Common use of Data Sources Clause in Contracts

Data Sources. Table 14 shows the data items and sources for ITHIM SACOG implementation. Rather than obtaining travel behavior data from regional travel surveys like the implementations in San Francisco Bay Area and NAMPO, ▇▇ et al. (2019) sourced travel behavior data from SACSIM15, which is an activity-based model built and calibrated to produce disaggregate travel data at the individual level (SACOG, 2015). SACSIM15 outputs for the 2016 MTP/SCS were used to estimate the average active transportation (walking and cycling) time (i.e., minutes per day) and average distance (i.e., miles per day) for each demographic group for all analysis scenarios. VMT outputs from SACSIM15 for each travel mode was estimated. Health data for all-cause mortality statistics were obtained from the California Department of Public Health (CDPH) vital records data and statistics (CDPH, 2020). Average annual all-cause mortality rates by age-sex-race/ethnicity and age-sex income level categories were calculated for each county in the SACOG region. Due to small African-American population in some counties, annual all-cause mortality rate for the Black population is only available for the entire region rather than for each county. The U.S. disease burden data for all age-sex categories were derived from the Global Burden of Disease (GBD) database (Institute for Health Metrics and Evaluation, 2017). The California Health Interview Survey (UCLA, 2012) data were used to identify characteristics of non-transport physical activities for residents of SACOG. MET-hours per week are calculated for occupational and exercise physical activity (non-travel METs) in the same way as the San Francisco Bay Area study by ▇▇▇▇▇▇▇▇ et al. (2013). Source Calibration Data Item Units Stratification Sacramento Activity- Based Travel Simulation Model (SACSIM15) (SACOG, 2015) Per capita mean daily travel distance Miles/person/ day Travel mode Per capita mean daily travel time Minutes/person/ day Travel mode Ratio: per capita mean daily active transportation time Walk, bike, age, and sex Standard deviation of mean daily active transportation time Minutes/person/ day Walking speed Miles/hour Ratio of daily per capita bicycling time to walking time Personal auto travel distance and time Miles and hours/day Driver and passenger Vehicle miles traveled (VMT) by facility type Miles/day Travel mode and road type US Census Distribution of population by age and gender % Age and sex California Health Interview Survey (UCLA, 2008) Per capita weekly non-travel related physical activity, expressed in Metabolic Equivalent Task (MET) hours MET-hours/ week Age and sex Proportion of colon cancers from all colorectal cancers California Department of Public Health (CDPH) Vital Records Data and Statistics (CDPH, 2020) Age-sex specific ratio of disease- specific mortality rate between the SACOG area and USA. Disease group, age, and sex Transportation Injury Mapping System (SafeTREC, 2020) Serious and fatal injuries between a striking vehicle and a victim vehicle in road traffic collisions Injuries Severity, striking mode, victim mode, and road type Traffic injury data for the Sacramento region were obtained from the Transportation Injury Mapping System from the Statewide Integrated Traffic System (SWITRS) geocoded by University of California, Berkeley Safety Transportation Resource and Education Center (SafeTREC, 2020). Due to data limitation, mortality and disease burden of traffic injures can only be disaggregated by race/ethnicity categories without income level differentiation. To perform equity analysis, travel behavior and health data for each race/ethnicity group and household income group are required (▇▇ et al., 2019). Due to unavailability of race/ethnicity and income variables in required datasets, the hot deck imputation method (D’Orazio et al., 2006), a type of data fusion, was used to synthesize the missing variables. The hot deck imputation method synthesized the race/ethnicity statistics for the entire SACOG region based on the limited number of samples in the 2012 American Community Survey Public Use Microdata Sample (ACS PUMS) and SACSIM15 as the recipient. Hot deck imputation was performed for the race/ethnicity variable in travel behavior data and mortality rate variables in health data.