Resources

HCI & HCAT Resources

Information regarding the development of the Healthy Community Index (HCI) and Healthy Community Assessment Tool (HCAT) can be found here, along with information about potential uses of the HCI and HCAT, User guides for data collection and the HCAT, other selected sustainable indicator projects, and ongoing information and research related to healthy communities.

Download Assessment Data

The Healthy Communities Index (HCI) neighborhood rankings and indicator values are available for download below. Data used in the HCI and Healthy Communities Assessment Tool (HCAT) come from a variety of sources which may be updated periodically. However, as data in the HCI and HCAT are not automatically updated, data available from the HCAT may not represent the most current indicator values available. For users interested in learning more about the data sources and/or recreating the HCI with the most current data, information about the data source and steps to collect the data for each indicator are provided below.

Indicator Data Download Locations

Indicators Download Location Additional Information
Abandoned Structures United States Postal Service Vacancy Data

Data Source: United States Postal Service Vacant Address Data for 2015, and U.S. Census TIGER Shapefiles (Tract)
Data Collection Steps:
1) From the USPS excel sheets for 2015 select fields
“geoid” Tract
“Ams_res”: Total Count of Addresses - Residential
“Ams_bus”: Total Count of Addresses - Business
“Ams_oth”: Total Count of Addresses - Other
“vac_3_6_?”: Vacant 3 Mos. to 6 Mos. Count
“vac_6_12_?”: Vacant 6 Mos. to 12 Mos. Count
“vac_12_24_?”: Vacant 12 Mos. to 24 Mos. Count
“vac_24_36_?”: Vacant 24 Mos. to 36 Mos. Count
• ? : Residential (r), Business (b), and Other (oth)
2) Sum the values in “vac_3_6“, “vac_6_12”, “vac_12_24”, and “vac_24_36” for Residential (r), Business (b), and Other (oth) separately to determine the total count of vacant addresses over 90 days for each category
3) Combine data in all fields related to the “business” and “other” to determine the values for the commercial addresses
4) The only columns remaining should be “geoid”, “Ams_res”, “Ams_Comm”, “vac_res_2015”, “vac_comm_2015”
5) Divide the “vac_res_2015” by “Ams_res”, and “vac_comm_2015” by “Ams_comm” to specify the percentage of abandoned properties for each category at the tract level
6) Use GIS software to open neighborhood layer
7) Input U.S. Census TIGER Shapefiles for Census Tracts
8) Join the results in the step 5 to census tract shapefile and using geoid as a join field
9) Assign the percentages from step 5 to the underling neighborhood layer based on the neighborhood boundaries. In the event where more than one tract is involved, take the average of values
10) Apply the percent of abandoned residential properties to the total unites in each neighborhood
11) Apply the percent of abandoned commercial properties to the total establishments in each neighborhood
12) Sum results in the step 10 and 11 to determine the total number of abandoned structures in each neighborhood
13) Divide the total number of abandoned structures by the sum of total units and establishments in each neighborhood to derive the percentage of abandoned structures in each neighborhood

Access to Mainstream Financial Services CFED Bank On

CFE Bank On information can be found at /www.joinbankon.org/#/resources#bank-on-national-account-standards. The Pew Charitable Trust also has some data and resources available at http://www.pewtrusts.org/en/projects/consumer-banking

Access to Parks and Open Space U.S. Census TIGER Shapefiles, Local Data Request

Data Source: City of Birmingham parks database, Census TIGER Shapefile
Data Collection Steps:
1) Use GIS software to open neighborhood layer created from census Block shapefile
2) Input polygon data on “parks and open space” locations
3) Calculate non-weighted centroid for each census block.
4) Create a 0.5-mile buffer around each block centroid and assign the block a value of ”1” if buffer touches “park and open space” layer otherwise “0”.
5) For every neighborhood, sum the population in blocks with assigned value equal to “1”
6) To determine the percentage of population located within a half mile distance from “parks and green space”, divide the value from step 5 by the total population in the neighborhood

Adult Educational Attainment US Census Data: Factfinder2

Data Source: Table B15003: “EDUCATIONAL ATTAINMENT FOR THE POPULATION 25 YEARS AND OVER” most recent ACS 5-year Estimates
Data Collection Steps:
1) Select Advanced Search, select "Show me All"
2) Search for table in topic or table name.
3) Under geographies, select Block Group - 150, State, and County.
4) Click Download, OK and open zip folder.
5) Select Columns
B: id2 (geographical level)
D: Total
AJ: Regular high school diploma
AL: GED or alternative credential
AN: Some college, less than 1 year
AP: Some college, 1 or more years, no degree
AR: Associate's degree
AT: Bachelor's degree
AV: Master's degree
AX: Professional school degree
AZ: Doctorate degree
6) Use the Neighborhood Definition File to assign block groups to a neighborhood name and aggregate values to the neighborhood level
7) Determine the proportion of neighborhood adults, aged 25 or older, with a high school diploma or higher by summing columns AJ, AL, AN, AP, AR, AT, AV, AX, AZ and dividing the total by Column D

Age of Housing US Census Data: Factfinder2

Data Source: Table: B25034: “YEAR STRUCTURE BUILT” most recent year ACS 5-Yr Estimates
Data Collection Steps:
1) Select columns:
B: id2 (geographical level)
D: Total
P: Built 1970 to 1979
R: Built 1960 to 1969
T: Built 1950 to 1959
V: Built 1940 to 1949
X: Built 1939 or earlier
2) Assign block groups to a neighborhood name by using the Neighborhood Definition File and aggregate values
3) Sum columns of years prior to 1980 and divide by column D to find the proportion of homes constructed prior to1980 for each neighborhood

Blood Lead Levels in Children Local Data Request

Local Request to the State Environment or Health agency.
Some state and local health departments may be reluctant to share blood lead data at the address level due to privacy concerns. However, they may be willing to report aggregate data at the census block or tract level.

