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76 A Guidebook for Using American Community Survey Data for Transportation Planning The opportunity to improve upon current descriptive analyses by taking advantage of more timely data and important caveats to using ACS data for descriptive analyses in terms of data availability are discussed in the next section. 5.2 Benefits and Limitations of ACS for Descriptive Analyses In discussions with transportation planners, the main perceived benefit of ACS identified was the timeliness of the data. For descriptive analyses of larger geographies, ACS will provide ana- lysts with more current data and pre-made Census Bureau descriptive analysis products at a quicker rate than the decennial Long Form data collection approach. With a Long Form data collection approach, an analyst interested in finding the percentage of households with no vehicles in their state could obtain a year 2000 estimate beginning in August or September of 2002. If a traditional decennial census Long Form data collection effort were to be performed, there would be no new estimate for the state available until the summer of 2012, when new year 2010 data became available to users. With the ACS, the analyst can obtain this estimate for the previous year as early as late summer of the next year. These data will be available for 2005 and beyond. If the analyst needed the data in the summer of 2011, with the ACS they will be able to get a 2010 estimate (as well as similar estimates for 2005 through 2009, depending on the geographic size of the area under study). By comparison, with the Long Form data collection approach they would still only have the year 2000 estimate in the summer of 2011. The migration to ACS, however, does raise a few challenges for data users performing descrip- tive analyses. First, the ACS sample sizes are significantly smaller than those of the census Long Form. While it is very common for analysts to present Long Form estimates as point estimates, without regard to sampling error, users probably will want to show or report the larger standard errors in the ACS estimates. In addition, with smaller geographic areas, analysts will need to rely on data accumulated from multiple years. For areas with populations of more than 65,000, annual ACS estimates will be available. For areas with populations of between 20,000 and 65,000, analysts will need to rely on three-year averages. For areas with populations of less than 20,000, including tracts and block groups, analysts will need to rely on five-year averages. For many descriptive analyses, the averaging across years of the estimates will not have too much of an effect. However, for some analyses of population and household characteristics that can vary over relatively small periods of time, it will be much more difficult for analysts to under- stand the characteristics of interest. The analysis of smaller geographic areas also will be complicated by the Census Bureau's disclosure avoidance procedures. The ACS's smaller sample sizes and the Census Bureau's stricter rules on avoiding publication of estimates where there is a possibility that an individual can be identified, will make descriptive analyses of many small areas more difficult. 5.3 Descriptive Analysis Case Studies The following case studies illustrate how a data user might compile descriptive analyses using ACS data, and provide a step-by-step description of how to obtain the data, do the computa- tions, and present the results.

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Policy Planning and Other Descriptive Analyses Using ACS Data 77 For this purpose, assume that you are a transportation analyst working in a Metropolitan Planning Organization (MPO). Your manager has asked you to compile descriptive statistics on commuting-to-work characteristics for a county in the area served by the MPO. In the first analysis, you will develop a change estimates profile. In the second analysis, you will compile a ranking profile. In the third analysis, you will develop descriptive statistics for a "state of the system" report.44 Section 3 of this guidebook provides detailed instructions on downloading ACS data, and Section 4 describes the basic procedures that are applied in the case studies. 5.3.1 Analysis 1: Change Estimates Profile You have been asked to produce a profile of Lake County showing selected commuting-to- work characteristics and how they have changed from 2002 to 2003. For presenting the results of the change profile analysis to your MPO director, it is important to show the year-to-year differ- ences and an assessment as to whether the change is statistically significant. It is not necessary to include the technical details of the standard error and confidence interval computations. You could produce some charts and graphs to help visualize the change in commuting char- acteristics between 2002 and 2003. Figures 5.4 and 5.5 are bar charts showing the distribution of workers by means of transportation to work and by departure time to work in 2002 and 2003, respectively. If additional detail is needed, you can also show the actual numbers in tabular for- mat, such as the estimates in each of the years, the percentages, the difference in percentages, and whether the difference is statistically significant (see Table 5.4). Percentage of Workers 100 Year 2002 90 Year 2003 80 70 60 50 40 30 20 Significant Difference 10 0 Drove Carpooled Public Motorcycle Bicycle Walked Other Worked at Alone Transportation Means Home Figure 5.4. Distribution of workers by means of transportation to work in Lake County, 2002 and 2003. 44 The data used for the first two example case studies are from ACS estimates for Lake County, Illinois, for 2002 and 2003, and on fictitious hypothetical estimates for the same county. The data used for the third case study are from ACS estimates for the San Francisco Bay Area, California, for 2000 through 2003. The Bay Area case study is based on a poster presentation at the TRB Census Data for Transportation Planning Conference in Irvine, Cal- ifornia, in May 2005 developed and presented by Shimon Israel of MTC.

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78 A Guidebook for Using American Community Survey Data for Transportation Planning Percentage of Workers 30 Year 2002 Year 2003 25 20 Significant Difference 15 10 5 0 12:00 a.m. 5:30 a.m. 6:30 a.m. 7:30 a.m. 8:30 a.m. 10:00 a.m. 12:00 p.m. Worked to 4:59 a.m. to 5:59 a.m. to 6:59 a.m. to 7:59 a.m. to 8:59 a.m. to 10:59 a.m. to 3:59 p.m. at Home 5:00 a.m. 6:00 a.m. 7:00 a.m. 8:00 a.m. 9:00 a.m. 11:00 a.m. 4:00 p.m. to 5:29 a.m. to 6:29 a.m. to 7:29 a.m. to 8:29 a.m. to 9:59 a.m. to 11:59 a.m. to 11:59 p.m. Figure 5.5. Distribution of workers by departure time to work in Lake County, 2002 and 2003. The following conclusions could accompany the graphs: For mode to work, except for "walk" and "other means," there were no significant changes between 2002 and 2003 in the percentage of workers using any given transportation mode for commuting to work. For workers who walked to work, the difference in percentages is 0.63 percent (increase), and it is a statistically significant increase not caused by sampling error. For workers who used "other means" to work, the difference in percentages is 0.27 percent (increase), and it also is a statistically significant increase. For departure time to work, except for the time period from 6:00 A.M. to 6:59 A.M., there were no significant changes between 2002 and 2003 in the percentage of workers departing from home to work in any given time period. The increase in the percentage of workers departing during the time period from 6:00 A.M. to 6:29 A.M. was 1.87 percent in 2003 relative to 2002, and this increase is statistically significant. The decrease in the percentage of workers depart- ing from 6:30 A.M. to 6:59 A.M. was 1.31 percent in 2003 relative to 2002, and this decrease is statistically significant. Available Data Since Lake County has a population greater than 65,000 (according to Cen- sus 2000, the total population of Lake County is 644,356), ACS data for Lake County will be released annually. Since Lake County was one of the test sites during the demonstration phase of the ACS, both the 2002 data and 2003 detailed data tables are available on the American FactFinder website. Beginning with the full implementation of ACS, these data will be available via the website for any county of this size. Table 5.1 shows selected commuting-to-work charac- teristics for Lake County for years 2002 and 2003. The data files and estimates included in the Census Bureau detailed tabulations are released with a lower bound and an upper bound corre- sponding to the 90 percent confidence interval. The 90 percent confidence interval means that 90 times out of 100 the true value of the parameter for that area falls between the lower and upper bounds of an estimate derived from a sample like the one taken. Analysis Steps The first part of this discussion summarizes the steps you would need to fol- low for analyzing the change in the percentage of workers who used public transportation to work between 2002 and 2003. The second part provides a means to determine if this change is statistically significant.

