4. What is SAVI?
SAVI helps organizations and
individuals make data-informed
decisions.
• Reliable Data
• Actionable Information
• Analysis and Visualization Tools
• Capacity Building
www.savi.org
6. What is SAVI?
• A dynamic GIS-based community information system
that provides:
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Data from over 30 data providers
A Website to access and analyze data in charts, maps,
reports, and tables
User support
Education program
• SAVI = Social Assets and Vulnerabilities Indicators
www.savi.org
7. SAVI Database
• The SAVI Database can be divided into three
components.
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Social Assets
Social Vulnerabilities
Geographies (Boundaries & Service Areas)
• These data can be used to understand and
model communities.
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Formats: Maps, charts, and tables
20+ years of data: 1988 – 2013
www.savi.org
8. SAVI Database
• SAVI provides data in the following major
categories:
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Arts, Culture, and Recreation
Demographics
Economy
Education
Environment
Health
Housing
Political and Administrative Boundaries
Public Assistance
Public Safety
Sites, Programs, and Agencies (Assets)
Transportation and Mobility
www.savi.org
9. Social Assets
• An asset is something that can be
a benefit to your community.
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Place of Worship
School
Youth Program
Hospital
and more…
• Assets are displayed as specific
points on a map.
• Some sites represent
vulnerabilities such as hazardous
waste sites.
www.savi.org
Community Centers
10. Social Vulnerabilities
• Social vulnerabilities are data that reveal
Social Vulnerabilities
needs in a community.
Crimes
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Crime
Poverty
Housing
and more…
www.savi.org
11. Social Vulnerabilities
• SAVI vulnerability data are
summarized by:
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Counties
Townships
School Corporations
Neighborhoods
Census Tracts
Census Block Groups
and more…
www.savi.org
13. SAVI Geographies
SAVI organizes data by geographic areas used by the
Census Bureau in its data collection and tabulation
operations:
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States
Metropolitan Statistical Areas (MSAs)
Counties
Cities
Townships
Census Tracts (~4,000 people or 1,500 housing units)
Block Groups (~1,500 people or 550 housing units)
Some data are also available using local geographies.
www.savi.org
14. Families Living in Poverty
By Townships
By Counties
By Census Tracts
(Marion County)
www.savi.org
Source: SAVI Community Information System and 2000 Census
By Block Groups
(Marion County)
15. What Questions Can SAVI Help Answer?
• Where are vulnerable populations?
• What are the demographic and social issues in
a particular neighborhood?
• Where are existing resources?
• How have things changed over time?
• Where should resources be targeted?
www.savi.org
16. Most Common Uses of SAVI
• Community Assessment
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Identifying need for services
Understanding community issues
of concern
• Grant Application Development
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Statement of need
Plan of work (identifying
collaborating partners)
Evaluation (measuring outcomes)
www.savi.org
17. Most Common Uses of SAVI
• Strategic Planning
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Membership/client mapping
Understanding the communities where members/clients live
Understanding need for services
Communicating with stakeholders (visualizing the problem)
• Research
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Mapping where clients, patients, students live
Understanding the neighborhood context in which these students live
(demographics, educational attainment, environmental factors,
infrastructure, economy, etc.)
Understanding public health issues such as obesity, health disparities,
STDs, etc.
www.savi.org
19. Data-Driven Decision Making Process
1. Framing the Issue
2. Question Development
3. Data Collection
4. Data Analysis
5. Data Interpretation
6. Decision Making
7. Communication
www.savi.org
Source: Measuring Success
20. 1. Framing the Issue
• How do you know what concerns to study and
understand better?
• What do you want to accomplish?
• What is your overall objective?
Ask the following questions about your issue:
How did this issue come to my attention?
What communities and/or populations are impacted?
How does the issue relate to your organization’s
mission?
www.savi.org
Source: Measuring Success
21. 2. Question Development
• Question development is a key step that organizations
often skip. This is a crucial step and if missed can
complicate the remaining steps of the process and can
make it difficult to draw clear conclusions.
Keep these in mind when developing your question(s):
Be specific and measurable.
Identify the need or the “state of the community.”
Determine how it changes over time. Getting better or worse?
Find out the extent of the problem.
