Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Transportation planners at different types of agencies perform a wide range of transportation market analyses using census data and other sources. This Section describes how ACS is likely to be used for transportation market analyses. Section 7.1 defines transportation market analysis and describes why transportation planners conduct trans- portation market analyses. This section also provides some examples of uses of census data for this purpose. A more detailed list of specific uses is provided at the end of this section. Section 7.2 describes some of the benefits and limitations of shifting from census to ACS data related to transportation market analysis. Section 7.3 provides a case study example of transportation market analysis. The case study shows how to compute the index of dissimilarity (an application of envi- ronmental justice analysis) using ACS data and how the results compare to those using census data. 7.1 Transportation Market Analysis Transportation market analysis consists of a variety of methods used to support the analysis of transportation demand. Transportation planners have used decennial census data for many types of transportation market analyses, including studies on transit market, non-motorized commuting, and travel model market segmentation. Transportation market analysis is performed for different purposes. For example, transit mar- ket studies can be used to perform the following analyses:75 â¢ Study of captive and choice transit riders to identify potential transit markets; â¢ Performance evaluation, which is important for addressing Title VI federal requirements, environmental justice, and for identifying needs for extended transit service; â¢ Demand projections and market evaluations; and â¢ Route planning to improve current service and plan future service extensions. 7.1.1 Examples of Use This section provides some examples of presenting market analyses that are based on census data. Figure 7.1 shows transit usage by income and race.76 Figure 7.2 is a thematic map showing 117 C H A P T E R 7 Transportation Market Analyses Using ACS Data 75 R. Cervero, 1994, âUse of Census Data for Transit, Multimodal, and Small-Area Analyses.â Transportation Research Board, Decennial Census Data for Transportation Planning, Conference Proceedings 4, Irvine, California, March 13-16, 1994. 76 TCRP Report 28: Transit Markets of the Future: The Challenge of Change, Transportation Research Board, National Research Council, Washington, D.C., 1998. See http://gulliver.trb.org/publications/tcrp/tcrp_rpt_28-a.pdf.
118 A Guidebook for Using American Community Survey Data for Transportation Planning Source: Unpublished tape readable data from the 1990 U.S. Census, 5% PUMS. 25 20 15 10 5 0 Percent Income Black Hispanic Asian White <5k 5-10k 10-15k 15-20k 20-25k 25-30k 30-40k 40-50k 50-60k 60-70k 70k+ Figure 7.1. Transit use to work in metropolitan areas, by race, ethnicity, and household income, 1990. Figure 7.2. Percent of persons age 65 years or older by county.
the distribution of elderly people by county,77 an analysis which is commonly done within envi- ronmental justice analyses. A list of specific examples of using census data to do market analysis also is provided at the end of this section. 7.2 Benefits and Limitations of ACS for Transportation Market Analysis This section summarizes the perceived benefits and limitations of using ACS data for trans- portation market analysis. Transportation planners contacted about the implications of ACS felt that the availability of ACS data on a continuous basis would allow for more timely analysis of route and service planning, as well as environmental justice issues. In contrast, the variability of ACS data at small area geography may severely limit some of the applications. In many cases (e.g., environmental justice analysis, corridor analysis, etc.), planners seemed more interested in obtaining a firm quantitative assessment, even if it used point-in-time data rather than qualitative âmoving averageâ data. Concerns were raised that in small areas where moving averages are used, some variables needed for many types of transit analysesâespecially environmental justice analysesâwould be hard to interpret. 7.3 Transportation Market Analysis: Environmental Justice Case Study The following case study illustrates how a data user might do transportation market analysis using ACS data. The case study provides a step-by-step description of how one might obtain the data, do the computations, and present the results. For this purpose, assume that you are a transportation analyst working in an MPO. In this analysis, your manager has asked you to perform an environmental justice analysis by comput- ing an index of dissimilarity (ID) for your area. Section 3 of this guidebook has detailed instruc- tions on downloading ACS data, and Section 4 and the previous case studies describe many of the generic ACS analyses that will be used in these analyses. You were asked to compute the ID, which often is used in planning to measure the evenness of sub-population groups across specific geographic areas,78 at different geographic levels for a target population consisting of non-Hispanic whites (âwhiteâ hereafter), non-Hispanic blacks (âblackâ hereafter) and Hispanic residents of Broward County, Florida. You have been asked to do the computations using both the 2000 ACS and census data. The goal of this analysis strategy is to ascertain the differential impact of moving from census to ACS data on this given measure of importance to public policy; transportation; and municipal, community, and regional planning personnel. In this case study exercise, begin by treating both the ACS estimates and the census counts as point estimates, without regard to the ACS sampling error. This will always be an option for analysts and, Transportation Market Analyses Using ACS Data 119 77 Missouri Department of Transportation Socio-Economic Indicator Resource, âThe Relationship of Environ- mental Justice Populations to Key Socio-Economic Indicators in the St. Louis Area District,â 2003. See http://oseda.missouri.edu/modot/planning/stlouis_analysis.shtml. 78 NCHRP Report 532: Effective Methods for Environmental Justice Assessment. Transportation Research Board, National Research Council, Washington, D.C., 2004.
