FORECASTING ST. LOUIS HOMICIDES2013

Looked at RegressionToo Small Sample Size

Sought CorrelationsToo Many

Looked at Linear Trend Lines

Neighborhood Statistics

What We Tried

St. Louis has 79 neighborhoods and 6 other areas that are patrolled

9 Districts & 3 Area Patrols Looked for High Risk Areas Columbus Square, North

Riverfront, and Near North Riverfront shown as high risk

Neighborhood Stats

Area Patrol

Police District

Neighborhood Number Neighborhood

Violent Crime /100k

High School GradRate Population

Poverty Level

People Per Household

Central 4 62 Columbus Square 2,795 51% 1,062 78.30% 2.8Central 4 & 5 79 North Riverfront 2,795 63% 2,055 38.20% 2.5

North 6 64 Near North Riverfront 2,795 61% 2,427 52.70% 1.7

Homicide Rarity1 Murder per 100,000 people on average

Small crime counts per area and limited data

Vast Number of Homicide CorrelatesOn both a macro and a micro scale

“When a researcher is interested in homicides, a clear definition must be presented so that no ambiguity remains as to whether she/he investigates homicide offenders or victims of a homicide. Official homicide rates usually measure the number of people killed rather than the number of people who have killed others (Holinger 1979).”

Difficulty of Predicting Homicides

Biological/Psychological Ecological

Age Gender Mental Illness Personality Disorders

Population Size Neighborhood Conditions Weather

Socioeconomic High Poverty Education Level Occupation Level

Gang Violence Drug/ Alcohol Usage Gun Ownership Home Vacancies

Source: Ellis, Beaver, et al. Handbook of Crime Correlates. 2009 Academic Press.

Cultural and Societal

Some Correlates of Crime

Based on previous studies, and our early attempts to produce a good model for prediction, we chose to use a simple linear model and apply exponential smoothing to forecast.

Due to the number of correlates, it is too difficult to come with a model that shows a meaningful connection and provides an accurate method of forecasting. We strictly followed the murder data to develop our predictions.

Our Approach

We used 134 months of murder data in our data set. This includes data from 2002 to 2012, and the first two recorded months of 2013.

  January Feb. March April May June July August Sept. Oct. Nov. Dec. Total

2002 13 11 5 10 8 4 12 9 14 15 3 9 113

2003 8 3 7 3 8 6 11 8 3 1 2 14 74

2004 4 3 12 5 8 17 2 21 4 13 16 9 114

2005 8 8 7 13 12 12 14 8 15 11 8 15 131

2006 13 3 8 12 12 8 15 12 12 6 17 11 129

2007 9 5 10 9 13 14 10 12 10 11 20 15 138

2008 9 14 7 16 17 23 11 18 21 12 9 10 167

2009 5 9 10 8 16 12 10 15 13 22 15 8 143

2010 13 8 7 14 10 10 9 7 13 13 26 14 144

2011 2 12 11 10 16 18 10 9 8 7 7 3 113

2012 11 6 6 10 12 13 12 11 8 9 6 9 113

TOTAL 95 82 90 110 132 137 116 130 121 120 129 117  

2013 15 5                      

Monthly Murder Data

"Is crime seasonal?” Seasonality refers to regular periodic fluctuations which

recur every year with about the same timing and with the same intensity and which, most importantly, can be measured and removed from the time series under review.

Any discussion of seasonal fluctuation in crime must be carefully qualified by several considerations: 1) the place 2) the conceptual and operational definitions of the crime 3) circumstances relating to public or private crime (weapon,

place of occurrence, injury, victim-offender relationship, property loss)

4) numerical aspects of the series that would increase the likelihood of significant results

Homicide and Seasonality

Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 Jul-09 Nov-10 Apr-12 Aug-130

5

10

15

20

25

30

Murders by Month

Homicide and Seasonality

In order to perform analysis on the table, we used a pivot table in excel, then applied the seasonal indices to our data to deseasonalize it.

MonthAverage of Murders

Jan 82.67%Feb 71.36%Mar 78.32%Apr 95.72%May 114.87%Jun 119.22%Jul 100.94%Aug 113.13%Sep 105.29%Oct 104.42%Nov 112.26%Dec 101.81%Grand Total 100.00%

Our Approach

We used monthly data to provide a larger sample size and more sensitivity to gradual changes.

We used exponential smoothing on the deseasonalized data to predict the number of homicides for the remaining 10 months of 2013.

Predicts approximately 112 homicides in St. Louis City.

