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18 An equity analysis begins by identifying populations for analysis using a combination of demographic methods and public engagement techniques. In addition to identifying the populations specified in federal requirements, agencies may identify unique communities or combinations of groups that present specific needs and concerns. Analysis techniques discussed in this chapter include the following: â¢ Developing heat maps based on population concentrations; â¢ Developing dot-density maps based on numbers; and â¢ Adding demographic information to the data used for the travel-demand modelâs TAZs. It is helpful to use more than one analysis method to identify the popula- tions of importance to a region. The data can tell a different story depending on how it is collected, grouped, and depicted. For example, it may be hard to pinpoint small pockets of low-income households from a thematic map that clusters income levels into just a few broadly defined ranges or assigns the characteristics to relatively large geographic areas such as TAZs or census tracts. Using a more fine-grained geographic scale or a more detailed set of themes may produce a map that is hard to read at a regional scale; in this case, it might be appro- priate to generate a broader snapshot of the region as well as subarea maps and narrative reports. Developing a thoughtful, well documented assessment of the characteristics of the regional population at the outset of the planning process can not only inform the development of long- range plans and programs (which is the focus of this guide), but also provide valuable con- textual data for subsequent project-level studies or processes. As noted numerous times in the Promising Practices report by the Federal Interagency Working Group on Environmental Justice and NEPA (the National Environmental Policy Act of 1970), the detailed analyses con- ducted for a meaningful NEPA assessment can be enriched by considering or comparing the characteristics of the affected environment to those of the region as a whole (Federal Inter- agency Working Group 2016). Define Population Groups for Analysis This guide focuses on required populationsâthat is, populations that are required to be included in federally compliant Title VI and EJ analyses. MPOs may also identify other under- served persons or communities unique to their regions. This section clarifies the required and optional population groups to define for analyses. C H A P T E R 3 Step 1: Identify Populations for Analysis
Step 1: Identify Populations for Analysis 19 â¢ Required populations or required population groups are terms that refer to the popula- tion groups for which analyses are required for an MPO to comply with federal laws and guidance relating to Title VI and EJ. These include minority and non-minority racial/ ethnic populations, low-income and non-low-income populations, and LEP and non-LEP populations. â¢ Underserved persons refers more broadly to any person of a population group that an MPO might want to consider for inclusion in an equity analysis. This term includes persons of the required population groups but may also include members of other populations of interest, such as older adults or persons with disabilities. â¢ Underserved communities refers to geographic areas or neighborhoods in which under- served persons live and includes areas that the MPO may have designated as high-priority areas in relation to particular populations of underserved persons. Each required population must have its own analysis and is likely to have different needs and burdens. For example, the Memphis Urban Area MPO found that minority individuals are likely to commute by carpool or rapid transit, but low-income persons are more likely to walk, rideshare, or use a bus (Memphis Urban Area MPO 2016). Define Required Populations Title VI prohibits discrimination on the basis of race, color, and national origin. Agencies that do not comply with Title VI risk lawsuits, termination of current federal grants, and loss of eligibility for future grants. E.O. 12898 includes minority populations and low-income individuals on the list of required populations. Failure to comply with EJ guidance can lead to the loss of federal funding or failure to pass certification reviews. All of the required population groups associated with EJ and Title VI requirements repre- sent important demographic characteristics for MPOs to identify. Characteristics associated with Title VI include race, color, and national origin (which is often linked to LEP). The Title VI groups are required populations even when they are not low-income and even when the impacts are not disproportionately high and adverse, and they apply to all future actions carried out by transportation agencies, such as changes in service. The EJ groups (low-income populations and minority populations) are required only if there is a disproportionately high and adverse impact. Language for describing a personâs race, color, and national origin is continually evolving. Table 4 summarizes definitions of required populations listed in FTA Title VI Circular 4702.1B. Identify Appropriate Data Sources Most agencies use U.S. Census Bureau data to identify and map required populations. As the decennial census is updated once every 10 years, many agencies refer to the annual American Community Survey (ACS) for more current data (https://www.census.gov/programs-surveys/ acs). The ACS includes data on limited English-speaking households, household income, and racial and ethnic populations (see also U.S. Census Bureau 2009). Additional tools and data sources are available. For example, the EPAâs online EJSCREEN mapping and screening tool (https://www.epa.gov/ejscreen) provides demographic and envi- ronmental information that has helped many MPOs, such as the Puget Sound Regional Council (PSRC) in Washington State, identify underserved communities as well as their exposure to environmental risks (PSRC 2018).
