 Looked at Regression Too Small Sample Size  Sought Correlations Too 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 NumberNeighborhood Violent Crime /100k High School GradRatePopulation Poverty Level People Per Household Central462Columbus Square2,79551%1, %2.8 Central4 & 579North Riverfront2,79563%2, %2.5 North664Near North Riverfront2,79561%2, %1.7

 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

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 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 JanuaryFeb.MarchAprilMayJuneJulyAugustSept.Oct.Nov.Dec.Total TOTAL 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

 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

 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  Predicts approximately 112 homicides in St. Louis City. DateMurdersDeseasForecast 2012 Actual Feb Mar Apr Forecasted May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Forecasted Dec Our Model

 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: 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

 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 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

  police html police html  er_and_Thompson,_Short-term.pdf er_and_Thompson,_Short-term.pdf    ml ml  Sources

   mo.gov/government/departments/public- safety/neighborhood-stabilization- office/neighborhoods/neighborhood-maps.cfm mo.gov/government/departments/public- safety/neighborhood-stabilization- office/neighborhoods/neighborhood-maps.cfm  mo/neighborhoods/ mo/neighborhoods/   murder.html?pagewanted=all&_r=0 murder.html?pagewanted=all&_r=0  oseph_hawthorne_homicide_st_louis.php#more oseph_hawthorne_homicide_st_louis.php#more Sources