for complex analyses, may be the only logical way to make use of ACS estimates. However, the Cen- sus Bureau recommends including the sampling uncertainty in any calculations using ACS esti- mates, so a discussion of including this uncertainty in the calculations follows the initial discussion. The ID measures the evenness with which two mutually exclusive groups are distributed across the geographic units that comprise a larger geographic entity. An example is the distribu- tion of blacks and whites across the census tracts that define a metropolitan region, county, or state. The key considerations are: 1) the total population of two particular groups at a higher- level geography (i.e., whites and blacks in Broward County) and 2) the proportion of each groupâs population within a particular areal unit. In this process, âareal unitâ is used to describe smaller geographic areas within the larger study area. This can be a census tract, block group, or TAZ. The ID has a minimum value of zero and a maximum value of 100. It can be defined as ID = 100 Ã 1â2 â¢ (| ai / A â bi /B |) where ai = the population of Group A within the ith areal unit, e.g., census tract; A = the total population of Group A within the larger geographic entity for which the ID is being calculated, e.g., county; bi = the population of Group B within the ith areal unit, e.g., census tract; and B = the total population of Group B within the larger geographic entity for which the ID is being calculated, e.g., county. The calculation of ID values involves specifying both the overall geographic unit of analysis as well as the areal units that form the basis for the index. Depending on the goals and needs of a project, this step is essential to achieving efficient and robust results. In this study, the ID will measure the overall race/ethnic residential composition at the county level for Broward County, Florida. The next decision of importance is the identification of the areal unitâthe lowest level of geog- raphy where race/ethnic residential composition will be measured and then compared to the tar- get area (i.e., Broward County). Traditional analyses over the last half-century have focused on utilizing census tract-level data as the preferred areal unit of analysis. The use of tracts was prompted by convenience more than theoretical requirements. The average U.S. census tract in 2000 included approximately 4,200 residents and was neither initially developed nor intended to represent an average residential neighborhood. Unfortunately, very few generalizable data sources included units of analyses smaller than a census tract. This, combined with the relatively low level of computational resources needed to analyze a small number of areal units, resulted in the cen- sus tract becoming the standard reference areal unit in most geographic area analyses. As com- puter power has increased, many analysts prefer and can support analysis at the level of census block groups (about 1,300 residents) and census blocks (about 34 residents). Ideally, the smallest areal unit will âuncoverâ the truest picture of residential segregation in any given geographic unit. One can present the ID computed values at different geographic levels and using both ACS and census, as shown in Table 7.1. The lowest levels of race/ethnic segregation for all groups studied was at the PUMA level. This is to be expected because the PUMA represents the most aggregated data (aside from county level), with only 13 areal comparison units in the county. At the PUMA level, the following conclusions can be made: â¢ The first column of values shows that, on average, roughly 35 percent (ID value at 35.1) of all blacks would have to move from their current PUMA residence in order to be proportionally 120 A Guidebook for Using American Community Survey Data for Transportation Planning
distributed within all PUMAs in the county in the same way as whites. The converse relation- ship holds as well. â¢ The percentage of segregation between whites and Hispanics is roughly half the white/black value at 18. black/Hispanic segregation resides at approximately the same level as black/white difference, with ID value equaling 33. At this level of analysis, the percentage differences between the census and ACS are relatively small, with the highest value being within white/Hispanic residents at 3 percent. Moving to higher numbers of smaller population area units creates a resultant increase in ID values. This is to be expected as larger areal units can, and often do, mask greater race/ethnic neigh- borhood segregation. For the 279 tracts in the county, the following conclusions can be made: â¢ Both higher ID values and greater differences between the census and ACS are noted, particu- larly for the black/Hispanic comparison. ID values have approximately doubled for all groups. â¢ White/black ID value equals 63, thereby indicating a much higher residential segregation than previously detailed. Minority group data comparisons show about 3.7 percent difference between ACS and Census 2000. At the TAZ level (777 TAZs in the county), the following conclusions can be made: â¢ There is only a slight increase in overall ID values and percentage differences by data as compared to tract data. â¢ The highest percentage difference between ACS and Census 2000 detailed is among the white/Hispanic comparison, at 5.1 percent. 