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Policy Planning and Other Descriptive Analyses Using ACS Data 79 Table 5.1. Selected commuting-to-work characteristics for lake county, 2002 and 2003 ACS data. 2002 Data 2003 Data Estimate Lower Bound Upper Bound Estimate Lower Bound Upper Bound Total Population 654,067 ***** ***** 663,721 ***** ***** Total Households 222,841 221,092 224,590 226,074 224,274 227,874 Mode to Work: Total Workers 16+ 314,647 309,478 319,816 316,525 312,408 320,642 Car, Truck, or Van: 282,426 276,882 287,970 282,407 277,642 287,172 Drove Alone 252,516 247,114 257,918 249,687 244,713 254,661 Carpooled 29,910 27,176 32,644 32,720 29,361 36,079 All Public Transportation: 13,829 12,087 15,571 13,299 11,567 15,031 Bus or Trolley Bus 1,860 1,114 2,606 2,527 1,686 3,368 Streetcar or Trolley Car 117 0 307 138 6 270 Subway or Elevated 541 241 841 776 401 1,151 Railroad 11,110 9,508 12,712 9,557 8,121 10,993 Ferryboat 39 0 103 43 0 115 Taxicab 162 0 330 258 48 468 Motorcycle 249 72 426 72 0 155 Bicycle 728 314 1,142 916 360 1,472 Walked 3,459 2,390 4,528 5,459 4,167 6,751 Other Means 1,315 804 1,827 2,176 1,567 2,785 Worked at Home 12,641 10,994 14,288 12,196 10,652 13,740 Departure Time to Work Total Workers 16+ 314,647 309,478 319,816 316,525 312,408 320,642 Did Not Work at Home: 302,006 296,960 307,052 304,329 300,014 308,644 12:00 a.m. to 4:59 a.m. 8,499 6,950 10,048 10,439 9,008 11,870 5:00 a.m. to 5:29 a.m. 11,426 9,827 13,025 11,089 9,688 12,490 5:30 a.m. to 5:59 a.m. 17,732 15,472 19,993 16,619 14,774 18,464 6:00 a.m. to 6:29 a.m. 29,941 27,374 32,508 36,024 33,579 38,469 6:30 a.m. to 6:59 a.m. 38,229 35,096 41,362 34,311 32,047 36,575 7:00 a.m. to 7:29 a.m. 50,545 47,247 53,843 47,632 44,422 50,842 7:30 a.m. to 7:59 a.m. 37,398 34,357 40,439 37,845 35,023 40,667 8:00 a.m. to 8:29 a.m. 31,061 28,089 34,033 30,954 28,449 33,459 8:30 a.m. to 8:59 a.m. 16,582 14,818 18,346 15,951 14,473 17,429 9:00 a.m. to 9:59 a.m. 18,903 16,639 21,167 19,434 17,463 21,405 10:00 a.m. to 10:59 a.m. 7,585 6,315 8,856 7,202 5,982 8,422 11:00 a.m. to 11:59 a.m. 2,227 1,595 2,859 3,112 2,390 3,834 12:00 p.m. to 3:59 p.m. 17,191 15,223 19,159 18,379 16,159 20,599 4:00 p.m. to 11:59 p.m. 14,687 12,893 16,481 15,338 13,531 17,145 Worked at Home 12,641 10,994 14,288 12,196 10,652 13,740 Source: American FactFinder web site. Note: An `*****' entry in the lower and upper bound columns indicates that the estimate is controlled. A statistical test is not appropriate. In any given year i, the estimate of the proportion of workers P ^ i who used a particular mode ^ to work is equal to the estimate of the number of workers X i who used that mode divided by the estimate of the total number of workers Y ^ , as given by the following equation: i ^ ^i = X i P (5.1) ^i Y The difference in the percentages of workers who used a particular mode to work between two years is given by DIFF = 100% P ( ^final year - P ^initial year ) (5.2)

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80 A Guidebook for Using American Community Survey Data for Transportation Planning where P^ initial year Pfinal year and are the proportions of workers who used the given mode to work in the initial year and final year, respectively. For this analysis of public transportation, the proportions of workers who used public trans- portation to work in 2002 and 2003 are 13, 829 P2002 = = 0.0440 314, 647 13, 299 P2003 = = 0.0420 316, 525 The difference in the percentage of workers who used public transportation to work is given by ( ^2003 - P DIFF = 100% P ) ^2002 = -0.20% Now that the difference in percentages has been determined. To know whether this difference in percentages of workers who used a certain mode to work is statistically significant, the steps described below should be applied. These steps are based on the documents released by the Cen- sus Bureau on the accuracy of the data and the change profiles, and are summarized in Section 4 of this guidebook.45 Step 1--Compute the standard errors of the numerator X ^ and denominator Y ^ of the propor- tion P^ , given their lower and upper bounds. ^ ) of an estimate X The standard error SE(X ^ is computed as follows: i X ( ) ^ i - LB X ^i ( ) ^i = SE X 1.65 (5.3) where LB(X ^ ) is the lower bound of the 90 percent confidence interval for the characteristic i ^ X i, and 1.65 is the critical value of the t-statistic associated with a 90 percent confidence interval. For example, the standard error of the number of workers who used public transportation to work in 2002 is ^ 2002 ) = SE(public transportation 2002 ) = 13, 829 - 12, 087 = 1, 056 SE( X 1.65 The standard error of the total number of workers in 2002 is: ^2002 ) = SE(total workers 2002 ) = 314, 647 - 309, 478 = 3,133 SE(Y 1.65 Similarly, for year 2003, these standard errors are given by: ^ 2002 ) = SE(public transportation 2003 ) = 13, 299 - 11, 567 = 1, 050 SE( X 1.65 and ^2002 ) = SE(total workers 2003 ) = 316, 525 - 312, 408 = 2, 495 SE(Y 1.65 45 See "Change Estimates" at www.census.gov/acs/www/Downloads/ACS/accuracy2002change.pdf and "Accu- racy of the Data (2003)" at www.census.gov/acs/www/Downloads/ACS/accuracy2003.pdf.

