TECH MEMO #3: ENVIRONMENTAL JUSTICE
ANALYSIS
February 2016
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ENVIRONMENTAL JUSTICE ANALYSIS | FINAL
Contents
Transit Markets IN Greater Minnesota ........................................................................................................................... 1
Market Analysis ..................................................................................................................................................... 2
Environmental Justice Populations ............................................................................................................................. 7
Transit Dependency Index .................................................................................................................................... 8
Minority Populations ................................................................................................................................................. 21
District Minority Maps .......................................................................................................................................... 21
Appendix: Transit Dependency Index .......................................................................................................................... 31
Data Sources: ...................................................................................................................................................... 31
Methodology: ....................................................................................................................................................... 31
POTENTIAL TRANSIT SERVICE DESIGN INDEX .................................................................................................. 31
Data Sources:...................................................................................................................................................... 31
Methodology: ....................................................................................................................................................... 32
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Transit Services Hierarchy Pyramid....................................................................................................... 2
Supported Transit Service by Density.................................................................................................... 3
Statewide Population Density by County ............................................................................................... 4
Statewide Employment Density by Census Block.................................................................................. 5
Statewide Job Density by Census Tract ................................................................................................ 6
Greater Minnesota Demographics ......................................................................................................... 8
District 1 Transit Dependency .............................................................................................................. 12
District 2 Transit Dependency .............................................................................................................. 13
District 3 Transit Dependency .............................................................................................................. 14
District 4 Transit Dependency .............................................................................................................. 15
District 6 Transit Dependency .............................................................................................................. 16
District 7 Transit Dependency .............................................................................................................. 17
District 8 Transit Dependency .............................................................................................................. 18
Statewide Transit Dependency ............................................................................................................ 19
Minority Population .............................................................................................................................. 21
District 1 Minority Populations.............................................................................................................. 23
District 2 Minority Populations.............................................................................................................. 24
District 3 Minority Populations.............................................................................................................. 25
District 4 Minority Populations.............................................................................................................. 26
District 6 Minority Populations.............................................................................................................. 27
District 7 Minority Populations.............................................................................................................. 28
District 8 Minority Populations.............................................................................................................. 29
Statewide Minority Populations ............................................................................................................ 30
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TRANSIT MARKETS IN GREATER MINNESOTA
Quantifying the need for public transportation services allows communities to see where concentrations of people,
jobs and vulnerable populations live. This informs transit provision, including route alignment, service levels
(frequency, hours of operation) and fares. Need is an output of mathematical and Census calculations; however,
quantifying need does not automatically translate into transit demand. Need is always greater than demand. Those
who need transportation find many options for getting around, including human services transportation, taxis, family,
friends or transit. Translating transportation need into demand for public transit services is a challenge; however, as
a first step quantifying need provides an initial benchmark. National experience suggests there are two factors that
have more influence over the need for transit service than any other:
•
Density – places with high concentrations of workers and/or residents – is the most important factor in
determining transit ridership. Densely developed areas – like downtowns in large or small cities, university
and college campuses and hospitals – have many people traveling to and from them. This common trip
pattern can be easily served by public transit. In addition, densely developed areas are also more likely to
have safe walking environments with sidewalks and crosswalks, so people can safely get to and from
transit routes.
•
Demographic Characteristics – Research and experience of transit agencies shows that people with
certain demographic characteristics tend to use public transportation due to lack of an alternate option.
Households without access to a vehicle or people with low incomes, for example, often rely on public
transportation for all or a large portion of their travel. Likewise, teenagers who may not have access to a car,
or older adults who may be less inclined to drive due to age or a disability, also suggest a reliance on public
transportation.
The types of public transportation that can meet demand vary by community context. In dense urban areas, a larger
variety of services exists due to higher population densities and land use mixes. In rural areas, driving may be the
predominant travel option due to long distances between dispersed destinations, but a basic service might be in
place to serve those who have no other transportation option. Figure 1 graphically displays this relationship. A wide
range of transit services, ranging from high capacity modes such as rail and bus rapid transit (BRT) to lower capacity
modes such as dial-a- ride and community shuttles, can serve transit needs. Each of these services has different
strengths and weaknesses and is designed to accommodate different types of communities and riders. Rail systems,
for example, are very expensive to construct but can carry high volumes of passengers efficiently on a fixed-route
when operating through densely populated corridors. Dial- a-ride services carry far fewer riders in comparison but
work well in areas with lower populations and offer a higher level of service for people with unique specialized
destinations or who need more assistance traveling.