20 Equity Analysis in Regional Transportation Planning Processes Define Optional Population Groups Many MPOs analyze additional socio-economic characteristics in order to identify popula- tion groups that may be underserved in their region. Generally, supplemental approaches to identifying populations tend to take two forms: â¢ Modifying the national definition of low-income to reflect regional characteristics, or â¢ Identifying unique population groupsâbeyond the required underserved groupsâwhose transportation needs may differ from those of the general population. Stakeholders engaged through an equitable public participation process can provide valu- able insights about the issues of most importance to underserved populations. For exam- ple, the Mid-Ohio Regional Planning Commission (MORPC) analyzes the impacts of its activities on racial and ethnic subgroups, persons with disabilities, older adults, low-income households, and zero-vehicle households. Based on feedback from stakeholders, however, the analysis accompanying their latest TIP focused on minority persons and low-income house- holds rather than also providing analyses of impacts on additional underserved populations (MORPC 2017). Modify the Definition of Low-Income A complete equity analysis ensures an adequate examination of potential disparate impacts/ DHAE that may be experienced by those with the lowest incomes. The U.S. DOTâs EJ guidance Required Populations Definition Persons with LEP (Title VI) Persons for whom English is not their primary language and who have a limited ability to read, write, speak, or understand English. This group includes people who reported to the U.S. Census Bureau that they speak English less than very well, not well, or not at all. Low-income persons (EJ) Any person whose median household income is at or below the United States Department of Health and Human Services (HHS) poverty guidelines. Find the current HHS poverty guidelines at https://aspe.hhs.gov/poverty-guidelines. The term low-income population refers to any readily identifiable group of low-income persons who live in geographic proximity, and, if circumstances warrant, to geographically dispersed/transient persons (such as migrant workers or Native Americans) who will be similarly affected by a proposed FTA program, policy, or activity. Note that FTA encourages agencies to use a locally developed threshold, such as the definition found in 49 U.S.C. 5302 as amended by MAP-21, which ârefers to an individual whose family income is at or below 150% of the poverty line (as that term is defined in Section 673(2) of the Community Services Block Grant Act (42 U.S.C. 9902(2)), including any revision required by that section) for a family of the size involvedâ or another threshold, provided that the threshold is at least as inclusive as the HHS poverty guidelines. Minority persons (Title VI and EJ) Non-white persons, specifically American Indian and Alaska Native; Asian; Black or African American; Hispanic or Latino; and Native Hawaiian or Other Pacific Islander. Minority population means any readily identifiable group of minority persons who live in geographic proximity and, if circumstances warrant, geographically dispersed/transient populations (such as migrant workers or Native Americans) who will be similarly affected by a proposed DOT program, policy, or activity. FTAâs EJ Circular notes the importance of not conflating low-income and minority populations, stating âthere are minority populations of all income levels, whereas low-income populations may be minority, non-minority, or a mix in a given areaâ (FTA Circular 4703.1, 2012). Table 4. Defining required populations.