7.3.1 Available Data Selection of Geography For this case study, ID values were calculated for several subcounty areal units available in Census 2000 and ACS data. These include â¢ PUMAâa geographic area with a minimum population of 100,000 residents. From within these PUMA areas, the census extracts a 5 percent sample of census Long Form data for pub- lic use. In Census 2000, there were 13 PUMAs within Broward County; â¢ Census tract; â¢ TAZ; â¢ Census block group; and â¢ Census block. Transportation Market Analyses Using ACS Data 121 PUMA Tract Block Group Block Data Source Census 2000 ACS 1999-2001 Net Difference % Difference W/B 35.1 35.3 0.2 0.6 W/H 17.5 18.0 0.5 3.0 B/H 32.9 33.0 0.1 0.4 W/B 62.7 63.0 0.3 0.5 W/H 31.5 32.5 1.1 3.3 B/H 53.5 55.5 2.0 3.7 W/B 66.0 66.2 0.2 0.3 TAZ W/H 35.0 36.9 1.9 5.1 B/H 56.6 58.7 2.1 3.6 W/H 33.2 -- -- -- W/B 64.5 -- -- -- W/B 70.4 -- -- -- W/H 41.5 -- -- -- B/H 60.2 -- -- -- B/H 54.8 -- -- -- N 13 279 777 689 20,136 *Source: Census 2000 & American Community Survey, 1999-2001 (weighted) â Broward County, Florida. *Note: W/B = White/Black, W/H = White/Hispanic, B/H = Black/Hispanic. Table 7.1. Index of dissimilarity values for Broward County, Florida, by area, data source, and race/ethnicity.
The Census 2000 data could be used at all of the listed geographies, but the ACS estimates do not contain information at the block group or block levels. Therefore, data comparisons for this study will concentrate on the levels that are possible in both datasets: PUMA, TAZ, and tract.79 However, data at the block group and block levels will be presented in order to assess the impact of how much detail may be lost due to the lack of small area geography in the ACS. Table 7.2 summarizes the characteristics of the areal units for this case study. Identification of Required Data Tables The analysis relied on custom Census 2000 and 1999-2001 ACS data tables provided by the Census Bureau in the Statistical Analysis System (SAS). ACS data with similar levels of geographic detail will be available for full ACS implementa- tion. Given the race/ethnic groups under study, the following tables in both the ACS and census custom tables were identified as relevant for this case study: â¢ Total Hispanic population, â¢ Total non-Hispanic white population, and â¢ Total non-Hispanic black population. If accessing the census data using American FactFinder, the appropriate tables would be as follows: â¢ ACSâP003: Hispanic or Latino by race; and â¢ Census (Summary File 1)âP4: Hispanic or Latino, and not Hispanic or Latino by race. Within each table, all of the included geographic variables and their associated documenta- tion should be retained. This includes Sumlev, State, County, Place2, TAZ, Tract, and Puma5. 7.3.2 Analysis Steps The ID measures the evenness with which two mutually exclusive groups are distributed across the geographic units that comprise a larger geographic entity. As will be detailed later in this case study, the values for each areal unit are transformed into absolute values. They are then summed for the specific areal units under study (e.g., census tracts or block groups). The result- ant statistic (the ID) theoretically indicates the extent to which the sub-population groups of interest are evenly distributed and, if not, identifies the proportion of either group that would 122 A Guidebook for Using American Community Survey Data for Transportation Planning 79 It should be noted that the ACS data used in this analysis are custom tables prepared from 1999-2001 average data, where the sampling rate has been increased so that the three-year data would possess the level of accuracy obtained from five-year data under normal sampling rates. Thus, when analysts compute the ID at the tract or TAZ level, they should be aware that multiple-year data are needed. Areal Unit N County 1 PUMA 13 Tract 279 TAZ 777 Block Group 689 Block Average Population 1,623,018 124,848 5,817 2,089 2,356 81 Average Area (Miles2) 1,320.0 101.1 37.5 13.5 15.2 0.5 20,136 *Source: Census 2000. Table 7.2. Areal unit characteristics, Census 2000 Broward County, Florida.