Who is impacted? What communities are impacted?
What level of geographic detail is necessary?
Consider community “assets,” not just “needs” (what resources
already exist to address this problem?)
www.savi.org
Source: Measuring Success
22. Examples of “Good” vs. “Bad”
Questions
• Can you tell me everything there is to
know about the demographics in the
Mid-North Neighborhood?
• How have the demographics of MidNorth changed over the past 5-10
years, and how does that impact my
program’s service delivery? Is our
service area boundary still appropriate
given the changes?
www.savi.org
23. 3. Data Collection
• Use these tips to collect the right data:
Determine what data you need. Don’t just go after what’s
easiest but create a plan to gather the data you truly
need.
Seek guidance from subject matter experts.
Look for existing quantitative data then determine if
additional collection methods (focus groups, surveys, etc.)
are necessary.
Be aware of data quality and limitations.
Don’t forget the data already within your organization.
Be aware of the timeliness of data. Historical data is
needed when doing trend analysis.
If the ideal data isn’t available, consider proxy data to
stand in.
www.savi.org
Source: Measuring Success
24. 4. Data Analysis
• You don’t need to be a statistician to successfully analyze and later
interpret data.
You just need to know how to:
Digest and assess statistical data provided
Calculate summary statistics (average, median, standard deviation and
distributions)
Look for anomalies, trends, relationships, and patterns
Use maps to look geographically at the issue
• Contextualization is critical. Compare your data several ways
Longitudinally to see history and trends
Demographically
Against peers
www.savi.org
Source: Measuring Success
25. 5. Data Interpretation
• This is where you make meaning
out the data analysis to answer
the “So what?” question.
What does the data reveal about the
issue? Did it answer your questions?
Did you identify any gaps or
opportunities?
• Translate into understanding the
questions trying to answer
www.savi.org
Source: Measuring Success
26. 6. Decision Making
• Data is there to inform your decision-making process, but often times, it
leads to more questions than it does answer your original question
• What were revealed in the data: Where there any surprises? If so, did it
change your understanding of the issue?
• What were the gaps and opportunities and how does that relate to your
mission?
• Use the data analysis results to develop options (e.g., choose 3
underserved neighborhoods where we might target our program), which
can be the starting point for gathering stakeholder input
• What else do you need to know to make a decision? Do you need to
revisit your questions and do more data collection?
• Examples
Strategic planning – engage community, assess mission and goals
Grant Development – justify need
www.savi.org
Source: Measuring Success
27. 7. Communication
• In the final stage of the process, you must focus on
communicating the results of your analysis. Not everyone
will be receptive to the information, especially if it
highlights weaknesses or challenges anecdotal beliefs
commonly held in the organization.
For each stakeholder group:
Create a “story” that helps your audience understand what the
data says.
Consider using data visualizations (charts, maps, infograms,
etc.) for communication.
Use data to communicate more effectively.
www.savi.org
Source: Measuring Success
30. 2. Question Development
• Where are the assets in NEC?
• Is crime getting worse?
• Can education lead a turnaround?
• Is unemployment in NEC worse than
Marion County?
• Where are the concentrations of
poverty in NEC?
www.savi.org
31. 3. Data Collection
Data sources
• IMPD
• Decennial Census
• American Community Survey
• SAVI
• Indiana Dept. of Education
• U.S. Postal Service
• MIBOR
• Marion County Assessor
• Others
www.savi.org
33. 5. Data Interpretation
• What did the data tell us?
Age
Gender
Crime
Education
www.savi.org
34. 6. Decision Making
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Housing
Culture and Livability
Education
Safety and Crime
Jobs and Training
Business Growth
Youth Engagement
Health and Wellness
Connectivity
www.savi.org
Nine “Action Teams” were
established based on what
the data told us
37. Audience Participation
• Pose a problem
• Formulate the questions
• Walk through the 7 step data-driven decision
making process
www.savi.org
Source: Measuring Success
39. Contact Information
Jay Colbert
Tammy Robinson
GIS Project Manager
The Polis Center at IUPUI
317.278.9212
jtcolber@iupui.edu
Managing Principal
Engaging Solution, LLC
317.283.8300
tammy@engagingsolutions.net
