |
Violent crime |
3174 | 3165.4009559846 |
Murder and nonnegligent manslaughter |
64 | 64.2627077813 |
Forcible rape |
50 | 51.7887222942 |
Robbery | 637 | 635.089397512 |
Aggravated assault |
2423 | 2414.2601283972 |
2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1995 | 1990 | 1985 | 1980 | |
Violent crime |
3174.882629108 | 3148.4741784038 | 2441.8355312534 | 2647.2118043199 | 2879.5239914843 | 2568.0045653415 | 2652.9346741797 | 2524.9384985537 | 1968.2813215219 | 1491.2918248136 | 1847.8495785606 | 1540.4643089607 | 3022.7383730396 | 3178.8539264549 | 5484.1647273095 | 5191.6093272171 | 3107.1683309558 | 3216.278306531 |
Murder and nonnegligent manslaughter |
64.5539906103 | 61.6197183099 | 64.3293663557 | 38.3257138164 | 46.399912659 | 73.3715590098 | 48.5292928204 | 40.5504041524 | 51.1591588357 | 18.8430374976 | 51.32915496 | 35.2571056628 | 51.5547837412 | 22.3164472216 | 55.6511180816 | 35.8371559633 | 15.6027104137 | 35.0071108194 |
Forcible rape |
49.882629108 | 67.4882629108 | 83.0920982095 | 68.4387746722 | 114.635078334 | 67.9366287127 | 56.6175082904 | 91.914249412 | 83.4702065214 | 88.8314624889 | 83.747568619 | 43.3933608158 | 70.5486514354 | 128.9394728359 | 204.8972983912 | 222.1903669725 | 187.2325249643 | 126.9007767203 |
Robbery | 636.7370892019 | 627.9342723005 | 552.1603945534 | 531.0848914561 | 646.8693705988 | 627.7344493057 | 649.7533094282 | 619.0695033927 | 457.7398422144 | 288.029287464 | 437.6485843959 | 368.8435669343 | 697.3462853422 | 662.0546009075 | 1558.2313062835 | 1017.7752293578 | 815.7988587732 | 1207.7453232688 |
Aggravated assault |
2423.7089201878 | 2391.4319248826 | 1742.2536721347 | 2009.3624243752 | 2071.6196298925 | 1798.9619283133 | 1898.0345636408 | 1773.4043415966 | 1375.9121139503 | 1095.5880373631 | 1275.1242705857 | 1092.9702755478 | 2203.2886525208 | 2365.5434054899 | 3665.3850045533 | 3915.8065749236 | 2088.5342368046 | 1846.6250957226 |
Property crime |
3606.220657277 | 3879.1079812207 | 3355.8486115578 | 3556.0787319664 | 3788.416398275 | 3614.2286475176 | 3957.8334366828 | 4000.9732096997 | 3473.437626215 | 2543.810062182 | 3317.4843311001 | 3221.9570405728 | 4862.430129701 | 5482.4072007736 | 8714.4591723161 | 7530.5810397554 | 6851.8188302425 | 7679.6849360026 |
Burglary | 1364.4366197183 | 1625.5868544601 | 1249.0618634073 | 1335.9248816009 | 1228.2329821497 | 1187.5322698986 | 1197.0558895689 | 1297.6129328756 | 1066.2645736288 | 619.1283749226 | 645.6667387076 | 659.0366673899 | 1240.0282194606 | 1522.4776215627 | 2592.8361833451 | 2119.1704892966 | 2342.6355206847 | 3369.4344163658 |
Larceny- theft |
1866.1971830986 | 1836.8544600939 | 1710.0889889568 | 1730.1322237127 | 1948.7963316775 | 1725.5903693035 | 1995.0931492815 | 1919.3857965451 | 1658.6337812004 | 1152.117149856 | 1745.1912686406 | 1659.7960512042 | 2243.9897975796 | 2534.1565622753 | 4062.5316199535 | 3741.3990825688 | 3586.3944365193 | 3435.0727491522 |
Motor vehicle theft |
299.2957746479 | 416.6666666667 | 396.6977591937 | 490.0216266528 | 611.3870844478 | 701.1060083154 | 765.6843978324 | 783.974480279 | 748.5392713859 | 699.8842499125 | 842.8787551329 | 816.3376003471 | 1191.1868453899 | 1157.9756502765 | 2059.0913690175 | 1670.0114678899 | 922.7888730385 | 875.1777704846 |
Arson | 76.2910798122 | 76.2910798122 | 88.4528787391 | 109.5020394755 | 81.88219881 | 84.2414196038 | 67.4017955838 | 97.3209699657 | 72.6998572929 | 72.6802874909 | 83.747568619 | 86.7867216316 | 187.2252672709 | 267.7973666592 |
I follow the study written on the message the groups of my study article
We start with the first screen that colorize my first group explained. This first group describes the most hard and poor people where we found violent crime problems.
