Equity Analysis in Regional Transportation Planning Processes, Volume 1: Guide (2020)

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52 After assessing the impacts of plans, programs, and projects on tradi- tionally underserved persons, determine whether the levels of impacts are disparate and/or whether any impacts are disproportionately experienced among differing population groups. It is important to remember that the assessment should consider not only proposed future investments but also the present needs and concerns of underserved populations. The laws and regulations relating to Title VI and E.O. 12898 require agencies responsible for federally funded programs and activities to assess and address potential disparate impacts and DHAE generated by their activities. The FTA’s Title VI Circular 4702.1 defines terms related to dis- parity as follows (FTA 2012): f. Disparate impact refers to a facially neutral policy or practice that dis- proportionately affects members of a group identified by race, color, or national origin, where the recipient’s policy or practice lacks a substantial legitimate justification and where there exists one or more alternatives that would serve the same legitimate objectives but with less dispropor- tionate effect on the basis of race, color, or national origin. g. Disproportionate burden refers to a neutral policy or practice that dis- proportionately affects low-income populations more than non-low- income populations. A finding of disproportionate burden requires the recipient to evaluate alternatives and mitigate burdens where practicable. h. Disparate treatment refers to actions that result in circumstances where similarly situated persons are intentionally treated differently (i.e., less favorably) than others because of their race, color, or national origin. DHAE include individual and cumulative impacts as well as “the denial of, reduction in, or significant delay in the receipt of, benefits.” FTA’s EJ Circular 4703.1 provides step-by-step guid- ance on determining DHAE consistent with the requirements of the U.S. DOT Order 5610.2(a) (U.S. DOT 2012), which defines a DHAE as one that: (1) is predominately borne by a minority population and/or a low-income population, or (2) will be suffered by the minority population and/or low-income population and is appreciably more severe or greater in magnitude than the adverse effect that will be suffered by the non-minority population and/or non-low-income population. MPOs must assess the potential for disparate impacts and DHAE on underserved persons that may result from any activities involving federal funds. To support this evaluation, this chapter guides MPOs in identifying whether underserved persons are disproportionately adversely affected by burdens or by benefits that have been denied, reduced, or significantly delayed. In Step 4, MPOs review the data collected in Step 2 and Step 3 to analyze the distribution of existing benefits and burdens, agency actions, and forecast outcomes, using both quantitative C H A P T E R 6 Step 4: Determine Whether Impacts Are Disparate or Have DHAE

Step 4: Determine Whether Impacts Are Disparate or Have DHAE 53 and qualitative methods. Quantitative methods serve as preliminary screening tools for poten- tially disparate impacts. Then, incorporating qualitative information through supplemental sources and public engagement allows MPOs to validate findings and better understand how underserved persons experience the benefits and burdens associated with transportation deci- sions. Robust evaluation of disparate impacts is an iterative process: new insights from qual- itative analysis may warrant that MPOs adjust and rerun quantitative screening tools used initially. Step 4 ends with guiding questions to explore causes and potential mitigation options for disparate impacts that have been identified. This diagnosis process leads into the final phase of equity analysis, mitigating issues in Step 5. Review Data to Identify Differences Among Population Groups Through the actions taken under Step 2 and Step 3, MPOs can produce data tables of indi- cators that summarize the differing experiences of various population groups in relation to agency outputs (such as agency investments) and existing and forecast outcomes relating to accessibility, safety, environmental quality, and health risks. Outputs and outcomes are the major categories of measures of agency impact. This section provides guidance on using the data gathered previously to identify whether any population group is experiencing different impacts. Later sections of this chapter will provide guidance for discerning whether those different impacts are disproportionate. Outputs Outputs such as funding distributions are an important piece of an equity analysis, though they are insufficient on their own for determining disparate impacts. Investment amounts do not capture the full range of benefits or burdens conferred by investments, and funding distri- bution assessments often inaccurately assume that the transportation investment provides net benefits only to the adjacent areas and no effects to other areas. Building on the example results calculated in previous steps, Table 10 illustrates a hypothetical equity analysis that uses the approach in previous steps to assess per capita spending on adjacent projects and total regional spending for minority, LEP, and low-income populations and for the control populations for each group. The example analysis shows that people residing in minority communities receive $3,000 in per capita investments, which is 25% less than the $4,000 per capita Measure Population Group Population Size Per Capita Spending Difference Underserved Persons/Other Per Capita Spending on Adjacent Projects Regional Total: $3,000 per capita ($6 billion/ 2 million people) Minority Communities 1,000,000 $3,000 25% less than non- minority areas Non-Minority Areas 1,000,000 $4,000 LEP Communities 200,000 $5,000 80% more than non-LEP areas Non-LEP Areas 1,800,000 $2,778 Low-Income Communities 600,000 $5,000 117% more than non-low- income areas Non-Low-Income Areas 1,300,000 $2,308 Table 10. Sample of per capita spending by geographic areas.