Business Retention InfoGroup

Data Source: InfoGroup Inc shapefile for Business Classification
Data Collection Steps:
1) Use GIS to open the InfoGroup Inc Shapefile
2) Overlay the neighborhood layer
3) Query the number of establishments within each neighborhood in the current year and previous year
4) Use the formula below to calculate the percent increase or decrease in the number of business establishments within the Neighborhood:
(# of establishments current year 20XX – # of establishments in previous year 20YY)/(# of establishments in current year 20XX)

Chronic School Absence Local Data Request

Data Source: Alabama State Department of Education (http://alabamaschoolconnection.org/2016/06/08/chronic-absenteeism-a-seri...)
Data Collection Steps:
1) Assign neighborhoods to schools based on school(s) district
2) Record school level estimates for chronic absence
3) Chronic absence for each neighborhood is the average of values for chronic absence schools assigned to the neighborhood. If data on school student population is available, compute a weighted average by weighting for school based on its student population.

Commute Mode Share US Census Data: Factfinder2

TO WORK” (for workers 16 years and over) most recent ACS 5-year estimates
Data Collection Steps:
1) Select Columns:
B: id2 (geographical level)
J: Carpooled
V: Public Transportation (excluding taxicab)
AL: Bicycle
AN: Walked
2) Assign neighborhoods to the block groups by using the Neighborhood Definition File and aggregate values to the neighborhood level
3) For every neighborhood sum the values in fields “Carpooled” (Column J), “Public Transportation (excluding taxicab)” (Column V), “Bicycle” (Column AL), and “Walked” (Column AN), and divide by the “Total” (Column D) field to find the percentage of workers commuting by transit, bicycle, foot or, carpool.

Concentrated Poverty US Census Data: Factfinder2

Data Source: Table B17021: “POVERTY STATUS OF INDI-VIDUALS IN THE PAST 12 MONTHS BY LIVING ARRANGE-MENT” (Population for whom poverty status is deter-mined) 2015 ACS 5-year estimates
Data Collection Steps:
1) Save Columns
B: id2 (geographical level)
D: Total
F: Income in the past 12 months below poverty level
2) Assign neighborhoods to the block groups by using the Neighborhood Definition File and aggregate values to the neighborhood level
3) Percentage of neighborhood residents below the poverty level is calculated by dividing Column F by Column D

Employment Rate US Census Data: Factfinder2

Data Source: Table B23025: ”EMPLOYMENT STATUS” (for the population 16 years and over) most recent ACS 5-year Estimates
Data Collection Steps:
1) Select Columns
B: id2 (geographical level)
D: Total
J: In labor force: - Civilian labor force: - Employed
2) Use the Neighborhood Definition File to assign Neighborhoods to block groups and aggregate values to the neighborhood level
3) Divide Column J by D to calculate the percentage of working age population who are employed for each neighborhood

Excessive Housing Cost Burden US Census Data: Factfinder2

Data Source: Table B25091: “MORTGAGE STATUS BY SE-LECTED MONTHLY OWNER COSTS AS A PERCENTAGE OF HOUSEHOLD INCOME IN THE PAST 12 MONTHS” and Ta-ble: B25070: “GROSS RENT AS A PERCENTAGE OF HOUSE-HOLD INCOME IN THE PAST 12 MONTHS”
Data Collection Steps:
1) Go to Factfinder2, Advanced Search, “Show me all”
2) Search for table B25091 and B25070.
3) Under geographies, select Block Group - 150, State, and County.
4) Select “Download,” “OK” to create a zip file, and “Download” to open the zip fold-er with the file.
5) The following columns are necessary to determine excessive housing cost burden:
Table B25070:
B: id2 (geographical level)
D: Estimate; Total (Renters)
R: 35.0 to 39.9 percent
T: 40.0 to 49.9 percent
V: 50.0 percent or more
Table B25091:
B: id2
F: Housing units with a mortgage
T: Housing units with a mortgage: - 35.0 to 39.9 percent
V: Housing units with a mortgage: - 40.0 to 49.9 percent
X: Housing units with a mortgage: - 50.0 percent or more
6) Use the Neighborhood Definition File to assign neighborhoods to block groups and aggregate values to the neighborhood level
7) Use the formula below to determine percent of neighborhood households with excessive housing cost burden:
{ColumnR + ColumnT (from B25070) + ColumnV (from B25070) + ColumnT (from B25070) + ColumnV (from B25070) + Column X} / [ColumnD + ColumnF]

Food Desert USDA Food Access Research Atlas

Data Source: USDA Food Deserts.
Data collection steps:
1) Download the Food Access Research Atlas Data File
2) Select the following Columns:
A: provides census tract number
E: (LILATracts_halfAnd10) which identifies Low Income/Low Access census tracts at 0.5-mile (urban) or 10 miles (rural) areas (LILATracts_1And10) in a dichotomous fashion (0=not a food desert, 1=food desert).
3) Assign census tracts to neighborhoods by using the Neighborhood Definition File
4) For every neighborhood sum the values in the column D
5) The presence of food desert for each neighbor-hood is calculated by dividing the results found in the step 4 by the total number of tract in that neighborhood
For more information on Food Deserts and the USDA da-ta: (https: //apps.ams.usda.gov/fooddeserts/ foodDe-serts.aspx)