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Policy Planning and Other Descriptive Analyses Using ACS Data 81 ^ of the proportion P, given the standard errors of the Step 2--Compute the standard error SE(P) ^ is given by the following equation: numerator and the denominator. The standard error SE(P) ^2 ( ) ^i = 1 SE P ^i Y ( ) ^ 2 - X SE Y SE X i Y^i 2 ^i 2 ( ) (5.4) If the value under the square root in Equation 5.4 is negative, the minus sign under the square root is replaced by a plus sign, which results in a conservative estimate of the standard error. In the above example, the standard error of the proportion of workers who used public trans- portation to work in 2002 and in 2003 is given by ^2002 ) = SE(proportion public transportati SE(P ion 2002 ) = 1 13, 8292 [1, 056]2 - [ 3,133] = 0.00333 2 314, 467 314, 647 2 ^2003 ) = SE(proportion public transportati SE(P ion 2003 ) = 1 13, 2992 [1, 050]2 - 2 [ 2, 495 ] = 0.00331 2 316, 525 316, 525 Step 3--Compute the standard error of the difference in percentages. This is given by the following equation: ( ) ( ) 2 2 SE(DIFF ) = 100% ^ ^ SE Pfinal year + SE Pinitial year (5.5) In the above example, the standard error of the difference in the percentages of workers who used public transportation to work in 2002 and in 2003 is given by: ( ) ( ) 2 2 SE(DIFF ) = 100% ^ ^ SE P2003 + SE P2002 = 100% [0.00331]2 + [0.00333]2 = 0.47% Step 4--Compute the 90 percent margin of error of the difference in percentages. This is given by the following equation: ME(DIFF ) = 1.65 SE(DIFF ) (5.6) In the above example, the 90 percent margin of error of the difference in the percentages of workers who used public transportation to work is given by: ME(DIFF ) = 1.65 0.47% = 0.77% Step 5--Compute lower and upper bounds of the difference in percentages. These bounds are given by the following equations: LB(DIFF ) = DIFF - ME(DIFF ) (5.7) UB(DIFF ) = DIFF + ME(DIFF ) (5.8)

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82 A Guidebook for Using American Community Survey Data for Transportation Planning In the above example, the lower and upper bounds of the 90 percent confidence interval cor- responding to the difference in the percentages of workers who used public transportation to work are given by: LB(DIFF ) = -0.20% - 0.77% = -0.97% UB(DIFF ) = -0.20% + 0.77% = 0.57% Step 6--Determine the statistical significance of the difference in percentages according to the following rules: If either the lower bound or upper bound is equal to zero, then the difference is not statisti- cally significant; If the lower bound is negative and the upper bound is positive, then the difference is not statistically significant; If both the lower and upper bounds have the same sign (that is both are positive or both are negative), then the difference is statistically significant; and If ME(DIFF) is undetermined (e.g., due to an estimate of zero for the denominator (universe) or if the numerator and denominator estimates are controlled), then the significance cannot be computed. For the above example, since the lower bound is negative (-0.97 percent) and the upper bound is positive (0.57 percent), the change in the percentage of workers who used public transporta- tion to work from 2002 to 2003 is not statistically significant at the 90 percent level of confidence. The difference in the percentages is attributed to sampling error. The computation details for the full set of commuting variables are shown below. Tables 5.2 and 5.3 show the calculations of the standard errors of the estimates, proportions, and standard errors of the proportions for the 2002 and 2003 data, respectively. Table 5.4 shows the calculations related to the difference between the 2002 and 2003 percent- ages. Specifically, it shows the 2002 and 2003 percentages, difference in percentages, standard error of the difference, margin of error of the difference, lower and upper bounds of the 90 per- cent confidence interval, and whether the difference is statistically significant at the 90 percent level of confidence. 5.3.2 Analysis 2: Ranking Profile You have been asked to produce a profile of Hypothetical Lake County showing the percent- age of zero-vehicle households in year 2010 by county subdivision and how the different subdi- visions compare to the county average.46 Understanding how vehicle availability varies across the county is important, for example, for determining whether transit service is adequate in areas with higher concentrations of zero-vehicle households. This section presents some different graphical options for presenting the results and conclu- sions from the analysis of the percentage of zero-household vehicles. If additional detail is needed, the actual numbers also can be shown in tabular format (as appears later in Table 5.9). Figure 5.6 shows the ranges of the percentage of zero-vehicle households in each of the county subdivisions, where each range is colored differently. Figure 5.7 compares the county subdivi- 46 This case study relies on synthetic data similar to what will be available from future ACS data releases. These synthetic data are used because actual full-implementation ACS data with three- and five-year averaging are not available at the time that this report is being written.

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Policy Planning and Other Descriptive Analyses Using ACS Data 83 Table 5.2. 2002 calculations worksheet. Given Data Calculations Standard Standard Lower Upper Error of Error of Estimate Bound Bound Estimate Proportion Proportion Total Population 654,067 ***** ***** Total Households 222,841 221,092 224,590 1,063 Mode to Work Total Workers 16+ 314,647 309,478 319,816 3,142 Car, Truck, or Van: 282,426 276,882 287,970 3,370 0.8976 0.0059 Drove Alone 252,516 247,114 257,918 3,284 0.8025 0.0067 Carpooled 29,910 27,176 32,644 1,662 0.0951 0.0052 Public Transportation: 13,829 12,087 15,571 1,059 0.0440 0.0033 Bus or Trolley Bus 1,860 1,114 2,606 453 0.0059 0.0014 Streetcar or Trolley Car 117 0 307 71 0.0004 0.0002 Subway or Elevated 541 241 841 182 0.0017 0.0006 Railroad 11,110 9,508 12,712 974 0.0353 0.0031 Ferryboat 39 0 103 24 0.0001 0.0001 Taxicab 162 0 330 98 0.0005 0.0003 Motorcycle 249 72 426 108 0.0008 0.0003 Bicycle 728 314 1,142 252 0.0023 0.0008 Walked 3,459 2,390 4,528 650 0.0110 0.0021 Other Means 1,315 804 1,827 311 0.0042 0.0010 Worked at Home 12,641 10,994 14,288 1,001 0.0402 0.0032 Departure Time to Work Total Workers 16+ 314,647 309,478 319,816 3,142 Did Not Work at Home: 302,006 296,960 307,052 3,067 0.9598 0.0018 12:00 a.m. to 4:59 a.m. 8,499 6,950 10,048 942 0.0270 0.0030 5:00 a.m. to 5:29 a.m. 11,426 9,827 13,025 972 0.0363 0.0031 5:30 a.m. to 5:59 a.m. 17,732 15,472 19,993 1,374 0.0564 0.0043 6:00 a.m. to 6:29 a.m. 29,941 27,374 32,508 1,560 0.0952 0.0049 6:30 a.m. to 6:59 a.m. 38,229 35,096 41,362 1,905 0.1215 0.0059 7:00 a.m. to 7:29 a.m. 50,545 47,247 53,843 2,005 0.1606 0.0062 7:30 a.m. to 7:59 a.m. 37,398 34,357 40,439 1,849 0.1189 0.0058 8:00 a.m. to 8:29 a.m. 31,061 28,089 34,033 1,807 0.0987 0.0057 8:30 a.m. to 8:59 a.m. 16,582 14,818 18,346 1,072 0.0527 0.0034 9:00 a.m. to 9:59 a.m. 18,903 16,639 21,167 1,376 0.0601 0.0043 10:00 a.m. to 10:59 a.m. 7,585 6,315 8,856 772 0.0241 0.0024 11:00 a.m. to 11:59 a.m. 2,227 1,595 2,859 384 0.0071 0.0012 12:00 p.m. to 3:59 p.m. 17,191 15,223 19,159 1,196 0.0546 0.0038 4:00 p.m. to 11:59 p.m. 14,687 12,893 16,481 1,091 0.0467 0.0034 Worked at Home 12,641 10,994 14,288 1,001 0.0402 0.0032 sions' percentage of zero-vehicle households to that of the entire county, using different colors to show subdivisions that have a smaller rate and those that have a larger rate. Figure 5.8 shows another method for presenting the conclusions. For every county subdi- vision, this graph shows the estimate of the percentage of zero-vehicle households and its lower and upper bounds. The confidence interval for the percentage of zero-vehicle households at the county level is shown as dotted lines (lower bound, estimate, and upper bound). It would be common to accompany these graphs with conclusions like the following: Waukegan has the largest percentage of zero-vehicle households (8.1 percent); West Deerfield has the smallest estimated percentage of zero-vehicle households (0.8 percent); and There are 11 county subdivisions where the percentage of zero-vehicle households is smaller than the county average (3.1 percent), and 7 county subdivisions where the percentage of zero- vehicle households is larger than the county average.