have to move from their current areal unit to another in order for both groups to be proportion- ally distributed within the larger geographic unit of interest. The following basic steps undertaken to calculate an ID for Broward County are: 1. Following the index formula of ID = 100 Ã 1â2 Î£ (| ai /A â bi /B |), calculate the necessary param- eters (columns) for formula completion. This is done for each group pair (white/black, white/Hispanic, and black/Hispanic). a) For the two groups to be compared, calculate the proportion of each group within each areal unit i you have chosen to utilize (tracts, TAZs, etc.). Every row in the data file is a particular level of i, or areal unit. Divide each a or b value within the ith row by the corre- sponding total population (A or b) for the geographic unit you are studying (the county in this case). b) Subtract a/A from b/B within the ith row. c) Take the absolute value of Step 1b. d) Sum the column values from Step 1c for the ith rows that make up areal units you are uti- lizing (i.e., all tracts, TAZs, etc.) e) Multiply summed values from Step 1d by 0.5. 2. The summed result multiplied by 100 is the ID value for Broward County. Table 7.3 is a worksheet that shows the ACS data used in the Broward County ID calculations involving the comparison of non-Hispanic whites (Groups a and A) and non-Hispanic blacks (groups b and B). Column and row letters/numbers are shown in the table to help guide the reader through the calculation steps. Within this table, data rows consist of population totals and counts by the 13 PUMAs. The columns represent summary variables such as geography (B = PUMA5), and race/ethnicity (C = white, D = black). Note that â¢ Row 1 represents the county population totals for both whites and blacks. Transportation Market Analyses Using ACS Data 123 A B C D E F G H SUMLEV PUMA5 WHITE BLACK wh/WH blk/BLK |a/A-b/B| 0.5*sum (|a/A-b/B|) 1 50 929090 337925 PUMA ID 2 795 03601 94565 12415 0.10 0.04 0.07 35.3 3 795 03602 74905 13555 0.08 0.04 0.04 4 795 03603 72855 21185 0.08 0.06 0.02 5 795 03604 74620 18940 0.08 0.06 0.02 6 795 03605 75560 44945 0.08 0.13 0.05 7 795 03606 85775 52435 0.09 0.16 0.06 8 795 03607 31270 59930 0.03 0.18 0.14 9 795 03608 89740 25955 0.10 0.08 0.02 10 795 03609 73680 11155 0.08 0.03 0.05 11 795 03610 90910 4095 0.10 0.01 0.09 12 795 03611 72195 13735 0.08 0.04 0.04 13 795 03612 23150 40585 0.02 0.12 0.10 14 795 03613 69865 18990 0.08 0.06 0.02 *Data source: American Community Survey: 1999-2001. Table 7.3. ID calculations utilizing five percent of PUMA for non-Hispanic whites and non-Hispanic blacks in Broward County, Florida.
â¢ Rows 2 through 14 show the population counts for each group within the 13 Broward County PUMAs (This example assumed the calculation of ID at the PUMA level). â¢ Column A shows the summary level of the data. SUMLEV = 50 is the row with county data, SUMLEV = 795 identifies the rows with PUMA-level data. â¢ Column B represents the unique identifier of each PUMA in Broward County, Florida. â¢ Beginning with the data in Rows 2 through 14, Columns E, F, and G show the construction of the PUMA-level components of the ID. c) Column E shows the outcome of dividing the white population in each PUMA by the total white population in Broward County (for example, E2 is the result of dividing C2 by C1). d) Column F repeats this exact same calculation for the black population (for example, F2 = D2/D1). e) Column G calculates the absolute value of the difference between Columns E and F (|E2- F2| â with rounding). f) Lastly, Column H, Row 2 sums the values in column G and then multiplies that value by 0.5. This is the ID value for this areal unit (PUMA) for the selected geography (Broward County, Florida). The process outlined above was repeated for the white/Hispanic and black/Hispanic groups in order to develop an overall ID for the region. The results are shown in Table 7.1, discussed previously. In this table â¢ The columns show the different areal units (PUMA, tract, TAZ, block group, and block). â¢ The subcolumns show the ID for each comparison group pair (W/B is white/black, W/H is white/Hispanic, and B/H is black/Hispanic). â¢ The first two rows (Data Source) show the ID for each comparison group pair calculated first using the Census 2000 data, then using the ACS data. Given data availability, there is no block group or block data available through the ACS, so those cells