340 N 12th St, Suite 402 Philadelphia, PA 19107
215.925.2600 [email protected]
www.hunchlab.com
2.0 - Getting Started
Agenda • Technical Overview
– SaaS
– Authentication
– End-user Requirements
• Setup
– Required Data
– Uploading Crime Data
– Defining Crime Models
• Additional Data Sets
Places
People
Patterns } Prioritization
Places
People
Patterns } Prioritization
SaaS Architecture
Software as a Service Model
• Subscription – Bug fixes – Updates – Hosting / backups / etc. – 2nd tier support – Training
• Amazon Web Services infrastructure – High availability – Elastic resources
• User load • Model building processes
AWS Infrastructure & Security
• AWS data centers – Data residency
• US or EU
– Physical security • AWS employees with permission / 2 factor auth
– Logical access • Azavea employees with permission / 2 factor auth
– Redundant network / power – Continuous penetration testing – 3rd party evaluations
• Best-of-breed services
Authentication
Authentication
• Options – Standalone
• HunchLab managed credentials
– Integrated • Active Directory / LDAP compatible • Requires SaaS application to contact internal servers
• Security Considerations – CJIS requires 2 factor authentication – HunchLab can provide this in standalone mode
Authentication
End-user Requirements
Client Requirements / Browsers
• Core requirements – Modern browser – Network connectivity
• TLS 1.1+
– HTML5 app • Geolocation API (GPS for Sidekick)
• Browsers – Desktop
• Internet Explorer: last 2 releases • Firefox: last 2 rapid releases and extended support release • Chrome: last 2 rapid releases
– Mobile • Safari 7 for iOS • Chrome current rapid release for Android
Client Requirements / Browsers
Client Requirements / Browsers
• TLS version support – http://en.wikipedia.org/wiki/Transport_Layer_Security#Web_browsers
Client Requirements / Browsers
• Testing – http://test.hunchlab.com
Required Data
Required Data
• Boundaries – ShapeFile format – Uploaded in application – Types
• Jurisdiction boundary (required) • Organizational layers (divisions, districts, etc.)
• Event data (crimes, calls for service) – CSV format – Uploaded via API
Required Data
• Event data (crimes, calls for service) – CSV format
• First row is headers with names as below
– Columns • datasource (string) - identifies data source
– example: rms
• id (string) - unique identifier for event within data source – example: 1
• class (string) - class(es) for event separated by pipe – example: agg|1|23
• pointx (numeric) – longitude – example: -105.0255345
• pointy (numeric) – latitude – example: 39.7287494
• address (string) - street address – example: 340 N 12th Street
Required Data
• Event data (crimes, calls for service) – Columns (continued)
• datetimefrom (ISO8601 datetime) - start time – example: 2012-01-01T13:00:00Z
• datetimeto (ISO8601 datetime) - end time – example: 2012-01-01T13:00:00Z
• report_time (ISO8601 datetime) - report time – example: 2012-01-01T13:00:00Z
• last_updated (ISO8601 datetime) - record update time – example: 2012-01-01T13:00:00Z
Required Data
• Event data (crimes, calls for service) – Upload via API
• Allows automation of upload process • Workflow
– Query your database for recent changes – Transform into CSV format – POST CSV to HunchLab URL – Check for import to complete
– Example scripts • https://github.com/azavea/azavea-hunchlab-examples
Crime Models
Crime Models
• Generate predictions – Automatically built on a regular basis
• Represents one or more crime classes • Choices to make:
– Crime classes – Color – Severity weight – Patrol Efficacy
Crime Models
• Which crimes to model? – Start with serious events
• Part 1s, etc.
– Add ‘problem’ crime types for your department
• How many models? – Aim for up to 10 models
• Single crime type vs. combination? – Does the event happen often enough on its own?
• Example: Homicides as part of Violence
– Is the strategy the same as related crime types? • Example: Homicides vs. Aggravated Assaults
Lincoln Example
# Assaults x
$87,238
# Burglary x
$13,096
# MVT x
$9,079
Sum to Predicted Cost of Crime
# Rape x
$217,866
# Robbery x
$67,277
Crime Models
• Severity weights – How important is it to prevent these crimes? – RAND cost of crime
• http://www.rand.org/content/dam/rand/pubs/occasional_papers/2010/RAND_OP279.pdf
– NIH publications • http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2835847/table/T5/
Crime Models
Crime Models
• Patrol Efficacy – What proportion of these events are preventable via patrol
activities? • Example: rape (stranger vs known assailant)
– How effective is patrol against the preventable events? • Example: street crimes vs indoor crimes
– Expressed as percent (0-100%) – Examples:
• Robbery: 50% • Residential Burglary: 20% • Rape: 5%
Crime Models
1. Define set of models via crime classes 2. Assign severity weights 3. Assign patrol efficacy values 4. Assign colors
• Overall Goal – Craft a set of models that generate predictions for real
opportunities for your officers to prevent crime.
Optional Data
Optional Data
• Geographic POIs – Points, lines, polygons (Shapefile) – Examples
• Schools • Transit stops • Parks • Bars
• Temporal feeds – Schedules (CSV) – Examples
• School calendar • Sporting events
Choosing Data Sets
• Usefulness vs. Complexity – How strong do you believe the correlation is?
• Example: bars vs hospitals
– How big is the data set? • Example: schools vs bus stops
– How often does the data change? • Example: hospitals vs bars
• Availability – Start with what you have
• Police stations, fire stations, public housing
– Layer in data from other city departments • Schools, bus stops, liquor licenses
– Fill in gaps (once things are going)
Choosing Data Sets
• Risk Terrain Modeling – Literature reviews
• http://www.rutgerscps.org/pubs.htm
– Factors in 5 or more reviews: • Drug Activity • Bars • Nightclubs • Schools • Transportation Hubs
Agenda • Technical Overview
– SaaS
– Authentication
– End-user Requirements
• Setup
– Required Data
– Uploading Crime Data
– Defining Crime Models
• Additional Data Sets
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
