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Taz2MarginalsCsv
These TAZ2-level variables are used as marginals or contraints for PopulationSim. This table is dependent on calculations in the MazMarginals table so run that one first. The new variables are bolded below. The table should be set up with a row for each TAZ2 and columns for each of these variables. The internal names are constant year-to-year and the table is produced for each forecast year. The divisions below are for clarity but shouldn't be in the table. A template of what the table should look like is at
- TAZ2: can be taken from bayarea_urbansim/data/taz2_forecast_inputs.csv
These are categories used to segment the HH Loc Choice and other models in BA UrbanSim. So you just sum how many of each are in each TAZ
- hh_inc_30: refered to in the model as hhq1
- hh_inc_30_60: hhq2
- hh_inc_60_100: hhq3
- hh_inc_100_plus: hhq4
These are done much like the size HH Size categories in the MAZs. So they are forecast by combining two types of data:
- The proportion of each TAZ2’s HHs that are in that worker-count category in 2010. This data comes from bayarea_urbansim/data/taz2_forecast_inputs.csv.
- The shift in the region-wide forecast for these categories. This data comes from the regional forecast for HH size which is from regional_demographic_forecast.csv
- hh_wrks_0: get the forecast year region-level proportion from shrwo (short for "SHaRe of households in Worker category 0) in regional_demographic_forecast.csv
- hh_wrks_1: get the forecast year region-level proportion from shrw1 in regional_demographic_forecast.csv
- hh_wrks_2: get the forecast year region-level proportion from shrw2 in regional_demographic_forecast.csv
- hh_wrks_3_plus: get the forecast year region-level proportion from shrw3 in regional_demographic_forecast.csv
Now we move to person counts. For this one please use the HH_size counts for each TAZ2 (calculated in the MAZ step) to estimate the count of people in each TAZ. You can do this by multiplying the count of HHs with 1 person by 1; HHs w 2 people by 2; HHs w 3 people by 3; AND HH with 4plus people by 4.781329. Then adjust them with those two types of data:
- The proportion of each TAZ2’s persons that are in that age category in 2010. This data comes from bayarea_urbansim/data/taz2_forecast_inputs.csv.
- The shift in the region-wide forecast for these categories. This data comes from the regional forecast for age which is from regional_demographic_forecast.csv
- pers_age_00_19: get the forecast year region-level proportion from shra1 (short for SHaRe of households in Age category 1) in regional_demographic_forecast.csv
- pers_age_20_34: get the forecast year region-level proportion from shra2 in regional_demographic_forecast.csv
- pers_age_35_64: get the forecast year region-level proportion from shra3 in regional_demographic_forecast.csv
- pers_age_65_plus: get the forecast year region-level proportion from shra4 in regional_demographic_forecast.csv
- THEN SCALE THE COUNTS EVENLY UP SO THAT THEY MATCH THE TOTPOP VAR IN REGIONAL_CONTROLS.CSV (more important that they match the totpop than the shares
This is another HH count done like the size categories. The idea here is produce forecasts for each future year that depend on two things:
- The proportion of each TAZ2’s HHs that are in that children-presence category in 2010. This data comes from bayarea_urbansim/data/taz2_forecast_inputs.csv. A few TAZ don’t have share listed (#DIV/0! in the table) for 2010 because there are no HHs; for these please sub in the regional average 2010 proportion which is the variables shrn_2010 and shry_2010 in the bayarea_urbansim/data/region_forecast_inputs.csv.
- The shift in the region-wide forecast for these categories. This data comes from the regional forecast for HH size which is from regional_demographic_forecast.csv
- hh_kids_no: get the forecast year region-level proportion from shrn (short for SHare of household with No children present) in regional_demographic_forecast.csv
- hh_kids_yes: get the forecast year region-level proportion from shry in regional_demographic_forecast.csv