Data Set Information:

Many variables are included so that algorithms that select or learn weights for attributes could be tested. However, clearly unrelated attributes were not included; attributes were picked if there was any plausible connection to crime (N=122), plus the attribute to be predicted (Per Capita Violent Crimes). The variables included in the dataset involve the community, such as the percent of the population considered urban, and the median family income, and involving law enforcement, such as per capita number of police officers, and percent of officers assigned to drug units.

The per capita violent crimes variable was calculated using population and the sum of crime variables considered violent crimes in the United States: murder, rape, robbery, and assault. There was apparently some controversy in some states concerning the counting of rapes. These resulted in missing values for rape, which resulted in incorrect values for per capita violent crime. These cities are not included in the dataset. Many of these omitted communities were from the midwestern USA.

 Data is described below based on original values. All numeric data was normalized into the decimal range  0.00-1.00 using an Unsupervised, equal-interval binning method. Attributes retain their distribution and skew (hence for example the population attribute has a mean value of  0.06 because most communities are small). E.g. An attribute described as 'mean people per household' is actually the normalized (0-1) version of that value.

 The normalization preserves rough ratios of values WITHIN an attribute (e.g. double the value for double the population within the available precision - except for extreme values (all values more than 3 SD above the mean are normalized to 1.00; all values more than 3 SD below the mean are nromalized to  0.00)).

 However, the normalization does not preserve relationships between values BETWEEN attributes (e.g. it would not be meaningful to compare the value for whitePerCap with the value for blackPerCap for a community)

 A limitation was that the LEMAS survey was of the police departments with at least 100 officers, plus a random sample of smaller departments. For our purposes, communities not found in both census and crime datasets were omitted. Many communities are missing LEMAS data.

.arff header for Weka:

@relation crimepredict

@attribute state numeric
@attribute county numeric
@attribute community numeric
@attribute communityname string
@attribute fold numeric
@attribute population numeric
@attribute householdsize numeric
@attribute racepctblack numeric
@attribute racePctWhite numeric
@attribute racePctAsian numeric
@attribute racePctHisp numeric
@attribute agePct12t21 numeric
@attribute agePct12t29 numeric
@attribute agePct16t24 numeric
@attribute agePct65up numeric
@attribute numbUrban numeric
@attribute pctUrban numeric
@attribute medIncome numeric
@attribute pctWWage numeric
@attribute pctWFarmSelf numeric
@attribute pctWInvInc numeric
@attribute pctWSocSec numeric
@attribute pctWPubAsst numeric
@attribute pctWRetire numeric
@attribute medFamInc numeric
@attribute perCapInc numeric
@attribute whitePerCap numeric
@attribute blackPerCap numeric
@attribute indianPerCap numeric
@attribute AsianPerCap numeric
@attribute OtherPerCap numeric
@attribute HispPerCap numeric
@attribute NumUnderPov numeric
@attribute PctPopUnderPov numeric
@attribute PctLess9thGrade numeric
@attribute PctNotHSGrad numeric
@attribute PctBSorMore numeric
@attribute PctUnemployed numeric
@attribute PctEmploy numeric
@attribute PctEmplManu numeric
@attribute PctEmplProfServ numeric
@attribute PctOccupManu numeric
@attribute PctOccupMgmtProf numeric
@attribute MalePctDivorce numeric
@attribute MalePctNevMarr numeric
@attribute FemalePctDiv numeric
@attribute TotalPctDiv numeric
@attribute PersPerFam numeric
@attribute PctFam2Par numeric
@attribute PctKids2Par numeric
@attribute PctYoungKids2Par numeric
@attribute PctTeen2Par numeric
@attribute PctWorkMomYoungKids numeric
@attribute PctWorkMom numeric
@attribute NumIlleg numeric
@attribute PctIlleg numeric
@attribute NumImmig numeric
@attribute PctImmigRecent numeric
@attribute PctImmigRec5 numeric
@attribute PctImmigRec8 numeric
@attribute PctImmigRec10 numeric
@attribute PctRecentImmig numeric
@attribute PctRecImmig5 numeric
@attribute PctRecImmig8 numeric
@attribute PctRecImmig10 numeric
@attribute PctSpeakEnglonly numeric
@attribute PctNotSpeakEnglWell numeric
@attribute PctLargHouseFam numeric
@attribute PctLargHouseOccup numeric
@attribute PersPerOccupHous numeric
@attribute PersPerOwnOccHous numeric
@attribute PersPerRentOccHous numeric
@attribute PctPersOwnOccup numeric
@attribute PctPersDenseHous numeric
@attribute PctHousLess3BR numeric
@attribute MedNumBR numeric
@attribute HousVacant numeric
@attribute PctHousOccup numeric
@attribute PctHousOwnOcc numeric
@attribute PctVacantBoarded numeric
@attribute PctVacMore6Mos numeric
@attribute MedYrHousBuilt numeric
@attribute PctHousNoPhone numeric
@attribute PctWOFullPlumb numeric
@attribute OwnOccLowQuart numeric
@attribute OwnOccMedVal numeric
@attribute OwnOccHiQuart numeric
@attribute RentLowQ numeric
@attribute RentMedian numeric
@attribute RentHighQ numeric
@attribute MedRent numeric
@attribute MedRentPctHousInc numeric
@attribute MedOwnCostPctInc numeric
@attribute MedOwnCostPctIncNoMtg numeric
@attribute NumInShelters numeric
@attribute NumStreet numeric
@attribute PctForeignBorn numeric
@attribute PctBornSameState numeric
@attribute PctSameHouse85 numeric
@attribute PctSameCity85 numeric
@attribute PctSameState85 numeric
@attribute LemasSwornFT numeric
@attribute LemasSwFTPerPop numeric
@attribute LemasSwFTFieldOps numeric
@attribute LemasSwFTFieldPerPop numeric
@attribute LemasTotalReq numeric
@attribute LemasTotReqPerPop numeric
@attribute PolicReqPerOffic numeric
@attribute PolicPerPop numeric
@attribute RacialMatchCommPol numeric
@attribute PctPolicWhite numeric
@attribute PctPolicBlack numeric
@attribute PctPolicHisp numeric
@attribute PctPolicAsian numeric
@attribute PctPolicMinor numeric
@attribute OfficAssgnDrugUnits numeric
@attribute NumKindsDrugsSeiz numeric
@attribute PolicAveOTWorked numeric
@attribute LandArea numeric
@attribute PopDens numeric
@attribute PctUsePubTrans numeric
@attribute PolicCars numeric
@attribute PolicOperBudg numeric
@attribute LemasPctPoliconPatr numeric
@attribute LemasGangUnitDeploy numeric
@attribute LemasPctOfficDrugUn numeric
@attribute PolicBudgPerPop numeric
@attribute ViolentCrimesPerPop numeric

@data

Attribute Information:

Attribute Information: (122 predictive, 5 non-predictive, 1 goal)
 -- state: US state (by number) - not counted as predictive above, but if considered, should be consided nominal (nominal)
 -- county: numeric code for county - not predictive, and many missing values (numeric)
 -- community: numeric code for community - not predictive and many missing values (numeric)
 -- communityname: community name - not predictive - for information only (string)
 -- fold: fold number for non-random

Creator: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA
   -- culled from 1990 US Census, 1995 US FBI Uniform Crime Report, 1990 US Law Enforcement Management and Administrative Statistics Survey, available from ICPSR at U of Michigan.
 -- Donor: Michael Redmond (redmond '@' lasalle.edu); Computer Science; La Salle University; Philadelphia, PA, 19141, USA
 -- Date: July 2009