High School Graduation Rate Local Data Request

Data Source: Alabama State Department of Education
Data Collection Steps:
Data commonly calculated by local school districts and often are often available through the website of the local school district or state Department of Education.
Most graduation rates are published at the school level. The integration of data between traditional public schools versus charter schools varies across districts, consequently charter school data, as well as data for students attending private or parochial schools, may need to be calculated separately.
1) Assign neighborhoods to schools based on school(s) district
2) High school graduation rate for each neighborhood is the average of values for graduation rate in schools assigned to the neighborhood.

Household Transportation Costs HUD Location Affordability Index (LAI)

Data Collection Steps:
1) Select "Download Data" from the dropdown menu on the "Location Affordability Index" tab.
2) Download the Census block group data for your specific MSA.
3) Select Columns
A: blkgrp (the geographic ID)
B: households
Z: hh_type1_t (percent of household income a typical regional household spends on transportation)
4) Use the Neighborhood Definition File to assign neighborhoods to census block groups
5) To aggregate data at the neighborhood level, the values in the Column Z should be averaged (Weighted household Average)

Income Inequality US Census Data: Factfinder2

Data Source: Table B19001: “HOUSEHOLD INCOME IN THE PAST 12 MONTHS” (2015 ACS 5-year estimates)
Data is available at Census Block Group level.
Data Collection Steps:
1) Select Geography (Place -160), your city. Add to your selection.
2) Select geography (Census Block Group – 150), Select your State, County(ies), and all Census Block Group within selected County(ies). Add to your Selection.
3) Select Table B19001, most recent 5-year esti-mates
4) Select “Download” and OK to create a zip file.
5) Select Columns:
B: id2 (geographical level)
D: Estimate; Total
F: Less than $10,000
H: $10,000 to $14,999
J: $15,000 to $19,999
L: $20,000 to $24,999
N: $25,000 to $29,999
P: $30,000 to $34,999
R: $35,000 to $39,999
T: $40,000 to $44,999
V: $45,000 to $49,999
X: $50,000 to $59,999
Z: $60,000 to $74,999
AB: $75,000 to $99,999
AD: $100,000 to $124,999
AF: $125,000 to $149,999
AH: $150,000 to $199,999
AJ: $200,000 or more
6) Utilizing Neighborhood Definition File which determine appropriate allocation of data to each neighborhood enables aggregation of data from block groups to the neighborhood level
7) Calculate GINI Index for each neighborhood
NOTE:The City data will either be found in the first row or last.

Land Use Mix InfoGroup

Data Source: InfoGroup Inc database for 2015
According to the methodology used in EPA Smart Location (www.epa.gov/smartgrowth/pdf/sld_userguide.pdf), this indicator measures the average neighborhood-level diver-sity of destinations based on the mix of eight different em-ployment types (office, retail, industrial, service, enter-tainment, education, health, and public sector) within each neighborhood.
InfoGroup codes for these eight sectors are:
Retail (Code 44-45), Entertainment (Code 71), Education (Code 61), Public Administration (Code 92), Health Care (62), Office (55, 52), Industrial (11,21,23,31-33), Service (51,53,54,56,72,81)
Data collection Steps:
1) Open neighborhood layers and overlay with the NAICS Employment Shapefile in current year
2) Record the total number of employment for each sector within every neighborhood
Ret: Total retail employment
Off: Total Office employment
Ind: Total Industrial employment
Svc: Total Service employment
Ent: Total Entertainment employment
Ed: Total Education employment
Hlth: Total Health care employment
Pub: Total Public Administration employment
3) Sum values in the step 2 to calculate total employment (TotEmp) in each neighborhood.
4) Apply formula below to specify the employment mix:
E= (Ret/TotEmp)*ln(Ret/TotEmp) + (Off/TotEmp)*ln(Off/TotEmp) + (Ind/TotEmp)*ln(Ind/TotEmp) + (Svc/TotEmp)*ln(Svc/TotEmp) +
(Ent/TotEmp)*ln(Ent/TotEmp) +
(Ed/TotEmp)*ln(Ed/TotEmp) +
(Hlth/TotEmp)*ln(Hlth/TotEmp) +
(Pub/TotEmp)*ln(Pub/TotEmp)
5) Land Use Mix for each neighborhood is equal to:
= - E/(ln(8))

Life Expectancy Local Data Request

Data for either measure are available via local or state vital statistics systems, which requires a local data request. This indicator is most likely only available at the City level; however, if a city is interested in displaying the value at the neighborhood level, the Robert Wood Johnson Foundation may be able to provide some guidance about acquiring data at a smaller geographic scale. RWJ has been researching the impact location has on life expectancy and has produced maps in several jurisdictions depicting the results at a smaller scale than City-wide (see http://www.rwjf.org/en/about-rwjf/newsroom/features-and-articles/Commiss... for more information).

Local Business Vitality InfoGroup

Data Source: InfoGroup Inc shapefile for Business Classification
Data Collection Steps:
1) Open the InfoGroup Inc shapefile and use neighborhood layer to determine the total number of establishments in each neighborhood
2) Use “Employment” column to find and record the number of establishments with 0-4 employees
3) Divide the total number of establishments with 0-4 employees by the total number of businesses for each neighborhood.