are blank. â¢ The next two rows (Net Difference and % Difference) are calculated differences in magnitude between the ID calculated using Census 2000 data versus that calculated using ACS data for the same geographic unit and comparison group pairing. â¢ The final row provides the number of areal units used in the calculations for each geographic unit. There are substantial differences between the results of the ID calculations at the PUMA level and those at the tract level, indicating that the PUMA level of geography is probably too large to do meaningful environmental justice analyses. However, there are only moderate differences between the tract-level results and the TAZ-level results, and the TAZ-level results are quite similar to the block group and block-level analyses. This would indicate that, at least for Broward County, analyses at the tract or TAZ level are probably adequately capturing the over- all level of dissimilarity in the county. Since the block-level analyses do not vary by a large amount from the analyses for which ACS data will be available, such analyses using ACS data (and supplemented with decennial census Short Form data) would appear to be valuable and useful. Accounting for the Uncertainty of ACS Estimates Analysts can reflect the ACS sampling in the ID calculations by applying the equations that have been presented above. The following dis- cussion describes these calculations for the PUMA level of geography. The actual reported ACS estimates would include lower and upper bounds such as the rep- resentative figures shown in the columns on the left of Tables 7.4 and 7.5. The 90 percent con- fidence level margin of error of the estimates can be calculated by finding the differences between these estimates and the lower and upper bounds. The standard errors of the estimates can then be calculated by dividing the margins of error by 1.65, as has been previously shown. The margin of error and standard error calculations are shown in the middle columns of the two tables. 124 A Guidebook for Using American Community Survey Data for Transportation Planning
The next step in the previous point estimate analysis was to develop the (ai/A) and (bi/B) fac- tors. The formula for calculating the standard error of this type of ratio was provided previously in Section 4. The results of applying this calculation for the estimates of (ai/A) and (bi/B) are shown in the rightmost column of Tables 7.4 and 7.5. The next step in the ID calculation is to calculate the differences between (ai/A) and (bi/B), and then to sum these differences. Table 7.6 shows the results of performing these steps and calculating the corresponding standard errors. SE X Y Y SE X X Y SE Y( Ë Ë ) Ë Ë Ë Ë ( Ë)= ( )â¡â£ â¤â¦ + â¡â£ â¤â¦1 2 22 2 Transportation Market Analyses Using ACS Data 125 PUMA Estimate LB UB ME SE PUMA a/A SE(a/A) County Total 929090 921936 936244 7154 4336 3601 94565 89487 99643 5078 3078 3601 0.102 0.0033 3602 74905 72321 77489 2584 1566 3602 0.081 0.0017 3603 72855 69737 75973 3118 1890 3603 0.078 0.0021 3604 74620 72075 77165 2545 1542 3604 0.080 0.0017 3605 75560 73271 77849 2289 1387 3605 0.081 0.0015 3606 85775 82327 89223 3448 2090 3606 0.092 0.0023 3607 31270 29853 32687 1417 859 3607 0.034 0.0009 3608 89740 85459 94021 4281 2595 3608 0.097 0.0028 3609 73680 70409 76951 3271 1982 3609 0.079 0.0022 3610 90910 90210 91610 700 424 3610 0.098 0.0006 3611 72195 69560 74830 2635 1597 3611 0.078 0.0018 3612 23150 22458 23842 692 419 3612 0.025 0.0005 3613 69865 67070 72660 2795 1694 3613 0.075 0.0019 PUMA Estimate LB UB ME SE PUMA b/B SE(b/B) County Total 337925 335086 340764 2839 1721 3601 12415 12039 12791 376 228 3601 0.037 0.0007 3602 13555 13010 14100 545 330 3602 0.040 0.0010 3603 21185 20225 22145 960 582 3603 0.063 0.0018 3604 18940 18037 19843 903 547 3604 0.056 0.0016 3605 44945 42949 46941 1996 1210 3605 0.133 0.0036 3606 52435 52031 52839 404 245 3606 0.155 0.0011 3607 59930 57743 62117 2187 1325 3607 0.177 0.0040 3608 25955 25179 26731 776 470 3608 0.077 0.0014 3609 11155 10709 11601 446 270 3609 0.033 0.0008 3610 4095 3875 4315 220 133 3610 0.012 0.0004 3611 13735 13261 14209 474 287 3611 0.041 0.0009 3612 40585 38848 42322 1737 1053 3612 0.120 0.0032 3613 18990 18342 19638 648 393 3613 0.056 0.0012 Table 7.4. Estimates of non-Hispanic white population of Broward County. Table 7.5. Estimates of non-Hispanic black population of Broward County.