We can see that the value is shared on the entire map. Certain states are more represented as California New York, Michigan, Delaware and Kansas
|
Group around the violent criminality |
The group of the ideal family |
The group of poor workers |
The group of the managers |
Method
|
RMSE
|
MAE
|
MSE
|
ARV
|
likelihood, gaussian mixture
|
0.1241
|
0.087554
|
0.015401
|
0.42164
|
Full data set
|
0.13475
|
0.098797
|
0.018157
|
0.49709
|
Full data set cut in 2 classes
|
0.13686
|
0.097323
|
0.01873
|
0.51276
|
Full data set cut in 3 classes
|
0.13521
|
0.094228
|
0.018282
|
0.50052
|
Removed Variables
|
0.13406
|
0.097274
|
0.017972
|
0.49202
|
Removed Communities
|
0.12757
|
0.092739
|
0.016275
|
0.44557
|
Mixte
avec 2 classes
|
0.1241
|
0.087554
|
0.015401
|
0.42164
|
linear regression
|
0.12437
|
0.087327
|
0.015467
|
0.42344
|
Full data set
|
0,13499
|
0.099144
|
0.018222
|
0.49888
|
Full data set cut in 2 classes
|
0.13763
|
0.099092
|
0.018942
|
0.51857
|
Full data set cut in 3 classes
|
0.13501
|
0.096144
|
0.018227
|
0.49899
|
Removed Variables
|
0,134
|
0,097173
|
0,017957
|
0,49161
|
Communautés
supprimées
|
0.12747
|
0.092553
|
0.016248
|
0.44483
|
Mixed with 2 classes
|
0.12437
|
0.087327
|
0.015467
|
0.42344
|
PLS regression 1st
|
0.12438
|
0.08572
|
0.015472
|
0.42357
|
Full data set
|
0,13347
|
0.09774
|
0.017815
|
0.48772
|
Full data set cut in 2 classes
|
0.13245
|
0.094019
|
0.017542
|
0.48025
|
Full data set cut in 3 classes
|
0.13047
|
0.091678
|
0.017021
|
0.466
|
Removed Variables
|
0,13291
|
0,09554
|
0,017665
|
0,48362
|
Communautés
supprimées
|
0.12764
|
0.091114
|
0.016292
|
0.44602
|
Mixed with 2 classes
|
0.12438
|
0.08572
|
0.015472
|
0.42357
|
PLS regression advanced
|
0.1207
|
0.085773
|
0.01457
|
0.39888
|
Full data set
|
0.12743
|
0.093526
|
0.016238
|
0.44455
|
Full data set cut in 2 classes
|
0.12396
|
0.089755
|
0.015366
|
0.42067
|
Full data set cut in 3 classes
|
0.12021
|
0.087285
|
0.014451
|
0.39562
|
Removed Variables
|
0.12829
|
0.094293
|
0.016458
|
0.45057
|
Communautés
supprimées
|
0.12429
|
0.088444
|
0.015448
|
0.4229
|
Mixed with 2 classes
|
0.1207
|
0.085773
|
0.01457
|
0.39888
|
SVM Polynomial
|
0.12175
|
0.08589
|
0.014822
|
0.40579
|
Full data set
|
0.12985
|
0.092377
|
0.01686
|
0.46268
|
Full data set cut in 2 classes
|
0.12911
|
0.088887
|
0.01667
|
0.45637
|
Full data set cut in 3 classes
|
0.13302
|
0.092129
|
0.017695
|
0.48444
|
Removed Variables
|
0,12925
|
0,089951
|
0,016705
|
0,45733
|
Communautés
supprimées
|
0.12797
|
0.090735
|
0,017175
|
0,47019
|
Mixed with 2 classes
|
0.12175
|
0.08589
|
0.014822
|
0.40579
|
Neural network
|
0,11787
|
0,086258
|
0,013893
|
0,40909
|
Full data set
|
0,11787
|
0.086258
|
0.013893
|
0.40909
|
Full data set cut in 2 classes
|
0,13692
|
0.10066
|
0.018747
|
0.51323
|
Full data set cut in 3 classes
|
0.13393
|
0.094034
|
0.017938
|
0.4911
|
Removed Variables
|
0,13351
|
0,095503
|
0,017824
|
0,48797
|
Communautés
supprimées
|
0.13552
|
0.094944
|
0.018367
|
0.50283
|
Mixed with 2 classes
|
0,13283
|
0,097711
|
0,017645
|
0,48306
|