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The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz de Martinez, PhD, MLS, CPP Ruth Gassman, PhD Desiree Goetze, MPH National Prevention Network Annual Conference Lexington, Kentucky August 28, 2006
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Page 1: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention:

The Case of Tobacco

Indiana Prevention Resource CenterBarbara Seitz de Martinez, PhD, MLS, CPP

Ruth Gassman, PhDDesiree Goetze, MPH

National Prevention Network Annual Conference

Lexington, KentuckyAugust 28, 2006

Page 2: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

What You Will Learn:

• Components of GIS system and costs• How GIS Can Help You with Program Planning

– Obtain Demographic Background– Profile Needs, Resources– Locate Problem Area or Target Audience– Inform Decisions about Strategy Selection– Enhance Cultural Competency– Obtain Funding

• How GIS Can Help You with Program Evaluation– Create a Risk/Protection Surveillance System– Track Change

• How GIS Can Help You Do Research– Conduct Research to identify relationships among

environmental and health variables

Page 3: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

I. Components of a GIS System

Minimal Equipment and

Personnel Skill Requirements

Page 4: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stages for Data Import/Analysis

Stage 3: Complex data import & analyze

Stage 2: Simple data imports & analysis

Stage 1: Extract data

Page 5: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage One

Objective: GIS to Inform Program Planning– Identify Problem Area or Find Target Audience– Obtain Demographic Background– Inform Decisions about Strategy Selection– Enhance Cultural Competency

Equipment: Computer Hardware and Software – Standard Desktop/Laptop and Printer– GIS software: MapInfo and PCensus for MapInfo. Or

ArcView equivalentData: Purchased Databases

– AGS Core Demographics, Consumer Spending, and MRI Lifestyle Variables or Claritas Equivalent

Kinds of Skills (Capacity Building)– Computer Literacy and Intro to Microsoft Excel

Page 6: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Software Tools

MapInfo, PCensus, Maploader

(1)

Page 7: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Data Complexity

Purchased GIS data

(1)

Page 8: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Examples of Data

AGS, Claritas™Map files

(1)

Page 9: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Skill Complexity

Basic ComputerAnd Printer

(1)

Page 10: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage Two

Objectives: GIS to Monitor Program Effectiveness– Create a Risk/Protection Surveillance System– Track Change

Additional Equipment: Geocoding Software – MapMarker Geocoding Software– Color Printer

Additional Data: – Local program and local geographic location data to be

imported

Additional Skills (Capacity Building)– Patience and precision– Microsoft Excel and some Microsoft Access preferable

Page 11: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Software Required

MapInfo, PCensus, Maploader

(1)

Mapmarker Geocoding software, Excel & Access

(2)

Page 12: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Data Complexity

Purchased GIS data

(1)

Imported data (free or purchased)

(2)

Page 13: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Examples of Data

AGS, Claritas™,Map files

(1)

Program Data, Address Data, Health Data (public

or purchased)(2)

Page 14: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Skills Required

Basic Computer(1)

Geocoding, Excel and Access

(2)

Page 15: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage Three

Objectives: GIS to Support Research– to study relationships among environmental and health

variables

Additional Equipment: Geocoding Software – SPSS Software

Additional Data (to be imported): – Local data– Public data– Purchased data

Additional Skills (Capacity Building)– Excellent Microsoft Excel and Access skills– Excellent statistical analysis and SPSS skills

Page 16: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Software Required

MapInfo, PCensus, Maploader

(1)

Mapmarker Geocoding software, Excel & Access

(2)

SPSS (3)

Page 17: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Data Complexity

Purchased GIS data

(1)

Imported data (free or purchased)

(2)

More complex imported data(free or purchased)

(3)

Page 18: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Examples of Data

AGS, Claritas™(1)

Program Data, Address Data, Health Data (public

or purchased)(2)

Mortality Report Data Morbidity Data

(public or purchased) (3)

Page 19: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Levels of Skill Complexity

Basic Computer(1)

Geocoding, Excel and Access

(2)

Excel, Access, SPSS(3)

Page 20: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Examples of Data

• AGS Core Demographics from Tetrad• Program Enrollment/Completion Numbers• Pre and Post- Test Scores• Addresses (e.g., of programs, outlets, agencies)• Data that needs cleaning, linking, reordering (e.g.,