The differences in (ai/A) and (bi/B) are calculated directly. To obtain the standard error of this difference in estimates, apply the following formula (again, from Section 4 and Census Bureau guidance) as follows: In this case, X and Y refer to the (ai/A) and (bi/B) estimates. The differences in (ai/A) and (bi/B) are then summed, divided by two, and multiplied by 100 to obtain the ID. The standard error of this summation can be calculated using an extension of the same formula with 13 addends. The final standard error of the calculation can then be mul- tiplied by 1.65 to obtain the 90 percent confidence level margin of error, so the ID in this case is 35.34 Â± 0.84. Calculating a confidence interval on a measure like the ID becomes a useful exercise when the IDs for different geographies (e.g., one county compared to another), areal units (e.g., PUMA-level analysis versus tract-level analysis), or combinations of population groups (non- Hispanic white compared to non-Hispanic black versus non-Hispanic compared to Hispanic) are compared. 7.4 Conclusions from the Case Study This case study has demonstrated how to calculate the ID, which is one application of environ- mental justice analysis. The power of this measure is in its ability to be calculated for specific areal units, then imported into a geographic information system (GIS) for mapping and displaying the uniformity or diversity of a region. The ID plays an important role in estimating impacts within the environmental justice process and is also applicable to specific transportation projects. There are two important notes regarding weaknesses of this measure. The first issue involves the âaspatialâ nature of this measure. Although the ID does represent a summary measure of spa- tial âevenness,â it does so only in a very simplified, non-spatial way for a particular areal unit. SE X Y SE X SE YË Ë Ë Ë+( ) = ( )â¡â£ â¤â¦ + ( )â¡â£ â¤â¦2 2 126 A Guidebook for Using American Community Survey Data for Transportation Planning PUMA a/A SE(a/A) b/B SE(b/B) a/A-b/B SE(a/A-b/B) ID 35.34 3601 0.102 0.0033 0.037 0.0007 0.065 0.0034 3602 0.081 0.0017 0.040 0.0010 0.041 0.0020 SE(ID) 3603 0.078 0.0021 0.063 0.0018 0.016 0.0027 0.51 3604 0.080 0.0017 0.056 0.0016 0.024 0.0024 3605 0.081 0.0015 0.133 0.0036 0.052 0.0040 ME(ID) 3606 0.092 0.0023 0.155 0.0011 0.063 0.0025 0.84 3607 0.034 0.0009 0.177 0.0040 0.144 0.0041 3608 0.097 0.0028 0.077 0.0014 0.020 0.0032 3609 0.079 0.0022 0.033 0.0008 0.046 0.0023 3610 0.098 0.0006 0.012 0.0004 0.086 0.0008 3611 0.078 0.0018 0.041 0.0009 0.037 0.0020 3612 0.025 0.0005 0.120 0.0032 0.095 0.0032 3613 0.075 0.0019 0.056 0.0012 0.019 0.0022 Table 7.6. Calculation of index of dissimilarity with confidence intervals.
A calculated ID value does not indicate the type of spatial patterns that are present in the geographic unit of interest. An ID value of 50 could represent a situation where half of the geo- graphic unit is composed of 100 percent Group A concentrated in particular census tracts and the other half is composed of 100 percent Group B in different census tracts (such as when one group settles to the east of the railroad tracks and another group is west of the railroad tracks). Alternatively, an ID value of 50 also could represent a case where every other areal unit (e.g., census tract) is composed of alternating 100 percent populations of Group A and B (a checker- board scenario). Both patterns of residential segregation differ widely in both scope and policy recommendations. For the purposes of this case study, this issue was not paramount as our goals involved making comparisons of the same measure by data source. The second issue is that the ID only measures two groups at a time. Historically, this has not been as much of an issue as our society was dominated by segregation patterns between two dis- tinct groups: non-Hispanic whites and non-Hispanic blacks. Given the increasingly divergent and diverse nature of numerous U.S. communities, this weakness means that only two groups can be compared at a time. Here, we have analyzed the Broward County population in terms of the three race/ethnic groups that represent the overwhelming majority of residents (about 95 percent): whites, blacks, and Hispanics. As shown in Table 7.1, the ACS data compare favorably with the census data, as evidenced by no resultant large percentage difference in ID values between the two data sources for any race/ethnic comparison group. However, the ACS data do not include block data but the Cen- sus Summary File 1 data do. For the analysis of Broward County, the Census 2000 data indicate that the most detailed geography is not needed to understand the racial separation in the county. The ID calculations using ACS estimates can be performed in the same way as for the census data by treating the estimates as point estimates, but the analyses can be improved by account- ing for the statistical uncertainty of the ACS estimates due to sampling. By keeping track of the standard errors of estimates as they are calculated in the analysis process, data users are able to obtain an estimate of the margin of error of the results. This allows one to better compare the results to other similar results for which confidence intervals also are calculated. 7.5 Specific Uses of Census Data for Market Analyses CTPP Part 1 data on households and commuters and CTPP Part 3 commute flow data are often used for transit market studies. Several specific examples are provided below. 7.5.1 Study of Captive Riders Census data can be used to study transit-dependent populations by observing characteristics such as workers from households without vehicles, household income, age, etc. The analysis is often done within a GIS context to isolate populations within the service area of a transit route. Examples of some studies include the following: â¢ The Chicago Transit Authority80 periodically conducts a travel behavior and attitude survey. Combined, and weighted using the decennial census, these data have been instrumental in understanding the changing profile of the Chicago transit user, from the captive rider in the earlier decades to the choice rider in the last decade. Transportation Market Analyses Using ACS Data 127 80 Personal correspondence with Mary Kay Christopher, General Manager, Service Planning, Chicago Transit Authority, November 17, 2004.
