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 Looked at Regression Too Small Sample Size  Sought Correlations Too Many  Looked at Linear Trend Lines  Neighborhood Statistics What We Tried.

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Presentation on theme: " Looked at Regression Too Small Sample Size  Sought Correlations Too Many  Looked at Linear Trend Lines  Neighborhood Statistics What We Tried."— Presentation transcript:

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2  Looked at Regression Too Small Sample Size  Sought Correlations Too Many  Looked at Linear Trend Lines  Neighborhood Statistics What We Tried

3  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 NumberNeighborhood Violent Crime /100k High School GradRatePopulation Poverty Level People Per Household Central462Columbus Square2,79551%1,06278.30%2.8 Central4 & 579North Riverfront2,79563%2,05538.20%2.5 North664Near North Riverfront2,79561%2,42752.70%1.7

4  Homicide Rarity 1 Murder per 100,000 people on average  Small crime counts per area and limited data  Vast Number of Homicide Correlates On 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

5 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

6  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

7 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. JanuaryFeb.MarchAprilMayJuneJulyAugustSept.Oct.Nov.Dec.Total 2002 131151084129141539113 2003 8373861183121474 2004 43125817221413169114 2005 8871312 1481511815131 2006 133812 81512 61711129 2007 951091314101210112015138 2008 914716172311182112910167 2009 59108161210151322158143 2010 13871410 9713 2614144 2011 212111016181098773113 2012 116610121312118969113 TOTAL 958290110132137116130121120129117 2013155 Monthly Murder Data

8  "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

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10  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. Month Average of Murders Jan82.67% Feb71.36% Mar78.32% Apr95.72% May114.87% Jun119.22% Jul100.94% Aug113.13% Sep105.29% Oct104.42% Nov112.26% Dec101.81% Grand Total100.00% Our Approach

11  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. DateMurdersDeseasForecast 2012 Actual Feb-1268.40853711.37127065 113 Mar-1267.6611119.001083398 Apr-121010.446977.929105569 2012 Forecasted May-121210.446979.943396871 107.3923747 Jun-121310.904510.34625513 Jul-121211.8879310.792852 Aug-12119.72371811.66891523 Sep-1287.59779610.1127574 Oct-1298.618758.100788395 Nov-1265.3449618.515157679 Dec-1298.8397445.979000528 Jan-131518.144748.267594977 Feb-1357.00711416.16930847 Mar-138.83955311.28688.839552751 Apr-1310.7973511.2799610.79734999 May-1311.183449.73608611.18343705 Jun-1310.025568.40951410.02555594 Jul-138.7327238.6511678.732722693 Aug-138.6674787.6618288.667478213 Sep-137.8629587.4676447.862958441 Oct-137.5467077.227027.54670722 Nov-137.2909586.4949817.290957697 2013 Forecasted Dec-136.6541766.535696.654176379 112.0377998 Our Model

12  We decided to break our prediction down by districts. District Data

13 Based on historical % of homicides per district within the past 11 years. Of the 112 Predicted Murders: District 1: 8 District 2: 2 District 3: 11 District 4: 6 District 5: 17 District 6: 28 District 7: 19 District 8: 13 District 9: 7 Other: 1 District Data

14  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

15  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

16 Conclusion  We predict 112 murders in 2013 Highest 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

17  https://www.ncjrs.gov/pdffiles1/nij/grants/211973.pdf https://www.ncjrs.gov/pdffiles1/nij/grants/211973.pdf  http://www.kmov.com/news/editors-pick/St-Louis- police--195189401.html http://www.kmov.com/news/editors-pick/St-Louis- police--195189401.html  http://forprin.dev.zoe.co.nz/files/pdf/Gorr_Olligschalg er_and_Thompson,_Short-term.pdf http://forprin.dev.zoe.co.nz/files/pdf/Gorr_Olligschalg er_and_Thompson,_Short-term.pdf  http://www.predpol.com/ http://www.predpol.com/  http://research.stlouisfed.org/fred2/series/MOSSURN http://research.stlouisfed.org/fred2/series/MOSSURN  http://cad.sagepub.com/content/49/3/339.full.pdf+ht ml http://cad.sagepub.com/content/49/3/339.full.pdf+ht ml  http://ajp.psychiatryonline.org/article.aspx?articleID= 172630 http://ajp.psychiatryonline.org/article.aspx?articleID= 172630 Sources

18  http://www.slmpd.org/press_room.html# http://www.slmpd.org/press_room.html#  http://www.slmpd.org/crime_stats.html http://www.slmpd.org/crime_stats.html  http://stlouis- mo.gov/government/departments/public- safety/neighborhood-stabilization- office/neighborhoods/neighborhood-maps.cfm http://stlouis- mo.gov/government/departments/public- safety/neighborhood-stabilization- office/neighborhoods/neighborhood-maps.cfm  http://www.areavibes.com/st.+louis- mo/neighborhoods/ http://www.areavibes.com/st.+louis- mo/neighborhoods/  http://bjs.ojp.usdoj.gov/content/pub/pdf/ics.pdf http://bjs.ojp.usdoj.gov/content/pub/pdf/ics.pdf  http://www.nytimes.com/2009/06/19/nyregion/19 murder.html?pagewanted=all&_r=0 http://www.nytimes.com/2009/06/19/nyregion/19 murder.html?pagewanted=all&_r=0  http://blogs.riverfronttimes.com/dailyrft/2013/03/j oseph_hawthorne_homicide_st_louis.php#more http://blogs.riverfronttimes.com/dailyrft/2013/03/j oseph_hawthorne_homicide_st_louis.php#more Sources

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