54 Equity Analysis in Regional Transportation Planning Processes investments in non-minority areas. By comparison, per capita spending for LEP communities and low-income communities is higher than for their counterpart populations. Outcomes (Existing and Forecast) To evaluate outcomes, gather data from Step 2 and Step 3 to compare how different popula- tion groups fare under current conditions, the forecast year, and the percentage change over time. To comply with federal EJ guidance, equity analyses need to consider “the cumulative effect of a decision in combination with past actions [emphasis added] and all other reasonably foreseeable future actions” (FHWA 2015). Therefore, the analysis also needs to assess whether minority persons and low-income persons are currently receiving fewer benefits of the trans- portation system. If they are currently receiving a disproportionately smaller benefit, then the improvements offered by the plan should narrow that gap. Seek to evaluate and remedy existing inequities, not just ensure that the current activities do not create additional disparate impacts. Do no harm going forward and remedy past harms. The sample case illustrated in Table 11 shows that travel times improve for all groups, but not as much for minorities and LEP as for their counterpart populations. Travel times for the low-income population improve far more than those for the non-low-income group. How- ever, given that travel times for the low-income population are far higher than for any other group under the current condition, this disproportionately large improvement is appro- priate to remedy the current disparity. Line graphs are an effective way to visualize these differences, as shown in Figure 4. These graphs can make the data easier for the agency staff to review and easier to communicate the results with the public, equity stakeholders, and agency decision makers. Screen for Disparate Impacts Using Quantitative Methods Quantitative methods such as benchmarking, statistical significance, and location quotients (LQs) are helpful for screening for potential disparate impacts to identify impacts that may warrant additional investigation. For equity analyses of the required populations, compare the demographics and impacts on an affected population with a more general population, such as the population of the county or MPO planning area (Crenshaw Subway Coalition v. LACMTA). The LQ method applies this standard. MPOs will need to follow their quantitative screening with qualitative analysis to validate the findings and to obtain input as to why the disproportionate impacts are occurring; the next section in this chapter presents some qualita- tive approaches. Performance Measure Population Group Current Conditions Plan Forecast Percent Reduction Commute Travel Times (in minutes) and Time Savings Minority 50 48 4% Non-Minority 40 38 5% LEP 45 44 2% Non-LEP 40 38 5% Low-Income 60 50 17% Non-Low-Income 35 34 3% Regionwide 45 42 7% Table 11. Sample commute travel times.

Step 4: Determine Whether Impacts Are Disparate or Have DHAE 55 Establish Benchmarks to Flag Differences Establish benchmarks for percentage differences in indicator values between population groups to flag potentially disparate impacts for further investigation. Justify benchmark values with relevant contextual and historical information for each indicator. Comparison to existing conditions could be useful in establishing benchmarks. Consultation with equity stakeholders can help the agency develop appropriate benchmarks that can be used to flag when an impact needs further attention. For example, the MPO might work with equity stakeholders to deter- mine that a current difference of 25% in commute times for minority populations compared to non-minority populations is disparate and should be reduced. If the assessment of future con- ditions demonstrates little improvement—or worse, a growing disparity—consider mitigation strategies to reduce the percentage more significantly. Use Statistical Significance to Screen for Disparity Guidance from the U.S. EPA on EJ impacts recommends using statistical significance, which is a statistical method for confirming that an identified variation is not occurring by chance (U.S. EPA 2004). Statistical significance is particularly important for studies that use sample sizes that are much smaller than the full population. In an equity analysis, the population groups evaluated for statistically significant differences include test groups (each required population group) and control groups (non-required popula- tion groups that serve as benchmarks for comparison to the test groups). A t-test can be used to determine if the means of the indicators of interest (such as average travel times) for each group are statistically different. For data derived from the census and related data products, the U.S. Census Bureau makes available the Excel-based ACS Statistical Testing Tool (https://www.census.gov/programs- surveys/acs/guidance/statistical-testing-tool.html). A worksheet in the tool, “Statistical Testing for Multiple Estimates,” offers testing at the 10%, 5%, or 1% significance levels (U.S. Census Bureau 2018). The 5% significance level is most commonly used to identify statistically signifi- cant differences. If the agency is using U.S. Census Bureau data without the Excel tool, refer to the publication A Compass for Understanding and Using American Community Survey Data: What Figure 4. Change in sample commute travel times.

