Advice and answers from the mySidewalk Team

We firmly believe that data should be accessible to everyone–which is why our team is constantly working to bring you new local and national datasets from a variety of reliable sources.  In order to help you analyze, make sense of, and answer questions about these datasets, we are also designing a ton of new Templates

With so many things being added into mySidewalk weekly, it can be hard to keep track of what's new. Know that you can always check here to see what data and Templates are being released. Questions? Reach out and chat with an expert any time. 

View all of mySidewalk's datasets.

May 5, 2017

New National Data

Great news! We’ve added new datasets from the EPA National Walkability Index to help you see how walkable your community is and compare your score to other places.

Walkability Index
How walkable is your community?

On a scale of 1-20, with 1 being low and 20 being high, determine walkability down to a census block group. 

Proximity to Transit
How easy is it for someone to walk to a transit stop in your community? 

On a scale of 1-20, this dataset ranks the ease of walking to a transit stop. A score of 20 indicates an easy walk to a transit stop, while a lower score indicates an area in which it is difficult to walk to transit. Note that scores of 1 indicates areas that lack transit data.

Mixed-Use Ranking
Which areas have diverse employment types (such as office, retail, and service) plus a large quantity of occupied housing units?

This 1-20 ranking combines employment diversity and the number of occupied housing units. Higher values (near 20) are areas that have high employment diversity, a larger proportion of occupied housing, and are more likely to correlate with walk trips. Low values (near 1) are areas that lack employment diversity, have a low percentage of occupied housing, and are unlikely to have walk trips.

Walkability Intersection Density Ranking
Where does the built environment support connectivity? Where does the built environment support walkability? 

The walkability intersection density rankings are on a 1-20 scale. Areas with high intersection density (values near 20) correlate with more walking trips and low intersection density (values near zero) indicate fewer walking trips. The intersection density was calculated using different intersection types and the US Census geographies land area, and is weighted to account for the connectivity of pedestrian and bicycle travel. 

What’s intersection density? Intersection Density captures the intersection density of an area, measured by a ratio involving the number of intersections in the street/road network. It is one of the most important predictors of walking in a community, along with access to various destinations and the quality of sidewalks.

Employment Diversity Ranking
Which areas have the greatest diversity in employment types? Where do diverse employment opportunities lead to greater walkability?

The employment diversity ranking was calculated with employment types (industrial, office, retail, etc.) at a block group level. High values correlate with a higher number of employment types and more walk trips. Low values correlate with fewer walk trips and fewer employment types.

New Templates

To help you make the most of the new EPA Walkability data, here are a few quick analyses you can run by applying a Template to a project area.  

National Walkability Score
See the EPA’s Walkability Index score of your city, neighborhood, or other area of interest. Walkability is a measure used to indicate the ease of pedestrian travel in an area. Scores start out at 1 and go up to 20, with 1 indicating low walkability and 20 indicating high walkability.

Once you know which areas of your community are walkable and which are struggling, you can make more informed decisions about what kind of improvements are needed and where. The EPA Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability.  

Question this Template helps you answer: How walkable is my community compared to other places?  

Most Walkable Places
Analyze the most walkable areas in your community. Walkability is a measure used to indicate the ease of pedestrian travel in an area. Walkability scores start out at 1 and go up to 20, with 1 indicating low walkability and 20 indicating high walkability. Once you know which areas of your community are walkable and which are struggling, you can make more informed decisions about what kind of improvements are needed and where.

This Template is filtered to show block groups in the top quintile by walkability score. To display this data, mySidewalk uses the EPA National Walkability Index score, a nationwide geographic data resource that ranks block groups according to their relative walkability.

Question this Template helps you answer: Where are the most walkable places in my community? 

March 24, 2017

New National Data

MindMixer Participants
This dataset contains the counts of MindMixer participants from April 2011 through March 17, 2017. MindMixer is an engagement platform that helps organizations build stronger relationships with their community. It is available for only the zip code geography within mySidewalk. 

New Local Data

Citizen Survey:

New and expanded data includes 275 choropleths, 74 charts. All of these datasets fall under a new tag - Citizen Survey.

  • Arlington, VA (2015)
  • Austin, TX (2016)
  • Durham, NC (2015, 2016)
  • Ft. Lauderdale, FL (2016)
  • Olathe, KS (2014, 2015, 2016)
  • Kansas City, MO (Fiscal years 2015, 2016, 2017 Q1 and Q2)
  • Gladstone, MO (2016)
  • Independence, MO (2016)
  • Miami Beach, FL (2016)
  • Overland Park, KS (2016)
  • Saint Joseph, MO (2016)
  • Shawnee, KS (2015)
  • Wyandotte County, KS (2016)

The surveys are split by year (2014, 2015, 2016, and 2017). There are five datasets for each year that contain the average score of survey responders. Each of these average score graph contains between 3 and 11 variables (characteristic types).

Average score graphs:

Citizen Survey

  • Effectiveness of Communication with Public
  • Feeling of Safety
  • Level of Public Involvement in Decisions
  • Maintenance of City Streets in Neighborhood
  • Place to Live
  • Place to Raise Children
  • Place to Work
  • Quality of City Services
  • Value Received for Tax Dollars
  • Image of the City
  • Quality of Life in the City

Citizen Survey Code Enforcement

  • Exterior Maintenance of Business Property
  • Clean-up of Debris on Private Property
  • Removal of Temporary Signs
  • Mowing and Cutting Weeds on Private Property
  • Exterior Maintenance of Residential Property

Citizen Survey Parks and Recreation

  • Maintenance of City Parks
  • Communication from Parks and Recreation
  • Quality of City Parks and Recreation Programs/Facilities

Citizen Survey Infrastructure

  • Quality of Public Transportation
  • Condition of Sidewalks in Neighborhood
  • Satisfaction with On-Street Bicycle Infrastructure

Citizen Survey Communication

  • Quality of City's 311 Service
  • Quality of Customer Service Received from City Employees
  • Availability of Information about City Programs and Services
  • Overall Usefulness of City's Website
  • Quality of City Video Programing
  • Content of City's Magazine
  • City's Use of Social Media

The two most recent years, 2016 and 2017, have pie charts that provide a the count of survey responders who are satisfied, neutral, and dissatisfied. There are 29 citizen survey questions for each of 2016 and 2017 that contain the count of satisfied, neutral, and dissatisfied.

  • Effectiveness of Communication with Public
  • Feeling of Safety
  • Level of Public Involvement in Decisions
  • Maintenance of City Streets in Neighborhood
  • Place to Live
  • Place to Raise Children
  • Place to Work
  • Quality of City Services
  • Value Received for Tax Dollars
  • Image of the City
  • Quality of Life in the City
  • Exterior Maintenance of Business Property
  • Clean-up of Debris on Private Property
  • Removal of Temporary Signs
  • Mowing and Cutting Weeds on Private Property
  • Exterior Maintenance of Residential Property
  • Maintenance of City Parks
  • Communication from Parks and Recreation
  • Quality of City Parks and Recreation Programs/Facilities
  • Quality of Public Transportation
  • Condition of Sidewalks in Neighborhood
  • Satisfaction with On-Street Bicycle Infrastructure
  • Quality of City's 311 Service
  • Quality of Customer Service Received from City Employees
  • Availability of Information about City Programs and Services
  • Overall Usefulness of City's Website
  • Quality of City Video Programing
  • Content of City's Magazine
  • City's Use of Social Media

Please note: due to survey differences year-to-year and from city-to-city, data is not available for all of these survey questions for each year or every city. 


February 28, 2017

New Templates

Low Ownership Rates

Use this template to locate areas with low rates of homeownership. The homeownership rate is defined as the percentage of owner-occupied housing units out of all the housing units in a block group. The map is filtered to show block groups that fall within the bottom 25% of the homeownership rate. 

