predicting the 2013 saint louis city homicide rate

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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: http://www.sciencedirect.com/science/article/pii/S0049089X10001882http://www.sciencedirect.com/science/article/pii/S0049089X12002554

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 (1980-1994)• St. Louis• Homicide rate related to neighborhood characteristics• Patterns differ according to homicide subtypes – general

altercation, felony and domestic

Sources: http://hsx.sagepub.com/content/13/3/242.shorthttp://onlinelibrary.wiley.com/doi/10.1111/j.1533-8525.2003.tb00536.x/abstract

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 PRINCIPLETimeliness

- 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 isRegression model can’t be trendedBest model based on all available data

EXAMPLE MODEL

Rolling average it is Best model based on all available dataOur model:

Prediction based on last 4 quarters Last quarter: weighted by 0.4 2nd last: 0.4 3rd last: 0.1 4th last: 0.1

MEASURING THE MODEL

1 2 3 4 5 6 7 8 90.0

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Homicide Predictions vs. Actual for Q4-2012

Pred Actual

Districts

Hom

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Microsoft Excel Worksheet

OUR PREDICTIONS

1 2 3 4 5 6 7 8 90.0

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Homicide Predictions for Q1-2013

Districts

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CONCLUSION

Prediction is difficult

QUESTIONS?

SOURCES• FBI 2011 National Gang Threat Assessment – Emerging Trends. http://

www.fbi.gov/stats-services/publications/2011-national-gang-threat-assessment

• Saint Louis Police Department Statistics - http://www.slmpd.org/Crimereports.shtml

• American Fact Finder - http://factfinder2.census.gov/faces/nav/jsf/pages/guided_search.xhtml

• Saint Louis Homicide Map - http://blogs.riverfronttimes.com/dailyrft/2013/01/st_louis_city_homicide_map_nextstl.php