Long-Term Unemployment US Census Data: Factfinder2

Data Source: Table B23027: “FULL-TIME, YEAR-ROUND WORK STATUS IN THE PAST 12 MONTHS BY AGE FOR THE POPULATION 16 YEARS AND OVER” (2015 ACS 5-year esti-mates)
Data Collection Steps:
1) Select columns:
B: Id2 (Block groups)
F: 16 to 19 years
N: 16 to 19 years: - Did not work in the past 12 months
P: 20 to 24 years
X: 20 to 24 years: - Did not work in the past 12 months
Z: 25 to 44 years
AH: 25 to 44 years: - Did not work in the past 12 months
AJ: 45 to 54 years:
AR: 45 to 54 years: - Did not work in the past 12 months
AT: 55 to 64 years:
BB: 55 to 64 years: - Did not work in the past 12 months
2) Use Neighborhood Definition File to allocate data to each neighborhood
3) Proportion of the population 16 years to 64 years out of work for more than 12 months is determined by summing columns N, X, AH, AR, BB and dividing that value by sum of columns F, P, Z, and AT

Low Birth Weight Local Data Request

Local Data Request
Data Collection Steps:
1. Contact the state or local vital record agency to determine if they estimate and publish low-weight birth rates for the desired geography (ZIP will be converted to Census Tract). A list of vital record offices for all 50 states is available from the Center for Disease Control (CDC): http://www.cdc.gov/nchs/w2w.htm.
2. If the agency does not report low birth weight, request the following data to do the computation:
(a) Annual count of live births at lowest available geographic level (at least ZIP).
(b) Annual count of births with low birth weight (live births where baby is less than 2,500 grams).
3. Divide the number of low birth weight births by the number of live births for each geographic area (e.g., ZIP).
4. If data is at zip level, use crosswalk to convert to Census Tract.

Motor Vehicle Collisions U.S. Census TIGER Shapefiles, Critical Analysis Reporting Environment (CARE)

Data Source: CARE Database, U.S. Census
TIGER Shapefiles
In CARE database, each fatality or person injured is recorded with the precise location of the collision.
Data Collection Steps:
NOTE: Variable should include both fatalities and injuries resulting from motor vehicle collisions, not the number of collisions.
Data Collection Steps:
1) Use GIS software to open collisions shapefile from 2014 to 2016
2) Overlay with neighborhood layers to determine the number of fatalities and injuries within each neighborhood
3) Divide the count in each neighborhood by the neighborhood population and the number of years of data provided (this produces an annual rate of fatalities and injuries).
4) Multiply the result in step 3 by 1000. This produces indicator data comparable to HHS’s Healthy People 2020 and other potential targets.

Offsite Alcohol Outlets InfoGroup

Classification
Data Collection Steps:
1) Open InfoGroup Inc shapefile and use code 445310 to select Beer, wine and liquor stores.
2) Add Neighborhood layer to determine the number of alcohol outlets within each neighborhood
3) Use the following formula to determine the number of alcohol outlets per 1000 people:
[total number of industry 445310 establishments] / [Neighborhood population / 1000]

Step 4: Under Industry Codes (lower left hand menu), select Individual codes, 445310: Beer, wine and liquor stores
Step 5. Click Download, Download, OK to create zip folder and open file.
Step 6. Sort data to find total number of industry 445310 establishments (code 1 in column G: Employment Size of Establishments) in Zip codes within the jurisdiction.
Step 7: Use Zip Code to Census Tract Crosswalk to determine appropriate Census Tracts . See "Resources" for information re: using Zip to Census Tract crosswalks.
Step 8: Use the following formula to determine the number of alcohol outlets per 10,000 people:
[total number of industry 445310 establishments] / [census tract population / 10,000]

Park Quality U.S. Census TIGER Shapefiles, Local Data Request

Data Source: Census TIGER Shapefile, Freshwater Land Trust Birmingham Park Assessment, and City of Birmingham Capital Budget
The analysis is based on the methodology used at Park Score® (http://parkscore.tpl.org/methodology.php).
Five variables are considered that represent the important characteristics of park system.
- Median park size
- Parkland as a percentage of Neighborhood area
- Spending per resident
- Amenities
- Access to Parks
Each neighborhood is given a score from 0 to 120:
The first four variables are given 20 points per each and points for Access to Parks are 40. Then each neighborhood score is normalized to be out of 100.
Data Collection Steps:
Step1: (Median Park Size)
1) Use GIS software to open neighborhood layer
2) Input polygon data on “park and open space” locations
3) Assign neighborhoods to parks and calculate the park size within each neighborhood
4) Calculate the median park size for each neighborhood
Step2: (Parkland as a percentage of Neighborhood area)
1) Overlay with the parcel layer which enables detecting unpopulated rail yard and airport areas
2) Calculate neighborhood area after removing unpopulated rail yard and airport areas
3) Sum all park areas within each neighborhood
4) Divide the result from step 3 by step 2 for every neighborhood
Step3: (Spending per resident)
1) Use City of Birmingham Capital Budget for a three-fiscal year that reflect agency spending on parks and recreation.
2) Report the average annual spending for each park
3) Spending per resident for each neighbor-hood is the average of values in (2) for parks assigned to the neighborhood divided by total number of neighborhood residents
Step4: (Amenities)
1) In “Freshwater Land Trust Park Quality Grade Definition” database, select fields “Clean Grade” and “Exercise/Activity Grade”
2) Assign a numerical point from 25 to 100 to each park grade
A=100
B=75
C=50
D=25
3) Calculate the average neighborhood amenity score by averaging the values in (2) for parks assigned to neighborhood
Step4: (Access to Parks)
Use the same values calculated in the “Access to Parks and Open Space”

Payday Loans InfoGroup

Data Source: InfoGroup, Inc
Data Collection Steps:
1) Use GIS software to open neighborhood layer
2) Utilized NAICS code to identify and extract Payday loan establishments
3) Create one-mile buffer around neighborhoods
4) Record the number of Payday loans that are inside the one-mile buffer for every neighborhood
5) Divide the count in each neighborhood by the neighborhood population and multiply the result by 1000.