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Figure 1 Transit Services Hierarchy Pyramid
Ultimately, transportation connects people to jobs, activities and basic services, like medical appointments and
shopping. Every community has people who cannot reach jobs and basic services on their own. For the most part,
these individuals use transportation services provided by other federal and state human and medical service
programs, like Medicaid (see bottom two levels of pyramid). These services are typically mandated by the federal
government, and are available statewide, but are limited to specific clients for specific trip purposes. Public
transportation, on the other hand, includes transportation services available to members of the general public traveling
for any purposes. The market analysis focuses on identifying the need for general public transportation services as
determined by development patterns and demographic characteristics.
MARKET ANALYSIS
Successful fixed-route public transportation (service running on a set path with time points) achieves highest
efficiency levels in communities where clusters of people and destinations exist. The purpose of public transportation,
however, is also to provide opportunities and mobility to disadvantaged populations. Therefore to gain an
understanding of where potential transit needs exist, an analysis of both population and job density overall was
conducted, with an additional assessment of disadvantaged populations specifically.
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Population & Employment
Population and employment densities are important factors because the clustering of people and jobs helps
determine where transit routes should run. Most transit systems consist of a mix of “choice riders,” or people who own
a car or have access to a car but choose to take transit, and “transit dependent” riders, or those who do not have any
other option. This first step of analyzing overall population and employment density provides insights into the choice
rider market. Serving dense population centers also makes transit more financially efficient (Figure 2).
Figure 2 Supported Transit Service by Density
Population
Figure 3 shows statewide population density by county. Overall, the counties of Greater Minnesota have a far lower
population density than the seven counties that comprise the Twin Cities region. With the exception of Olmsted
County in District 6, Greater Minnesota Counties with the highest population densities are located around the urban
fringe of the Twin Cities Area in counties such as Sherburne and Wright. Lower population densities are widely
distributed across the western and northern halves of the state with many of the counties along the border of the
Dakotas and Canada at countywide population densities no higher than 10 people per square- mile.
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Figure 3 Statewide Population Density by County
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Employment in Greater Minnesota
Figure 4 displays proportionally-sized symbols representing employment density by Census Block. Many of the state’s
largest employers are located in the Twin Cities region; however, considerable nodes of employment density exist
around Greater Minnesota’s largest cities such as Rochester, Duluth, St. Cloud and Mankato. Despite the low density
of jobs throughout much of the state (Figure 5), the wide distribution of employment sites shown in Figure 4 highlights
the importance of countywide and regional commuter options in Greater Minnesota.
Figure 4 Statewide Employment Density by Census Block
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Figure 5 Statewide Job Density by Census Tract
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Environmental Justice Populations
Transportation is often a primary barrier cited by individuals who are unable to access employment, medical services, and
educational opportunities (among other key public services). With this taken into consideration, analysis of environmental justice
is a vital component of a broader evaluation of statewide transportation policies and investment priorities. Presidential Executive
Order 12898, issued in 1994, directed each federal agency to “make achieving environmental justice part of its mission by
identifying and addressing, as appropriate, disproportionately high and adverse human health or environmental effects of its
programs, policies and activities on minority and low-income populations.” The order builds on Title VI of the Civil Rights Act of
1964 which prohibits discrimination on the basis of race, color, or national origin. There are three fundamental principles of
environmental justice:
•
To avoid, minimize, or mitigate disproportionately high and adverse human health and environmental
effects, including social and economic effects, on minority and low-income populations.
•
To ensure the full and fair participation by all potentially affected communities in the transportation decision
making process.
•
To prevent the denial of, reduction in, or significant delay in the receipt of benefits by minority and lowincome populations.
The Executive Order and subsequent orders by the U.S. Department of Transportation define minority and low-income
populations as:
•
Black – a person having origins in any of the black racial groups of Africa.
•
American Indian and Alaskan Native – a person having origins in any original people of North America and
who maintains cultural identification through tribal affiliation or community recognition.
•
Asian – a person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian
subcontinent.
•
Native Hawaiian or Other Pacific Islander – a person having origins in any of the original peoples of Hawaii,
Guam, Samoa, and other Pacific Islands.
•
Hispanic – a person of Mexican, Puerto Rican, Cuban, Central or South American, or other Spanish culture
or origin, regardless of race.
•
Low-income – a person whose household income (or in the case of a community or group, whose median
household income) is at or below the U.S. Department of Health and Human Services poverty guidelines.
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While not specifically identified by Title VI or the Executive Order, MnDOT chooses to expand its Environmental
Justice analyses to include persons age 65 and older, persons age 16 and younger, persons with limited English
proficiency, and households with zero vehicles because these additional population groups have unique
transportation needs. Figure 6 shows an overview of demographic and environmental justice indicators in Greater
Minnesota compared to the state and nation as a whole.
