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340 N 12 th St, Suite 402 Philadelphia, PA 19107 215.925.2600 [email protected] www.hunchlab.com Missions: Under the Hood
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340 N 12th St, Suite 402 Philadelphia, PA 19107
215.925.2600 [email protected]
www.hunchlab.com
Missions: Under the Hood
Amelia Longo Business Development Associate [email protected] 215.701.7715
Jeremy Heffner HunchLab Product Manager [email protected] 215.701.7712
Places
People
Patterns } Prioritization
Predictive Missions
It’s the fourth Tuesday in January and school is in session. There were 3 burglaries and 2 robberies yesterday. Six bars, three take-out stores, and a school are in the neighborhood. The forecast is 17° with cloudy skies. Where do you focus your 2 vehicles?
How would you do it?
Analyst Process
• Identify relevant factors – Training / Literature – Experience
• Use heuristics – high concentration of past crime è higher risk – near a bar on a Friday night è higher risk – near the police station è lower risk – concentration of ex-offenders è higher risk – near transit stops è higher risk
?
How HunchLab Works
A computer system designed to learn how to accomplish a task by using historic data sets. There are different ways (algorithms) to accomplish this training process.
term: machine learning
The step-by-step procedure to accomplish a given calculation. Different algorithms have different qualities. Algorithms are used to train a machine learning model.
term: algorithm
Overall Process
1. Generate training examples of outcomes
2. Enrich with relevant variables
3. Build models
4. Evaluate accuracy
5. Select best performing model
Generate Examples
~ 500 ft cells & 1+ hour time slices
Data Volume
• Space – Lincoln, NE is 90 sq miles – 500 ft cell size creates 12,000 cells
• Time – 3 years of data – 1 hour resolution – 26,000 hour blocks
• Space x Time – 312,000,000 hour block cells (examples)
Data Volume
• Space – Lincoln, NE is 90 sq miles – 500 ft cell size creates 12,000 cells
• Time – 3 years of data – 1 hour resolution – 26,000 hour blocks
• Space x Time – 312,000,000 hour block cells (examples)
• Sampling FTW! – Outcomes are sparse (small % of examples have crimes) – Sampling strategy preserves crime events
Representing Crime Theories
• Crime predictions based on: – Baseline crime levels
• Similar to traditional hotspot maps
– Near repeat patterns • Event recency (contagion)
– Risk Terrain Modeling • Proximity and density of geographic features • Points, Lines, Polygons (bars, bus stops, etc.)
– Collective Efficacy • Socioeconomic indicators (poverty, unemployment, etc.)
Predictive Missions
• Crime predictions based on: – Routine Activity Theory
• Offender: proximity and concentration of known offenders • Guardianship: police presence (AVL / GPS) • Targets: measures of exposure (population, parcels, vehicles)
– Temporal cycles • Seasonality, time of month, day of week, time of day
– Recurring temporal events • Holidays, sporting events, etc.
– Weather • Temperature, precipitation
Predictive Missions
Representing Crime Theories Risk Terrain Modeling
Gun shoo)ngs example Source: Rutgers, h8p://www.rutgerscps.org/rtm/irvrtmgoogearth.htm
crimes prior7 prior364 dayssincelast bardist dow
0 0 0 365 >2000ft Monday
0 0 1 234 >2000ft Monday
1 1 3 3 750ft Tuesday
0 0 2 43 500ft Wednesday
2 0 2 74 500ft Friday
Representing Crime Theories Aoristic Analysis
crimes probability
0 0
1 a
2 b
3 c
4 d
crimes weights prior7 prior364 dayssincelast bardist dow
0 1 0 0 365 >2000ft Monday
0 1 0 1 234 >2000ft Monday
0 0.5 1 3 3 750ft Tuesday
1 0.5 1 3 3 750ft Tuesday
0 0 0 2 43 500ft Wednesday
0 0.13 0 2 74 500ft Friday
1 0.32 0 2 74 500ft Friday
2 0.55 0 2 74 500ft Friday
Building Models
Models
• Baseline – Baseline models (6)
• Counts – 28 day – 56 day – 364 day
• Kernel Densities – 28 day – 56 day – 364 day
– HunchLab models • Variations of a stacked ensemble:
– examples è gradient boosting machine (gbm) è y/n probabilities
– y/n probabilities è generalized additive model (gam) è counts
A machine learning algorithm that recursively partitions a data set based upon variable values forming a tree-like structure.