56 Equity Analysis in Regional Transportation Planning Processes Researchers Need to Know, which provides guidance and instructions for manually calculating statistical significance, including adapting an indicator’s margin of error according to signifi- cance level and geography (U.S. Census Bureau 2009). A finding of statistical significance indicates that an observed difference was unlikely to have occurred by chance. However, this finding provides limited information about potential dis- parate impacts on the test group, as it does not convey the magnitude of the difference and its meaning for the everyday lives of underserved persons. A statistically significant difference may not be particularly meaningful or relevant to the issues being considered, whereas an observed difference that is not statistically significant might be quite important. For example, the rate of pedestrian injuries within a region’s low-income population may not differ enough from the region’s overall rate of pedestrian injuries to be statistically significant. Nonetheless, if the low- income population primarily relies on walking as its main mode of transport, whereas other people in the region usually choose other options, the higher urgency of the low-income popula- tion’s need for pedestrian safety is important to take into account. An assessment of statistical significance only serves as a preliminary screening tool to flag potential concerns or issues, and should be considered in light of other information such as the expressed concerns of underserved populations. It should not be used as the sole indicator of disparate impacts. U.S. DOT guidance uses terms and phrases such as disproportionately, predominantly, and appreciably more severe or greater in magnitude to describe differences that would warrant a finding of disproportionate impact regardless of whether they are statistically significant. FTA’s EJ Circular 4703.1 recommends considering the “totality of the circumstances.” The qualitative methods described later in this chapter can help MPOs to determine whether an identified impact is meaningful and warrants mitigation. Apply LQs LQs screen for potentially disparate impacts of indicators that are associated with particu- lar geographic areas. LQs compare the concentration of underserved persons in an affected geographic area to see if the demographics of the affected population closely resemble the demographic makeup of the regional population (as discussed in Crenshaw Subway Coalition v. LACMTA). Examples of geographic areas for which population demographics could be com- pared include: • Populations living within walking distance of transit compared to the regional population, • Populations living outside a 20-minute car or bus trip to a full-service grocery store compared to the regional population, and • Populations living in close proximity to high-volume roadways with elevated air and noise pollution as compared to the regional population. Calculating LQs will be straightforward for MPOs with access to population data that can be apportioned according to areas impacted by the existing transportation system or forecast changes. To calculate the LQ, consider the area of impact as the study area and the broader region as the reference area to apply the following formula: .Location quotient Underserved population in the study area Total population in the study area Underserved population in reference area Total population in reference area =         If the LQ equals one (LQ = 1), it indicates that the population within the study area is the same as that of the broader region (i.e., there are equal proportions of underserved persons