This map would be useful for those interested in evaluating an area’s potential for retail development. It could also be helpful for city departments interested in better understanding their communities. For example, fire analysts use this information to locate areas with potential for increased fire risk. Home ownership rates have been found to be inversely correlated with the incidence of fire in the home. 

Questions this template helps you answer: 

  • Where are there low rates of homeownership in your community or project area?
  • Where might there be a lower level of support for retail development?
  • Where might there be a greater risk for structural fires?

Affordable Housing Site Selection: Near Employment Centers

This template can be used to identify where affordable housing development, rehabilitation of housing units, inclusionary zoning, subsidies, and grant programs might be most effective. The map shows resident access to employment as well as the average percent of income spent on housing and transportation for a low-income individual. Including access to employment in this map helps to identify unaffordable areas near job centers. The charts in the toolbar identify low-income areas as well as the number of vacant housing units for rent.

HUD-qualified census tracts are included to indicate areas in which at least 50% of households have an income less than 60% of the Area Median Gross Income (AMGI). You can find more information on HUD-qualified census tracts here

Questions this template helps you answer:

  • What areas are the least affordable for low income residents?
  • Where do local residents have a high level of access to employment? 
  • What areas have the lowest number of vacant housing units for rent?
  • Which areas have the lowest income? 

New Local Data

Please note that local data will be added on a city-by-city basis. If you have a set of publicly available crime, appraisal, vehicle incidents, building permits, or 311 data that includes locational information and want it to be added, please contact mySidewalk using the Chat feature at the bottom of your screen.

Locally Reported Crime:  

This dataset is the aggregation of locally reported crime data from law enforcement agencies across the United States. All crime incidents are reclassified into one of 19 crime categories. The 19 crime categories are assault, automobile theft, bribery, burglary, drugs, embezzlement, fraud, homicide, kidnapping, property damage, public order, robbery, sexual offense (violent or non-violent), theft, weapons, and other crime.

We have locally reported crime data for the following locations:

  • Fairfield, OH
  • Lee's Summit, MO
  • Montebello, CA
  • Fountain Valley, CA
  • Anaheim, CA
  • Torrance, CA
  • Glendale, CA
  • Hyattsville, MD
  • Laurel, MD
  • Leesburg, VA
  • Santa Fe Springs, CA
  • Newport Beach, CA
  • South Gate, CA
  • Norwood, OH
  • Huntington Park, CA
  • Garland, TX
  • Arlington, TX
  • Plano, TX
  • Arvada, CO
  • Aurora, CO
  • Lewisville, TX
  • Bowie, MD
  • Covina, CA
  • Bonner Springs, KS
  • Monterey Park, CA
  • Long Beach, CA
  • Grand Prairie, TX
  • Broomfield, CO
  • Denver, CO
  • Irvine, CA
  • Montgomery County, MD
  • Pittsburgh, PA
  • Santa Monica, CA
  • Pomona, CA
  • Shawnee, KS
  • Leawood, KS
  • Frisco, TX
  • Mason, OH
  • Arlington, VA
  • Cary, NC
  • Chicago, IL
  • Cincinnati, OH
  • Dallas, TX
  • Washington, D.C.
  • Durham, NC
  • Fort Worth, TX
  • Indianapolis, IN
  • Kansas City, MO
  • Los Angeles, CA
  • Minneapolis, MN
  • Philadelphia, PA
  • Port St. Lucie, FL
  • Riverside, CA
  • Tacoma, WA
  • Austin, TX
  • Frederick, MD
  • Fullerton, CA
  • Huntington Beach, CA
  • Louisville, KY
  • Pasadena, CA
  • Santa Ana, CA

Locally Reported Vehicle Incidents

This dataset is the aggregation of locally reported vehicle incident data which has been acquired via open data portals or data requests to the jurisdiction. The data has been filtered, which applicable, to only include 2015 incidents. In addition, this data is representative of vehicle accidents or crashes. It does not represent moving violations.

We have locally reported vehicle incident data for the following locations: 

  • Westmoreland County, PA
  • Boulder, CO
  • Wyandotte County, KS
  • Johnson County, KS
  • Huntington Park, CA
  • Montebello, CA
  • Leesburg, VA
  • State of Missouri
  • State of Maryland
  • Redondo Beach, CA
  • Dallas Fort Worth area (Collin, Dallas, Tarrant, Denton Counties)
  • Monterey Park, CA
  • Fairfield, OH
  • Broomfield, CO
  • Allen, TX
  • Chino, CA
  • Denver, CO
  • Downey, CA
  • Fountain Valley, CA
  • Santa Monica, CA
  • Washington County, PA
  • Beaver County, PA
  • Butler County, PA
  • Allegheny County, PA
  • Washington, DC
  • McKinney, TX

Locally Reported Building Permits

This dataset is the aggregation of locally reported building permit data which has been acquired via open data portals or data requests to the jurisdiction. The data has been filtered, when applicable, to only include 2015 permits. The data has been broken up by residential and non-residential.  

We have locally reported building permit data for the following locations: 

  • Rockville, MD
  • Mission Viejo, CA
  • Overland Park, KS
  • Allen, TX
  • Fort Worth, TX
  • Grand Prairie, TX
  • Inglewood, CA
  • Upland, CA
  • Norwalk, CA
  • Pasadena, CA
  • Palmdale, CA
  • Garden Grove, CA
  • Flower Mound, TX
  • Plano, TX
  • Irving, TX
  • Frisco, TX
  • Richardson, TX
  • Pittsburgh, PA
  • Cranberry, PA
  • Laurel, MD
  • Los Angeles, CA
  • Anaheim, CA
  • Arlington, TX
  • Burbank, CA
  • Covington, KY
  • Fort Thomas, KY
  • Grandview, MO
  • Hempfield, PA
  • Independence, MO
  • Kansas City, MO
  • Leawood, KS
  • Lewisville, TX
  • McKinney, TX
  • Pomona, CA
  • Shawnee, KS
  • Torrance, CA
  • Wyandotte County, KS
  • Montgomery County, MD
  • Arlington, VA
  • Dallas, TX
  • Santa Ana, CA
  • Santa Clarita, CA
  • Blue Springs, MO
  • Cincinnati, OH

Locally Reported Appraisal: 

This dataset is the aggregation of locally reported appraisal values from jurisdictions across the United States. All appraised parcels are reclassified into one of 9 value categories. The 9 value categories are as follows: Less than $50,000, $50,000 to $100,000, $100,000 to $150,000, $150,000 to $200,000, $200,000 to $250,000, $250,000 to $500,000, $500,000 to $1,000,000, $1,000,000 to $5,000,000, and More than $5,000,000.

We have locally reported appraisal data for the following locations: 

  • Arlington, VA
  • Jackson County, MO
  • Parker County, TX
  • Douglas County, CO
  • Warren County, OH
  • Denver County, CO
  • Adams County, CO
  • Broomfield County, CO
  • Los Angeles County, CA
  • Hunt County, TX
  • Hood County, TX
  • Hamilton County, OH
  • Boston, MA
  • Washington, DC
  • Los Angeles County, CA
  • Allegheny County, PA
  • Collin County, TX
  • Tarrant County, TX
  • Dallas County, TX
  • Denton County, TX
  • Ellis County, TX
  • Kaufman County, TX
  • State of Maryland

Locally Reported 311:

This dataset is the aggregation of locally reported 311 data from municipalities across the United States. All 311 requests are reclassified into one of eleven 311 categories. The eleven 311 categories are zoning, health, housing, waste, landscape, road, nuisance, animal, vehicle, utilities, and other. In addition, the sum of all 311 requests for a given year, total 311, is calculated.

We have locally reported 311 data for the following locations: 

  • Kansas City, MO
  • Costa Mesa, CA
  • Dallas, TX
  • Cincinnati, OH
  • Los Angeles, CA
  • Pasadena, CA
  • Prince George County, MD


February 15, 2017

New Templates

Linguistically Isolated Households

Use this template to discover areas with high concentrations of linguistically-isolated households. The charts included in the toolbar on the left display primary languages spoken as well as the region of origin for those who are foreign born. This data could be useful to anyone looking to enhance community engagement by ensuring that public communication is carried out in the language of ALL households in an area. 