Health Department Reports • Data that involves coding, joins, restructuring (e.g.,

Religent Planning 2 Morbidity Data• Data from analyses involving statistical calculations

(e.g., importing data results from your analysis for mapping)

Page 21: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

II. How GIS Can Help You with Program Planning

Obtain Demographic BackgroundProfile Needs, Resources

Locate Problem Area or Target AudienceInform Decisions about Strategy Selection

Enhance Cultural CompetencyObtain Funding

Page 22: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Examples of Demographics

• Population• Age• Race/Ethnicity• Marital Status• Income• Occupation• Health Insurance Status• Health Status• Behaviors: Spending, Drug Use• Behaviors: Crime

Page 23: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Forest Manor / Martindale-Brightwood Neighborhoods

The Place: Neighborhood

Page 24: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

[ Children in Poverty ] / [ Total Children ]

Locate Problem Area: Child Poverty

AG

S Indiana C

ore D

emographics

Page 25: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

AG

S Indiana C

ore D

emographics

Locate Target Audience

Where are the 10-14 year olds in Marion County?

They are in the areas that are darkest green.

Page 26: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

AG

S Indiana C

ore D

emographics

Study a Place

Page 27: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

46218

AG

S Indiana C

ore D

emographics

The Place: Government Boundaries

Page 28: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

AG

S Indiana C

ore D

emographics

Risk/Protective Factors

AG

S Indiana C

ore D

emographics

Page 29: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Education, Less Than HS Diploma

AGS, Core Demographics,2004 est., 2005

Indiana Prevention Resource Center

Source: GIS in Prevention, County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 30: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

By Census Tract and w/in a 1 Mile Radius of John Marshall Middle School

Data for addresses of retail tobacco outlets were contributed bythe Indiana State Excise Police TRIP Inspection Program.

Less Than 9th Grade Education

Page 31: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Insurance Coverage

Insurance Coverage

AG

S Indiana C

ore Dem

ographics, 2002 est.

Page 32: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Insurance Coverage

Source: MRI Consumer Behavior

Page 33: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Single-Parent Families (#)

492

180

418

212

303

226

212

AG

S Indiana C

ore D

emographics

Page 34: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Combined Indicators: Single-Parent Families & Poverty

Number of Single Parent-Families in Poverty in Each Block Group in Blue Box

AG

S Indiana C

ore D

emographics

Page 35: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Working Parents

Children Aged 6-17 Living with…

MarionCounty

Forest Manor/ Brtwd-Mrtndle Neighborhood

# % # %

Two parents who work 57,972 43 383 11

One parent who works 4,1001 30 1,998 55

Source: U.S. Census 2000, SF3

Page 36: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

AGS Indiana, Crime Risk

Personal Crime Indices

MarionCounty

Forest Manor/ Brtwd-Mrtndle Neighborhood

Total Crime Index 202 283

Personal Crime Index 221 275

Murder 255 178

Rape 222 234

Robbery 223 356

Assault 185 328

Page 37: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

AGS Indiana, Crime Risk 2002 (2003)

Property Crime Indices

Marion County

Forest Manor/ Brtwd-Mrtndle Neighborhood

Total Crime Index 202 283

Property Crime Index 183 291

Burglary 183 297

Larceny 148 273

Car Theft 218 302

Page 38: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

AGS MRI Consumer Behavior 2002 (2003)

Voting and Volunteerism

Marion County

Forest Manor/ Brtwd-Mrtndle Neighborhood

In the Last Year, Percentage of Adults Who…

Voted in Federal, Stateor Local Election 44.0 33.0

Actively worked

as a Volunteer 16.8 10.0

Page 39: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Income

See Relationships between Data

10-17 Year Olds

Page 40: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

6.6 Household Spending on Alcohol

Table 6.6: Per Household Spending on Alcohol (AGS, Consumer Spending, 2004, 2005)

Per Household Spending on Alcohol, 2004 est. (AGS, 2005)

  Hamilton Indiana U.S.

