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Introduction 3 with population greater than 65,000, starting with 2005 data that will be released in the fall of 2006. So, for these larger population areas, data users will get new independent ACS estimates for the previous year. Three-year accumulated average estimates will be released for areas with population greater than 20,000 starting in year 2008. In that year, the Census Bureau will release estimates for those areas that are based on averaging the ACS data from 2005, 2006, and 2007. Five-year accumulated averages will be released for all areas starting in year 2010, using accu- mulated averages for the years 2005, 2006, 2007, 2008, and 2009. The three- and five-year accumulated averages will be developed every year using the preceding three or five years of ACS data, but analysts will need to be aware that multiyear estimates reported for a specific year are not independent of previous multiyear estimates that have overlapping years. In addition, data users will need to understand the potential impacts of using characteristics data accumulated over time when those characteristics are changing year to year. 1.2.4 Understanding and Reporting Sample Data Both the decennial census Long Form and ACS are samples of the overall population. There- fore, estimates from both data sources contain uncertainty. Despite the fact that the Census Bureau provides variance estimates for Long Form data, in most cases when using census Long Form data, analysts take the reported estimates as simple points and ignore the level of variance present. However, for the ACS, with its lower sample sizes, the Census Bureau is instructing and enabling users to account for the inherent sampling error in their analyses. The Census Bureau ACS data tables include 90 percent confidence intervals for all estimates so that users can readily see the relative level of uncertainty in the estimates. Data users will need to determine how the higher uncertainty levels affect their analyses, and they will need to develop effective ways of presenting information with uncertainty. 1.2.5 Data Disclosure Avoidance Before releasing any ACS data, the Census Bureau first determines whether the informa- tion could be used to identify specific households, individuals, or establishments. When the information is deemed to potentially result in wrongful disclosure, Census Bureau staff are required by law to take actions to prevent such identifications. Three types of data disclosure avoidance procedures will be applied to the ACS data: imputation, rounding, and data suppression. Disclosure avoidance is an important issue for transportation planning uses of Census data, because transportation data users rely on small-area data to a greater extent than almost all other data users. Analyses of journey-to-work flow data will be particularly affected, because home- to-work matrices, even for mid-sized geographic units, will generally consist of small numbers within individual cells, and thus will be subject to data disclosure avoidance. 1.3 Purpose and Organization of this Guidebook In this guidebook, attempts have been made to identify the key issues that will face transporta- tion planners as they use ACS data to complete analyses that have been heretofore performed with decennial census Long Form data. Potential new transportation planning analyses that ACS may enable also are outlined. Section 2 describes the implementation of ACS, including the operational steps that the Cen- sus Bureau follows to collect and disseminate ACS estimates. This section tries to highlight the