Step 1: Identify Populations for Analysis 21 recommends using the HHS poverty guidelines, but regional differences in the cost of living can mean that the national guidelines might not capture all persons with financial burdens in a region. Some agencies with high regional costs of living have chosen more inclusive definitions, such as using 200% of the federal poverty guideline. For example, the San Francisco Bay Area Metropolitan Transportation Commission (MTC) formed a Regional Equity Working Group to consider conditions specific to the Bay Area when defining what qualifies as low-income. In consideration of the high cost of living in the region compared to the national average, the agency defined low-income populations as those with income at approximately 200% of the federal poverty guideline (MTC 2015). Add Optional Underserved Populations In addition to the required populations, there may be other underserved populations to include in an analysis. These might be populations that have unique needs related to com- munications, public engagement, or transportation choices. These populations might include members of federally recognized tribes, older adults, persons with disabilities, or zero-car households. Work with the equity stakeholders in a region to identify the most appropriate populations to include. An MPO will need to balance the desire to be inclusive with the risk of becoming overwhelmed with data. Identify Regional Distribution of Underserved Persons Rather than identifying a limited number of geographic areas with significant concentra- tions of underserved persons, this guide recommends first developing an understanding of the overall distribution of these persons throughout the region. Developing this regional under- standing can reveal new insights that could be missed if an MPO focuses solely on areas with high concentrations of underserved persons, and it ensures compliance with Title VI and EJ guidance to consider the members of the required populations even when they do not live in underserved communities. This section of the chapter provides three approaches to identifying the regional distribution of the populations analyzed: (1) heat maps of concentrations; (2) dot-density maps of numbers; and (3) matching demographic characteristics to TAZs. Figure 2 shows how the MORPC used these three approaches in one map, showing the relative concentration of minority persons at the TAZ level using a heat map to show four gradations of concentrations and one dot to represent 200 minority persons. Heat Maps Heat maps use color gradations for a geographic area (such as census tracts) in proportion to the concentration of the population being analyzed. To be useful for comparison purposes, these maps should have at least three gradations. To develop such a map, select the desired geographic scale and download the relevant data from U.S. Census Bureau products such as the ACS. When creating a heat map, consider the following: â¢ Collect data for the relevant populations by geographic unit and calculate the concentra- tion of the analysis population for each geographic unit. For example, divide the number of minority persons in each TAZ by the total population in that TAZ. This can be calculated and then loaded in a GIS software package for analysis, or it can be calculated within most GIS programs.
22 Equity Analysis in Regional Transportation Planning Processes Source: MORPC (2017), 2017 Metropolitan Transportation Plan, Appendix D, âEnvironmental Justiceâ Figure 2. Dot-density map of the minority population in the MORPC area.
Step 1: Identify Populations for Analysis 23 â¢ Group the data into ranges of values (bins). The cutoff values used to define the bins can be set in a number of ways. The analyst could choose thresholds that align with the agency metrics. For example, the agency may define underserved communities to be any TAZ with a concentration of minority persons over the regional average by at least one standard devia- tion. The analyst also could group values into concentrations by standard deviationâthat is into concentrations at the regional average, and concentration bands below and above the average. Alternatively, the analyst could use the distribution of the values to establish the bins. For example, the analyst could group the TAZ minority concentrations into the bottom third, middle third, and top third. â¢ Assign each bin a color to distinguish each geographic unit by its population concentration. If a dot-density map will be overlaid on top of a heat map, be sure to select colors for the bins that can be distinguished from the dots. Dot-Density Maps A dot-density map displays dots representing the presence of persons or households through- out a selected geographic area (see Figure 2). The visual clustering depicts relative population densities, provides a visual representation of the absolute number of persons in an area, and complements heat maps that display the relative concentrations of a given population. A dot-density map can illustrate the locations and densities where low-income and/or minority populations reside. Dot-density maps overlaid on heat maps are particularly effective in con- veying both absolute numbers and relative concentrations of populations. Most agencies with GIS capabilities can create dot-density maps with relative ease. When creating a dot-density map, agencies should consider the following steps: â¢ Use a GIS software package to map relevant population groups in the analysis area. After loading the population data into the GIS application, adjust the symbology properties of the appropriate layer to use dot-density and specify the number of individuals that each dot represents and the size of the dot. The analyst may need to experiment with these settings to generate a map that conveys density without obscuring other information. â¢ Consider map legibility when choosing to represent multiple populations on the same dot- density map. Zoomed inset maps may help viewers decipher dot locations within areas with high concentrations of underserved persons. â¢ To minimize concerns regarding the subjectivity of dot-density maps, ensure that the number of persons represented per dot (i.e., one dot = X persons) does not overly exaggerate or mini- mize the population present in a given area. In Figure 2, each dot represents 200 people identified as minority, and the dots are overlaid on a heat map showing TAZs that have been shaded by the percentage of minority residents. Combining the dot-density map with the heat map allows an MPO to see which TAZs have many minority people, even within areas that may not have high concentrations of minority people. Conversely, the method can reveal sparsely-populated places with higher concentra- tions of minority people. Overall, the visualization contributes to a more robust discussion of regional spatial patterns of segregation, integration, and isolation. Add Demographics to the Travel-Demand Model at the TAZ Level Agencies frequently convey the benefits of their plans and programs using performance mea- sures from the regional travel-demand model. By matching demographic information to TAZs, an agency can use these same measures to conduct equity analyses, enabling the analyses to reveal whether the agencyâs plans or programs are forecast to benefit underserved populations