Consumer spending on alcoholic beverages 646 439 460

Spending on Alcohol for Consumption outside the Home 279 188 197

Beer and ale away from home 92 62 65

Wine away from home 43 29 30

Whiskey away from home 72 48 50

Alcohol On Out-Of-Town Trips 72 49 52

Spending on Alcohol for Consumption in the Home 366 250 261

Beer and ale at home 211 145 152

Wine at home 89 60 63

Whiskey and other liquor at home 66 45 46

Source: GIS in Prevention, Hamilton County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 41: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Spending on Beer/Ale for Home

AGS, Consumer Spending,2004 est., 2005

Indiana Prevention Resource Center

Source: GIS in Prevention, Hamilton County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 42: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

6.7 Household Spending on Tobacco

Table 6.7: Per Household Spending on Tobacco Products, Miscellaneous Reading and Personal Insurance (AGS, Consumer Spending, 2004, 2005)

Per Household Spending on Tobacco, 2004, est. (AGS, 2005)

Morgan Indiana U.S.

Per Household Spending on Tobacco Products 448 428 443

Cigarettes 405 388 400

Other Tobacco Products 43 41 44

Per Household Spending on Misc. Reading 254 245 257

Newspapers 113 109 114

Magazines 54 52 54

Books 87 84 88

Personal insurance 547 523 552

Source: GIS in Prevention, Morgan County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 43: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Race, Black

Source: GIS in Prevention, County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 44: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Race, Black

Source: GIS in Prevention, Dubois County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 45: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Ethnicity, Hispanic/Latino

Source: GIS in Prevention, County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 46: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Ethnicity, Hispanic/Latino

Source: GIS in Prevention, Dubois County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 47: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Ethnicity: Hispanic/Latino

Source: GIS in Prevention, County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 48: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Inform Decision about Strategy

• Which Curricula? For whom? Which problem?• Which Domain to focus on? Parent? Child?• Which Communication Strategy to use? Words or

pictures? Phone Calls? Literacy level?• Where to Focus your efforts? Program location?• What Criteria to apply? Poverty? Working

parents? • What Services to offer? Transportation? Food? • Extend of Need? Limit or expand service area?

Page 49: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage Two Enhancements

Importing Local Data

Geocoding

Percentages, Rates and Rankings

Analysis and Custom Mapping

Page 50: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage Two:Importing Local Data

Methamphetamine Lab Seizures

Page 51: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Imported Data:Meth Busts, 2005

Total lab busts to mid October, 846 Indiana Prevention Resource Center

Source: IN State Police, 2005

Page 52: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage Two:Geocoding Program Locations

and Studying Risk/Protective Factors

ARII Location Relative to

Persons in Poverty and

Families in Poverty

Page 53: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Geocoding ofAfternoons R.O.C.K. in Indiana Programs

Page 54: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Afternoons Rock in IN Programs

Fort Wayne, Indiana

Page 55: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Persons Living in Poverty (Percent)Fort Wayne city, IN, by BG

Over 25%

14 to 25%

7 to 14%

4 to 7%

0 to 4 %

Persons in Poverty and Program Placement

Fort Wayne, Indiana

Page 56: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Fort Wayne, Indiana

Families in Poverty and Program Placement

Page 57: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Numbered Block Groups Have Over 50% of Families w/ Children under 18 Living in Poverty

Fort Wayne, Indiana

Page 58: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage Two:Enhanced Analysisfor Risk/Protection

Adding Percentages, Rates and Rankings

Page 59: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

5.7 Educational Attainment

Table 5.7: Educational Attainment (AGS, 2004 est., 2005)

Educational Attainment (%), 2004 est. (AGS, 2005)

  Dubois Co. Indiana U.S.

Less than 9th grade 9.1 5.3 7.6

9th to 12th grade, no diploma 10.8 12.6 12

Total, Less Than 9th or Less Than HS Diploma 19.9 17.8 19.6

High school graduate 44.7 37.2 28.6

Some college, no degree 13.9 19.8 21.1

Associate degree 7.4 5.8 6.3

Bachelor's degree 9.2 12.2 15.6

Graduate or profession degree 4.9 7.2 8.9

Rank for % of Pop 25+ w/ less than HS diploma 39 26th of 51  

Rank for % of Pop 25+ w/ a college degree 22 43rd of 51  

Source: GIS in Prevention, Dubois County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 60: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

5.8 Households (Families, w/ Child, Income)

Table 5.8: Median Age and Household Income (AGS, 2004 est., 2005)

Households, Families, and Income, 2004 est.