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84 A Guidebook for Using American Community Survey Data for Transportation Planning Table 5.3. 2003 calculations worksheet. Given Data Calculations Standard Standard Lower Upper Error of Error of Estimate Bound Bound Estimate Proportion Proportion Total population 663,721 ***** ***** Total households 226,074 224,274 227,874 1,094 Mode to work Total workers 16+ 316,525 312,408 320,642 2,503 Car, truck, or van: 282,407 277,642 287,172 2,897 0.8922 0.0058 Drove alone 249,687 244,713 254,661 3,024 0.7888 0.0072 Carpooled 32,720 29,361 36,079 2,042 0.1034 0.0064 Public transportation: 13,299 11,567 15,031 1,053 0.0420 0.0033 Bus or trolley bus 2,527 1,686 3,368 511 0.0080 0.0016 Streetcar or trolley car 138 6 270 80 0.0004 0.0003 Subway or elevated 776 401 1,151 228 0.0025 0.0007 Railroad 9,557 8,121 10,993 873 0.0302 0.0027 Ferryboat 43 0 115 26 0.0001 0.0001 Taxicab 258 48 468 128 0.0008 0.0004 Motorcycle 72 0 155 44 0.0002 0.0001 Bicycle 916 360 1,472 338 0.0029 0.0011 Walked 5,459 4,167 6,751 785 0.0172 0.0025 Other means 2,176 1,567 2,785 370 0.0069 0.0012 Worked at home 12,196 10,652 13,740 939 0.0385 0.0029 Departure time to work Total workers 16+ 316,525 312,408 320,642 2,503 Did not work at home: 304,329 300,014 308,644 2,623 0.9615 0.0033 12:00 a.m. to 4:59 a.m. 10,439 9,008 11,870 870 0.0330 0.0027 5:00 a.m. to 5:29 a.m. 11,089 9,688 12,490 852 0.0350 0.0027 5:30 a.m. to 5:59 a.m. 16,619 14,774 18,464 1,122 0.0525 0.0035 6:00 a.m. to 6:29 a.m. 36,024 33,579 38,469 1,486 0.1138 0.0046 6:30 a.m. to 6:59 a.m. 34,311 32,047 36,575 1,376 0.1084 0.0043 7:00 a.m. to 7:29 a.m. 47,632 44,422 50,842 1,951 0.1505 0.0060 7:30 a.m. to 7:59 a.m. 37,845 35,023 40,667 1,716 0.1196 0.0053 8:00 a.m. to 8:29 a.m. 30,954 28,449 33,459 1,523 0.0978 0.0047 8:30 a.m. to 8:59 a.m. 15,951 14,473 17,429 898 0.0504 0.0028 9:00 a.m. to 9:59 a.m. 19,434 17,463 21,405 1,198 0.0614 0.0038 10:00 a.m. to 10:59 a.m. 7,202 5,982 8,422 742 0.0228 0.0023 11:00 a.m. to 11:59 a.m. 3,112 2,390 3,834 439 0.0098 0.0014 12:00 p.m. to 3:59 p.m. 18,379 16,159 20,599 1,350 0.0581 0.0042 4:00 p.m. to 11:59 p.m. 15,338 13,531 17,145 1,098 0.0485 0.0034 Worked at home 12,196 10,652 13,740 939 0.0385 0.0029 However, given the sampled nature of ACS data, it is more appropriate to present the results using the confidence intervals. For instance, one could say with 90 percent confidence that Waukegan has the largest percentage of zero-vehicle households (7.5 percent to 8.8 percent); West Deerfield (0.7 percent to 0.9 percent), Ela (0.7 percent to 1.0 percent), and Newport (0.8 percent to 1.0 percent) have the smallest percentage of zero-vehicle households; Ten county subdivisions have a statistically significant smaller percentage of zero-vehicle households than the overall county; Three county subdivisions (Grant, Waukegan, and Zion) have a percentage of zero-vehicle households that is statistically higher than the overall county; and Five county subdivisions (Antioch, Benton, Moraine, Shields, and Wauconda) have percent- ages of zero-vehicle households that are statistically the same as the county percentage.

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Policy Planning and Other Descriptive Analyses Using ACS Data 85 Table 5.4. 2002-2003 change calculations worksheet. Given Data Calculations 2002 2003 Diff: 2003 % SE ME UB Statistically Estimate Estimate 2002 % 2003 % -2002 % (Diff) (Diff) LB (Diff) (Diff) Significant? Total population 654,067 663,721 100% 100% Total households 222,841 226,074 100% 100% Mode to work Total workers 16+ 314,647 316,525 Car, truck, or van: 282,426 282,407 89.76 89.22 -0.54 0.83 1.36 -1.90 0.82 No Drove alone 252,516 249,687 80.25 78.88 -1.37 0.99 1.62 -2.99 0.25 No Carpooled 29,910 32,720 9.51 10.34 0.83 0.82 1.36 -0.52 2.19 No Public transportation: 13,829 13,299 4.40 4.20 -0.20 0.47 0.77 -0.97 0.57 No Bus or trolley bus 1,860 2,527 0.59 0.80 0.21 0.22 0.36 -0.15 0.56 No Streetcar or trolley car 117 138 0.04 0.04 0.01 0.03 0.06 -0.05 0.06 No Subway or elevated 541 776 0.17 0.25 0.07 0.09 0.15 -0.08 0.23 No Railroad 11,110 9,557 3.53 3.02 -0.51 0.41 0.68 -1.19 0.17 No Ferryboat 39 43 0.01 0.01 0.00 0.01 0.02 -0.02 0.02 No Taxicab 162 258 0.05 0.08 0.03 0.05 0.08 -0.05 0.11 No Motorcycle 249 72 0.08 0.02 -0.06 0.04 0.06 -0.12 0.00 No Bicycle 728 916 0.23 0.29 0.06 0.13 0.22 -0.16 0.28 No Walked 3,459 5,459 1.10 1.72 0.63 0.32 0.53 0.10 1.16 Yes Other means 1,315 2,176 0.42 0.69 0.27 0.15 0.25 0.02 0.52 Yes Worked at home 12,641 12,196 4.02 3.85 -0.16 0.43 0.71 -0.88 0.55 No Departure time to work Total workers 16+ 314,647 316,525 Did not work at home: 302,006 304,329 95.98 96.15 0.16 0.37 0.62 -0.45 0.78 No 12:00 a.m. to 4:59 a.m. 8,499 10,439 2.70 3.30 0.60 0.40 0.67 -0.07 1.26 No 5:00 a.m. to 5:29 a.m. 11,426 11,089 3.63 3.50 -0.13 0.41 0.67 -0.80 0.54 No 5:30 a.m. to 5:59 a.m. 17,732 16,619 5.64 5.25 -0.39 0.56 0.92 -1.30 0.53 No 6:00 a.m. to 6:29 a.m. 29,941 36,024 9.52 11.38 1.87 0.67 1.10 0.76 2.97 Yes 6:30 a.m. to 6:59 a.m. 38,229 34,311 12.15 10.84 -1.31 0.73 1.20 -2.51 -0.11 Yes 7:00 a.m. to 7:29 a.m. 50,545 47,632 16.06 15.05 -1.02 0.86 1.42 -2.44 0.41 No 7:30 a.m. to 7:59 a.m. 37,398 37,845 11.89 11.96 0.07 0.78 1.29 -1.22 1.36 No 8:00 a.m. to 8:29 a.m. 31,061 30,954 9.87 9.78 -0.09 0.74 1.21 -1.31 1.12 No 8:30 a.m. to 8:59 a.m. 16,582 15,951 5.27 5.04 -0.23 0.44 0.72 -0.95 0.49 No 9:00 a.m. to 9:59 a.m. 18,903 19,434 6.01 6.14 0.13 0.57 0.94 -0.81 1.08 No 10:00 a.m. to 10:59 a.m. 7,585 7,202 2.41 2.28 -0.14 0.34 0.56 -0.69 0.42 No 11:00 a.m. to 11:59 a.m. 2,227 3,112 0.71 0.98 0.28 0.18 0.30 -0.03 0.58 No 12:00 p.m. to 3:59 p.m. 17,191 18,379 5.46 5.81 0.34 0.57 0.93 -0.59 1.28 No 4:00 p.m. to 11:59 p.m. 14,687 15,338 4.67 4.85 0.18 0.49 0.80 -0.62 0.98 No Worked at home 12,641 12,196 4.02 3.85 -0.16 0.43 0.71 -0.88 0.55 No Available Data Table 5.5 shows synthetic ACS estimates of population for each of the county subdivisions, as well as for the entire Hypothetical Lake County in each of the years 2005 to 2009 (although these annual population estimates are not released for each county subdivision, they are shown in this table to determine which types of ACS estimates are released in a given year). The Census Bureau releases the following types of estimates based on the population of a given area: For areas with population greater than 65,000, annual estimates, as well as three- and five-year average estimates are available; For areas with population between 20,000 and 65,000, three- and five-year average estimates are available; and For areas with population less than 20,000, only five-year average estimates are available.