Step 4: Determine Whether Impacts Are Disparate or Have DHAE 57 in the study area and the reference area). If the LQ is greater than one (LQ > 1), it indicates that underserved persons are concentrated in the study area relative to the reference area. If the study area represents a burden (such as risk exposure), then LQ > 1 signifies a dispropor- tionate adverse impact. If the LQ is less than one (LQ < 1), it indicates that there are fewer underserved persons in the study area relative to the reference area. If the study area represents a benefit (such as transit access), then LQ < 1 signifies a potentially disproportionate denial of receipt of benefits. By comparing the proportion of underserved populations in an impacted area to the propor- tion of the underserved population in the overall region, an agency can identify if underserved populations are disproportionately exposed to benefits or burdens. For an example, an agency could compare the proportion of low-income individuals living in TAZs with high crash rates to the regionwide proportion of low-income individuals. If the “high-crash TAZs” population is 1,000, of which 250 are low-income persons, and the total population is 100,000, of which 25,000 are low-income persons, the LQ will equal one, and would be calculated as follows: LQ 250 1,000 25,000 100,000 0.25 0.25 1.[ ] [ ]= = = In this example, LQ = 1 would indicate that low-income persons are not over-represented in high-crash zones. However, if the number of low-income persons in the high-crash zones is much higher, say 500, the LQ would double, as follows: LQ 500 1,000 25,000 100,000 0.5 0.25 2.[ ] [ ]= = = In the revised example, the value LQ = 2 could serve as a “red flag” prompting the MPO to examine why low-income persons were over-represented in high-crash zones. The Rhode Island DOT and its MPO used LQs to identify disproportionate exposure of EJ populations to pollution and asthma risk (see “Example in Practice: LQs” text box). Limitations of Quantitative Analysis Each of the quantitative analyses described previously is limited in its ability to determine disparate impacts. The values used for benchmarks are subjective choices set by stakeholders or determined by policies; communities that experience small impacts relative to the benchmark could still be experiencing disproportionate impacts that are not captured or revealed by quan- titative analyses alone. Tests of statistical significance depend heavily on sample sizes and only describe the likelihood of differences between populations. As sample sizes near the full population, any differences in the results are likely to be statistically significant. Therefore, it is important for MPOs to deter- mine whether a difference is meaningfully disproportionate, regardless of its statistical signifi- cance. Due to inevitable lack of precision in demographic data reported by geography (such as census tracts), LQs should be considered as estimates for potentially disparate impacts. Although LQs may be useful for identifying areas that may experience disparate impacts for further inves- tigation, they cannot be used to conclude that impacts are not disproportionate. Consider other impacts in the equity analysis before making this determination. The quantitative analyses described in this section should only be used to screen potentially disparate impacts for further investigation, and should not be used as adequate evidence that no disparate impacts exist. Supplementing quantitative analyses with qualitative methods incorpo- rating existing supplemental information and public engagement is critical to fully understand the full range of meaningfully disproportionate impacts on underserved persons.

58 Equity Analysis in Regional Transportation Planning Processes Example in Practice: LQs The State Planning Council in Rhode Island defined the study area as 250 feet around limited access roadways, based on academic research, and then determined the demographic makeup of the study area compared to the statewide demographics (see highway buffer map). The agency chose not to include other arterials because of the perceived offsetting benefits of providing access to transit and destinations (Rhode Island State Planning Council 2017). Using data obtained from the 2010 Census (see table), the agency calculated LQs for the minority population (1.68) and for the population below the poverty level (1.48). The agency concluded that there was a disproportionate impact. Source: Rhode Island State Planning Council (2017) Population-based LQ calculation, proximity to Interstate Highways. * Study Area Reference Area LQs (A/B)/(C/D) Targeted Underserved (A) Total Population (B) Targeted Underserved (C) Total Population (D) Minority 7,691 20,367 248,882 1,052,567 1.68 Population below poverty level 3,538 20,367 123,396 1,052,567 1.48 * Population numbers based on 2010 Census. Data Source: Rhode Island State Planning Council (2012)

Step 4: Determine Whether Impacts Are Disparate or Have DHAE 59 Validate Findings with Qualitative Methods and Stakeholder Engagement Qualitative methods inform analysis of disparate impacts with the values, attitudes, knowl- edge, and preferences of underserved persons. Qualitative methods should be used to determine which impacts are considered as benefits or burdens, and how significantly they are felt within the community. This section guides MPOs in gathering supplemental information from existing sources before filling in knowledge gaps with additional input from equity stakeholders. At this stage in an equity analysis, the scope of stakeholder engagement will likely be lim- ited to using the ongoing activities listed in the agency’s Public Engagement Plan or existing equity advisory committees. As described in Chapter 2, the methods used to lay the foundation with public engagement are particularly helpful for understanding what is important to various groups. Findings from qualitative analysis may lead MPOs to revisit and adjust assumptions underlying tests for statistical significance, LQs, and other quantitative screening tools as previ- ously discussed. Gather Supplemental Information Collect supplemental information to determine the priorities of the stakeholders and the context of potential planning decisions. This can include, for example, guidance from an equity advisory committee, previous studies, stakeholder interviews, public input gathered during prior engagement efforts, or public opinion about similar projects as documented in the media. It is also helpful to assess related indicators to see if the discrepancies are consistent across the board; if not, there may be issues with the data or calculation for the outlying indicator. Supplemental information provides MPOs with a better understanding of the burdens and benefits resulting from a plan or program on different population groups. For example, with- out understanding the priorities of a community, a finding that most of the region’s roadway investments were located outside of underserved communities could easily be interpreted in two distinct ways: a person could conclude that (1) the agency was not investing enough in the underserved community, or (2) the agency was protecting the cohesion of underserved com- munities by not disrupting them with construction or new facilities. Guiding Questions for Supplemental Information Guiding questions for supplemental information are outlined below to help determine whether differences are meaningful. • Are other positive benefits or actions in place to counteract or mitigate disparate impacts/ DHAE? For example, current conditions may seem to generate disparate impacts, but the MPO’s outputs may include additional funds to remedy those disparities. If the current transit commute times are poor for a given underserved community but the MPO is investing heavily in transit service for those communities, then the MPO is probably already working to miti- gate the disparity. • Do some of the indicators push in different directions? For example, extending transit from lower-income central city neighborhoods to affluent suburbs might appear to be generating a disproportionate benefit to higher-income populations. If, however, these transit connections provide new access to jobs for lower-income persons, then this benefit could outweigh the apparent disparity. • Is an existing disparity worse under the plan scenario? For example, if minority populations currently experience 55-minute commute times compared to 40-minute times for non- minority populations, then any activities that generate longer commute times for minorities