24 Equity Analysis in Regional Transportation Planning Processes in the same ways that they benefit other populations. This will enable the MPO to model the forecast outcomes (such as average travel time to work) for each demographic group being analyzed. (In this guide, the process of forecasting outcomes is described in more detail in Chapter 5, âStep 3: Measure Impacts of Proposed Agency Activity.â) To match demographics to TAZs, agencies can follow the steps listed below. These steps describe the process as used by the MORPC and test-piloted by the Mid-America Regional Council (MARC), which serves Kansas and Missouri (MARC 2015): 1. Download demographic data from the ACS (https://www.census.gov/programs-surveys/acs), or from a locally developed source. 2. Create an equivalency table to convert demographic data to TAZs. For data through 2018, existing equivalency tables (available through the U.S. Census Bureauâs Transportation Plan- ning Products [CTPP] program) also can be used. These equivalency tables automatically match census demographic data to TAZ boundaries. (The CTPP program tools have tradi- tionally been used for this purpose, but CTPPs released in 2019 and beyond will no longer include TAZ-level information.) 3. Develop forecasts for each demographic subgroup within each TAZ by changing its future numbers to match the overall TAZ growth rate that had been generated previously in accor- dance with regional land use forecasts. Assumptions for the first round are: a. Hold the regional population count steady. b. Hold the population count for each TAZ steady. c. Hold the demographic makeup (percentage of each demographic group) steady for each TAZ. (See the section titled âUnderstand Demographic Changeâ for a discussion of the flaws of this assumption. All uses of the model rely on imperfect assumptions.) d. Hold the demographic makeup steady for the region (or allow it to fluctuate if changes are anticipated). 4. Conduct a quality control review: Did the regional population totals for each demographic group hold steady or change in a way that reflects the anticipated changes in the regionâs demographics? Or did the demographics for the region change in a way that was contrary to what is expected? In the MORPC exercise, the initial forecast produced a total percentage of underserved persons that was smaller than that of the base year, which was not consistent with what was expected in the region. 5. If something unexpected is found, ask why. The MORPC staff realized that the model assumed that most of the regionâs population growth would occur in TAZs that currently had low percentages of underserved persons. The MORPC had held the demographic makeup of their TAZs constant (matching the present-day data), which meant that the model showed more of an increase in the population of non-underserved persons and less of an increase in the population of underserved persons than would be expected. 6. Refine the assumptions to correct any quality control issues identified. The problem that the MORPC found is likely to occur. Here is how to fix it: a. Identify the demographic makeup (percentage of each group) expected in the regional forecast. For example, socio-economic and cultural trends analyses may indicate that the future proportion of low-income households may be higher in some neighborhoods and lower than others. b. Slightly revise the demographic proportions within each TAZ to correct the assumptions while holding constant the number of people in each TAZ and at the regional level, and while holding constant each TAZâs relative share of the population group for which the adjustment is made. For example, if the forecast number of low-income households needs to be increased by 10,000, then the agency can distribute an additional 10,000 among the TAZs based on each TAZâs current share of the regionâs low-income persons. In other
Step 1: Identify Populations for Analysis 25 words, the TAZs that have 1% of the regionâs low-income households today should also have 1% of the regionâs low-income households in the forecast year. Make these adjust- ments to the TAZsâ demographics while continuing to hold constant the total population numbers of each TAZ and at the regional level. For example, 200 low-income persons may have just been added to a TAZ; to maintain that TAZâs control total, reduce the non-low- income persons in that TAZ by 200. This should bring it back to a regional demographic profile that matches those of the base year. The resulting datasets of base-year and forecast demographic profiles enabled the MORPC to run numerous impact analyses using its travel-demand model and spreadsheet tools, as docu- mented within the MORPCâs equity appendix to plans and programs. Smaller agencies with- out the in-house or contractor resources to add travel demographics to travel-demand models can use free, web-based demographic forecasting tools such as the U.S. Census Bureauâs free Subnational Projections Toolkit Software, available at https://www.census.gov/data/software/ sp-toolkit.html. Chapter 5 in this guide describes Step 3 and provides additional information about these resources. Identify High-Priority Areas Knowing the regional distribution of required populations is valuable, but an agency that wants to apply targeted activities to address equity will need to know which areas to prioritize for these efforts. Also, reviewers for federal certification programs often require an agency to identify areas that are high-priority areas under Title VI or EJ. This section begins by describing how to identify high-priority areas for each population group being analyzed by using heat maps and dot-density maps. The section next describes an optional approach that creates an index to identify areas that have multiple types of under- served populations. Identify Areas with High Numbers and Concentrations of Each Required Population This chapter previously described how to generate heat maps to show the relative concen- trations of various populations and dot-density maps to show where high numbers of under- served populations live. These concentrations and population values can be used to identify which areas have the highest concentrations and which areas have the highest numbers of each population group included in the analysis. Many agencies currently identify high-priority equity areas as areas that have a concentration of underserved populations that exceeds a selected threshold concentration, often the regional average. Although easy to apply, this approach has many potential drawbacks: â¢ Agencies are required to identify disparate impacts and DHAE on required populations regardless of where they live. When an agency limits its equity analysis to only the geographic areas (such as census tracts) that meet a predetermined threshold, then the equity analysis will overlook the experiences of underserved persons that live in other areas. â¢ When the thresholds are based on the concentration of underserved populations, the analysis risks overlooking areas that have high numbers of underserved populations in favor of focus- ing on the underserved communityâs share of the overall population. â¢ An analysis based on high-priority equity areas might wash out dissimilarities that exist between areas that have larger differences in their concentrations.