â¢ Sandra Rosenbloom (Transit Cooperative Research Program, Report 28) studied future tran- sit markets by using data from the decennial census, Nationwide Personal Transportation Sur- vey, and the American Housing Survey.81 â¢ Dowell Myers studied the changing commuting behavior of immigrants and their depend- ence on transit in Southern California using 1980, 1990, and 2000 census data.82 â¢ The MTCâs research on the attitudes and level of dependence of California commuters on transit, through the stratification of workers by number of vehicles. â¢ Other studies of primary transit riders include the analysis done by the planners at Iowa Northland Regional Council, Hampton Roads Planning District Commission, and Denver Regional Transit District (based on interviews and correspondence with agency planners). 7.5.2 Performance Evaluation Examples of performance evaluation studies include Title VI and environmental justice analy- sis (see examples below), accessibility studies (e.g., studies conducted by Massachusetts Bay Transportation Authority), and corridor density analysis (e.g., analysis conducted by the Munic- ipality of Anchorage, Alaska). Most of these analyses rely heavily on demographic and socioeconomic data from the census (espe- cially related to race, income, and minority areas). This analysis is frequently done at small area geog- raphy such as TAZs, census block groups or tracts. The GIS spatial analysis is often used to identify and display sensitive areas. Analysis results can be used to develop policies and procedures, identify expansion projects within or near sensitive areas, and for public involvement/outreach purposes. Many transportation planners contacted in the development of this guidebook have per- formed environmental justice analyses.83 Some specific examples include the following: â¢ Missouri DOTâs environmental justice analysis utilized structural equation modeling/cluster analysis to ascertain the quality of life in neighborhoods comprised of protected populations (minorities, low-income, disabled, and elderly).84 The geographic detail used for the structural equation models was census block group. â¢ An Atlanta benefits and burdens study examined journey-to-work travel patterns (mode, travel time, origin/destination) by race/ethnicity and income, by matching characteristics of workers at residence locations with characteristics of workers at work locations; the study also examined vehicle availability by race/ethnicity, income, and geography.85 â¢ Chicago Transit Agency has used decennial census data on minority status and income as a primary source of quantitative analyses to ensure that transit service is fairly distributed, and any cuts in service (due to budget constraints) do not disproportionately affect low-income or minority populations.86 128 A Guidebook for Using American Community Survey Data for Transportation Planning 81 Sandra Rosenbloom, TCRP Report 28: Transit Markets for the Future: The Challenge of Change, Transportation Research Board, National Research Council, Washington, D.C., 1998. See http://gulliver.trb.org/publications/ tcrp/tcrp_rpt_28-a.pdf. 82 See, for example, D. Myers, 1996, âChanges Over Time in Transportation Mode for Journey to Work: Effects of Aging and Immigration,â Transportation Research Board, Decennial Census Data for Transportation Plan- ning, Case Studies and Strategies for 2000, Conference Proceedings 13, April 28-May 1, 1996. 83 Examples include: Minnesota DOT, Hampton Roads Planning District Commission, Chittenden County MPO, Mid-Ohio Regional Planning Commission, Municipality of Anchorage, Chicago Area Transportation Study, Iowa Northland Regional Council, Pima Association of Governments, Yakima Valley Conference of Gov- ernments, King County Transit, METRA, and Denver Regional Transit District. 84 See http://oseda.missouri.edu/modot/planning/stlouis_analysis.shtml, August 2003. 85 Personal correspondence with Chris Porter, Cambridge Systematics, Inc., November 17, 2004. 86 Personal correspondence with Mary Kay Christopher, General Manager, Service Planning, Chicago Transit Authority, November 17, 2004.
