PREDICTING THE 2013 SAINT LOUIS CITY HOMICIDE RATE SPENCER SCHNEIDENBACH SHAILESH LANJEWAR XUN ZHOU BEN HOLTMAN

BACKGROUND Annual homicide rates for 157 large US cities Analyzed for 30 years – 1976 to 2005 Factors Resource deprivation/concentrated poverty Higher income inequality Higher percentage of divorced adult male population Higher unemployment rates Study in 30 nations Significant association between poverty and homicide Sources:

DIVERSITY Characteristics of neighborhoods Very significant in predicting homicide Conclusion: immigrant concentration unrelated or inversely related to homicide language diversity consistently linked to lower homicide 15 years of data ( ) St. Louis Homicide rate related to neighborhood characteristics Patterns differ according to homicide subtypes – general altercation, felony and domestic Sources:

NATIONAL GANG TRENDS Source: FBI 2011 National Gang Threat Assessment – Emerging Trends

MISSOURI GANG TRENDS Source: FBI 2011 National Gang Threat Assessment – Emerging Trends

MISSOURI GANG TRENDS  Seems to be lower than other states with only 0-2 members per 1000 people  Rise in gang “promotional teams”  Increased gang use of social media directed towards youth  Presence small as it may be of 490 gangs according to the FBI Gang Threat Assessment Source: FBI 2011 National Gang Threat Assessment – Emerging Trends

DATA SELECTION PRINCIPLE Timeliness - Annually? Quarterly? Monthly? Sufficiency - Sample size – St Louis City, at least 5 years, the factors can have potential impact on criminal Level of detail or aggregation - Amount for reported criminal annually, criminal ratio distribute by district and possible influence factors such as poverty level, education attainment, population, Income etc Understandability - Readable for the crime data. Freedom from bias - How to avoid that? Keep it simple Decision relevance - How to determine the boundary? Geographical? How many factors are relevant to the criminal occurs geographically?

DATA SELECTION PRINCIPLE Comparability - Each city is individual case for analytic, avoid comparing the other cites’ data and cut off the data which influenced by abnormal factors. Reliability - We can not control, however there may be un-reported and un- detected crime which can influence the analysis Redundancy - Mulit-resources? Cost efficiency - Costs concern update data annually Quantifiability - Use Ratio level data Appropriateness of format - Which is the appropriate way to demonstrate

DIMENSIONALITY OF MODELS Representation -Reported crime Time Dimension -How much of the activity of decision environment is being considered Linearity of the Relationship -Determine if categorized data are linear or nonlinear Deterministic Versus Stochastic -Linear regression, Stochastic modeling Descriptive Versus Normative - Descriptive - used for prediction

DIMENSIONALITY OF MODELS Causality versus correlation -How to determine? - use criminal distribution graph and the other possible factor which has the positive or negative relate on them Methodology Dimension -Complete enumeration, algorithmic, heuristic simulations and analytical -Complete enumeration – large sample amounts required -Algorithmic – extremeness' value method -Heuristic - if math would not help -Simulation – external influence? Hard to identify -Analytical – speared parts for the whole process

MODELS WE CONSIDERED  Linear regression  Model based on Census, American Community Survey data  Predict crime based on population factors -Saint Louis Police Department Neighborhood Statistics -U.S. Census American Fact Finder Statistics -American Community Survey -Poverty Level -Educational Attainment -Lack of Core Family Stability Single Parent Families – Mothers with no husband present -Income -Race

MODELS WE CONSIDERED (CONT)  Linear Regression  Census data  Research only occurs once a decade  Hard to measure trends for predictions  American Community Survey  Broken down at the macro level (entire city)  Can’t measure by neighborhood, district  Conclusion:  Still useful for identifying problem areas inside a city  Best for a one-time “snapshot” to see what correlations exist and attack those problems  Largely outside the scope of what the SLMPD does

MODELS WE CONSIDERED (CONT)  Rolling average  Model based on past homicides  Weighs more recent data higher than other data  Pros  Data is easily accessible and accurate  Model is simple and pretty accurate  Cons  Does not predict big, one time events  Model data varies the more homicides are committed

MODELS WE CONSIDERED (CONT)  Rolling average  District vs. neighborhood  SLMPD uses districts  Most crime seems to be concentrated in several large areas  Districts it is  Quarters, months, years?  Years – too macro  Months – too micro – data is too wildly distributed  Measuring by quarters provides a nice balance between micro vs. macro and data accuracy

DECISION POINT  Rolling average it is  Regression model can’t be trended  Best model based on all available data

EXAMPLE MODEL  Rolling average it is  Best model based on all available data  Our model:  Prediction based on last 4 quarters  Last quarter: weighted by 0.4  2 nd last: 0.4  3 rd last: 0.1  4 th last: 0.1

MEASURING THE MODEL

OUR PREDICTIONS

CONCLUSION  Prediction is difficult

QUESTIONS?

SOURCES FBI 2011 National Gang Threat Assessment – Emerging Trends. services/publications/2011-national-gang-threat-assessmenthttp:// services/publications/2011-national-gang-threat-assessment Saint Louis Police Department Statistics - American Fact Finder - Saint Louis Homicide Map -