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86 A Guidebook for Using American Community Survey Data for Transportation Planning Figure 5.6. Percentage of zero-vehicle households by county subdivision based on the point estimates for Hypothetical Lake County. Figure 5.7. Percentage of zero-vehicle households by county subdivision as compared to the county average based on the point estimates for Hypothetical Lake County.

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90 A Guidebook for Using American Community Survey Data for Transportation Planning Table 5.7. Three-Year average estimate (2007-2009), ACS data for Hypothetical Lake County. Total Zero- Occupied Lower Upper Vehicle Lower Upper County Subdivision Households Bound Bound Households Bound Bound Antioch Township 9,773 9,046 10,500 291 244 338 Avon Township 19,812 18,687 20,937 485 412 558 Benton Township Cuba Township Ela Township 14,654 13,712 15,596 116 95 137 Fremont Township 9,222 8,522 9,922 94 76 112 Grant Township Lake Villa Township 13,199 12,316 14,082 179 148 210 Libertyville Township 19,794 18,670 20,918 493 419 567 Moraine Township 14,209 13,284 15,134 470 399 541 Newport Township Shields Township 11,730 10,910 12,550 343 289 397 Vernon Township 25,694 24,402 26,986 259 216 302 Warren Township 24,984 23,710 26,258 596 510 682 Wauconda Township Waukegan Township 31,727 30,299 33,155 2,444 2,177 2,711 West Deerfield Township 12,237 11,395 13,079 92 74 110 Zion Township 8,399 7,742 9,056 490 417 563 County Total 245,745 244,910 246,580 7,129 6,542 7,716 Table 5.8. Five-year average estimate (2005-2009), ACS data for Hypothetical Lake County. Total Zero- Occupied Lower Upper Vehicle Lower Upper County Subdivision Households Bound Bound Households Bound Bound Antioch Township 9,613 9,056 10,170 304 266 342 Avon Township 19,487 18,624 20,350 507 449 565 Benton Township 7,036 6,586 7,486 192 166 218 Cuba Township 6,709 6,273 7,145 87 74 100 Ela Township 14,388 13,666 15,110 121 104 138 Fremont Township 9,064 8,528 9,600 98 84 112 Grant Township 7,614 7,138 8,090 284 248 320 Lake Villa Township 12,969 12,292 13,646 187 162 212 Libertyville Township 19,475 18,612 20,338 515 456 574 Moraine Township 13,996 13,286 14,706 490 433 547 Newport Township 1,706 1,550 1,862 16 13 19 Shields Township 11,549 10,920 12,178 358 314 402 Vernon Township 25,265 24,273 26,257 270 236 304 Warren Township 24,586 23,607 25,565 622 553 691 Wauconda Township 6,729 6,292 7,166 230 200 260 Waukegan Township 31,314 30,214 32,414 2,551 2,337 2,765 West Deerfield Township 12,025 11,380 12,670 96 82 110 Zion Township 8,285 7,781 8,789 512 453 571 County Total 241,810 241,147 242,473 7,440 6,972 7,908

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Policy Planning and Other Descriptive Analyses Using ACS Data 91 Table 5.9. Proportion and standard error computations for the example. Total Zero- SE(Total SE(Zero- Percentage of Zero-Vehicle Households Occupied Vehicle Occupied Vehicle Estimate County Subdivision HHs HHs HHs) HHs) (%) SE (%) LB (%) UB (%) Antioch Township 9,613 304 339 23 3.2 0.2 2.8 3.5 Avon Township 19,487 507 523 35 2.6 0.2 2.3 2.9 Benton Township 7,036 192 273 16 2.7 0.2 2.4 3.1 Cuba Township 6,709 87 264 8 1.3 0.1 1.1 1.5 Ela Township 14,388 121 438 10 0.8 0.1 0.7 0.9 Fremont Township 9,064 98 325 8 1.1 0.1 1.0 1.2 Grant Township 7,614 284 288 22 3.7 0.2 3.3 4.1 Lake Villa Township 12,969 187 410 15 1.4 0.1 1.3 1.6 Libertyville Township 19,475 515 523 36 2.6 0.2 2.4 2.9 Moraine Township 13,996 490 430 35 3.5 0.2 3.1 3.9 Newport Township 1,706 16 95 2 0.9 0.1 0.8 1.1 Shields Township 11,549 358 381 27 3.1 0.2 2.8 3.4 Vernon Township 25,265 270 601 21 1.1 0.1 0.9 1.2 Warren Township 24,586 622 593 42 2.5 0.2 2.3 2.8 Wauconda Township 6,729 230 265 18 3.4 0.2 3.0 3.8 Waukegan Township 31,314 2,551 667 130 8.1 0.4 7.5 8.8 West Deerfield Township 12,025 96 391 8 0.8 0.1 0.7 0.9 Zion Township 8,285 512 305 36 6.2 0.4 5.6 6.8 County Total 241,810 7,440 402 284 3.1 0.1 2.9 3.3 The percentage of zero-vehicle households for the entire Hypothetical Lake County is 3.1 per- cent. Table 5.9 shows the percentage of zero-vehicle households for all county subdivisions. The percentages of zero-vehicle households computed as shown above are point estimates. They can be compared across county subdivisions as well as with respect to the overall county percentage of zero-vehicle households. In addition to examining the point estimates, it also is important to examine the standard errors of these estimates to see whether the conclusions are significantly altered. First, the standard errors of total occupied households and zero-vehicle households are com- puted given the estimates and their lower and upper bounds, using Equation 5.3. For example, for Antioch, the standard error of total occupied households is (9,613 - 9,056)/1.65 = 338 The standard error of zero-vehicle households is (304 - 266)/1.65 = 23 Second, the standard error of the percentage of zero-vehicle households is computed using Equation 5.4. For example, for Antioch, the standard error of the percentage of zero-vehicle households is 1 304 2 [ 23]2 - [339]2 = 0.00212 = 0.2% 9, 613 9, 6132