Pedestrian & Bicycle Injuries by Motor Vehicles Critical Analysis Reporting Environment (CARE)

Data Source: CARE Database, U.S. Census
TIGER Shapefiles
In CARE database, each fatality or person injured is recorded with the precise location of the collision.
Data Collection Steps:
NOTE: Variable should include both fatalities and injuries of Pedestrians and Bicyclers resulting from being hit by motor vehicles, not the number of collisions.
Data Collection Steps:
1) Use GIS software to open collisions shapefile from 2014 to 2016
2) Overlay with neighborhood layers to determine the number of fatalities and injuries within each neighborhood
3) Filter the fatalities and injuries of pedestrians and bicyclists involved in the collision by motor vehicle
4) Divide the count in each neighborhood by the neighborhood population and the number of years of data provided (this produces an annual rate of fatalities and injuries).
5) Multiply the result in step 3 by 1000. This produces indicator data comparable to HHS’s Healthy People 2020 and other potential targets.

Pedestrian Connectivity U.S. Census TIGER Shapefiles

Data Source: Census TIGER Shapefile
Two variables are used to determine Pedestrian Connectivity, the street intersection density which reflect connectivity for pedestrian and bicycle travel, and access to sidewalk that provides information regarding the presence of sidewalks. The denominator used for street intersection density is total neighborhood area (AC_Tot), and access to sidewalk is calculated by dividing the average mile of sidewalks in both direction of travel by roadway miles. Each neighborhood is given a score from 0 to 200, 100 points for intersection density and 100 points for access to sidewalk.
Data Collection Steps:
1) Calculate the intersection density and access to sidewalk for every neighborhood
2) Assign points to each neighborhood by breaking the data range from all neighborhoods into 100 brackets. Lowest bracket receives least points and highest brackets receives the most points.
3) Final score for each neighborhood is sum of points for intersection intensity and access to sidewalk

Population US Census Data: Factfinder2

Data Source: Table B01003: “TOTAL POPULATION” – most recent 5-year estimates
1) Select geography (Block Group - 150), your State, County(ies), and all Block Groups within selected County(ies). Add to your Selection
2) Select Table B01003, most recent 5-year estimates; Select “Download,” OK to create a zip file
3) Column B (id2) provides the block group number;
Column D (provides the estimated population)
4) Use the Neighborhood Definition File to aggregate population at the neighborhood level

Preschool Enrollment US Census Data: Factfinder2

Data Source: Table B14007: “SCHOOL ENROLLMENT BY DE-TAILED LEVEL OF SCHOOL FOR THE POPULATION 3 YEARS AND OVER” and Table B01001: “SEX BY AGE” (most recent 5-year estimates)
Data Collection Steps:
1) In Table B14007 select Columns:
B (id2) provides the block group number
H Enrolled in school: - Enrolled in nursery school, preschool
2) In Table B01001 select Columns:
B (id2) provides the block group number
H Male: - Under 5 years
BD Female: - Under 5 years
3) Use the Neighborhood Definition File to aggregate data at the neighborhood level
4) To determine the percentage of under 5-year old enrolled in preschool, divide column H (Table B14007) by sum of columns BD, and H (Table B01001) for each neighborhood

Preventable Hospitalizations Local Data Request

Local/State Data Request:
Step 1. Request age-adjusted preventable hospitalization rates from City or State health department at a Zip, ZCTA or alternative smaller geographical census unit.
Step 2. If the health department does not estimate the rates or cannot estimate preventable hospitalization rates at the ZIP or a smaller census geographic area, request annual counts of hospitalization discharges for a three to five year period. Assess preventable discharges according to the set of Prevention Quality Indicators (PQIs) used by the Agency for Healthcare Research and Quality (AHRQ).

Proximity to Brownfield Sites U.S. Census TIGER Shapefiles, Local Data Request, U.S. EPA Cleanups in My Community

Data Source: Census TIGER Shapefile, Environ-mental Protection Agency (EPA), and Alabama Department of Environment Management (ADEM)
Data Collection Steps:
1) Use GIS software to select neighborhood layers created from most recent census block shapefile.
2) Overlay with brownfield point shapefile
3) Create a 500 feet buffer around each brownfield site
4) If buffer touches blocks, the block is coded with a ‘1’, otherwise the block is coded as ‘0’.
5) Percentage of neighborhood population within 500 feet of brownfield sites is calculated per the following formula:
[Sum of population in the neighborhood block coded with ‘1’]/ [total population in the neighborhood]

Proximity to Superfund Sites U.S. Census TIGER Shapefiles, EPA CERCLIS Database

Data Source: U.S. Census TIGER Shapefiles, Environmental Protection Agency (EPA) CERCLIS Public Access Database
Data Collection Steps:
1) Use GIS software to select neighborhood layers created from most recent census block shapefile
2) Overlay with CERCLIS point shapefile
3) Create 1km buffer around each CERCLIS site
4) If buffer touches blocks, the block is coded with a ‘1’, otherwise the block is coded as ‘0’.
5) Percentage of neighborhood population within 1km of CERCLIS sites is calculated per the following formula:
[Sum of population in the neighborhood block coded with ‘1’]/ [total population in the neighborhood].

