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From page 102... ... Figure 6.1 shows a national trend of zero-vehicle households,51 and Figure 6.2 shows a national trend of means of transportation to work.52 A list of specific examples of using census data to do trend analysis also is provided at the end of this section. 102 C H A P T E R 6 Trend Analyses Using ACS Data 51 Murakami, E., 2003, "Households without Vehicles, 2000." See www.fhwa.dot.gov/ctpp/sr0103.htm. Read the entire page →
From page 103... ... 0 10 20 30 40 50 60 70 80 90 100 1960 1970 1980 1990 2000 Year Percent Worked At Home Other Means Walked Public Transportation Private Vehicle Figure 6.2. National trend of means of transportation to work. Read the entire page →
From page 104... ... – Another application identified by users is to use the more frequently reported data to validate and enhance demographic projections for use in travel demand models.53 • Given the lower sample sizes in ACS and the need to examine corresponding confidence intervals, however, users need to be wary of the following new issues: – ACS data come with a lot of variability and standard errors for each yearly estimate. – Because several potential data releases are possible for a particular year, (e.g.: one-year estimate, three- and five-year average) Read the entire page →
From page 105... ... The following conclusions can be drawn from this analysis: • The confidence intervals at the city level are larger than at the county level, as expected, because of smaller sample sizes at smaller geographies; and • Overall, the percentage of workers using public transportation to work is smaller in Fairview City than in Central County as a whole. 6.3.1 Available Data For areas with population over 65,000, the Census Bureau will release annual ACS estimates as well as three- and five-year moving averages. Read the entire page →
From page 106... ... Every estimate is associated with a lower bound and an upper bound corresponding to the 90 percent confidence interval. The 90 percent confidence interval means that 90 times out of 100 the true value of the characteristic for that area falls between the lower and upper bounds of an estimate derived from a sample like the one taken. Read the entire page →
From page 107... ... Year Total Workers Lower Bound Upper Bound Workers by Public Transportation Lower Bound Upper Bound 1996-2000 314,997 312,868 317,126 35,552 34,579 36,524 1997-2001 319,163 316,986 321,339 36,770 35,717 37,822 1998-2002 321,188 319,035 323,341 36,351 35,202 37,499 1999-2003 322,387 320,385 324,389 36,834 35,627 38,040 2000-2004 322,703 320,556 324,850 37,151 35,872 38,431 2001-2005 320,237 317,906 322,568 36,345 34,988 37,703 2002-2006 318,729 316,231 321,227 35,617 34,181 37,052 Table 6.3. Five-Year moving average data for Central County, 1996-2006. Read the entire page →
From page 108... ... Three-Year Moving Average Data Given the lower and upper bounds of the confidence intervals for the three-year average total number of workers and the number of workers using public transportation to work, the other computations of the percentage of workers using public transportation to work and its confidence interval would be the same as those described for the annual data case. As mentioned earlier, the three-year moving average data were not available for the 1996-2003 data and, therefore, are synthesized for this case study. Read the entire page →
From page 109... ... Three-Year moving average data computation worksheet for the percentage of workers who used public transportation to work with 90 percent confidence interval. Read the entire page →
From page 110... ... The standard error of the difference in the percentage of workers who used public transportation to work between 1996 and 1997 is given by Equation 5.5 and is equal to The 90 percent margin of error of the difference in the percentage of workers who used public transportation to work between 1996 and 1997 is given by Equation 5.6 and is equal to: The lower and upper bounds of the 90 percent confidence interval of the 1996-1997 difference in percentages are given by Equations 5.7 and 5.8, respectively, and are equal to LB DIFF1996 1997 1 91 0 79 1 12−( ) Read the entire page →
From page 111... ... Figures 6.5 and 6.6 show these plots for the three- and five-year moving average data. Similar to the conclusions drawn from the analysis of annual estimates, Figures 6.5 and 6.6 indicate that the change in the percentage of workers using public transportation to work was minimal; the plots of the three- and five-year moving averages are almost flat. Read the entire page →
From page 112... ... Often, when the confidence intervals for the areas being compared are significantly different and not overlapping, an analyst will know that the difference is statistically significant without calculating the standard errors of the differences. For example, the confidence interval at the city level corresponding to the 2001-2005 moving average does not overlap with the confidence interval at the county level. Read the entire page →
From page 113... ... One can, for example, compare a moving average of data from years 1996-1998 with a moving average of data from years 1999 and beyond, because these two intervals do not include overlapping years. 6.4.3 Modeling the Trend In addition to visually observing the pattern of change in the percentage of workers using public transportation to work and computing differences in percent distributions, statistical methTrend Analyses Using ACS Data 113 Read the entire page →
From page 114... ... When using a regression to model a trend, it is preferable to use the actual annual rates rather than the moving averages because of the high correlations between moving averages from overlapping years. The dependent variable can be the actual percentage of workers using public transportation, as in the following model: Percentage of workers using public transportation t = Intercept + Slope * Read the entire page →
From page 115... ... Paul Region.66 • Puget Sound Regional Council's analysis of trends in the Central Puget Sound Region using 1980, 1990, and 2000 Census data.67 • Northwest Michigan Council of Government's use of census data to map trends in population, employment, and commute for the 10 counties constituting Northwest Michigan.68 Trend Analyses Using ACS Data 115 58 Personal correspondence with Jane Traynham, Maryland State Data Center. For an example of the application, see www.mdp.state.md.us/msdc/military/military_site.htm. Read the entire page →
From page 116... ... 73 Ed Christopher, 1996, "Census Data Use in Illinois by a Large Metropolitan Planning Organization," Transportation Research Board, Decennial Census Data for Transportation Planning, Case Studies and Strategies for 2000, Conference Proceedings 13, April 28-May 1, 1996. 74 TCRP Report 28: Transit Markets of the Future: The Challenge of Change, Transportation Research Board, National Research Council, Washington, D.C., 1998. Read the entire page →

From page 102...

... Figure 6.1 shows a national trend of zero-vehicle households,51 and Figure 6.2 shows a national trend of means of transportation to work.52 A list of specific examples of using census data to do trend analysis also is provided at the end of this section. 102 C H A P T E R 6 Trend Analyses Using ACS Data 51 Murakami, E., 2003, "Households without Vehicles, 2000." See www.fhwa.dot.gov/ctpp/sr0103.htm.