Date Murders Deseas Forecast 2012 Actual

Feb-12 6 8.408537 11.37127065 113

Mar-12 6 7.661111 9.001083398

Apr-12 10 10.44697 7.929105569 2012 Forecasted

May-12 12 10.44697 9.943396871 107.3923747

Jun-12 13 10.9045 10.34625513

Jul-12 12 11.88793 10.792852

Aug-12 11 9.723718 11.66891523

Sep-12 8 7.597796 10.1127574

Oct-12 9 8.61875 8.100788395

Nov-12 6 5.344961 8.515157679

Dec-12 9 8.839744 5.979000528

Jan-13 15 18.14474 8.267594977

Feb-13 5 7.007114 16.16930847

Mar-13 8.839553 11.2868 8.839552751

Apr-13 10.79735 11.27996 10.79734999

May-13 11.18344 9.736086 11.18343705

Jun-13 10.02556 8.409514 10.02555594

Jul-13 8.732723 8.651167 8.732722693

Aug-13 8.667478 7.661828 8.667478213

Sep-13 7.862958 7.467644 7.862958441

Oct-13 7.546707 7.22702 7.54670722

Nov-13 7.290958 6.494981 7.290957697 2013 Forecasted

Dec-13 6.654176 6.53569 6.654176379 112.0377998

Our Model

District 1 District 2 District 3 District 4 District 5 District 6 District 7 District 8 District 9 Other* Total2002 9 2 15 5 17 29 17 12 7 0 1132003 4 1 10 4 10 11 13 12 9 0 742004 7 2 10 3 22 20 21 12 10 7 1142005 10 2 9 2 18 35 19 29 7 0 1312006 6 1 15 8 14 35 19 22 7 2 1292007 10 0 12 7 18 41 33 9 7 1 1382008 10 1 11 12 19 43 35 17 17 2 1672009 9 2 13 13 25 27 29 17 8 0 1432010 10 4 10 9 30 39 21 14 7 0 1442011 12 2 15 10 17 27 15 13 2 0 1132012 12 2 13 6 18 32 15 8 7 0 113Total 99 19 133 79 208 339 237 165 88 12 1379

% of Total 7.18% 1.38% 9.64% 5.73% 15.08% 24.58% 17.19% 11.97% 6.38% 0.87%

We decided to break our prediction down by districts.

District Data

Based on historical % of homicides per district within the past 11 years.

Of the 112 Predicted Murders:

District 1: 8District 2: 2District 3: 11District 4: 6District 5: 17District 6: 28District 7: 19District 8: 13District 9: 7Other: 1

District Data

The model assumes “business as usual conditions”Does not account for changes in police

policy or trends in possible correlated factors Greater amounts of data can allow for a

more complicated model and can account for more variables.

Ways to Improve Model

St. Louis has initiated a new “Hot-Spot Initiative.”Will focus on Downtown, Shaw, and Baden

neighborhoods.

Domestic Abuse Response Team(DART)

PredPol (Predictive Policing)

Other Considerations

Conclusion

We predict 112 murders in 2013Highest Predicted Crime Area: District 6 (28)Lowest Predicted Crime Area: District 2 (2)

With small scale data sets and lack of abundant data, forecasting accurately is a challenge.

“It’s tough to make predictions, especially about the future.”

-Yogi Berra

https://www.ncjrs.gov/pdffiles1/nij/grants/211973.pdf http://www.kmov.com/news/editors-pick/St-Louis-poli

ce--195189401.html

http://forprin.dev.zoe.co.nz/files/pdf/Gorr_Olligschalger_and_Thompson,_Short-term.pdf

http://www.predpol.com/ http://research.stlouisfed.org/fred2/series/MOSSURN http://

cad.sagepub.com/content/49/3/339.full.pdf+html http://

ajp.psychiatryonline.org/article.aspx?articleID=172630

Sources

http://www.slmpd.org/press_room.html# http://www.slmpd.org/crime_stats.html http://stlouis-mo.gov/government/department

s/public-safety/neighborhood-stabilization-office/neighborhoods/neighborhood-maps.cfm

http://www.areavibes.com/st.+louis-mo/neighborhoods/

http://bjs.ojp.usdoj.gov/content/pub/pdf/ics.pdf http://www.nytimes.com/2009/06/19/nyregion/

19murder.html?pagewanted=all&_r=0

http://blogs.riverfronttimes.com/dailyrft/2013/03/joseph_hawthorne_homicide_st_louis.php#more

Sources