  Fayette Indiana U.S.

Households (2004) 10,462 2,465,349 112,708,665

Families (2004) 7,191 1,659,694 75,740,018

Households with children (2004) 3,482 864,296 40,102,709

Average Household Income 51,906 57,000 63,396

Per capita income 22,059 22,807 24,583

Rank for Ave HH Income High-Low 57 28th of 51  

Rank for Per Cap Income H-L 31 25th of 51  

Average Age of Householder 45-54 yrs. 45-54 yrs.  

Source: GIS in Prevention, Fayette County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 61: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

5.9 Families (by type)

Table 5.9a: Types of Households with Children (AGS, 2004 est., 2005); Median Family Income (AGS, 2004 est., 2005)

Types of Households w/ Children and Median Family Income, 2004 est. (AGS, 2005)

County Hamilton Co. Indiana U.S.

HHs w/ children (2004) 36,645 864,296 40,102,709Married Couple Family (Percent) 84.1 70 69Lone Parent Male (Percent) 3.9 6.9 6.8Lone Parent Female (Percent) 11.4 21.8 23.2Non-family Male Head (Percent) 0.5 1.1 0.8Non-family Female Head (Percent) 0.1 0.2 0.2Median Family Income 86,222 54,393 54,087Rank for Married Couple Family (% of HHs w/ children) 1 26th of 51  Rank for Median Family Income 1 21st of 51  

Source: GIS in Prevention, Hamilton County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 62: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

6.12a Crime Indices

Table 6.12b: Specific Crimes, Indices (AGS Crime Risk 2004, 2005)

Crime Indices, 2004 (AGS, 2005, based on FBI UCR)

County DeKalb Indiana U.S. IN Rank in US

Total Crime Index 17 93 101 30th of 51

Personal Crime Index

14 74 101 26th of 51

Property Crimes 17 110 102 27th of 51

Crime Indices, 2004 (AGS, 2005, based on FBI UCR) -- Rankings

  DeKalb IN Rank in US

Rank Total Crime Index 75 30th of 51

Rank Personal Crime 75 26th of 51

Rank Property Crimes 71 27th of 51

Source: GIS in Prevention, DeKalb County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 63: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

6.12b Crime Indices

Table 6.12b: Specific Crimes, Indices (AGS Crime Risk 2004, 2005)

Crime Indices, 2004 (AGS, 2005, based on FBI UCR)  Tippecanoe Indiana US

Personal Crime Index 48 74 101

Murder Index 48 107 100

Rape Index 104 94 101

Robbery Index 27 76 101

Assault Index 45 70 101       

Property Crime Index 97 110 102

Burglary Index 86 98 102

Larceny Index 153 109 102 Motor Vehicle Theft Index 41 142 101

Crime Indices, 2004 (AGS, 2005, based on FBI UCR) -- Rankings

  Tippecanoe IN Rank in US

Rank Personal Crime 16 26th of 51

Rank Murder 31 18th of 51

Rank Rape 7 28th of 51

Rank Robbery 15 25th of 51

Rank Assault 30 29th of 51

     

Rank Property Crime 12 27th of 51

Rank Burglary 15 21st of 51

Rank Larceny 4 24th of 51

Rank Motor Vehicle Theft 14 7th of 51

Source: GIS in Prevention, Tippecanoe County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 64: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

6.18 Food Stamp Recipients

Table 6.18: Food Stamp Recipients per Month in 2004 (FSSA, Division of Family and Children, 2005) and Rate per 1,000 Total Population for 2004 and 2005 and Change in Rate (calculations from the IPRC based on data from FSSA, Division of Family and Children, 2004 and 2005).

CSAP calculates this as the average number of persons who receive food stamps each month, stated as the rate per 1,000 persons in the total population. This statistic for Indiana comes from Indiana Family and Social Services Administration, Family Resources Bureau as reported in the Indiana Youth Institute Kids Count in Indiana 2005. The rate calculation comes from the Indiana Prevention Resource Center. The following table shows the rate for 2004 for Marion County with comparisons for the state and nation.