Figure 6 Greater Minnesota Demographics
Geography
Total
Populations
Senior
Population
(65+)
Youth
(Under
18)
Low-Income
Population
(1)
Zero-vehicle
Households
Population
with
Disabilities
Population
with Limited
English(3)
United States
311,536,594
13%
11%
32%
9%
15%
4.5%
Minnesota
5,347,740
13.3%
23.9%
11.5%
7.1%
10.1%
2.1%
Greater
Minnesota
2,458,193
15.7%
23.5%
12%
6.1%
11.4%
1%
[1] Low-income populations are defined by households making up to 150% of the poverty level. [2] Age 18 or older. [3] Age 5 or
older who speak English “less than well.”
Source: ACS 5-YR Estimates 2013
As shown in Figure 6, Minnesota as a whole has above national average rates of environmental justice populations
such as seniors and youth, however it ranks considerably below national averages for groups such as low-income,
disabled, and limited English proficiency. Compared to the state as a whole, shares of environmental justice
populations are fairly similar in Greater Minnesota, with a slightly higher percentage of seniors, persons with
disabilities, and low-income households. Greater Minnesota’s share of zero-vehicles households is slightly below
that of the state as a whole, while the population with limited English proficiency is less than half that of the
statewide average.
TRANSIT DEPENDENCY INDEX
A transit dependency index was developed to highlight areas with groups that have a higher than average propensity
for transit use. Factors considered in this analysis include densities of the following: population, employment, youth
(under 18), older adults (age 65+), households without a vehicle, persons with a disability, limited English ability, and
low-income households. Further explanation of the methodology for this analysis is provided in the Appendix.
•
Population and Employment Density – Population and employment sites are key indicators of where
transit may succeed. In order to tie together the other factors of the transit dependency index spatially,
population and employment density is used to break the statistical values for each demographic factor.
•
Youth Density – Youth, many of whom do not have a driver’s license or access to a vehicle, exhibit a higher
overall need for transit than the general population. In Greater Minnesota the percentage of the population
below the age of 18 is 23.5%, which is similar to the total statewide share of 23.9%. Greater Minnesota
counties with the highest shares of youth population include Mahnomen, Wright, Sherburne, and Dodge
Counties with those aged under 18 comprising over 24% of the total population.
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•
Older Adult Density – Older adults (age 65 and older) typically use public transportation more frequently
than the general population. Older adults often exhibit higher demand for transit as they become less
capable or willing to drive themselves, or can no longer afford to own a car on a fixed income. Greater
Minnesota counties with the highest shares of senior population include Aitkin, Traverse, Big Stone, and
Lincoln Counties with those aged 65 and older comprising over 28% of the total population.
•
Zero Vehicle households – One of the most influential indicators of transit need is whether a household
has access to a car. This indicator may represent households without the economic means of owning a
vehicle, households that choose not to own a car or individuals who are unable to drive, such as senior
citizens and persons with disabilities. In Greater Minnesota, 6.1% of households do not have a vehicle
available, which is slightly less than the statewide share of 7.1%. In particular, Greater Minnesota counties
with the highest percentages of zero vehicle households include Mahnomen (10.7%), St. Louis (9.5%), and
Koochiching (9%).
•
Low-Income Populations – Households are deemed low-income if they earn up to 150% of the federal
poverty threshold. In Greater Minnesota, 12% of households are classified as low-income, slightly above the
statewide share of 11.5%. In particular, Greater Minnesota counties with the highest percentages of lowincome households include Mahnomen (26.2%), Beltrami (21.9%), and Blue Earth (19.2%).
•
Persons with Disabilities – Persons with disabilities often are heavily dependent on public transit service.
Of residents over the age of 17 in Greater Minnesota, 11.4% have a disability, which is slightly higher than
the statewide average of 10.1%. In particular, Greater Minnesota counties with the highest percentages of
persons with disabilities include Aitkin (18.6%), Clearwater (17.1%), and Koochiching (17.1%).
•
Persons with Limited English Proficiency – Limited English proficiency correlates closely to income and
can be another indicator of a household’s relative dependency on transit. In Greater Minnesota, only 1% of
residents speak English “less than well,” which is relatively low compared to the statewide average of 2.1%
and the nationwide average of 4.5%.
The index aggregates all segments of population that are most likely to depend on transit for their transportation
needs, and shows where the highest densities of these populations are located in Greater Minnesota. Maps showing
the transit dependency index for each of the districts in Greater Minnesota are shown in Figure 7 through Figure 13
and summarized below.