term: decision tree
crimes prior7 prior364 dayssincelast bardist dow
0 0 0 365 >2000ft Monday
0 0 1 234 >2000ft Monday
1 1 3 3 750ft Tuesday
0 0 2 43 500ft Wednesday
2 0 2 74 500ft Friday
A machine learning algorithm that uses a series of weaker models (typically decision trees) that are trained upon the residuals of prior iterations (boosting) to form one stronger model.
term: gradient boosting machine (GBM)
Build Decision Tree 1
Predict with 1
Calculate errors
1 Build Decision Tree 2
Predict with 1 & 2
Calculate errors
2 Build Decision Tree 3
Predict with 1-3
Calculate errors
3 …
A regression model that fits smoothed functions to the input variables. Compare to a generalized linear model which fits just a single coefficient to each variable.
term: generalized additive model (GAM)
HunchLab Model Building
1. Build a GBM – examples è gradient boosting machine è y/n probabilities
312 million
4 million
1 mil 1 mil 1 mil 1 mil
Sampling
4 folds
GBM
}
1 mil
Evaluate
43
200
312 million
4 million
Sampling
GBM 43
HunchLab Model Building
1. Build a GBM – examples è gradient boosting machine è y/n probabilities
• Segment examples into several folds – For each fold build a GBM model on the rest of the data – For each iteration in the GBMs:
» Randomly sample a portion of the data (stochastic) » Adjust weights of observations (adaptive boosting)
• Determine how many iterations result in the most accurate model • Build a GBM on all of the data for that many iterations
HunchLab Model Building
2. Build a GAM – y/n probabilities è generalized additive model è counts
• Transforms (“bends”) GBM output into counts • Calibrates count levels with other key variables
Example
Lincoln NE
Lincoln Assaults
Lincoln Assaults
Lincoln Assaults
Lincoln Assaults
Lincoln Assaults
Selecting Models
Selecting Models
1. Build models holding out last 28 days of data
2. Score each model
– Combine different metrics into a selection score
3. Select best score
4. Rebuild the best model (including last 28 days data)
Cells ranked highest to lowest
A map represented as a grid of cells
0% 100%
Crime Location
Cells ranked highest to lowest
0% 100%
Percent of Patrol Area to Capture All Crimes
Average Crime Rank
0%
50%
100%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Percent of Crimes Captured vs. Percent of Patrol Area
0% 20% 40% 60% 80% 100%
Assault
Burglary
MVT
Ra
pe
Robb
ery
Percent of Patrol Area to Capture All Crimes
0
0.1
0.2
0.3
0.4
0.5
0.6
Assault Burglary MVT Rape Robbery
Average Crime Rank
0
0.2
0.4
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16
Perc
ent
of C
rime
s C
ap
ture
d
Percent of Land Area
Theft of Motor Vehicle
Overall Process
1. Generate training examples of outcomes
2. Enrich with relevant variables
3. Build models
4. Evaluate accuracy
5. Select best performing model
Our Solution • Learns from several years of your data
• Automatically determines which theories apply
– more than just crime data
• Prevents over-fitting
• Calibrates predictions
• Selects a model based upon a blind evaluation
– prioritization and count-based metrics
Our Solution • Learns from several years of your data
• Automatically determines which theories apply
– more than just crime data
• Prevents over-fitting
• Calibrates predictions
• Selects a model based upon a blind evaluation
– prioritization and count-based metrics
• But it still cannot make your morning coffee
Additional Information • How did HunchLab originate?
• How does HunchLab represent crime theories?
• What data is needed?
• How does the modeling work specifically?
Questions
340 N 12th St, Suite 402 Philadelphia, PA 19107
215.925.2600 [email protected]
www.hunchlab.com
340 N 12th St, Suite 402 Philadelphia, PA 19107
215.925.2600 [email protected]
www.hunchlab.com
Amelia Longo Business Development Associate [email protected] 215.701.7715
Jeremy Heffner HunchLab Product Manager [email protected] 215.701.7712