Food Stamps, 2004 (FSSA, Family Resources Bureau, 2006)

  Marion Indiana

Population, 2004 864,200

6,230,346

Food Stamp Recipients per mo., 2004 104,832

516,360

Rate per 1000 persons, 2004 121.3 82.9

Rate per 1000 persons, 2003 105.1 73.1

Change in Rate per 1,000 from 2003 to 2004 16.2 9.8

Rank for 2004 Rate per 1,000 Persons 3  

Source: GIS in Prevention, Marion County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 65: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Stage Two:Analyzing Data and Custom Mapping

Property Crime Indices

To show County

Relative to IN and US Rates

Page 66: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Map: Property Crime Indices

Bottom Quarter, Mid Range, Top Quarter (includes over IN & over US)

Above US (9), 101.55-194

Above IN (12), 95.55-194

Top Quarter (23), 64-194

Mid Range (46), 19-64

Lowest Quarter (23), 4-19

AGS, Crime Indices2004 (2005)Indiana Prevention Resource Center

Page 67: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

How GIS Helps You Obtain Funding

Provides Demographic Background

Facilitates Profile of Needs/Resources

Documents Locate Problem Area/ Target Audience

Helps Justify Decisions about Strategy Selection

Explains Aspects of Cultural Competency

Page 68: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Outcomes-Based Prevention

Substance- related problems

Effective Prevention:

Intervening Variables

Strategies/Programs

Planning, Monitoring, Evaluation and Re-Planning

Source: U.S. Department of Health and Human Services, SAMHSA, CSAP

Page 69: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Outcomes-Based Prevention

Sustainability & Cultural Competence

Source: U.S. Department of Health and Human Services, SAMHSA, CSAP

Profile population needs, resources, and readiness to address

needs and gapsMonitor, evaluate,

sustain, and improveor replace those that

fail

Develop a Comprehensive Plan

Implement evidence-based prevention

programs andactivities

Mobilize and/or build capacity to address needs

Page 70: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

How GIS Helps Obtain Funding

Source: U.S. Department of Health and Human Services, SAMHSA, CSAP

Assessment CONVINCE THEM

OF THE NEED

Describe Your PlanBase on Literature, Logic Model

Step-by-Step BlueprintBuild in Evaluation

CapacityHighlight AWARENESS,

WHAT YOU BRINGWHAT YOU GAIN

Evaluation: Plan for on-going Monitoring

and Evaluation

Implementation:Explain Rationale for choice

of evidence-based strategyand activities

Cultural CompetenceSustainability

Page 71: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

III. GIS for Program Evaluation (a Stage Two Activity)

Create a Risk/Protection Surveillance System

Track Change

Page 72: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

GeocodingFailed TRIP Inspections

Indiana Prevention Resource Center

Source: IN State Excise Police, TRIP

Source: GIS in Prevention, County Profiles, Series 3 (Indiana Prevention Resource Center, 2006)

Page 73: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Schools in Proximity to Failed TRIP Inspections

Indiana Prevention Resource Center

Source: IN State Excise Police, TRIP

Allen County

Source: GIS in Prevention, Allen County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 74: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Schools in Proximity to Failed Trip Inspections

Clark County

Source: GIS in Prevention, Clark County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Page 75: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

Schools in Proximity to Failed Trip Inspections, Close-up

Clark County -- Clarksville

Source: GIS in Prevention, Clark County Profile, Series 3 (Indiana Prevention Resource Center, 2006)

Middle School

Outlet SellingTo Minor

Page 76: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

IV. GIS for Research

Conduct Research to Identify Relationships among Environmental and Health Variables

Page 77: The Potential of Geographic Information Systems to Facilitate Data-Driven Prevention: The Case of Tobacco Indiana Prevention Resource Center Barbara Seitz.

What You Have Learned:

• Components of GIS system and costs• How GIS Can Help You with Program Planning

– Obtain Demographic Background– Profile Needs, Resources– Locate Problem Area or Target Audience– Inform Decisions about Strategy Selection– Enhance Cultural Competency– Obtain Funding

• How GIS Can Help You with Program Evaluation– Create a Risk/Protection Surveillance System– Track Change

• How GIS Can Help You Do Research– Conduct Research to identify relationships among

environmental and health variables


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