Public Assisted Households US Census Data: Factfinder2

Data Source: Table B22010 “RECEIPT OF FOOD STAMPS/SNAP IN THE PAST 12 MONTHS BY DISABILITY STA-TUS FOR HOUSEHOLDS”, Table B19057 “PUBLIC ASSISTANCE INCOME IN THE PAST 12 MONTHS FOR HOUSEHOLDS”, and Table B19056
“SUPPLEMENTAL SECURITY INCOME (SSI) IN THE PAST 12 MONTHS FOR HOUSEHOLDS”
1) From the Table B22010 select the following columns:
B: (id2) provides the block group number
D: Total (Households)
F: Household received Food Stamps/SNAP in the past 12 months

From Table B19057 select Columns:
B: (id2) provides the block group number
F: Total: - With public assistance income

From Table B19056 select Columns:
B: (id2) provides the block group number
F: Total: - With Supplemental Security Income (SSI)
2) Use the Neighborhood Definition File to aggregate data at the neighborhood level
3) Sum Column F from all three tables and divide by Column D to determine the percent of households within each neighborhood receiving public assistance.

Public Health Nuisances Local Data Request

Data Source: Jefferson County Health Department
Data Collection Steps:
1) Record the count of complaints per neighborhood divided by neighborhood population per capita (1000).

NOTE: All types of Complaint should be included such as Sewer/Plumbing Repair, Gar-bage/Residential/Rubbish, Animal Bites, Mainte-nance/Repair/Furnishings, No Plumbing /Facilities, etc.

Racial and Ethnic Diversity US Census Data: Factfinder2

Data Source: Table B03002: “HISPANIC OR LATINO ORIGIN BY RACE” (2015 ACS 5-year estimates)
Data Collection Steps:
Calculation of the Shannon-Weiner Diversity Index requires several columns from Table B03002
Step1:
1) Select the following Columns:
B: Id2 (Block groups)
D: (Total population)
H: Not Hispanic or Latino: - White alone
J: Not Hispanic or Latino: - Black or African American alone
L: Not Hispanic or Latino: - American Indian and Alaska
N: Not Hispanic or Latino: - Asian alone
P: Not Hispanic or Latino: - Native Hawaiian and Other
R: Not Hispanic or Latino: - Some other race alone
T: Not Hispanic or Latino: - Two or more races
AN: Hispanic or Latino: - Two or more races
Step2:
1) Sum Columns H, J, L, N, P, R, T, AN and Subtract from Column D to determine Hispanic or Latino Total Population – One Race.
2) Sum Columns T and AN to determine Total Population – Two or More Races
3) Delete columns T and AN. The only columns remaining should be Id2, Total Population, each race/ethnicity – one race, other race, and two or more races (10 columns including the geography)
4) Delete the Columns marked: "Total - Number; Total population - One race" and " Not Hispanic or Latino - Number; Total population - One race". The only columns remaining should be Id2, Total Population, each race/ethnicity – one race, other race, and two or more races (10 columns including the geography)
Step3:
Use the Neighborhood Definition File for block groups and neighborhood to assign neighborhoods to block groups and sum the number of residents for each race/ethnic group for every neighborhood
Step4:
Create the diversity spreadsheet using the following steps:
1) Divide the population of each race/ethnic group by the total population (for each neighborhood).
2) If the resulting number is zero for a race/ethnic group, the value is zero; otherwise find the natural logarithm of the value (i.e., IMLN in excel) using the following if/then excel function:

=IF (COLUMN/ROW=0, 0, IMLN(COLUMN/ROW [e.g., =IF(L2=0,0,IMLN(L2)]

3) Multiple the results found in Step b) by the results of Step a) [e.g., =L2 X T2]
4) The inverse sum of the races/ethnicities represents the diversity index
[e.g., =- SUM(AB2:AI2)

Reading Proficiency Local Data Request

Data Source: Alabama State Department of Education
Data Collection Steps:
Depending on the school district, testing for reading proficiency is done at either 3rd or 4th grade. The percent of students meeting or exceeding proficiency is calculated by dividing the number of students who met or exceeded “proficient” reading levels by the total number of students taking the test.
1) Request reading proficiency data per school
2) Sum the values in fields Level III (Meets Academic Content Standards) and Level IV (Exceeds Academic Content Standards) for All Students (2014-2015)
3) Assign neighborhoods to school(s) based on school(s) district and average the values for each neighborhood

Residential Mobility US Census Data: Factfinder2

Data Source: Table B07201: “GEOGRAPHICAL MOBILITY IN THE PAST YEAR FOR CURRENT RESIDENCE” (2015 ACS 5-year estimates)
Data Collection Steps:
1) Select columns:
B: Id2 for geographic FIPS Code (Block Group)
D: Total (Residents age 1 and over)
F: Total: - Same house 1 year ago
2) Use the Neighborhood Definition File for Block Groups and Neighborhood to assign Neighborhoods to Block groups and sum the number of residents for both columns
3) Calculate the percent of residents living the same house as one year ago for every neighborhood