The U.S. Census Bureau defines a limited English-speaking household as “a household in which no member of the household 14 years old and over speaks only English, or speaks a non-English language and speaks English very well.” 

Questions this template helps you answer:

  • Where are there concentrations of limited English speaking households?
  • Where would it be beneficial to produce public engagement materials in additional languages?
  • In which languages should public communications be disseminated?

Locations with Highest Fire Risk - 50+ Units in Structure (for KC)

Find locations with the highest fire risk due to a high concentration of housing units within a single structure. This template identifies areas with a high concentration of buildings with with 50+ housing units. It is suggested that these locations have a higher fire risk due to the large square footage of structures with 50+ housing units. Charts attached allow you to explore the number of units in structures within each tract (Units in Structure) and and predict the growth in housing units in these areas based on historical time series and projections through 2020 (Time Series: Housing Units).

Questions this template helps you answer:

  • What locations are at the highest risk for fire?
  • What is the expected change in housing units by 2020? 

February 1, 2017

New National Data

LODES 2014 Update
(LEHD Origin-Destination Employment Statistics)

LODES data includes details describing the location of residents and the types of industries they are employed within, as well as the location of workplaces and the industries they serve. 

Datasets:

  • Employment Industry by Job Location 2003
  • Employment Industry by Job Location 2013
  • Local Job Density
  • Local Retail Job Density
  • Number of Jobs
  • Number of Jobs 2003
  • Number of Jobs 2013
  • Number of Jobs Between 2003 and 2013
  • Percent Change - Jobs 2003 to 2013
  • Household Density
  • Employment Industry by Job Location 2014
  • Number of Jobs 2014
  • Number of Jobs Between 2003 and 2014
  • Percent Change - Jobs 2003 to 2014

New Templates

1. Trends

Employment Growth & Decline 2000-2013

Use this template to see changes in the level of employment between 2000 and 2013. Employment levels can help show a community’s degree of economic activity and security over time. 

Areas in green saw a decrease in employment levels between 2000 and 2013, areas in pink and purple saw a significant increase, light blue areas were low in both 2000 and 2013, and dark blue areas had relatively high levels of employment at both points in time. Time Series link

Questions this Template helps you answer:

  • What areas have seen an increase/decrease in the level of employment (the number of employed persons) from the 2000 Decennial census estimates to 2010-2014 ACS 5-year estimates?
  • What areas saw employment growth/decline?
  • Are there recognizable spatial/geographic patterns in the change in employment over time? (For example, KC metro saw the greatest employment growth in the outer-ring suburbs.)

2. Building Conditions

Buildings Potentially Eligible for National Register of Historic Places

Use this template to identify potential locations for buildings eligible for the National Register of Historic Places. In the United States, age determination is one of the steps used when evaluating a structure for potential listing on the National Register of Historic Places. Generally, structures 50 years or older must be evaluated using the National Register Criteria for Evaluation.

In 2017, structures built in 1967 or earlier meet the guideline of being at least 50 years old. Since the American Community Survey breaks down building age by decade the count of buildings in this map is set to 1969 or earlier, therefore, this is a slight overestimate of the number of buildings built before 1967.

Questions this Template helps you answer:

  • How many buildings are potentially eligible for the National Register of Historic Places?
  • Where are there high or low concentrations of buildings potentially eligible for the National Register of Historic Places (built 50 years ago or more)?

Potential Buildings with Lead Paint

Use this template to find the locations of buildings in your project area that have the potential to contain lead paint. This map displays housing units built in 1979 or earlier from the latest ACS 5-year estimates since this dataset mostly closely approximates housing units built before lead paint was banned in 1978. 

The Center for Disease Control (CDC) states that "all houses built before 1978 are likely to contain some lead-based paint.” Lead exposure can have negative health impacts, especially among children*. Check out mySidewalk's blog for more information. 

Questions this Template helps you answer:

  • Where might residents have greater potential lead exposure?
  • What is the breakdown of housing unit age in my area of interest? (use building age of housing units chart)

3. Education

High Levels of Student Absenteeism

Use this template to identify areas where the rate of student absenteeism is greater than the most recently reported national average. The charts included in the toolbar allow you to investigate factors that could influence student absenteeism in your project area.

According to the U.S. Department of Education*, over 6 million students were chronically absent from school (missing 15 days or more) in the 2013–2014 school year. That’s 1 in 7 students, or 14 percent of the student population. A 2010 study found that the main factors associated with school absenteeism include younger age, male sex, and lower levels of parental income and education.

Questions this Template helps you answer:

  • Where is the rate of student absenteeism higher than the national average?
  • What factors might play a role in student absenteeism in my area of interest?

High Level of Educational Attainment

Identify areas with the highest level of educational attainment in your area. This template lets you explore the characteristics of areas in the top quartile of percent college educated. In 2015, 32.6% of the population aged 25 and older held a bachelor’s degree or higher. Charts attached show trends in the number of people over 25 holding a bachelor’s degree or graduate degree. Educational attainment metrics are often used in planning, economic development, and general demographic analyses.

Questions this Template helps you answer:

  • What areas have the highest level of educational attainment?
  • What areas are in the top quartile for percent college educated?

Percent High School Educated

Explore the distribution of residents with a high school degree or equivalent credentials. The map displays the percent of residents 25 and over who have received a regular high school degree, GED, or alternative credentials. The high school graduation rate in the U.S. reached a record high for the 5 year in a row during the 2014-2015 school year. The U.S. Department of Education reported a high school graduation rate of 83% for the 2014-2015 school year. The 2014-2015 school year marked the fifth consecutive year of record-setting high school graduation rates. This information can be used to compare the overall rate of high school graduation with the graduation rate for specific demographic groups to identify at-risk groups.

Questions this Template helps you answer:

  • What is the distribution of residents with a high school degree or equivalent credentials?
  • How does your area compare to the record-setting national rate of high school graduation?

4. Housing 

Median Age of Housing 

This template shows the average age of buildings in your project area. Use the chart in the toolbar to explore the number of housing units built each decade from 1940 until today.

Building age is an important metric used to identify concentrations of older homes that are likely to be less energy-efficient. This map can be used to identify areas where energy-efficient renovation is most likely needed.

Questions this Template helps you answer:

  • When did a particular area see the greatest boom in housing development?
  • When was housing constructed in my community or project area?
  • In which areas might residents be living in homes that are less energy efficient? 

What areas are likely to have the least energy-efficient homes? 

Use this template to identify areas where where energy-efficient renovation might be most effective. The lighter the gradient of green, the older the housing unit.

Building age is an important metric used to identify concentrations of older homes that are likely to be less energy-efficient. This information can be used to identify areas where residents are likely to qualify for energy efficiency and conservation resources, which are available through several state and federal programs.

Questions this Template helps you answer:

  • In which areas might residents be living in homes that are less energy efficient? 
  • To which areas should Community Development groups (for CDBG Grants, state grants/funds) send mailers in order to get people to apply for grants? Which residents might qualify?
  • When was housing constructed in my community or project area?

Opportunity for New & Rehabilitated Affordable Housing

Use this template to identify areas that would benefit the most from affordable housing efforts. The map shows the average location affordability for very low income individuals. HUD-qualified census tracts are included to indicate areas in which at least 50% of households have an income less than 60% of the Area Median Gross Income (AMGI). Read this article for more information on HUD-qualified census tracts.

Location affordability is a combined measure of housing and transportation costs—this measure indicates the percentage of income spent on housing and transportation for low income individuals. This information helps identify where the rehabilitation of housing units, inclusionary zoning, subsidies, and grant programs might be most effective.

