In the unzipped folder, the CSVs will be files we load into R in the chunk below. At the second link above, download the “Full Survey Data” to somewhere on your personal machine (this will determine your own individual path). Like PUMS data, ths NHTS data has a complicated data dictionary and linkages between different datasets, but this section will help you navigate some of this data structure to get basic insights. The National Household Travel Survey is one such dataset that is available for analysis, and furthermore, the latest available survey from 2017 included an add-on survey for California, given the state’s size and complexity, which provides richer data for our Bay Area focus. Somewhere in the middle is mobility data in the form of travel diaries, where a large sample of respondents is asked to meticulously record information about their trips on a particular date, which, when compiled together, provide a rich statistical distribution of travel patterns and behaviors, comparable to PUMS data. On the other end would be something like cell phone device data, providing nearly raw information about the movement behavior of individuals, but needing a significant amount of processing to convert to interpretable insights, and presenting privacy concerns. While the ACS includes some measures of mobility, like commute mode and commute travel time, this represents a far end of a spectrum of mobility insights we might be able to use for urban data analytics.
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