def get_poly_avg(input_poly_fc):
# as of 11/26/2019, each of these outputs are dictionaries
accdata = acc.get_acc_data(input_poly_fc, p.accdata_fc, p.ptype_area_agg, get_ej=False)
collision_data = coll.get_collision_data(input_poly_fc, p.ptype_area_agg, p.collisions_fc, 0)
mix_data = mixidx.get_mix_idx(p.parcel_pt_fc, input_poly_fc, p.ptype_area_agg)
intsecn_dens = intsxn.intersection_density(input_poly_fc, p.intersections_base_fc, p.ptype_area_agg)
bikeway_covg = bufnet.get_bikeway_mileage_share(input_poly_fc, p.ptype_area_agg)
tran_stop_density = trn_svc.transit_svc_density(input_poly_fc, p.trn_svc_fc, p.ptype_area_agg)
emp_ind_wtot = lubuff.point_sum(p.parcel_pt_fc, input_poly_fc, p.ptype_area_agg, [p.col_empind, p.col_emptot], 0)
emp_ind_pct = {'emp_ind_pct': emp_ind_wtot[p.col_empind] / emp_ind_wtot[p.col_emptot]}
pop_x_ej = lubuff.point_sum(p.parcel_pt_fc, input_poly_fc, p.ptype_area_agg, [p.col_pop_ilut], 0, p.col_ej_ind)
pop_tot = sum(pop_x_ej.values())
pct_pop_ej = {'pct_ej_pop': pop_x_ej[1] / pop_tot}
job_pop_dens = lubuff.point_sum_density(p.parcel_pt_fc, input_poly_fc, p.ptype_area_agg, \
[p.col_du, p.col_emptot], 0)
total_dens = {"job_du_dens_ac": sum(job_pop_dens.values())}
out_dict = {}
for d in [accdata, collision_data, mix_data, intsecn_dens, bikeway_covg, tran_stop_density, pct_pop_ej,\
emp_ind_pct, total_dens]:
out_dict.update(d)
return out_dict
def get_poly_avg(input_poly_fc):
# as of 11/26/2019, each of these outputs are dictionaries
pcl_pt_data = params.parcel_pt_fc_yr()
accdata = acc.get_acc_data(input_poly_fc, params.accdata_fc, params.ptype_area_agg, get_ej=False)
collision_data = coll.get_collision_data(input_poly_fc, params.ptype_area_agg, params.collisions_fc, 0)
mix_data = mixidx.get_mix_idx(pcl_pt_data, input_poly_fc, params.ptype_area_agg)
intsecn_dens = intsxn.intersection_density(input_poly_fc, params.intersections_base_fc, params.ptype_area_agg)
bikeway_covg = bufnet.get_bikeway_mileage_share(input_poly_fc, params.ptype_area_agg)
tran_stop_density = trn_svc.transit_svc_density(input_poly_fc, params.trn_svc_fc, params.ptype_area_agg)
emp_ind_wtot = lubuff.point_sum(pcl_pt_data, input_poly_fc, params.ptype_area_agg, [params.col_empind, params.col_emptot], 0)
emp_ind_pct = {'EMPIND_jobshare': emp_ind_wtot[params.col_empind] / emp_ind_wtot[params.col_emptot] \
if emp_ind_wtot[params.col_emptot] > 0 else 0}
pop_x_ej = lubuff.point_sum(pcl_pt_data, input_poly_fc, params.ptype_area_agg, [params.col_pop_ilut], 0, params.col_ej_ind)
pop_tot = sum(pop_x_ej.values())
key_yes_ej = max(list(pop_x_ej.keys()))
pct_pop_ej = {'Pct_PopEJArea': pop_x_ej[key_yes_ej] / pop_tot if pop_tot > 0 else 0}
job_pop_dens = lubuff.point_sum_density(pcl_pt_data, input_poly_fc, params.ptype_area_agg, \
[params.col_du, params.col_emptot], 0)
# total_dens = {"job_du_perNetAcre": sum(job_pop_dens.values())}
out_dict = {}
for d in [accdata, collision_data, mix_data, intsecn_dens, bikeway_covg, tran_stop_density, pct_pop_ej,\
emp_ind_pct, job_pop_dens]:
out_dict.update(d)
return out_dict
def poly_avg_futyears(
input_poly_fc, data_year
): #IDEALLY could make this part of get_poly_avg as single function with variable number of input args
pcl_pt_data = params.parcel_pt_fc_yr(data_year)
mix_data = mixidx.get_mix_idx(params.parcel_pt_fc_yr(data_year),
input_poly_fc, params.ptype_area_agg)
return mix_data
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
# 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, fc_pcl_poly, year)
# combine into dict
for d in [
ilut_buff_vals, job_du_tot, veh_occ_data, mix_index_data,
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, p.ptype_arterial,
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 = {
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