Use this map with mySidewalk’s “Access to Employment” and “Percentage of Population that Commutes by Public Transit” maps to identify unaffordable areas near job centers and public transportation.

Questions this Template helps you answer:

  • Where should we focus efforts to promote the development of affordable housing?
  • Where should decision-makers focus efforts to encourage development near job centers and discourage sprawl?

Cost Burdened Renters (>30% of Income)

Use this template to locate renters who are “cost burdened,” i.e. those who spending more than 30% of their income on housing. According to the U.S. Department of Housing and Urban Development (HUD), “Families who pay more than 30 percent of their income for housing are considered cost burdened and may have difficulty affording necessities such as food, clothing, transportation and medical care.” The information in this map can help local affordable housing advocates, city planners, and developers identify where there is a greater need for affordable housing.

HUD estimates that “12 million renter and homeowner households now pay more than 50 percent of their annual incomes for housing. A family with one full-time worker earning the minimum wage cannot afford the local fair-market rent for a two-bedroom apartment anywhere in the United States.”

For more affordable housing information and where residents can find assistance, visit the U.S. Department of Housing and Urban Development.

Questions this Template helps you answer:

  • Who needs affordable housing?
  • Where should we locate affordable housing programs/incentives?

Housing Density

Explore the density of housing units (housing units per acre) across your area of interest. According to the Community Development Initiative*, higher densities can provide benefits including “more efficient use of infrastructure such as roads and water pipes,” and can help provide new options for housing. Housing density can be a controversial issue; with some advocating for high density development that allows for more walkable places and increased housing options, with others fearing increased traffic and parking problems. This map lets you explore the distribution of housing unit density across your area of interest. Charts attached let you create a more detailed profile of housing density in your community or project area; trends in the estimated number of housing units from 1990 through 2020, and the estimate number of housing units by units in each structure.

Questions this Template helps you answer:

  • What is the number of housing units per acre in your area of interest?
  • What is the distribution of housing unit density across your city?
  • Which areas have the highest/lowest density?
  • What are the most densely populated areas of your city?

Average Household Size

Examine the distribution of household size (the number of family members per household) in your community or project area. The map displays average household size for households in each census tract. Average household size is calculated by dividing the number of family members by the total number of families (or family householders) within the area. Average household size has declined from 3.29 people in 1960 to 2.58 in 2010; with a steady increase in one-person households. This information allows you to identify outliers in the trend of increasingly smaller household size, and the general tendency of urban areas to have smaller households and suburbs to have slightly larger households.

Questions this Template helps you answer:

  • What is the average household size (number of family members per household) of your area of interest?
  • What is the distribution of household size across your city?

5. Jobs & Workforce

Middle-Skill Jobs

Use this template to identify the estimated level of employment in middle-skill occupations. In accordance with the Urban Institute and the National Skills Coalition, we define middle skill jobs as those persons employed in occupations that "generally require some significant education and training beyond high school but less than a bachelor's degree." These include three occupational categories: office and administrative support occupations; construction and extraction occupations; and installation, maintenance, and repair occupations.

Areas showing a darker gradient of green have a higher level of residents employed in middle-skill jobs. Use this information to inform business development decisions; including the attraction, and retention of businesses employing local residents. Using this information you can also identify where residents and local businesses would benefit from the promotion of certificate and associate degree programs, including employer-provided training and community college programs.

Questions this Template helps you answer:

  • Where are there enough middle skill workers to support new business development in need of workers with these skills?
  • What areas are succeeding in supporting middle-skill jobs for residents with more than a high school degree but less than a college diploma?
  • What areas might attract workers with more than high school education but less than a traditional four-year college degree?
  • Where can middle-skill workers afford to live?

Access to Retail

Better understand the pattern of retail activity across your area. The map presents the retail job access index from the U.S. Department of Housing and Urban Development Location Affordability Program. The retail job access index is a measure developed to estimate both the quantity of and residents' access to the retail jobs in a region. This information is useful for understanding where residents have greatest access to retail activity. Access to retail activity helps planners and developers understand the pattern of retail access across a particular area, and where there is the greatest opportunity (or need) for retail development. Use this information with a measure of household income to identify areas with greatest potential for new development.

Questions this Template helps you answer:

  • How accessible are retail establishments to residents?
  • What areas have high/low access to retail?
  • Where are the retail centers located?

6. Income & Wealth

Local Income Inequality 

What is the level of income inequality in your area? The Gini Index is a statistical measure of income inequality ranging from 0 to 1. “A measure of 1 indicates perfect inequality, i.e., one household having all the income and rest having none. A measure of 0 indicates perfect equality, i.e., all households having an equal share of income” (U.S. Census Bureau). It is the most commonly used index of income concentration and inequality.

Charts attached let you further explore the impact of income inequality in your area of interest, such as a high housing cost burden (>30% rent as a percentage of income), racial segregation, and high levels of unemployment.

Question this Template helps you answer:

  • What is the state of income inequality in your community or project area?

Highly Affluent Households

Identify the most affluent areas across your community or project area. The map shows the percentage of households within each census tract estimated to have a household income of $200,000 or greater. The Time Series: Median Income chart shows you trends in median household income from 1990 through the most recent ACS 5-year Estimates and projections for 2016, 2018, and 2020. This template also includes a breakdown of household income and home value. Among other insights, this information helps you identify areas likely to have the highest amount of disposable income.

Questions this Template helps you answer:

  • What are the most affluent areas across your community?
  • What areas are likely to have the highest amount of disposable income?

January 17, 2017

1. American Community Survey (ACS)

The ACS is an ongoing survey that samples about 3.5 million addresses per year, collecting data on a wide range of demographic, social, economic, and housing characteristics. The 5-year estimates were released December 8, 2016 via the Census Bureau, and all mySidewalk datasets now reflect these updates. 

Datasets:

  • Families with Related Children
  • Middle-Skill Jobs
  • Building Built 1979 or Earlier - Potential for Lead Paint
  • Building Built 1969 or Earlier - Potentially Eligible for National Register of Historic Places
  • Building Age - Median
  • Heating Fuel for Housing Units (Utility Gas, Bottle/Tank/LP Gas, Electric, Fuel Oil/Kerosene, Coal/Coke, Wood, Solar, Other, None) [chart]

2. Time Series

mySidewalk’s time series data is a collation of data from multiple source years and projections for future years. This dataset makes it easy to compare past data to current data, or make predictions about how places may change in the future. We have data from 1990-2020, meaning you can make data-based past vs. current comparisons or future predictions within these time frames. 

All time series charts have the following years: 1990, 2000, 2010, 2012, 2016, 2018, 2020.

Datasets:

  • Time Series: Employed
  • Time Series: Family Householder - Female, No Husband
  • Time Series: Family Householder - Male, No Wife
  • Time Series: Family Householder - Married
  • Time Series: Family Household Size - 2 People
  • Time Series: Family Household Size - 3 People
  • Time Series: Family Household Size - 4 People
  • Time Series: Family Household Size - 5 People
  • Time Series: Family Household Size - 6 People
  • Time Series: Family Household Size - 7 or more People
  • Time Series: Nonfamily Household Size - 1 Person
  • Time Series: Nonfamily Household Size - 2 People
  • Time Series: Nonfamily Household Size - 3 People
  • Time Series: Nonfamily Household Size - 4 People
  • Time Series: Nonfamily Household Size - 5 People
  • Time Series: Nonfamily Household Size - 6 People
  • Time Series: Nonfamily Household Size - 7 or more People
  • Time Series: Income to Poverty Ratio Below 50%
  • Time Series: Income to Poverty Ratio 50 to 99%
  • Time Series: Income to Poverty Ratio 100 to 124%
  • Time Series: Income to Poverty Ratio 125 to 149%
  • Time Series: Income to Poverty Ratio 150 to 184%
  • Time Series: Income to Poverty Ratio 185 to 199%
  • Time Series: Income to Poverty Ratio 200%
  • Time Series: Marital Status - Divorced
  • Time Series: Marital Status - Married
  • Time Series: Marital Status - Never Married
  • Time Series: Marital Status - Widowed
  • Time Series: Family Households with Related Children
  • Time Series: Population Over 18
  • Time Series: Units in Structure - 1 Attached
  • Time Series: Units in Structure - 1 Detached
  • Time Series: Units in Structure - 2
  • Time Series: Units in Structure - 3 to 4
  • Time Series: Units in Structure - 5 to 9
  • Time Series: Units in Structure - 10 to 19
  • Time Series: Units in Structure - 20 to 49
  • Time Series: Units in Structure - 50 or more
  • Time Series: Units in Structure - Mobile Homes
  • Time Series: Occupied Housing Units, Vehicles Available - None
  • Time Series: Occupied Housing Units, Vehicles Available - 1
  • Time Series: Occupied Housing Units, Vehicles Available - 2
  • Time Series: Occupied Housing Units, Vehicles Available - 3 or More