District 1
Overall, transit dependency across District 1 is low with a handful of census tracts exhibiting moderate transit
dependency spread across the region (Figure 7). As can be expected with larger urban areas, tracts with a high level
of transit dependency exist around the Cities of Duluth and Hibbing, albeit their presence is limited.
District 2
Overall, transit dependency across District 2 is low to moderate with higher levels more widely distributed across the
eastern half of the region, particularly in Beltrami, Cass, Clearwater, and Koochiching Counties (Figure 8). Tracts with
the highest levels of moderate dependency exist around the District’s areas of greatest population density around
Bemidji, Crookston, and East Grand Forks.
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District 3
Overall, transit dependency across District 3 is low; however there are a few areas of population that exhibit higher
levels of transit dependency such as Baxter, Buffalo, St. Cloud and Little Falls (Figure 9). Contained within central St.
Cloud are a couple of tracts that rate high on transit dependency.
District 4
Overall, transit dependency across District 4 is low to moderate. As with other districts, clusters of higher
dependency are located near population centers such as Detroit Lakes, Moorhead, and Pelican Rapids. Higher
transit dependency is particularly acute in Moorhead along the border with Fargo, ND in western Clay County (Figure
10).
District 6
Although the transit dependency index registers low across the rural areas of District 6, the region contains a
considerable number of cities and towns which contain tracts with a wide range of transit dependencies (Figure 11).
As can be expected with the largest urban center in Greater Minnesota, Rochester contains census tracts with some
of the highest transit dependencies across all seven districts. Other communities with higher than moderate levels of
transit dependency include Austin, Faribault, Northfield, and Winona.
District 7
Overall, transit dependency is low across District 7; however the City of Mankato as a significant urban center and
area with a considerable university population stands out as a location with moderate to high transit dependency.
Other communities with moderate transit dependency include Waseca and Worthington (Figure 12).
District 8
Overall, transit dependency across District 8 is low with moderate dependency centered on areas with higher
population densities. The City of Willmar is the main exception with a considerable portion of the community
registering as tracts with high transit dependency (Figure 13).
Statewide
Shown in Figure 14 is a map displaying transit dependency across Greater Minnesota. As showcased in the district
specific maps, the highest levels of transit dependency are reserved for areas of highest population density. In
general, higher levels of transit dependency in rural areas are wider spread across the northern half of the state, with
a band of lower transit dependency radiating from the fringes of the Twin Cities Metropolitan Area.
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Figure 7 District 1 Transit Dependency
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Figure 8 District 2 Transit Dependency
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Figure 9 District 3 Transit Dependency
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Figure 10 District 4 Transit Dependency
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Figure 11 District 6 Transit Dependency
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Figure 12 District 7 Transit Dependency
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Figure 13 District 8 Transit Dependency
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Figure 14 Statewide Transit Dependency
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Minority Populations
Distributions of minority populations have been included as part of this analysis to ensure that minority populations
are not disproportionately affected in an adverse manner from the outcomes of the Greater Minnesota Transit
Investment Plan. For this analysis minority populations are defined as all Census race categories except White Alone
(Not Hispanic or Latino) (Figure 15).
Figure 15 Minority Population
Total
Population
White
Alone
Black
Alone
Hispanic/
Latino
Asian
Alone
American
Indian
/Alaska
Native
United
States
311,536,594
63.3%
12.2%
16.6%
4.8%
0.7%
0.2%
0.2%
2.1%
Minnesota
5,347,740
82.6%
5.1%
4.8%
4.1%
1%
0%
0.1%
2.2%
Greater
Minnesota
2,458,193
90.6%
1.5%
3.5%
1.3%
1.5%
0%
0.1%
1.5%
Geography
Native
Hawaiian/
Pacific
Islander
Other
Alone
Two or
More
DISTRICT MINORITY MAPS
District 1
Minority populations above 10% are dispersed across the district. Areas with the highest shares of minority
populations (above 25%) include northern Carlton County, central Pine County and central Duluth. As shown in the
previous section, central Pine County and central Duluth register as areas with moderate transit dependency (Figure
15).
District 2
The eastern half of District 2 which includes Beltrami, Cass, Clearwater and Itasca Counties contains census tracts
that demonstrate high shares of minority populations (above 25%). These tracts, which register as areas with
moderate transit dependency, generally correspond to the locations of tribal lands (Figure 16).
