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Community Safety Scorecard: City of Los Angeles 2011

JCF Convening

Mina Brock

on 18 September 2013

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Transcript of Community Safety Scorecard: City of Los Angeles 2011

Community Safety Scorecard
City of Los Angeles
What is it?
•Advancement Project, in collaboration with the Violence Prevention Coalition (VPC), has developed a Community Safety Scorecard for the City of Los Angeles which simulates a report card with letter grades from A to F for each ZIP code in the City of Los Angeles using these 4 community conditions:
-violence levels
-the state of its schools
-risk factors
-protective factors
What are the goals of the report?
To develop a way to measure public safety
inequity among Los Angeles' communities
To develop a tool that tracks the impact of
ongoing violence reduction strategies over time
The Scorecard highlights where the community
and the public and private sectors need to concentrate their resources to ensure basic safety to neighborhoods that are most heavily impacted by violence.
What does the Scorecard
tell us?
Safety Score by ZIP code, City of Los Angeles
What does the Scorecard tell us?
Reveals public safety inequity in Los Angeles

Pinpoints the most safe & most unsafe ZIP codes

Offers data by categorizing risk, protective factors, & school

School conditions, risk factors, and protective factors are all closely interrelated and jointly inform the level of safety in a community
Despite the inequity, each community has a
unique array of assets.
More specifically....
When any one category of the
Scorecard is at an “F” the rest of
the categories are also
affected (ex: 90068).
Many of the ZIP codes graded “least safe” are located across multiple jurisdictions, i.e. cross-city
areas, unincorporated County areas, or multiple City Council districts (ex: 90058, 90044, 90002).
Some ZIP codes, where protective factors were high, and thus risk factors were expected to be low, actually had grades of a "D" or "F" in risk factors. A closer look at these ZIP codes shows how investments in a single community sector are not enough to improve outcomes in the rest of the sectors.
How can you use it?
This tool can be used in a number of ways:
Advocacy efforts to inform policymaking decisions
Promote investment into highest need areas & community sectors
Use recommendations to implement a CVRS
Use HealthyCity.org to view data
& overlay with other critical data
Logic Model Process
Ten root conditions of violence in a community
1) Lack of Targeted Suppression that Follows a Community Policing Model
2) Lack of Comprehensive Primary Prevention Infrastructure
3) Lack of Community Economic Investment Workforce Development & Family
Economic Success
4) Lack of Effective Re-entry Strategies & Support
5) Early Academic Failure & Lack of School Attachment
6) Family Isolation & Lack of Access to Support Structures
7) Lack of Community Cohesion to Improve Public Safety
8) Inadequate Government Coordination & Accountability
9) Poor Access to Quality Health & Mental Health Care Services
10) Normalization of Violence
Logic Model
Visual Representation
Root Condition

Subcategory of Root Conditions




Literature Review

Data Indicators
Gang related crime
Violent crime
Child abuse rate
High School Academic Performance Index (API)
High school truancy
Middle school truancy
High school graduation rate
Risk Factors
Percent families in poverty
Percent unemployment
Percent single parent families
Percent high school students scoring below basic in English
Percent middle school students scoring below basic in English
Protective Factors
Violence prevention services rate
Youth violence prevention nonprofit revenue per capita
Percent high school teachers with full credentials
Percent middle school teachers with full credentials
Percent active voting population
Advancement Project
Data & Analysis Limitations
Grades are relative to each other

External grading criterion was not used

Some community factors that could represent conditions on the ground
were not used, due to the unavailability of data (e.g. community cohesion)

A few ZIP codes had missing data, adjustments were made
Identified available data set indicators
Correlation analysis was completed to identify factors most strongly related to
safety outcomes
Indicators were grouped into the 4 categories
Each category has a minimum of 3 indicators
Data indicators were indexed to create a standardized score for each ZIP code
Factors were weighted equally to construct the final index score
Index score was ranked among all City of LA ZIP codes
Quintile system was used
Future of the Scorecard
Gold standard scoring criterion
Data indicators will be updated
May possibly be released periodically
Will conduct trainings for other
Methodology will be updated
Advancement Project
Call to Action
Public Health Approach at the Neighborhood Level
Outcomes to be Achieved
How the idea came about...
Slides that follow could be presented during the "interview" portion of the Webinar
Full transcript