New Templates

1. Trends

Change in Private School Enrollment: 2000 to 2012

Use this template to identify areas that saw growing (or declining) private school enrollment between 2000 and 2012. This information can help educators, park planners, and city planners see how demographic changes affect the educational system. According to a recent report from the National Center for Education Statistics “the percentage of students in private elementary and secondary schools declined from 11.7 percent in fall 2001 to 9.6 percent in fall 2011,” while public school enrollment has been increasing.

Questions this template helps you answer:

  • What areas have seen an increase/decrease in the number of children enrolled in private schools (private school includes home schooling)?
  • How does your community or project area compare to the national trend of increasing public school enrollment and declining private school enrollment?
  • What areas have growing/declining private schools?
  • What areas are projected to see increased enrollment in private schools?
  • What areas are likely to see growth/decline in private school enrollment in the future (use time series chart included in this template)?

Change in Public School Enrollment: 2000 to 2012

Use this template to identify areas that saw growing (or declining) public school enrollment between 2000 and 2012. This information can help educators, park planners, and city planners see how demographic changes affect the educational system. According to a recent report from the National Center for Education Statistics “total public elementary and secondary enrollment is projected to increase every year from 2014 to 2024.”

Questions this template helps you answer:

  • What areas have seen an increase/decrease in the number of children enrolled in public schools?
  • How does your community or project area compare to the national trend of increasing public school enrollment and declining private school enrollment?
  • What areas have growing public schools?
  • What areas are projected to see increased enrollment in public schools?
  • What areas are likely to have seen a school closing between 2000 to 2012?
  • What areas are likely to see growth/decline in public school enrollment?
  • Are there areas that have seen an increase in the number of children (see time series: population under 18 chart) but a decrease in public school enrollment?

Change in Population Under 18: 2000 to 2012

This template displays changes in the number of children (population under 18) between 2000 and 2012. With this map you can identify areas where families with children are likely to be living—these locations could be ideal for public services (including parks and recreation activities) that address the needs of families with children.

Questions this template helps you answer:

  • What areas have seen an increase in the number of children under 18 from the 2000 to 2012?
  • What areas have seen a decrease in the number of children under 18 from the 2000 to 2012?
  • Where is it likely for there to be more families with children, and thus a greater need for public services (including parks and rec. activities) that address the needs of families with children?

Projected Change in Population Under 18: 2012 to 2020

Use this template to identify areas where it is projected that there will be a higher number of families with children in the future. The map and time series chart employed here show projected changes in the number of children (population under 18) between 2012 and 2020. This information is valuable for those involved in planning to provide services addressing the needs of families with children, such as education and parks & recreation activities.

Questions this template helps you answer:

  • What areas are projected to see an increase/decrease in the number of children under 18 from 2012 (the 2010-2014 ACS 5-year Estimates) to projected values for 2020?
  • What areas are projected to see an increase/decrease in children between 2012 and 2020?
  • Where is it likely for there to be more families with children, and thus a greater need for public services (including parks and rec. activities) that address the needs of families with children?

Change in Vacant Housing Units: 2000 to 2012

Use this template to identify changes in the number of vacant housing units between 2000 and 2012. Trends in the number of vacant housing units help to identify the degree of real estate activity occurring in particular areas over time. You can also use this map to identify areas where real estate investment or divestment has occurred between the 2000 Decennial Census and the 2010-2014 ACS 5-year estimates.

Questions this template helps you answer:

  • What the map demonstrates: What areas have seen an increase/decrease in the number of vacant housing units) from the 2000 Decennial census estimates to 2010-2014 ACS 5-year estimates?
  • What areas received greater/less attention in terms of real estate development?
  • Are there recognizable spatial/geographic patterns in the change in vacant housing units over time? (For example, KC metro saw the greatest increase in vacant HUs in east-side neighborhoods of Kansas City, notably splitting along Troost; while at the same time a decrease in areas just on the west side of Troost and in Kansas suburbs)

2. Families 

Percent Single Mother Families

Use this template to discover areas that have high concentrations of families headed by single females. This map would be a useful tool in determining areas that have the greatest need for programs supporting single mothers. According to U.S. Census Bureau data compiled by Single Mother Guide, “1 in 4 children under the age of 18 — a total of about 17.4 million — are being raised without a father and nearly half (45%) live below the poverty line.” 

Questions this template helps you answer:

  • Are there areas in my community or project area that contain a high percentage of single mothers?
  • What areas would benefit most from programs to support single mothers?
  • What is the age breakdown of children within single-mother homes?

Chronic Student Absenteeism and Single Mothers

Use this map to see where chronic student absenteeism and single mothers with children overlap. Read the corresponding blog post.

Questions this template helps you answer:

  • Are there areas in my community or project area that contain a high percentage of single mothers?
  • What areas experience a high level of chronic student absenteeism? 
  • What areas would benefit the most from programs to support single mothers and their children?

3. Vacancy

Percent Vacant Housing Units

Use this template to find the percentage of vacant housing units present across your area of interest. The darker the gradient of green, the higher the concentration of vacant housing units. This template can identify which communities are facing higher costs and lost property tax income due to concentrations of vacant housing units.

Questions this template helps you answer:

  • Where are there high (or low) concentrations of vacant housing stock?
  • What communities (municipalities) are facing higher costs, and lost property tax income, due to concentrations of vacant housing units?
  • Where is there likely to be vacant housing units to satisfy the demand for housing across my community or project area?

Homeowner Vacancy Rate

Use this template to see homeowner vacancy rates across your area of interest. The darker the gradient of green, the higher the concentration of vacant homeowner housing units. You can also use this template to compare homeowner vacancy in your community or project area to the national rate of 1.8%. According to the U.S. Census Bureau*, “national vacancy rates in the third quarter of 2016 were 6.8% for rental housing and 1.8% for homeowner housing.”

Questions this template helps you answer:

  • Where are there high (or low) concentrations of vacant homeowner (non-rental) units (a high/low homeowner vacancy rate)? 
  • What communities (municipalities) are facing higher costs, and lost property tax income, due to concentrations of vacant  homeowner housing units?
  • How does the burden of homeowner vacancy in your community or project area vary in relation to the national rate of 1.8%?
  • Where is there likely to be vacant homeowner housing units to satisfy the demand for housing across my community or project area?

Rental Vacancy Rate

Use this template to see rental vacancy rates across your area of interest. The darker the gradient of green, the higher the concentration of vacant rental housing units. You can also use this map to compare the burden of rental vacancy in your community or project area to the national rate of 6.8%. According to the U.S. Census Bureau*, “national vacancy rates in the third quarter of 2016 were 6.8% for rental housing and 1.8% for homeowner housing.” 

Questions this template helps you answer:

  • Where are there high (or low) concentrations of vacant rental units (a high/low rental vacancy rate)? 
  • Where is there likely to be vacant rental housing units to satisfy the demand for rental housing across my community or project area?