Residential Proximity to Traffic US Census Data: Factfinder2, U.S. Census TIGER Shapefiles, FHA Highway Performance Monitoring System (HPMS)

Data Source: US Census Data: Factfinder2, U.S. Census with Federal Highway Administration Highway Performance Monitoring System
Data Collection Steps:
1) Use GIS software to select neighborhood layers created from census block shapefile.
2) Overlay with federal and state highway shapefiles from FHWA HPMS. These files include an estimate of volume determined by the annual average daily traffic (AADT).
3) Calculate non-weighted centroid for each census block.
4) Create buffers around 100, 200, and 300 meters each census block centroid.
5) Assess whether volumes on any road within a buffer exceeds the following volume thresholds:
- 30K AADT on any road within 100 meters of the centroid;
- 75K AADT within 200 meters; and
- 150K AADT within 300 meters.
6) Assign the block a value of ”0” if no volume/buffers are exceeded. Assign”1” if any volume/buffer threshold is exceeded.
7) For each neighborhood sum the population in blocks with assigned value of “1” and divide by the total population in the neighborhood

School Proximity to Traffic U.S. Census TIGER Shapefiles, Local Data Request, FHA Highway Performance Monitoring System (HPMS)

Data Source: U.S. Census with Federal Highway Administration, and Alabama State Department of Education
Data Collection Steps:
1) Use GIS software to select neighborhood layers created from census block shapefile.
2) Input schools point shapefile
3) Assign neighborhoods to schools based on school(s) districts
4) Use FHWA HPMS to overlay federal and state highway shapefiles. These files provide an estimate of annual average daily traffic (i.e., traffic volume).
5) Create buffers at three distances: 100, 200, 300 meters around each school.
6) Assess whether volumes on any road within a buffer exceeds the following thresholds:
- 30k AADT on any road within 100 meters of the school;
- 75k AADT within 200 meters; and
- 150k AADT within 300 meters.
7) Assign the school a value of ”1” if a volume/buffer is exceeded; otherwise assign a value of ”0.”
8) Neighborhood value is determined by summing the number of schools that exceed the volume threshold and dividing that value by the total number of schools within the neighborhood.

School Readiness Scores Local Data Request

Data Request to the Local School District(s)
Although data are collected via in-school assessments, the practice of collecting the data is not universal and there are a variety of variety of assessment tools employed. Comparison of the data may be difficult in cities with multiple school districts if the data are not collected in a uniform manner.
Step 1. Request school readiness data from the local school district(s) at the school or census tract level.
Step 2. Assign school(s) data/score to census tract/neighborhood regardless of where students attending the school reside.

Tax Delinquent Properties Local Data Request

Data Source: Alabama Department of Revenue (ADOR)
Data Collection Steps:
1) Request data from ADOR
2) Use GIS to code and open the tax parcels shapefile and overlay with neighborhood layer
3) Clip tax parcels to neighborhood boundaries
4) Sum the number of tax delinquent parcels in the resulting shapefile from step 3
5) Sum the number of parcels that lie within neighborhood boundaries
6) Divide the result from the step 4 by the step 5 for every neighborhood

Toxic Releases from Facilities US Census Data: Factfinder2, U.S. Census TIGER Shapefiles, EPA Toxic Release Inventory (TRI)

Data Source: US Census Data: Factfinder2, EPA Toxic Release Inventory (TRI)/Census TIGER Shapefile
Data Collection Steps:
1) Use GIS software to open neighborhood layers created from census block shapefile.
2) Overlay with Toxic Release Inventory point shapefile
3) Place 1km circular buffer around each facility.
4) If facility buffer contains/touches census block assign a value of “1,” otherwise assign a value of “0.”
5) Neighborhood value is determined by summing the block population with assigned value of “1” and dividing that value by the total population within the neighborhood.

Transit Accessibility EPA Smart Location Database

Data Source: EPA’s Smart Location Database (SLD)
The D4 (Transit Measures) variables from the SLD measure transit availability, proximity, frequency, and density. Two data sources are used to calculate transit metrics: Transit service data from more than 200 transit agencies across the United States, including the geographic location of all transit stops as well as the service schedules for all routes that serve those stops, and point location data for all existing fixed-guideway transit service in the U.S.
Data Collection Steps:
1) Use GIS software to open neighborhood layer
2) Input the EPA Smart Location Database
3) Report data from Column GEOID10 – this column provides the geographic ID (census block group FIPS).
4) Report data from Column D4c – this column provides Transit Accessibility data (i.e., ACS wa).
5) Join data to neighborhood shapefile (use geoid as a join field)
6) Use the dissolve tool on the resulting shapefile to aggregate data at the neighborhood level by averaging values in field “D4c” assigned to each neighborhood.
Note: these data can be extracted to Excel as necessary from GIS software.