26 Equity Analysis in Regional Transportation Planning Processes â¢ Using the regional average as a threshold could result in most of the region counting as an equity area, which is not very helpful to an agency that is trying to identify areas to prioritize. â¢ If an agency does not document its rationale for selecting a threshold, the selection can appear arbitrary to constituents, which may reduce equity stakeholdersâ willingness to engage in the process. Acknowledging the drawbacks to this approach, agencies can consider applying the following techniques to improve their equity analyses: â¢ Seek input and feedback from equity stakeholders. What thresholds would be appropriate to use as indicators of high-priority areas? Are the identified areas the appropriate areas to prioritize for equity analysis? â¢ Use standard deviations to set the thresholds that categorize areas into three groupings. Areas that exceed one standard deviation above the regional number for underserved popu- lations would be classified as high-priority equity areas. Areas that fall below one standard deviation below the regional for underserved populations would be the control group. Areas that fall within one standard deviation to either side of the regional number would be con- sidered neutral areas that might not contribute meaningfully to the comparative analysis. (Additional discussion of control groups is provided in Chapter 6 under the heading, âUse Statistical Significance to Screen for Disparity.â) â¢ Use a combination approach that identifies areas that have high numbers and areas that have high concentrations of underserved populations. By identifying both types of areas, the analysis will hedge against the weaknesses of either approach. Optional: Use Indices to Identify Areas with Multiple Underserved Populations To assist with prioritizing, agencies can use indices to help identify areas that have multiple categories of underserved persons. To create an index, start by identifying high-priority areas for each population group being studied, as described in the previous section. Then, overlay these maps to identify which areas qualify as high-priority areas for more than one popula- tion of underserved persons. For example, a census tract that is a high-priority area because of a high number of minority individuals might also be a high-priority area because of a high number of persons with disabilities. An index approach captures these overlapping vulner- abilities and ranks areas based on the degree of potential disadvantage. MPOs that are using these index approaches include the Atlanta Regional Commission (ARC), the Delaware Valley Regional Planning Commission (DVRPC), and the San Francisco Bay Areaâs MTC. The MTC identifies high-priority areas for eight different population groups that are at risk of disadvantages: minority populations, low-income populations, LEP popula- tions, zero-vehicle households, adults over 75 years of age, persons with a disability, single-parent families, and rent-burdened households. By overlapping these indicators into an index, the MTC can identify areas that may be at risk due to multiple types of potential disadvantages, which can help the agency prioritize areas that may need additional attention or efforts (MTC 2015). Figure 3 illustrates the equitable target area index approach used by the ARC. Indices of multiple characteristics can be useful prioritization tools to support agency deci- sion making and can supplement an equity analysis, but it is critical to have an approach that focuses specifically on the required populations. Relying solely on an index risks mixing the analysis for required populations with analyses of other populations that are not federally required.