â¢ An NCHRP (Project 8-36, Task 11) report87 on âTechnical Methods to Support Analysis of Environmental Justice Issuesâ prescribed the use of census data at small geography. 7.5.3 Demand Projections and Market Evaluations Examples of the use of census data in this category include the following: â¢ Utah Transit Authorityâs88 use of Census 2000 and PUMS data in attitude models linking trav- eler attitudes to existing socioeconomic and demographic data. This makes it possible to relate traveler attitudinal factors that were used to create the market segments to the socioeconomic data in the census and to identify the spatial distribution of the segments in the population. â¢ The I-287/TZB project and the impacts of transit and land use in Rockland County, New York.89 â¢ Transit market research studies90 using census and CTPP data in structural equations model- ing work performed for the Utah Transit Authority, San Diego Association of Governments, SamTrans Strategic Plan, I-580 BART study, and the San Francisco Water Transit Authority. â¢ Various studies in the Chicago region, such as bus service market analysis (using on-board travel survey results and demographic data from the census) conducted by the Chicago Trans- portation Authority to define appropriate marketing strategies; and analysis of non-CBD work trip origins by the Regional Transportation Authority to evaluate suburban transit feasibility. â¢ The use of 1980 UTPP, 1990 CTPP, and 2000 CTPP data by the Delaware Valley Regional Planning Commission to assess the ridership potential for several different potential transit improvements, including high-speed rail, express bus and park-and-ride service, and local bus service. â¢ Projection of the additional rail ridership induced by the introduction of congestion pricing on the Bay Bridge. Planners also evaluated latent demand for rail, through the examination of demographic profiles (based on CTPP, Part 1) and economic profiles (based on CTPP, Part 2) of non-rail users who reside close to rail stations, availability of free workplace parking, and adequacy of feeder bus services. â¢ Other work done by the Central Transportation Planning Staff and METRA defining distance- based marketsheds for each station (personal correspondence). 7.5.4 Route Planning Examples of route planning efforts done using census data include â¢ Chicago Transit Authorityâs use of population density and other variables at small area geog- raphy to plan their Night Owl service (buses that run all night);91 â¢ A study of the differences in origin-destination patterns between drive-alone automobile and streetcar modes in an effort to improve feeder services at major stations by Baltimore transit planners; â¢ Commuter rail feasibility studies (Central Transportation Planning Staff), and other work by the Delaware Valley Regional Planning Commission, where route planning was supplemented Transportation Market Analyses Using ACS Data 129 87 Cambridge Systematics, Inc., âTechnical Methods to Support Analysis of Environmental Justice Issues,â pre- pared for NCHRP Project 8-36 (11) support to the AASHTO Standing Committee on Planning, April 2002. 88 Cambridge Systematics Inc., âAttitudinal-Based Market Research,â Prepared for Utah Transportation Author- ity, December 2003. 89 Personal correspondence with Michael DâAngelo, Department of Planning, County of Rockland, New York, November 13, 2004. 90 Personal correspondence with Chris Wornum, Cambridge Systematics, Inc., November 10, 2004. 91 Personal correspondence with Mary Kay Christopher, General Manager, Service Planning, Chicago Transit Authority. November 17, 2004.
by on-board ridership surveys because journey-to-work data might be too coarse for detailed route-level transit planning; and â¢ Minnesota DOT, Pioneer Valley Planning Commission, King County Transit, and Denver Regional Transit District where population and employment densities were used to determine types and frequency of service needed. 7.5.5 Non-Motorized Commuting Examples of studies where census data have been used in this context include: â¢ The use of census data by the City of Portland92 to evaluate bicycle commuting in relation to the cityâs bicycle policies and benchmarks, as well as to test whether there is a statistical rela- tionship between the percentage of bicycle commuters and the bicycle network through a regression analysis performed at the tract level. For this analysis, socioeconomic variables and number of commuters by bicycle were derived from the 1990 and 2000 Census, as well as the 1996 ACS. â¢ Rockland County, New Yorkâs use of census data in ride sharing and ride matching.93 â¢ MTCâs analysis of means of transportation to work in California by various market segments, using 2000 PUMS data supplemented by 1990 PUMS data to examine shifts in travel patterns. 7.5.6 Other Market Analyses Other examples of market analyses using census data include: â¢ Travel model market segmentation derived from the use of PUMS data by MTC to adjust zonal household size averages to averages stratified by household income level. Household size is then used as an input to a nested workers-in-household-automobile-ownership choice model.94 â¢ The development of a proprietary segmentation product by Claritas, called Workplace PRIZM. PRIZM uses journey-to-work flows, and links characteristics of workers at place of work to their residential attributes. It classifies block groups into lifestyle âclustersâ based on key demographic characteristics. These clusters then serve as an efficient way to identify the distribution of demand for specific products and services (and media usage) across the land- scape. Use was made of a special tabulation of the Census Bureauâs journey-to-work data. 130 A Guidebook for Using American Community Survey Data for Transportation Planning 92 M. Leclerc, 2002, âBicycle Planning in the City of Portland: Evaluation of the Cityâs Master Plan and Statisti- cal Analysis of the Relationship between the Cityâs Bicycle Network and Bicycle Commute.â 93 Personal correspondence with Michael DâAngelo, Department of Planning, County of Rockland, New York, November 13, 2004. 94 C. Purvis, 1996, âUses of Census Data in Transportation Planning: San Francisco Bay Area Case Study,â Trans- portation Research Board, Decennial Census Data for Transportation Planning, Case Studies and Strategies for 2000, Conference Proceedings 13, April 28-May 1, 1996.