340 N 12th St, Suite 402 Philadelphia, PA 19107

215.925.2600 info@azavea.com

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 info@azavea.com

www.hunchlab.com

Amelia Longo Business Development Associate alongo@azavea.com 215.701.7715

Jeremy Heffner HunchLab Product Manager jheffner@azavea.com 215.701.7712