60 Equity Analysis in Regional Transportation Planning Processes (compared to the change in commute times for non-minorities) will make an already dis- proportionate indicator even worse. Remember that impacts can include a denial of benefits or a reduction in benefits. • Do the underserved persons in affected areas think the impact is disproportionate? For example, individuals in areas with high levels of zero-car households may be much more concerned than the general regional population with transit funding allocations or sidewalk quality indi- cators. Previous engagement efforts may have captured relevant input. If the relevant infor- mation is not available, additional stakeholder engagement may be needed to address this question. Use Public Engagement to Fill Gaps in Understanding Where there is a lack of existing qualitative information on impacts to different population groups, MPOs can fill gaps in understanding through additional public engagement. At the planning and programming level for MTPs and TIPs, consulting an advisory panel is likely sufficient to gather stakeholder input. At the project level, more intensive, targeted engagement is needed to involve affected underserved persons in the environmental review and project development process. Get Input from Equity Stakeholders Gathering input from equity stakeholders can strengthen relationships and streamline proj- ect development by offering the opportunity to identify and resolve issues long before the project goes into development. Develop a network of equity stakeholders with whom to consult iteratively throughout the planning and decision-making process, from the earliest stages of long-range visioning through design, deployment, construction, and ongoing maintenance. Guiding Questions for Public Engagement • How do underserved persons believe they will be impacted? What one community perceives as a burden, another community may perceive as a benefit. It is also possible that, within the same community, the same action may be perceived by various segments as both a burden and a benefit. Therefore, MPOs are advised to work with underserved populations through a transparent engagement process that fosters mutual understanding of the benefits and burdens of proposed projects. • What outcomes are most important? By understanding what outcomes are most important to underserved populations, agencies can focus their efforts on those outcomes, related outputs, and potential mitigation. • How well do the indicators and analysis methods reflect the experiences of underserved persons? Stakeholders should have the opportunity to provide input on any indicators that are being screened for potentially disparate impacts through quantitative methods, especially indicators with observed differences that were statistically significant or with LQs that can be interpreted as identifying disproportionate burdens or reduced benefits to underserved persons. • Do the expected outcomes align with the experiences of underserved persons? Many agencies justify planning decisions based on modeled or previous experiences, but these expected outcomes may not match the experiences of the underserved persons. For example, an agency may justify making investments to extend transit in wealthy suburban areas on the basis that the additional routes will provide underserved persons in the urban core access to job opportunities in the suburbs. It is important, however, to verify that the invest- ment truly provides underserved persons greater access to the available jobs. If, despite the transit investment, the underserved population cannot access the jobs (due to scheduling