Step 1: Identify Populations for Analysis 27 Understand Demographic Change An areaâs demographic makeup is always changing. Many urban areas are grappling with gentrification caused by neighborhood redevelopment and housing price trends. Meanwhile, numerous regions throughout the country have undergone rapid rises or falls in numbers of different demographic groups and/or economic conditions. It is difficult to accurately forecast future population composition and distribution based on jagged historic trend lines. For exam- ple, during the 25-year span of a typical long-range transportation plan, a downtown neighbor- hood that begins with a high concentration of low-income populations could easily transform into an upscale community of expensive homes and, during the same period, a homogeneous suburb could become an ethnically diverse community. In rapidly evolving communities, it may not make sense, to consider the potential equity impacts of long-range transportation plan investments and outcomes using maps and statistics of present-day underserved communities. Some MPOs, including the Oregon Metro, the San Francisco MTC, the PSRC, and the Southern California Association of Governments (SCAG) have begun to quantitatively analyze gentrification and displacement risk in their equity analyses. In response to stakeholder group concerns about the ability to accurately forecast the locations and concentrations of under- served persons over the long-range transportation planning horizon in the Portland, Oregon region, Metro has begun conducting a 10-year interim analysis of long-range transportation planning investment scenario transportation system performance outcomes (as opposed to only looking at the longer-term forecast). Innovative approaches such as these are helping to address uncertainties stemming from changing demographics (Oregon Metro 2016). Resources ARC (Atlanta Regional Commission). 2018. The Atlanta Regionâs Plan. Retrieved from: http://atlantaregionsplan. com/regional-transportation-plan/. DVRPC (Delaware Valley Regional Planning Commission). 2017. Long-Range Plan & Transportation Improve- ment Program. Retrieved from: https://www.dvrpc.org/LongRangePlanAndTIP/. Federal Interagency Working Group on Environmental Justice & NEPA Committee. 2016. Promising Practices for EJ Methodologies in NEPA Reviews. Retrieved from: https://www.epa.gov/sites/production/files/ 2016-08/documents/nepa_promising_practices_document_2016.pdf. ETA Index Categories Poverty Category Race Category ETA Index 1 1 Very High 2 High 3 2 1 Medium 2 3 3 1 Non-ETA 2 Source: 2016 Atlanta Regionâs Plan, Appendix J: Equitable Target Area Methodology Figure 3. ARC equitable target area index categories.
28 Equity Analysis in Regional Transportation Planning Processes FTA. 2012. Environmental Justice Policy Guidance for Federal Transit Administration Recipients. FTA C 4703.1. Retrieved from: https://www.transit.dot.gov/sites/fta.dot.gov/files/docs/FTA_EJ_Circular_7.14-12_FINAL.pdf. 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. Memphis Urban Area MPO. 2016. 2040 Regional Transportation Plan. Retrieved from: http://memphismpo.org/ sites/default/files/public/livability-2040-all-chapters.pdf. MTC (San Francisco Bay Area Metropolitan Transportation Commission). 2015. Regional Equity Working Group. Retrieved from: https://mtc.ca.gov/about-mtc/what-mtc/mtc-organization/interagency-committees/ regional-equity-working-group. MARC (Mid-America Regional Council). 2015. Transportation Outlook 2040. Retrieved from: http:// www.to2040.org/. MORPC (Mid-Ohio Regional Planning Commission). 2017. 2016â2040 Columbus Area Metropolitan Trans- portation Plan, Appendix 3: EJ Analysis. Retrieved from: http://www.morpc.org/wp-content/uploads/ 2017/12/MORPCTIP2018-2021Appendix3EJ.pdf. Oregon Metro. 2016. Strategic Plan to Advance Racial Equity, Diversity and Inclusion. Retrieved from: https:// www.oregonmetro.gov/strategic-plan-advance-racial-equity-diversity-and-inclusion. PSRC (Puget Sound Regional Council). 2018. Regional Transportation Plan. Retrieved from: https://www.psrc. org/our-work/rtp. SCAG (Southern California Association of Governments). 2016. The 2016â2040 Regional Transportation Plan/ Sustainable Communities Strategy: A Plan for Mobility, Accessibility, Sustainability and a High Quality of Life. Retrieved from: http://scagrtpscs.net/Documents/2016/final/f2016RTPSCS.pdf. U.S. Census Bureau American Community Survey Public Use Microdata Sample. Available at: https:// www.census.gov/programs-surveys/acs/data/pums.html. U.S. Census Bureau. 2017. Subnational Projections Toolkit (SPToolkit) Software. Available at: https:// www.census.gov/data/software/sp-toolkit.html.