January 5, 2017

1. Civil Rights Data

“The purpose of the CRDC is to obtain data related to the obligation of public school districts and of elementary and secondary schools to provide equal educational opportunity. Since 1968, the CRDC has collected a variety of information, including student enrollment and educational programs and services data that are disaggregated by race/ethnicity, sex, English learner status, and disability, from public schools across the nation.” For more information, please read this article.

Datasets:

  • Student Enrollment
  • Student Enrollment Total
  • Student Enrollment with Limited English Proficiency 
  • Student Enrollment by Disability Designation [chart]
  • Student Enrollment by Race/Ethnicity [chart]
  • Student Enrollment by Sex [chart]
  • Gifted & Talented
  • Gifted and Talented Students
  • Gifted and Talented Students - Individuals with Disabilities Act
  • Gifted and Talented Students with Limited English Proficiency 
  • Gifted and Talented Students by Race/Ethnicity [chart]
  • Gifted and Talented Students by Sex [chart]
  • Teachers (Full-Time Equivalent)
  • Teachers (FTE)
  • Teachers (FTE) in First Year of Teaching
  • Teachers (FTE) in Second Year of Teaching
  • Teachers (FTE) Absent More than 10 Days
  • Schools Counselors (FTE)
  • Schools with Law Enforcement Officers
  • Teachers (FTE) Certification [chart]
  • SAT/ACT Participation 
  • Student SAT/ACT Participation
  • Student SAT/ACT Participation - Individuals with Disabilities Education Act
  • Student SAT/ACT Participation - Limited English English Proficiency
  • Student SAT/ACT Participation by Race/Ethnicity [chart]
  • Student SAT/ACT Participation by Sex [chart]
  • Absenteeism
  • Chronic Student Absenteeism
  • Chronic Student Absenteeism - Limited English Proficiency
  • Chronic Student Absenteeism by Disability Designation [chart]
  • Chronic Student Absenteeism by Race/Ethnicity [chart]
  • Chronic Student Absenteeism by Sex [chart]
  • School Expenditures 
  • School Salary Expenditures on Teachers
  • School Salary Expenditures on K-12 Teachers and Aides
  • School Salary Expenditures on Total Personnel
  • School Non-Personnel Expenditures

New Templates

1. Poverty

Poverty Dynamics: 2000-2010

Discover which areas saw an increase or decrease in the percent of households in poverty between 2000 and 2010. The datasets in this map are normalized by the total households in each respective year, which allows you to reliably compare changes in the level of household poverty by accounting for population growth—although it is important to note that several outliers may exist due to changes in household growth in areas that had a small number of households in our base year, 2000. 

Questions this template helps you answer:

  • What areas have seen an increase/decrease in households below the poverty level from 2000 to 2010?
  • What areas are projected to see high counts of households in poverty through 2020? (use time series chart: households below poverty)

Poverty Growth & Decline Through 2020

Determine which areas are projected to see growth or decline in poverty through 2020. Use the “Time Series: Households Below Poverty” chart to see counts of households below the poverty level projected through 2020. This template can help local decision-makers, including city planners, investigate the complex poverty dynamics in their area over time, and plan how to best promote the economic security of local residents.

Questions this template helps you answer:

  • What areas are projected to see an increase/decrease in households in poverty through 2020? (use time series: households below poverty chart)

Chronically Impoverished Areas

Identify areas where more than a quarter of households were living below the poverty level between 2000 and 2010. This template can help local decisions-makers, including city planners, investigate the complex poverty dynamics in the area over time, and plan how to best promote the economic security of local residents. The datasets in this map are normalized by the total households in each respective year, which allows you to reliably compare changes in the level of household poverty by accounting for population growth.

Questions this template helps you answer:

  • Where have there been persistently high levels of poverty from 2000 to 2010?
  • Have poverty levels increased or decreased in chronically impoverished areas?

2. Educational Attainment

Percent College Educated

Explore the distribution of residents with a college degree. The percentage of college- educated residents is presented alongside the type of bachelor’s degrees received and employment industry. This template also provides the breakdown of educational attainment. These datasets inform a local educational attainment profile useful for understanding the knowledge and skills of local residents.

Questions this template helps you answer:

  • What areas have high/low concentrations of college-educated residents (bachelor’s, master’s, professional school degree, or doctorate)?
  • What bachelor’s degrees have residents completed?

Disparity in Educational Attainment

Identify disparity in educational attainment across your area. Equitable access to education is a foundational aspect of thriving communities. Educational attainment has profound implications for residents’ ability to access living-wage employment and meet basic needs (e.g, adequate health care, access to food, shelter). The map shows areas - relative to other areas visible on the map - where there are a greater number of residents without a high school degree and areas where more residents have a bachelor’s degree. 

Questions this template helps you answer: 

  • Are residents with high educational attainment (a Bachelor’s degree) concentrated in particular areas? If so, what are the corresponding socio-economic and demographic characteristics of these areas?
  • Are residents with low educational attainment (no high school degree) concentrated in particular areas? If so, what are the corresponding socio-economic and demographic characteristics of these areas?
  • In which areas are residents struggling to access adequate education?

Very Low Educational Attainment

Identify areas where a higher number of residents have no high school education. Educational attainment has profound implications for residents’ ability to access living-wage employment and meet basic needs (e.g, adequate health care, access to food, shelter). This template can help decision-makers develop an educational attainment profile useful for understanding the knowledge and skills of local residents, and help to identify areas where efforts to promote increased educational attainment would be most impactful. 

Questions this template helps you answer:

  • Which areas have a higher/lower number of residents with low educational attainment?
  • Which areas have a higher/lower number of residents with less than a ninth-grade education?
  • In which areas are residents struggling to access adequate education?
  • Which areas have residents that may be struggling due to very low levels of educational attainment?

3. Other

High Housing & Transportation Costs

This map identifies areas where combined housing and transportation costs are greater the 55% of household income (10% higher than the affordability threshold used by the U.S. Department of Transportation). The map and the charts in the toolbar indicate possible factors that could lead to low location affordability. According to the U.S. Department of Transportation, the average American household spends 51% of income on housing and transportation.

Questions this template helps you answer:

  • In which areas do residents face high housing and transportation costs?
  • What factors might be leading to low location affordability?

Median Rent for a 2-Bedroom Dwelling

This template shows the median rent for two bedroom housing units in your community or project area. The data allows you to to see the affordability of different areas through the distribution of rent costs incurred by local residents. 

The template presents the median rent in each census tract for a two bedroom dwelling. The charts included provide an overview of housing characteristics and location affordability. 

Questions this template helps you answer:

  • What areas have relatively high/low median rent?
  • What areas are likely to be affordable for families looking to rent a two bedroom dwelling?
  • (Using chart 2) What is the corresponding amount of rent as a percentage of income in a particular area?
  • (Using chart 3) What is the corresponding average household size in a particular area?
  • (Using chart 1) What is the median rent for dwellings with one, two, or three bedrooms within a particular area? 

Commuters Who Bike to Work

Use this template to determine which parts of town have residents who commute by bicycle. This map can help pinpoint socioeconomic and demographic characteristics of areas where residents bike to work. 

The map is filtered to show only those areas where at least one resident is estimated to bike to work, so the chart data displays socioeconomic and demographic information only for those areas where residents bike to work. Compare resident travel time to work, household income, and other factors. The points on the map show where (if any) there have been fatal crashes involving cyclists in your community or project area.

Questions this template helps you answer:

  • What areas have residents who bike to work?
  • What are the socioeconomic and demographic characteristics of areas where some residents bike to work?
  • What areas have invested in infrastructure to support/encourage cycling as a mode of transportation?
  • Which areas are well suited for programs that encourage more residents to use a bicycle as means of transportation?
  • How safe are cyclists in different areas?

