def get_multiyear_data(project_fc, project_type, base_df, analysis_year):
ilut_val_fields = [p.col_pop_ilut, p.col_du, p.col_emptot, p.col_k12_enr, p.col_empind, p.col_persntrip_res] \
+ p.ilut_ptrip_mode_fields
fc_pcl_pt = p.parcel_pt_fc_yr(year)
fc_pcl_poly = p.parcel_poly_fc_yr(year)
fc_modelhwylinks = p.model_links_fc(year)
year_dict = {}
# get data on pop, job, k12 totals
# point_sum(fc_pclpt, fc_project, project_type, val_fields, buffdist, case_field=None, case_excs_list=[])
ilut_buff_vals = lu_pt_buff.point_sum(fc_pcl_pt, project_fc, project_type, ilut_val_fields,
p.ilut_sum_buffdist, case_field=None, case_excs_list=[])
ilut_indjob_share = {"{}_jobshare".format(p.col_empind): ilut_buff_vals[p.col_empind] / ilut_buff_vals[p.col_emptot]}
ilut_buff_vals.update(ilut_indjob_share)
ilut_mode_split = {"{}_share".format(modetrp): ilut_buff_vals[modetrp] / ilut_buff_vals[p.col_persntrip_res]
for modetrp in p.ilut_ptrip_mode_fields}
ilut_buff_vals.update(ilut_mode_split)
# cleanup to remove non-percentage mode split values, if we want to keep output CSV from getting too long.
# for trip_numcol in p.ilut_ptrip_mode_fields: del ilut_buff_vals[trip_numcol]
# job + du total
job_du_tot = {"SUM_JOB_DU": ilut_buff_vals[p.col_du] + ilut_buff_vals[p.col_emptot]}
# model-based vehicle occupancy
veh_occ_data = link_occ.get_linkoccup_data(project_fc, project_type, fc_modelhwylinks)
# land use diversity index
mix_index_data = mixidx.get_mix_idx(fc_pcl_pt, project_fc, project_type)
# housing type mix
housing_mix_data = lu_pt_buff.point_sum(fc_pcl_pt, project_fc, project_type, [p.col_du], p.du_mix_buffdist,
p.col_housing_type, case_excs_list=['Other'])
# acres of "natural resources" (land use type = forest or agriculture)
nat_resources_data = urbn.nat_resources(project_fc, project_type, fc_pcl_poly, year)
# combine into dict
for d in [ilut_buff_vals, job_du_tot, veh_occ_data, mix_index_data, housing_mix_data, nat_resources_data]:
year_dict.update(d)
# make dict into dataframe
df_year_out = pd.DataFrame.from_dict(year_dict, orient='index')
return df_year_out
fc_modelhwylinks)
# land use diversity index
mix_index_data = mixidx.get_mix_idx(fc_pcl_pt, project_fc,
project_type)
# housing type mix
housing_mix_data = lu_pt_buff.point_sum(fc_pcl_pt,
project_fc,
project_type, [p.col_du],
p.du_mix_buffdist,
p.col_housing_type,
case_excs_list=['Other'])
# acres of "natural resources" (land use type = forest or agriculture)
nat_resources_data = urbn.nat_resources(project_fc, fc_pcl_poly, year)
# combine into dict
for d in [
ilut_buff_vals, job_du_tot, veh_occ_data, mix_index_data,
housing_mix_data, nat_resources_data
]:
year_dict.update(d)
# make dict into dataframe
df_year = pd.DataFrame.from_dict(year_dict, orient='index')
# if it's base year, then append values to bottom of outdf_base,
# if it's future year, then left-join the values to the outdf.
# table has metrics as rows; years as columns (and will also append
if year == min(analysis_years):
p.model_links_fc)
# land use diversity index
mix_index_data = mixidx.get_mix_idx(p.parcel_pt_fc, project_fc,
p.ptype_arterial)
# housing type mix
housing_mix_data = lu_pt_buff.point_sum(p.parcel_pt_fc,
project_fc,
project_type, [p.col_du],
p.du_mix_buffdist,
p.col_housing_type,
case_excs_list=['Other'])
# acres of "natural resources" (land use type = forest or agriculture)
nat_resources_data = urbn.nat_resources(project_fc, p.parcel_poly_fc, 2016)
# combine all together-----------------------------------------------------------
out_dict = {
"project_name": proj_name,
"project_type": project_type,
'project_aadt': adt,
'project_pci': pci,
'project_speedlim': project_speedlim
}
for d in [
accdata, collision_data, complete_street_score, truck_route_pct,
pct_adt_truck, ag_acres, intersxn_data, npmrds_data,
ilut_buff_vals, job_du_dens, ej_data, veh_occ_data, transit_data,
mix_index_data, housing_mix_data, bikeway_data, infill_status,
def get_multiyear_data(fc_tripshedpoly, projtyp, base_df, analysis_year):
print("getting multi-year data for {}...".format(analysis_year))
ilut_val_fields = [params.col_pop_ilut, params.col_du, params.col_emptot, params.col_k12_enr, params.col_empind, params.col_persntrip_res] \
+ params.ilut_ptrip_mode_fields
fc_pcl_pt = params.parcel_pt_fc_yr(analysis_year)
fc_pcl_poly = params.parcel_poly_fc_yr(analysis_year)
year_dict = {}
# get data on pop, job, k12 totals
# point_sum(fc_pclpt, fc_tripshedpoly, projtyp, val_fields, buffdist, case_field=None, case_excs_list=[])
ilut_buff_vals = LandUseBuffCalcs(fc_pcl_pt,
fc_tripshedpoly,
projtyp,
ilut_val_fields,
params.ilut_sum_buffdist,
case_field=None,
case_excs_list=[]).point_sum()
ilut_indjob_share = {
"{}_jobshare".format(params.col_empind):
ilut_buff_vals[params.col_empind] / ilut_buff_vals[params.col_emptot]
}
ilut_buff_vals.update(ilut_indjob_share)
ilut_mode_split = {
"{}_share".format(modetrp):
ilut_buff_vals[modetrp] / ilut_buff_vals[params.col_persntrip_res]
for modetrp in params.ilut_ptrip_mode_fields
}
ilut_buff_vals.update(ilut_mode_split)
# cleanup to remove non-percentage mode split values, if we want to keep output CSV from getting too long.
# for trip_numcol in params.ilut_ptrip_mode_fields: del ilut_buff_vals[trip_numcol]
# job + du total
job_du_tot = {
"SUM_JOB_DU":
ilut_buff_vals[params.col_du] + ilut_buff_vals[params.col_emptot]
}
# land use diversity index
mix_index_data = mixidx.get_mix_idx(fc_pcl_pt, fc_tripshedpoly, projtyp)
# housing type mix
housing_mix_data = LandUseBuffCalcs(fc_pcl_pt,
fc_tripshedpoly,
projtyp, [params.col_du],
params.du_mix_buffdist,
params.col_housing_type,
case_excs_list=['Other']).point_sum()
# acres of "natural resources" (land use type = forest or agriculture)
nat_resources_data = urbn.nat_resources(fc_tripshedpoly, projtyp,
fc_pcl_poly, analysis_year)
# combine into dict
for d in [
ilut_buff_vals, job_du_tot, mix_index_data, housing_mix_data,
nat_resources_data
]:
year_dict.update(d)
# make dict into dataframe
df_year_out = pd.DataFrame.from_dict(year_dict, orient='index')
return df_year_out