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92 A Guidebook for Using American Community Survey Data for Transportation Planning Third, given the estimate of the percentage of zero-vehicle households and its standard error, the lower and upper bounds of the 90 percent confidence interval are computed as follows: LB = Estimate - 1.65 SE(Estimate) (5.9) LB = Estimate + 1.65 SE(Estimate) (5.10) For example, for Antioch, the lower bound of the 90 percent confidence interval is (3.2 0.2 1.65) = 2.8 % The upper bound is (3.2 + 0.2 1.65) = 3.5 % Computations of standard errors and confidence intervals are shown in Table 5.9. For each county subdivision, the 90 percent confidence interval means that 90 times out of 100 the true value of the percentage of zero-vehicle households for that area falls between the lower and upper bounds of an estimate derived from a sample like the one taken. Once the percentages and standard errors of the percentages are calculated, the differences between individual subdivisions and the county as a whole can be calculated and compared using the procedures previously described. The differences in the estimates are calculated directly. Therefore, for Antioch the difference between the subdivision and county estimate is 3.2 - 3.1 = 0.1 percent. The standard error of the difference can be calculated using a variant of Equation 5.5 from the first analysis. 2 SE(DIFF ) = 100% ^ + ^ SE(Pcounty ) SE(Ptownship ) (5.11) The 90 percent confidence-level margin of error of the difference is ME(DIFF ) = 1.65 SE(DIFF ) (5.12) The upper and lower bounds of the difference in percentages are as follows: LB(DIFF ) = DIFF - ME(DIFF ) (5.13) UB(DIFF ) = DIFF + ME(DIFF ) (5.14) For Antioch, the results of these calculations are SE(DIFF) = 0.2 percent, ME(DIFF) = 0.4 percent, LB(DIFF) = -0.3 percent, and UB(DIFF) = 0.5 percent. Table 5.10 shows the results of these calculations for each of the subdivisions. An interesting finding of this analysis is that one county subdivision, Libertyville, is found to be statistically different from the county average despite the fact that when one compares the esti- mates, lower bounds, and upper bounds (for instance, see Figure 5.8), the margins of error of that county subdivision and the county overlap. In order to correctly assess the statistical significance of the differences, it is necessary to calculate the standard errors of the differences, rather than to simply inspect the estimates, standard errors, and margins of error of the variable of interest.

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Policy Planning and Other Descriptive Analyses Using ACS Data 93 Table 5.10. Statistical difference computation results. Difference Between Township Statistically and County Zero-Vehicle SE ME LB UB Significant County Subdivision Households Percentages (Diff) (Diff) (Diff) (Diff) Difference? Antioch Township 0.1% 0.24% 0.40% -0.3% 0.5% No Avon Township -0.5% 0.20% 0.33% -0.8% -0.1% Yes Benton Township -0.3% 0.23% 0.38% -0.7% 0.0% No Cuba Township -1.8% 0.16% 0.26% -2.0% -1.5% Yes Ela Township -2.2% 0.13% 0.22% -2.5% -2.0% Yes Fremont Township -2.0% 0.14% 0.23% -2.2% -1.8% Yes Grant Township 0.7% 0.27% 0.46% 0.2% 1.1% Yes Lake Villa Township -1.6% 0.16% 0.26% -1.9% -1.4% Yes Libertyville Township -0.4% 0.20% 0.34% -0.8% -0.1% Yes Moraine Township 0.4% 0.25% 0.42% 0.0% 0.8% No Newport Township -2.1% 0.16% 0.26% -2.4% -1.9% Yes Shields Township 0.0% 0.24% 0.40% -0.4% 0.4% No Vernon Township -2.0% 0.14% 0.23% -2.2% -1.8% Yes Warren Township -0.5% 0.20% 0.33% -0.9% -0.2% Yes Wauconda Township 0.3% 0.26% 0.43% -0.1% 0.8% No Waukegan Township 5.1% 0.40% 0.65% 4.4% 5.7% Yes West Deerfield Township -2.3% 0.13% 0.22% -2.5% -2.1% Yes Zion Township 3.1% 0.39% 0.64% 2.5% 3.7% Yes 5.3.3 Analysis 3: Monitoring the State of the System You have been asked to compile various descriptive statistics related to commuting-to-work characteristics and vehicle ownership to develop a "state of the system" report for the Bay Area for the years 2000 to 2003. You were asked to use any available data sources and to track changes over time where data are available. This section describes different options that might be used for presenting the descriptive sta- tistics to policymakers. First, one can show some important transportation variables such as commuting mode shares by various means of transportation, percentage of zero-vehicle house- holds, and average commute time. Table 5.11 shows a summary of these statistics using Census 2000 data and 2000 to 2003 ACS data. Second, one can show some of these statistics graphically along with the confidence intervals for the ACS estimates. This is shown in Figure 5.10 for the number of public transportation com- muters, along with information on the statistical significance of the difference estimates. Some conclusions that can be drawn from this analysis include Statistically, the 2000 ACS estimate is significantly larger than the Census 2000 estimate; Statistically, the 2001 ACS estimate is significantly smaller than the 2000 ACS estimate; and Statistically, the 2002 ACS estimate is not significantly different from the 2001 ACS estimate, and statistically the 2003 ACS estimate is not significantly different from the 2002 ACS estimate. Third, one could compare trends from various data sources. For example, Figure 5.11 shows the change in the number of employed civilians over time using Census 2000, 2000-2003 ACS, and 2000-2003 Bureau of Labor Statistics-Local Area Unemployment Statistics (BLS-LAUS)