4. Locally Reported 

Locally Reported Non-Violent Crimes in Kansas City

This map displays the distribution of locally reported nonviolent crimes using data from law enforcement agencies across the country. Here you can see the total counts of locally reported nonviolent crimes by census tract, which allows you to identify high and low crime areas. Charts break down locally reported crimes by nonviolent and violent. 

Compare this map side-by-side with mySidewalk’s “Locally Reported Violent Crimes” template to see the varying distribution of nonviolent and violent crime.

Questions this template helps you answer:

  • What areas across your community or project area saw a relatively higher/lower number of nonviolent crimes reported in 2015?
  • What was the total number of nonviolent crimes reported in your community or project area in 2015?

Locally Reported Violent Crimes in Kansas City

This map displays the distribution of locally reported violent crimes using data from law enforcement agencies across the country. The map presents the total counts of locally reported violent crimes by census tract, showing high and low crime areas. The charts break down locally reported crimes by nonviolent and violent. 

Questions this template helps you answer:

  • What areas across your community or project area saw a relatively higher/lower number of violent crimes reported in 2015?
  • What was the total number of violent crimes reported in your community or project area in 2015?


December 21, 2016

1.  American Community Survey 2011-2015 (ACS)

The ACS is an ongoing survey that samples about 3.5 million addresses per year, collecting data on a wide range of demographic, social, economic, and housing characteristics. The 5-year estimates were released by the Census Bureau in December, and all ACS mySidewalk datasets now reflect these updates. 

New Datasets:

  • Median Age of Workers
  • Independent Living Difficulty [disability]
  • Average Household Size - Owner Occupied
  • Average Household Size - Renter Occupied 
  • Place of Birth [chart]
  • Median Rent by Bedrooms [chart]
  • Living Arrangements for Adults (18+)

2. Time Series 

Time Series data is a collation of data from multiple source years and projects for future years. This data can be useful to see how past data compares to current data, how a particular location has changed or is going to change over the years, as well as what the future projection for data might be in a particular location. 

Datasets:

  • Time Series: Population Under 18 (Children)
  • Time Series: Rent as a Percentage of Household Income
  • Time Series: Average Per Capita Income
  • Time Series: People Below Poverty Level
  • Time Series: Households Below Poverty Level
  • Time Series: Families Below Poverty Level
  • Time Series: Average Household Size
  • Time Series: Vacant Housing Units
  • Time Series: Occupied Housing Units
  • Time Series: Unemployed
  • Time Series: Not in Labor Force
  • Time Series: Enrolled in Public School
  • Time Series: Enrolled in Private School
  • Time Series: Not Enrolled in School
  • Time Series: Highest Education Attained - Graduate Degree
  • Time Series: Highest Education Attained - Bachelor's Degree
  • Time Series: Highest Education Attained - Some College
  • Time Series: Highest Education Attained - High School Diploma
  • Time Series: Highest Education Attained - No High School Diploma
  • Time Series: Commute Mean Travel Time
  • Time Series: Commute 12:00pm to 11:59pm
  • Time Series: Commute 10:00am to 11:59am
  • Time Series: Commute 8:30am to 9:59am
  • Time Series: Commute 7:30am to 8:29am
  • Time Series: Commute 6:30am to 7:29am
  • Time Series: Commute 5:30am to 6:29am
  • Time Series: Commute 12:00am to 5:59am

New Templates

1. Location of Industry-Specific Jobs

Location of Healthcare Jobs

This template is designed to help you find areas with a high/low concentration of healthcare and social assistance employment, while also allowing you to see the overall composition of employment industries in each area. Healthcare and social assistance jobs play an important role in any local economy. According to a report by the Bureau of Labor Statistics, the healthcare and social assistance sector is projected to become the largest employing sector by 2024, reaching 13.6 percent of total employment.

Questions this Template helps you answer:

  • Where is the healthcare industry concentrated?
  • How many people are employed in the healthcare industry?
  • What areas are likely to be in need of healthcare professionals?

Location of Manufacturing Jobs

This template is designed to see manufacturing jobs by job site location, as well as to identify areas with high and low concentrations of manufacturing activity. Manufacturing jobs continue to be an integral part of the U.S. economy. The manufacturing sector employed 12 million workers in 2013, representing nearly 9% of total employment in the United States. For more on the importance of manufacturing in the U.S., see this recent report from the Economic Policy Institute: “The Manufacturing Footprint and the Importance of U.S. Manufacturing Jobs.”

Questions this Template helps you answer:

  • What areas offer manufacturing jobs? 
  • How many people are employed in the manufacturing industry?
  • What is the economic condition of manufacturing districts?

Location of Jobs in Arts & Recreation

This template is designed to help you discover locations with high/low numbers of arts and recreation jobs. You can use these findings to determine where employment in the arts and recreation industry likely has the greatest impact on the local economy. 

The arts, entertainment, and recreation sector includes an array of establishments that provide services and/or operate facilities that meet the various cultural and recreational interests of residents. Often this sector plays a predominant role in attracting people to a particular area for tourism and increasing the attractiveness of an area for prospective residents.

Questions this Template helps you answer:

  • Where is the arts and recreation jobs located?
  • How many people are employed in arts and recreation industries?

Location of Jobs in Education

This template displays areas with high/low numbers of jobs in the education industry. Accessory charts allow you to further investigate the industry composition of jobs and employment trends in each area. The locations of colleges and universities are visualized within the map template to show where the largest employers in the education sector are located.

Questions this Template helps you answer:

  • Where is the education industry concentrated?
  • How many people are employed in the education industry?
  • What areas are likely to be in need of education professionals?

2. Millennials

Median Income and Millennials

This template is designed to help you see median income and the percent millennial population across your community or project area. Exploring the economic situation of millennials provides projection of future economic security of a particular area. 

This map is filtered to identify the median income in areas with a high concentration of millennials. This is accomplished by highlighting areas where the percent millennial population is in the 50th percentile. You can also explore the corresponding estimates of household income, employment industry, and wage breakdown by looking at the charts in the toolbar.

Questions this Template helps you answer:

  • Where might there be concentrations of high/low income millennials?
  • What areas are attracting high/low income millennials?

Millennials and Renting 

This template is designed to see areas in the region that are densely populated by millennials, and to see where the most millennials tend to rent. Millennials tend to make less money and rent longer than the generations before them. The charts in the toolbar show that many of the places densely populated by millennials also have high percentages of renters and low percentages of homeowners. 

Questions this Template helps you answer:

  • Where are high concentrations of millennials living?
  • Are millennials in this area renting or buying? 
  • What rental prices are millennials paying?

3. Other

Commuting Burden

This template identifies areas where average commute time is higher than the national average. This information can help planners identify areas where citizens spend an increased amount of time commuting to work. The charts included let you see commute type, vehicles per household, how many workers commute to each area, and commute origin and destination.

Identifying areas with a relatively high average commute time can help planners understand commuting patterns. According to the 2010-2014 ACS 5-year estimates, the national average commute time is 23.91 minutes.

Questions this Template helps you answer:

  • Where do residents have longer commute times?

Low Access to Primary Care

Access to adequate healthcare is an issue in both rural and urban settings. This template displays areas of ZIP codes with low counts of primary care physicians, identifying regions that could potentially struggle to access primary care. The map is filtered to show ZIP codes in the 50th percentile for counts of primary care physicians—it also shows the number of residences with and without health insurance. 

Questions this Template helps you answer:

  • Where do residents suffer from a lack of access to primary care physicians?
  • What is the number of uninsured residents in areas with low access to primary care?

Total Locally Reported Crimes 

Explore locally reported crime data from select law enforcement agencies across the country using this template. The map presents the total counts of locally reported crimes by census tract allowing you to identify high and low crime areas. Charts break down locally reported crimes by those which were nonviolent or violent. Income-to-poverty and employment levels for each census tract allow you to investigate commonly cited correlates of high/low levels of crime. 