Travel Time to Work US Census Data: Factfinder2

Data Source: Table: B08303: “TRAVEL TIME TO WORK” (Workers 16 years and over)
Data Collection Steps:
1) Select Column:
B: (id2)
D: Total (Number of workers)
F: Less than 5 minutes
H: 5 to 9 minutes
J: 10 to 14 minutes
L: 15 to 19 minutes
N: 20 to 24 minutes
P: 25 to 29 minutes
R: 30 to 34 minutes
T: 35 to 39 minutes
V: 40 to 44 minutes
X: 45 to 59 minutes
Z: 60 to 89 minutes
AB: 90 or more minutes
2) Use the Neighborhood Definition File to assign neighborhoods to block groups and sum the number of workers for travel time group for every neighborhood
3) Mean travel time to work is calculated by multiplying the number of workers to the average time of travel in each travel time group and dividing that value by the total number of workers in every neighborhood

Tree Cover U.S. Census TIGER Shapefiles, National Land Cover Database 2011 (NLCD2011)

Data Source:
Data Collection Steps:
1) Use GIS software to open the most recent census TIGER/line Shapefile.
2) Select "census blocks" to create neighborhood layers.
3) Download the NLCD 2011 USFS Tree Canopy Cartographic Layer [NOTE: this is a national database with a long download. Pilot Cities may also access state specific data at: https://www.dropbox.com/sh/qye4szm9n00sz7c/AAB4 MjW-plAo_SBqmPsEby6v2a?dl=0]
4) Overlay the NLCD .img file with the TIGER Shapefile (Note: the NLCD data may need to be re-projected or geo-referenced for it to align correctly).
5) Use the spatial analyst > extract by mask tool to select the portions of the NLCD .img file that lie within the boundary of the census block (or neighborhood). This will produce a new .img file specifically for the city, making it unnecessary to process the entire nationwide dataset.
6) Use the conversion > raster to polygon tool to convert the resulting .img file to a shapefile based on the “value” field in the .img file. This creates a shapefile with polygons for each of the raster cells. Title this file TC_polygon. The percentage of each polygon that is covered by tree canopy, in values from 0-100, will be in the GRIDCODE field.
7) Use the dissolve tool on the resulting shapefile to consolidate cells with the same value.
8) Use an analysis > overlay > spatial join tool to join TC_Polygon and the census block or neighborhood shapefile. Use TC_polygon as the target field and the neighborhood/census block shapefile as the join field and select JOIN_ONE_TO_MANY in the Join operation menu. Name the output TC_nhood. TC_nhood will be a new shapefile with the neighborhood name or ID number appended to each polygon from the TC_polygon file.
9) Create a new field in the TC_nhood attribute table titled AREA_AC. Right-click the heading of this field, select Calculate Geometry…, and select Area in the Property menu and Acres in the Units menu.
10) Create another new field in the TC-nhood attribute table titled COVER. Right-click the heading of this field, select Field Calculator… and set this field equal to (GRIDCODE * AR-EA_AC) / 100.
11) Use the summarize command to get a table with a sum of COVER and AREA_AC by neighborhood.
12) In the resulting table, use the field calculator to divide
COVER by AREA_AC. This provides the percent of the neighborhood with tree cover.

Vacancy Rates US Census Data: Factfinder2

Data Source: Table B25002: “OCCUPANCY STATUS” 5- year estimates (most recent available data)
Data Collection Steps:
1) Select Column:
B: (id2)
D: Total (Number of Housing Units)
F: Occupied
H: Vacant
2) Use the Neighborhood Definition File to assign neighborhoods to block groups and aggregate values to the neighborhood level
3) Percentage of vacant residential units are the number of vacant units divided by total housing units by neighborhood

Violent Crime Local Data Request

Data Source: City of Birmingham Police Department
Data Collection Steps:
1) Record the count of violent crimes per neighborhood divided by per capita (1000) neighborhood population.
NOTE: Violent crimes include criminal homicide, forcible rape (or attempt), armed robbery, aggravated assault, and assault with intent to commit murder.

Visual Property Nuisances Local Data Request

Data Source: Regional Planning Commission of Greater Birmingham, and the City of Birmingham Property Conditions Assessment
Data Collection Steps:
1) Use GIS to open the tax parcels shapefile and overlay with neighborhood layer
2) Clip the deteriorated, dilapidated, and vacant overgrown parcels to the neighborhood boundaries
3) Sum the number of deteriorated, dilapidated, and vacant parcels in the resulting shapefile from step 3
4) Sum the number of parcels that lie within neighborhood boundaries
5) Divide the result from the step 4 by the step 5 for every neighborhood

Voter Participation Local Data Request

Data Source: Jefferson County Board of Registrar’s Office
Data Collection Steps:
1) Open the neighborhood layer and overly with precinct region polygon shapefile
2) Assign neighborhoods to precincts if the neighborhood touches the precinct region
3) Divide the number of residents that voted by the number of registered voters to determine the voter participation percentage in each precinct
4) Average the values assigned to each neighborhood

Walkability InfoGroup

Data Source: InfoGroup Inc database for 2015
Some local jurisdictions may compute walkability scores for their communities or have access to metrics such as Streetsmart Walkscore or Maponics Walkability. Alternatively, walkability may be computed using the EPA Smart Location Database methodology that measures employment density to provide a simple and available neighborhood proxy for potentially walkable destinations. Three sectors are considered for measuring employment density are Retail (Code 44-45), Entertainment (Code 71), and Education (Code 61).
Data collection Steps:
1) Open neighborhood layers and overlay with the NA-ICS Employment Shapefile in current year
2) Record the number of employment related to Retail, Entertainment, and Education sectors within each neighborhood
3) Neighborhood serving employment density (EDn) is calculated simply as the sum of values in the step 2 and dividing the result by total neighborhood acres.
A user guide for the EPA’s SLD can be accessed at: (www.epa.gov/smartgrowth/pdf/sld_userguide.pdf)