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94 A Guidebook for Using American Community Survey Data for Transportation Planning Table 5.11. Important transportation variables for the annual MTC San Francisco Bay area "state of the system" report. Census American Community Survey 2000 2000 2001 2002 2003 Commute Share by: Public Transportation 9.7% 10.7% 10.0% 9.6% 9.4% Bicycle 1.1% 1.1% 1.0% 1.0% 1.0% Walk 3.2% 2.8% 2.9% 3.1% 3.1% Drive Alone 68.0% 67.5% 69.2% 69.9% 69.2% Carpool 12.9% 12.9% 11.3% 11.1% 11.5% Worked at Home 4.0% 4.0% 4.4% 4.4% 4.7% Percent Zero-Vehicle Households 10.0% 9.0% 9.2% 8.9% 8.6% Average Commute Time (Minutes) 29.4 28.5 27.7 27.5 26.7 data. The ACS and BLS-LAUS show a trend of decrease in employed civilians over this time- frame, but the BLS estimates are larger than the ACS estimates. In the analysis of ACS data, it will be important for analysts to perform validity checks using other available data sources whenever possible. Available Data This section describes the data that were available for this analysis. Figure 5.12 shows the distribution of places in the Bay Area by population size. This has impli- cations for the types of ACS data that will be available for each of these areas. Only five-year average ACS data will be available for 14 percent of places, three- and five-year average ACS data will be available for 24 percent of places, and annual estimates, three- and five-year average ACS data will be available for 62 percent of places. All estimates used in this case study use annual estimates. Table 5.12 shows the distribution of number of workers by means of transportation to work using Census 2000 data and 2000-2003 annual ACS data. Commuters (in Thousands) 450 400 350 300 250 200 150 100 50 0 Census 2000 ACS 2000 ACS 2001 ACS 2002 ACS 2003 Survey Year Statistically Significant Increase [ACS Relative to Census 2000] Statistically Significant Decrease Figure 5.10. Total commuters on public transportation, Census 2000 and ACS 2000-2003.

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Policy Planning and Other Descriptive Analyses Using ACS Data 95 Employed Civilians (in Millions) 3.65 BLS-LAUS BLS, % Change, 2000-2003 = -4.7% ACS, % Change, 2000-2003 = -7.4% 3.60 3.55 175,300 3.50 266,000 ACS 3.45 3.40 145,700 3.35 Census 2000 70,800 3.30 3.25 3.20 2000 2001 2002 2003 Year Figure 5.11 Employed civilians, Census 2000, ACS, and BLS-LAUS. Analysis Steps This section describes the computations that were performed to reach the conclusions presented earlier. First described are the methods for working with confidence inter- vals when the analysis involves a geography for which ACS data are not directly available. Fol- lowing this discussion is a description of how to compute the statistical significance of the dif- ference between two estimates. When working with ACS confidence intervals, one should note the following two important rules of thumb: The standard error is larger, and confidence intervals are wider (as a percentage of the esti- mate), for geographic areas with smaller populations and for characteristics that occur less frequently. For example, the estimate for Bay Area bicycle commuters (a relatively small percentage of total commuters) has a confidence interval that is proportionately wider than that for carpool (2+) commuters. Places <20,000 + Remainder of County 14% Places 65,000+ 62% Places 20,000-65,000 24% Figure 5.12. Bay area population for ACS reporting.

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96 A Guidebook for Using American Community Survey Data for Transportation Planning Table 5.12. Means of transportation to work, Census 2000 and ACS. Census American Community Survey 2000 2000 2001 2002 2003 Total: 3,306,100 3,337,500 3,236,400 3,203,700 3,186,900 Car, Truck, or Van: 2,674,600 2,683,800 2,604,100 2,596,300 2,571,300 Drove Alone 2,248,100 2,252,300 2,239,900 2,240,700 2,205,800 Carpooled 426,500 431,500 364,200 355,600 365,500 Public Transportation: 321,100 357,700 324,300 306,100 300,800 Bus or Trolley Bus 178,900 199,400 180,000 178,400 176,900 Streetcar or Trolley Car 14,300 14,300 15,100 12,800 10,400 Subway or Elevated 98,700 107,500 94,800 88,100 85,300 Railroad 20,100 24,600 21,900 19,000 20,400 Ferryboat 5,800 6,700 7,900 4,100 6,100 Taxicab 3,300 5,100 4,500 3,600 1,900 Motorcycle 11,900 13,700 13,800 9,400 9,400 Bicycle 36,000 36,800 31,300 33,000 31,000 Walked 106,100 92,200 92,900 100,800 100,100 Other Means 23,700 19,000 27,600 15,800 23,900 Worked at Home 132,700 134,400 142,500 142,300 150,300 Performing fewer data calculations generally produces the tightest confidence intervals. For the Metropolitan Transportation Commission (MTC), error reporting is typically better if its 9-county ACS estimates are derived by subtracting Santa Cruz PMSA from the 10-county CMSA (fewer calculations) than by summing its 9 counties or 5 PMSAs (more calculations). The following example demonstrates how the estimates and standard errors for different geographic areas may be combined to analyze custom geographies. In this example, we derive the ACS 2003 estimate and confidence interval for MTC's planning jurisdiction. MTC's metropolitan planning jurisdiction, the nine-county San Francisco Bay Area, does not have a single census-equivalent geography for which published ACS datasets are available. Instead, MTC must derive study data by Summing ACS estimates for its nine constituent counties; Summing ACS estimates for its five constituent PMSAs; or Subtracting ACS estimates for one PMSA (Santa Cruz PMSA) from the ACS estimates for the San Francisco CMSA (which is composed of 10 counties, or equivalently, 6 PMSAs). To accomplish any of these tasks, we must derive new standard errors and confidence inter- vals based on those provided in the available ACS dataset. Table 5.13 shows some of the relevant PMSA and CMSA data that are available from the American FactFinder website. With these data, we can develop estimates for the MTC region by either subtracting the last row estimates (Santa Cruz PMSA) from the CMSA total or by summing the other five PMSA estimates. To combine geographies, the estimates may be added or subtracted directly, but we also need to account for the confidence intervals by calculating combined standard errors from the com- ponent standard errors. The following four analysis steps are required: 1. Calculate the combined estimate by adding or subtracting the component geography estimates, 2. Calculate standard errors for the component geography estimates, 3. Calculate the standard errors of the estimates for the combined geography, and

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Policy Planning and Other Descriptive Analyses Using ACS Data 97 Table 5.13. Estimated number of carpool and bicycle commuters for selected Bay Area geographic areas from 2003 ACS. Carpool Commuters Bay Area Bicycle Commuters Lower Upper Lower Upper Geographic Area Estimate Bound Bound Estimate Bound Bound San Francisco CMSA 373,018 353,631 392,405 34,561 28,350 40,772 Oakland PMSA 148,912 134,587 163,237 11,635 7,980 15,290 San Francisco PMSA 75,418 67,238 83,598 10,149 8,007 12,291 San Jose PMSA 79,052 68,397 89,707 6,743 2,826 10,660 Santa Rosa PMSA 24,377 18,945 29,809 1,795 372 3,218 Vallejo PMSA 37,723 31,846 43,600 723 29 1,417 Santa Cruz PMSA (outside MTC area) 7,536 5,062 10,010 3,516 1,183 5,849 Source: 2003 ACS Base Table P047: Means of Transportation To Work for Workers 16 Years and Over. 4. Convert the combined geography standard errors to margins of error. This process shown below is for the carpool estimate. Step 1--Calculate combined estimates. For the first approach, in which the non-MTC PMSA estimate is subtracted from the CMSA estimate, the combined estimate is: Carpool CommutersMTC Area = 373,018 - 7,536 = 365,482 For the second approach, in which the five MTC PMSAs are summed, the combined estimate is: Carpool CommutersMTC Area = 148,912 + 75,418 + 79,052 + 24,377 + 37,723 = 365,482 Step 2--Calculate component geography standard errors. The standard error calculations for each component geography are similar to the previous translations from census margins of error to standard errors: Standard error = 90 percent confidence margin of error/1.65 Margin-of-error = max(upper bound estimate, estimate lower bound) Hence, the standard errors for the carpool estimates are those shown in Table 5.14. Table 5.14. Standard error calculations for geographic areas that comprise the MTC study area. Carpool Commuters Standard Error Calculation Lower Upper Critical Value 90% Geographic Area Estimate Bound Bound ME Confidence SE San Francisco CMSA 373,018 353,631 392,405 19,387 1.65 11,750 Oakland PMSA 148,912 134,587 163,237 14,325 1.65 8,682 San Francisco PMSA 75,418 67,238 83,598 8,180 1.65 4,958 San Jose PMSA 79,052 68,397 89,707 10,655 1.65 6,458 Santa Rosa PMSA 24,377 18,945 29,809 5,432 1.65 3,292 Vallejo PMSA 37,723 31,846 43,600 5,877 1.65 3,562 Santa Cruz PMSA (outside MTC area) 7,536 5,062 10,010 2,474 1.65 1,499

