San Francisco Crime

DSO 510 Business Analytics | Group Project

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Crime in San FranciscoDSO 510 Business Analytics | Group Project

Phase 4 Presentation

Andrew Chen | Yile Wu | Chi Zhang | Chulsoon Pak


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PHASE I

Define business analytics proposal, data required, data analysis approach,

and decision making and innovation framework


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• 2014 Population: 852,4691

• 13th most populous city in the nation

• Separated into 10 districts

• Top global innovation center, with

highest concentration of technology-

related jobs in the U.S.

• Crime in San Francisco has historically

been higher than the U.S. average• Crime Index of SF is rated 3 out of 100

(safer than 3% of other U.S. cities)2

BACKGROUND: SAN FRANCISCO

1. United States Census Bureau, July 2014

2. "Crime rates for San Francisco, CA", NeighborhoodScout, 2013


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In order to make San Francisco a safer place,

we aim identify factors that promote criminal

behavior to predict crime more accurately.

GOAL DEFINITION


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DEFINING OUR VARIABLES

Dependent Variables

1. Number of Crimes

2. Time of Crime

3. Date of Crime

4. Severity of Crime

5. Type of Crime

6. Location of Crime

Independent Variables

1. Day of Week

2. Season of Year

3. Weather

4. Daylight

5. Income Level of District

6. Average Housing Price of District

7. Age Composition of District

8. Population density

9. Degree of Urbanization

10. Modes of Transportation

11. Level of Education

12. Divorce rate of Families


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• San Francisco Crime Data:

• https://data.sfgov.org/Public-Safety/

• 700,000+ data points (5 years of data)

• San Francisco Weather, Population,

Housing, Hazard Risk, and Demographics

• www.sfclimatehealth.org/

• http://aa.usno.navy.mil/data/docs/RS_

OneYear.php

• San Francisco Housing, Income Level,

Employment, and Transportation by

District

• www.sf-planning.org

DATA COLLECTION

https://data.sfgov.org/Public-Safety/

http://www.sfclimatehealth.org/

http://aa.usno.navy.mil/data/docs/RS_OneYear.php

http://www.sf-planning.org/


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INTERPRETATION & ACTION

1. Identify to what degree different variables contribute to crime

2. Predict probability and severity of crimes in terms of time and location

Implementation

1. Assist SFPD in efficient deployment of its police force

2. Integrate data with mapping algorithms to provide the safest real-time routes

3. Organize anti-crime education in high crime areas (how to handle crimes under different

situations)

4. Utilize data in product development and marketing of security-related products

5. Enhance San Francisco city-planning to reduce crimes


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PHASE II


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In order to make San Francisco a safer place,

we aim identify factors that promote criminal

behavior to predict crime more accurately.

GOAL DEFINITION


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Dependent Variables

1. Number of Crimes per Day

2. Number of Crimes per Month

3. Time Slot of Crime

4. Date of Crime

5. Severity of Crime

6. Location of Crime


1. Day of Week

2. Month

3. Weather

4. Daylight





9. Employment of District


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SUMMARY STATISTICS

• 5 Years of Data• From August 2010

• To August 2015

• 726,245 Crimes

Reported

Monthly Statistics


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MONTHLY CRIME DATA (2010 – 2015)


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CRIME PATTERNS BY MONTH OF YEAR


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WHICH CRIMES ARE MOST FREQUENTLY

COMMITTED?

Top 5 Crimes*

1. Theft

2. Assault

3. Vandalism

4. Drug Violation

5. Vehicle Theft

*Other Offenses, Non-Criminal

Offenses, and Warrants are excluded


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CRIMES THAT DEMAND GREATER ATTENTION

Assault,

Robbery,

Missing Person

Theft,

Vandalism

Forcible Sex Offenses,

Murder,

Kidnapping

Disorderly Conduct,

Gambling,

Loitering

HIGH FREQUENCY

LOW FREQUENCY

HIGH

SEVERITY

LOW

SEVERITY


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CRIMES PER DAY OF THE WEEK

• Friday and Saturday’s have

the most crimes committed

– Late night parties/Events

• Sunday and Monday’s have

the least crimes committed

– Church, Family gatherings

– Back to Work/School


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CRIME PER DISTRICT

• Number of Crimes per District

• Some districts have significantly higher crime than others

• A good indicator to help SFPD deploy police forces by districts


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INNER JOIN WITH DAYLIGHT DATA

Crime Data Sunrise and Sunset Data

Inner join by date


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DAYLIGHT AFFECTS SOME TYPES OF CRIMES

• Crime breakdown based on day or nighttime (in percentages)

– Data eliminated our initial hypothesis that crimes are more likely committed during the night


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LOOKING AHEAD

Data Manipulation

• Clean up and join other demographic data to existing data

• Categorize meaningful variables into numeric values in order to

run further statistical models

• Assign values for severity and frequency of each crime

Further Insights

• Dig deeper into crimes by district, day of week, and time of day

• Produce a spatial map of crime


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PHASE III


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Dependent Variables

1. Number of Crimes per Day

2. Number of Crimes during the Day

3. Number of Crimes during the Night

4. Number of Crimes per Month

5. Time Slot of Crime


1. Day of Week

2. Month

3. Average Temperature

4. Precipitation

5. Daylight





10. Employment of District


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TEN CRIMES TO FOCUS ON

• Weighted based on frequency and severity of crime sentence

Frequency Low.yr High.yr Avg.yr Weight

LARCENY/THEFT 168,901 0 25 13 2,136,598

ASSAULT 62,449 1 25 13 811,837

DRUG/NARCOTIC 31,180 1 40 20 631,395

ROBBERY 18,652 15 30 23 419,670

BURGLARY 29,020 3 20 12 333,730

SEX OFFENSES,

FORCIBLE

3,927 20 100 60 235,620

FRAUD 14,237 1 25 13 185,081

VEHICLE THEFT 31,002 5 5 5 155,010

KIDNAPPING 2,162 0 100 50 108,208

WEAPON LAWS 7,444 0 20 10 74,812


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CORRELATIONS


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LINEAR REGRESSION MODEL


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LINEAR REGRESSION MODEL

• Dependent Variable:

• Total Daily Crime

• Independent Variables:

• Day of Week

• Average Temperature

• Precipitation

• Significance level: <.0001

• R-Squared Value: 0.1956


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RESIDUALS ANALYSIS


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ANOVA


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TEN CRIMES TO FOCUS ON

• Weighted based on frequency and severity of crime sentence

Frequency Low.yr High.yr Avg.yr Weight

LARCENY/THEFT 168,901 0 25 13 2,136,598

ASSAULT 62,449 1 25 13 811,837

DRUG/NARCOTIC 31,180 1 40 20 631,395

ROBBERY 18,652 15 30 23 419,670

BURGLARY 29,020 3 20 12 333,730

SEX OFFENSES,

FORCIBLE

3,927 20 100 60 235,620

FRAUD 14,237 1 25 13 185,081

VEHICLE THEFT 31,002 5 5 5 155,010

KIDNAPPING 2,162 0 100 50 108,208

WEAPON LAWS 7,444 0 20 10 74,812


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PHASE IV


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BINARY LOGISTIC REGRESSION


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PREDICTIVE MODELING


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PREDICTIVE MODELING


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PREDICTIVE MODELING


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PREDICTIVE MODELING


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PREDICTIVE MODELING

Date post:	22-Jan-2018
Category:	Data & Analytics
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San Francisco Crime

Data & Analytics