Step 4: Determine Whether Impacts Are Disparate or Have DHAE 61 problems or barriers related to the skills or education that may be required for those jobs), the expected outcomes clearly do not align with the experiences of underserved persons. Explore Causes and Mitigation Options If an MPO identifies disparate impacts or DHAE in its plans or programs, the next actions to be taken are (1) to diagnose the factors contributing to the existing or forecast disparity/ DHAE, and (2) to ensure that future actions mitigate and remedy those impacts or effects. As stated in the FHWA EJ Reference Guide (FHWA 2015): If the recipient determines that a program causes disproportionately high and adverse impacts to a given population group relative to other population group(s), then the recipient must analyze the dis- parate impact. The analysis should seek to demonstrate that the disparate impact is nondiscriminatory in nature and that less discriminatory alternatives were not available. This section provides some tips for diagnosing the reasons behind disparate impacts/DHAE so that the agency can develop and implement appropriate mitigations in Step 5. A meaningful equity analysis includes a concerted effort by the MPO (and partner transpor- tation agencies) to account for, mitigate, and remedy systemic disparities faced by underserved persons. If an indicator for any population group diverges greatly from that of its control group and/or the regional average, seek to understand reasons for the discrepancy. The public engagement process is an important source of supplemental information and feedback from the affected communities to correctly diagnose disparate impacts. An accurate diagnosis of disparate impacts/DHAE often points to opportunities to mitigate them. Guiding Questions for Exploring Causes and Mitigation Options • How do existing conditions contribute to disparate impacts/DHAE? Where disparities/ DHAE already exist, the agency can take immediate action to ensure that its investment decisions are strategically targeted toward remedial activities. • How do agency outputs and investments interact with existing conditions? Investments should counteract existing conditions that contribute to disparate impacts/DHAE while avoiding the creation of new problems. • Do multiple disparate impacts/DHAE stem from the same cause(s)? Disparate impacts/ DHAE that have been identified across several different outcome indicators may share the same causal factors. For example, roadway expansion and traffic growth within underserved communities may cause disproportionate exposure to air pollution, noise pollution, and safety risk. • What capacity exists within underserved communities to contribute toward solutions, such as applying for grants to build needed infrastructure? Lack of investment in underserved communities with respect to a specific program area, such as pedestrian and bicycle facili- ties, may result from insufficient staff or volunteer capacity to successfully advocate for their needs or to deploy strategies such as applying for competitive grants. • How is the funding process structured? An MPO could set aside a portion of funding for a safety countermeasure program to reduce traffic fatalities in underserved communities that experience disparate safety impacts/DHAE. This strategy can also help to ensure that the MPO achieves safety targets set under federally required performance management standards. If the program requires communities to proactively apply for funds, however, the agency could unintentionally favor neighborhoods whose residents have the time and know-how to navigate the bureaucracy.

62 Equity Analysis in Regional Transportation Planning Processes It is important to work with public engagement staff and consultants to integrate discussions of both the potential causes of disparity and mitigation options throughout the planning and decision-making process. These insights inform quantitative screening, qualitative validation, and diagnosis. The next chapter, which covers Step 5, provides detailed guidance on mitigating disparate impacts. Resources Crenshaw Subway Coalition v. LACMTA, C.D Cal., Sept. 15, 2015. Retrieved from: AASHTO Case Law Updates on the Environment.Retrieved from: https://environment.transportation.org/clue/case_details.aspx?case_id=212. FHWA. 2015. Federal Highway Administration Environmental Justice Guide. Retrieved from: https://www.fhwa. dot.gov/environment/environmental_justice/publications/reference_guide_2015/section00.cfm. FTA. 2012. Title VI Requirements and Guidelines for Federal Transit Administration Recipients. FTA C 4702.1B. Retrieved from: https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/FTA_Title_VI_FINAL.pdf. Rhode Island State Planning Council. 2012. Transportation 2035. Retrieved from: http://www.planning.ri.gov/ documents/trans/LRTP%202035%20-%20Final.pdf. Rhode Island State Planning Council. 2017. Transportation Improvement Program: FFY 2018–2027. Retrieved from: http://www.planning.ri.gov/documents/tip/2018/STIP_Full%204-16-18.pdf. U.S. Census Bureau. 2009. A Compass for Understanding and Using American Community Survey Data: What Researchers Need to Know. Retrieved from: https://www.census.gov/content/dam/Census/library/ publications/2009/acs/ACSResearch.pdf. U.S. Census Bureau. 2018. “American Community Survey Statistical Testing Tool.” Webpage. Retrieved from: https://www.census.gov/programs-surveys/acs/guidance/statistical-testing-tool.html. U.S. DOT. 2012. Final DOT Environmental Justice Order 5610.2(a). Retrieved from: https://www.fhwa.dot.gov/ environment/environmental_justice/ej_at_dot/orders/order_56102a/. U.S. EPA. 2004. Toolkit for Assessing Potential Allegations of Environmental Injustice. Retrieved from: https:// www.epa.gov/sites/production/files/2015-02/documents/ej-toolkit.pdf.