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98 A Guidebook for Using American Community Survey Data for Transportation Planning Step 3--Calculate the combined standard errors. The standard error of a sum or difference can be calculated as ^ +Y SE( X ^ ) 2 + SE(Y ^ ) = SE( X ^ ) 2 Applying this equation to the two alternative approaches for developing MTC area-specific estimates, provides the following estimates. For the first approach, in which the non-MTC PMSA estimate is subtracted from the CMSA estimate, the combined standard error is SE MTC = [11, 750]2 + [1, 499]2 = 11, 845 For the second approach, in which the five MTC PMSAs are summed, the combined standard error is: SE MTC = [8, 682]2 + [ 4, 958]2 [6, 458]2 + [3, 292]2 + [3,5 562 ] = 12, 852 2 Step 4--Calculate the margins of error. The margin of error for the 90 percent confidence level is ME MTC = 1.65 SE MTC Therefore, for the first approach, in which the non-MTC PMSA estimate is subtracted from the CMSA estimate, the combined estimate is: Carpool CommutersMTC Area = 365,482 19,544 For the second approach, in which the five MTC PMSAs are summed, the combined estimate is Carpool CommutersMTC Area = 365,482 21,206 Although the two approaches have the same central point estimate, the first approach that combines only two ACS estimates provides a more precise estimate than the second approach where five separate estimates are combined. If ACS data were published at the MPO level, the need to combine geographic areas like this would be obviated, but it is likely that many transportation planners will need to create custom geographic combinations, so the example will probably remain useful. Figures 5.13 and 5.14 show the resulting confidence intervals for carpool commuters and bicy- cle commuters, respectively, using the two methods mentioned above for deriving the MPO- level 2003 ACS data estimates. This section shows an example of how year-to-year statistical significance computations, such as those shown in Figure 5.10, can be accomplished. The standard errors of the individual ACS estimates are computed using Equation 5.3. For example, the standard errors of the 2000 and 2001 ACS estimates are 357, 661 - 338, 774 SE ( ACS2000 ) = = 11, 481 1.645 324, 287 - 308, 877 SE ( ACS2001 ) = = 9, 368 1.645

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Policy Planning and Other Descriptive Analyses Using ACS Data 99 2+ Commuters (in Thousands) 450 400 90% Confidence Interval 350 300 250 200 150 365,482 365,482 + 19,544 + 21,206 100 50 0 San Francisco CMSA less Santa Cruz PMSA Sum of 5 PMSAs Method for Deriving MPO Data from Census Geography Figure 5.13. Bay Area carpool (2+) commuters, with confidence intervals, ACS 2003. The standard error of the difference in estimates is computed using the following equation: SE(DIFF ) = SE X1 ( ) ^ 2 + SE X ^ 2 2 ( ) (5.15) ^1 and X where X ^2 are the estimates used to compute the difference. For example, the standard error of the difference between the ACS 2000 and 2001 estimates is SE ( DIFF2001-2000 ) = (11, 481)2 + (9, 368)2 = 14, 818 The margin of error of the difference is computed using Equation 5.6 as follows: ME ( DIFF2001-2002 ) = 1.645 14, 818 = 24, 376 Bicycle Commuters (in Thousands) 40 35 90% Confidence Interval 30 25 20 15 31,045 31,045 10 + 6,635 + 5,983 5 0 San Francisco CMSA less Santa Cruz PMSA Sum of 5 PMSAs Method for Deriving MPO Data from Census Geography Figure 5.14. Bay Area bicycle commuters, with confidence intervals, ACS 2003.

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100 A Guidebook for Using American Community Survey Data for Transportation Planning The lower and upper bounds of the difference are computed using Equations 5.7 and 5.8 as follows: LB ( DIFF2001-2000 ) = (324, 287 - 357, 661) - 24, 376 = -57, 750 UB ( DIFF2001-2000 ) = (324, 287 - 357, 661) + 24, 376 = -8, 998 Since both the lower bound and upper bound of the difference have the same sign, the differ- ence between the 2000 and 2001 estimates is statistically significant. These calculations can be performed for each successive year to track year-to-year changes or they can be performed on non-consecutive years to measure whether accumulated differences are statistically significant. 5.3.4 Conclusions from these Analysis Case Studies Just as with the decennial census Long Form dataset, there are many different types of descriptive analyses that analysts can produce using ACS data. These case studies demonstrate how to produce change profiles and ranking profiles for a county and its subdivisions, create descriptive statistics for compiling a state-of-the-system report for a multicounty area, and interpret the results in light of the lower and upper bounds of the 90 percent confidence inter- val that are released with the data. One of the clear advantages of ACS data with respect to census data is the timeliness of the data. For example, the first case study example that was described demonstrates that the availability of ACS data in years 2002 and 2003 for the county in question enabled the analyst to determine the change in a given characteristic between the two years. With census Long Form data, such an analysis would be based on data points that correspond to a difference of at least 10 years. The sec- ond case study also emphasizes the value of having ACS data in years just prior to the decennial census year (i.e., using 2005-2009 ACS data as opposed to using Census 2000 data for an analysis conducted in year 2010). The third case study also shows that ACS data will be important in the identification of key annual trends in transportation-related variables, and in supporting agen- cies' efforts to advocate for the transportation needs of the elderly, disabled, low-income, and youth populations. In the absence of ACS data, agencies would have to rely on the decennial cen- sus, and on non-census releases for intercensal years, which would sometimes produce data dis- crepancies. As the second case study shows, one of the ACS analysis challenges is the need to deal with the varying availability of estimates. When comparing estimates across different geographic areas where multiple types of estimates are available (annual, as well as three-and five-year moving aver- age), it is likely that users will need to use the same type of estimate for all geographic areas to main- tain consistency and to avoid the bias resulting from the different periods of data accumulation. Some further issues that users of the data should be aware of when doing similar types of analyses are the following: Annual estimates of change (Case Study 1) cannot be computed in cases where only multiyear average data are available if the multiyear average estimates include data from overlapping years. This is because when standard statistical procedures are used to test for significant differences between estimates over time, it is assumed that the two estimates are drawn from independent samples, an assumption that is violated in the case of two overlapping multiyear averages. The averaging of estimates over three or five years increases the survey sample sizes from which the estimates are derived, and thus reduces the sampling error and the size of the statistical confidence intervals. However, these statistics do not account for any bias that may