District 3
District 3 contains areas with high shares of minority populations in rural areas such as northwestern Mille Lacs
County and central Todd County in addition to clusters of moderate to high shares of minority populations in and
around the City of Saint Cloud. Some areas which exhibit rates of minority populations above 25%, such as
northwestern Mille Lacs County are the locations of tribal lands. Areas that exhibit higher minority populations in
District 3 show corresponding rates of moderate transit dependency; however, this is not exclusive to these areas
(Figure 17).
District 4
Census tracts with high shares of minority populations (above 25%) exist in the northern most portion of District 4 in
Becker County, Mahnomen County and the City of Pelican Rapids (Figure 18). Areas with the highest shares of
minority populations correspond to census tracts with moderate levels of transit dependency.
District 6
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Minority populations above 25% in District 6 primarily exist in major centers of population such as Rochester, Albert
Lea, Faribault and Owatonna. These communities, along with others such as Austin and Red Wing are
encompassed by census tracts with minority populations above 10% (Figure 19). Areas with the highest shares of
minority populations in District 6 correspond to census tracts with moderate to high levels of transit dependency.
District 7
Minority populations above 10% are dispersed across the district. Areas with the highest shares of minority
populations (above 25%) exist in the Cities of Saint James and Worthington. Worthington has moderate levels of
transit dependency; however, the areas in the District with the highest levels of transit dependency, located in central
Mankato, are census tracts with low to moderate shares of minority populations (Figure 20).
District 8
Census tracts with the highest shares of minority populations in District 8 exist in the Cities of Marshall and Willmar.
Census tracts in the City of Willmar with minority populations of 25% have correspondingly high levels of transit
dependency (Figure 21). Counties with large census tracts with moderate minority populations (11%-25%) include
Kandiyohi, Lyon, Redwood Renville and Yellow Medicine.
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Figure 16 District 1 Minority Populations
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Figure 17 District 2 Minority Populations
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Figure 18 District 3 Minority Populations
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Figure 19 District 4 Minority Populations
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Figure 20 District 6 Minority Populations
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Figure 21 District 7 Minority Populations
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Figure 22 District 8 Minority Populations
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Figure 23 Statewide Minority Populations
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APPENDIX: TRANSIT DEPENDENCY INDEX
The Transit Dependency Index presents concentrations of populations with higher public transportation needs based
on a set of demographic indicator characteristics. This index is relative to the study area as a whole, so the resulting
values do not hold any numeric significance other than providing a relative scale for low – moderate – high
dependency.
DATA SOURCES:
•
2013 ACS 5-year estimates (for all demographics except employment)
•
2013 LEHD On the Map (for employment)
Demographic Indicators: For Minnesota, we are including population density, employment density, youth (under 18)
density, senior (age 65+) density, percentage of households with zero vehicles, percentage of population with a
disability, percentage of population with limited English language ability, and percentage of population in poverty.
METHODOLOGY:
1. Download ACS (by tract) and LEHD (by block) data; tabulate LEHD data to tract-level
2. Join datasets to a Census tract shapefile
3. Calculate densities and percentages for each demographic indicator
4. Determine three natural break values (statistical) for each demographic. Assign each tract a category 1, 2 or
3 based on these break values.
5. Sum the category values (1, 2, or 3) for each demographic indicator to determine a summary index value for
all eight category values combined.
6. Symbolize based on this range of index values – low, moderate, and high (the higher the index value, the
more transit dependent the population).
Potential Transit Service Design Index
The purpose is to geographically illustrate population and employment density in a manner that shows the overall
transit level of service supported by each geography’s composite density. Nelson\Nygaard has compiled empirical
evidence of minimum population and employment densities required to support transit at various levels of service
(frequency). The evidence has been compiled and averaged for the tables below.
DATA SOURCES:
•
Total Population from the 2013
•
ACS 5-year estimates
•
Total Jobs (all jobs) from the 2013 LEHD On the Map
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METHODOLOGY:
1. Download ACS (by tract) and LEHD (by block) data; tabulate LEHD data to tract-level
2. Join both datasets to a Census tract shapefile
3. Calculate population density and job density for each tract
4. Determine the number of buses per hour supported for each density value (pop and emp)
Population/Acre
•
Buses per Hour
Employment/Acre
Buses per Hour
< 2.5
0
<2
0
2.5 – 8
0.5
2–4
0.5
8 – 16
1
4–8
1
16 – 31
2
8 – 16
2
31 – 47
4
16 – 24
4
47 – 92
6
24 – 48
6
> 92
12
> 48
12
Add the population buses per hour and the employment buses per hour to generate total buses per hour.
The results are then symbolized based on the following table:
Frequency
Buses per Hour
> 60 min
0 – 0.75
30 - 60 min
.76 - 1.75
<= 30 min
>1.75