(We’re adding locally reported crime data by request. If you’d like to visualize local crime data for your community, email [email protected].)

Questions this Template helps you answer:

  • What areas saw a relatively higher/lower number of crimes reported in 2015?
  • What was the total number of crimes reported in your community or project area in 2015?
  • Do areas with higher total crime counts also have lower levels of employment and income?


December 7, 2016

1. County Business Patterns (CBP): 

County Business Patterns data—such as total establishments—are highly useful for studying the economic activity of small areas, analyzing economic changes over time, and as a benchmark for other statistical series, surveys, and databases between economic censuses. Businesses use this data for analyzing market potential, setting sales quotas, and developing budgets. Government agencies use the data for administration and planning. State and local government offices often use Business Patterns data to assess business changes, develop fiscal policies, and plan future policies and programs.

Datasets:

  • Number of Business Establishments 2014 
  • Total Employed 2014

Templates:

Business Patterns: 2014 Total Employment & 2014 Total Establishments 

Questions these templates help you answer:

  • What is the employment count in this city?
  • What is the breakdown of employment by industry in my community or project area?
  • What is the business establishment count in this city?
  • What is the level of economic activity in my community or project area? 

2. Social Vulnerability Index:

The Social Vulnerability Index (SoVI) lets you compare the vulnerability of local residents to environmental hazards and natural disasters. It depicts the degree to which areas have access to the resources needed to adequately respond to environmental disasters. This data is available in 30 states (coastal and Great Lakes).  

Datasets: 

  • Average Vulnerability to Environmental Hazards
  • Vulnerability to Environmental Hazards (by count of Census Tracts) - High
  • Vulnerability to Environmental Hazards (by count of Census Tracts) - Medium
  • Vulnerability to Environmental Hazards (by count of Census Tracts) - Low
  • Vulnerability to Environmental Hazards (by count of Census Tracts) - Unknown

Templates:

Low Vulnerability to Environmental Hazards, Medium Vulnerability to Environmental Hazards, High Vulnerability to Environmental Hazards, and Vulnerability to Environmental Hazards 

Questions these templates help you answer:

  • What areas have a low, medium, and high level of societal vulnerability to environmental hazards? 
  • What is the social vulnerability score for areas in my community? 

3. Crash Fatalities

Looking at data for crash fatalities helps address how transportation planners across the country can better focus efforts to increase transportation safety and mobility. Use these datasets and Templates to help understand what factors could be contributing to crash fatalities in your community.

Datasets:

  • Motor Vehicle Crash Total Fatalities 2015
  • Motor Vehicle Fatal Crash for Non-Occupants 2015 - Cyclists
  • Motor Vehicle Fatal Crash for Non-Occupants 2015 - Pedestrians
  • Motor Vehicle Fatal Crashes 2015 - Selected Factors - Distracted Driver
  • Motor Vehicle Fatal Crashes 2015 - Selected Factors - Drowsy Driver
  • Motor Vehicle Fatal Crashes 2015 - Selected Factors - Drunk Driver

Templates:

Drunk Driver Involved, Drowsy Driver Involved, Distracted Driver Involved, Pedestrian(s) Involved,  Cyclist(s) Involved, and Crash Fatalities 2015

Questions these templates help you answer:

  • Where were drunk, drowsy, or distracted drivers involved in fatal crashes?
  • Where were pedestrians or cyclists involved in fatal crashes?

4. Physicians

Determine the number of clinically active primary care physicians, specialist physicians, OB-GYNs, and dentists in an area. This data can also help determine the degree of access to different types of healthcare in a community or project area, as well as the breakdown of local physicians by sex and age. This data aims to provide information about primary care resources and populations within small standardized areas, such as census tracts, that reflect patient utilization patterns and access to health care.

Datasets:

  • OB-GYNs (by count, age, sex)
  • Primary Care Physicians (by count, age, sex)
  • Specialist Physicians (by count, age, sex)
  • Dentists
  • Nurses

Templates: 

Primary Care Physicians, Specialist Physicians, Number of OB-GYNs, and Number of Dentists

Questions these templates help you answer:

  • Where are the primary care physicians located?
  • Are there areas that may struggle to access to primary care physicians?
  • What is the breakdown of primary care physicians by age and sex?

5. Minority Population

The percent minority population variable is used for demographic analysis in several planning contexts. Minority population is constructed here using ACS 2010-2014 5-Year Estimates, and is defined as the total population minus the white non-Latino population.

Datasets:

  • Total Population (Non-Hispanic White)

Templates:

Percent Minority Population

Questions this template helps you answer:

  • Where are there high/low concentrations of minority populations?
  • Where are there clusters of minority populations?

6. Employment Diversity & Housing

Employment diversity can be used to determine whether an area has a diverse labor market, while the employment-to-housing ratio can show whether an area has adequate housing for workers to reside near places of employment. A balance between a diverse labor market and a beneficial jobs-to-housing ratio can help identify areas likely to have shorter commute times, less single driver commutes, ample job opportunities for workers without vehicles, less traffic congestion, and better air quality.

Templates: 

Insufficient Housing Near Employment Centers 

Questions this template helps you answer:

  • What areas lack adequate levels of housing near employment centers?
  • What parts of town would benefit from increased affordable housing?

Employment Diversity

Questions this template helps you answer:

  • How diversified is the labor market in your community or project area?
  • How do different areas compare in terms of employment diversity?

Balanced Employment Diversity and Adequate Housing Stock

Questions this template helps you answer:

  • What areas have diverse employment and adequate housing?
  • What areas have ample job opportunities for workers without vehicles?
  • Where is it easy for workers to find adequate housing near areas with diverse employment opportunities?
  • What areas have potential for less traffic congestion? 

7. Time Series: Median Home Value

Median home value is a useful indicator of household wealth, and it's especially useful for helping planners and developers understand conditions in local housing markets. 

Templates:

Median Home Value: 2000-2010 & Median Home Value 2010-2020 

Questions these templates help you answer:

  • How did median home value change in my community between 2000-2010?
  • How might median home value change between 2010-2020?
  • What is the building age of housing units in my community?

8. Time Series: Median Household Income

Median income is a useful indicator of household wealth and economic security.

Templates: 

Median Household Income: 2000-2012 & Median Household Income 2010-2020

Questions these templates help you answer:

  • How did median household income change in my community between 2000-2012?
  • How might median household income change between 2010-2020?

9. Women in Workforce with Birth in Past Year 

Compare different areas by the number of women in the workforce with a birth in the last year. These datasets and Templates present the number of women employed and unemployed with a birth in the past year by those married and unmarried.

Datasets:

  • Women in Workforce Totals - with Birth in Past Year - Employed
  • Women in Workforce Totals - with Birth in Past Year - Unemployed
  • Women in Workforce with Birth in Past Year - Married and Employed
  • Women in Workforce with Birth in Past Year - Married and Unemployed
  • Women in Workforce with Birth in Past Year - Unmarried and Employed
  • Women in Workforce with Birth in Past Year - Unmarried and Unemployed

Templates:  

Women in Workforce with Birth in Past Year 

Questions this template helps you answer:

  • How many unmarried and married women in the workforce gave birth in the last year? 
  • How many unmarried and married women not in the workforce gave birth in the last year?

10. Patents

Patent data can be used to study the patterns of innovation. This data can inform regional and national policy makers of the comparative technological performances and profiles of different areas, the importance of geographical proximity for innovation, the spatial distribution of innovative and productive activity across regions, and the interaction and technical co-operation within and across regions. 

Datasets:

  • Total Patents 2010-July 2016
  • Patent Types 2010-2016 (Human Necessity - medical devices/machines, food processing, agriculture, cooking/kitchen; Performing Operations/Transporting, Chemistry/Metallurgy, Textiles/Paper, Fixed Constructions, Mechanical Engineering/Lighting/Heating/Weapons/Blasting Engines or Pumps, Physics, and Electricity)

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