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Transportation Market Analyses Using ACS Data 127 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: 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. 80 Personal correspondence with Mary Kay Christopher, General Manager, Service Planning, Chicago Transit Authority, November 17, 2004.

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128 A Guidebook for Using American Community Survey Data for Transportation Planning 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 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 The MTC's research on the attitudes and level of dependence of California commuters on transit, through the stratification of workers by number of vehicles. 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: Missouri DOT's 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. 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 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 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, "Changes Over Time in Transportation Mode for Journey to Work: Effects of Aging and Immigration," 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.

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Transportation Market Analyses Using ACS Data 129 An NCHRP (Project 8-36, Task 11) report87 on "Technical Methods to Support Analysis of Environmental Justice Issues" 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: Utah Transit Authority's88 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. The I-287/TZB project and the impacts of transit and land use in Rockland County, New York.89 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. 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. 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. 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. 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 Chicago Transit Authority's use of population density and other variables at small area geog- raphy to plan their Night Owl service (buses that run all night);91 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; Commuter rail feasibility studies (Central Transportation Planning Staff), and other work by the Delaware Valley Regional Planning Commission, where route planning was supplemented 87 Cambridge Systematics, Inc., "Technical Methods to Support Analysis of Environmental Justice Issues," pre- pared for NCHRP Project 8-36 (11) support to the AASHTO Standing Committee on Planning, April 2002. 88 Cambridge Systematics Inc., "Attitudinal-Based Market Research," Prepared for Utah Transportation Author- ity, December 2003. 89 Personal correspondence with Michael D'Angelo, 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.

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130 A Guidebook for Using American Community Survey Data for Transportation Planning by on-board ridership surveys because journey-to-work data might be too coarse for detailed route-level transit planning; and 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: The use of census data by the City of Portland92 to evaluate bicycle commuting in relation to the city's 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. Rockland County, New York's use of census data in ride sharing and ride matching.93 MTC's 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: 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 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 "clusters" 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 Bureau's journey-to-work data. 92M. Leclerc, 2002, "Bicycle Planning in the City of Portland: Evaluation of the City's Master Plan and Statisti- cal Analysis of the Relationship between the City's Bicycle Network and Bicycle Commute." 93 Personal correspondence with Michael D'Angelo, Department of Planning, County of Rockland, New York, November 13, 2004. 94 C. Purvis, 1996, "Uses of Census Data in Transportation Planning: San Francisco Bay Area Case Study," Trans- portation Research Board, Decennial Census Data for Transportation Planning, Case Studies and Strategies for 2000, Conference Proceedings 13, April 28-May 1, 1996.