def get_singleyr_data(fc_tripshedpoly,
projtyp,
analysis_year,
out_dict_base={}):
fc_pcl_pt = params.parcel_pt_fc_yr(analysis_year)
fc_pcl_poly = params.parcel_poly_fc_yr(analysis_year)
print("getting accessibility data for base...")
accdata = acc.get_acc_data(fc_tripshedpoly,
params.accdata_fc,
projtyp,
get_ej=False)
print("getting ag acreage data for base...")
ag_acres = GetLandUseArea(fc_tripshedpoly, projtyp,
fc_pcl_poly).get_lu_acres(params.lutype_ag)
# total job + du density (base year only, for state-of-good-repair proj eval only)
print("getting ILUT data for base...")
job_du_dens = LandUseBuffCalcs(
fc_pcl_pt, fc_tripshedpoly, projtyp,
[params.col_emptot, params.col_du],
params.ilut_sum_buffdist).point_sum_density()
comb_du_dens = sum(list(job_du_dens.values()))
job_du_dens['job_du_perNetAcre'] = comb_du_dens
# get EJ data
print("getting EJ data for base...")
ej_data = LandUseBuffCalcs(fc_pcl_pt,
fc_tripshedpoly,
projtyp, [params.col_pop_ilut],
params.ilut_sum_buffdist,
params.col_ej_ind,
case_excs_list=[]).point_sum()
ej_flag_dict = {
0: "Pop_NonEJArea",
1: "Pop_EJArea"
} # rename keys from 0/1 to more human-readable names
ej_data = utils.rename_dict_keys(ej_data, ej_flag_dict)
ej_data["Pct_PopEJArea"] = ej_data["Pop_EJArea"] / sum(
list(ej_data.values()))
accdata_ej = acc.get_acc_data(fc_tripshedpoly,
params.accdata_fc,
projtyp,
get_ej=True) # EJ accessibility data
ej_data.update(accdata_ej)
# for base dict, add items that only have a base year value (no future year values)
for d in [accdata, ag_acres, job_du_dens, ej_data]:
out_dict_base.update(d)
outdf = pd.DataFrame.from_dict(out_dict_base, orient='index')
return outdf
def get_singleyr_data(fc_project, projtyp, adt, posted_speedlim, out_dict={}):
pcl_pt_fc = p.parcel_pt_fc_yr(2016)
pcl_poly_fc = p.parcel_poly_fc_yr(2016)
accdata = acc.get_acc_data(fc_project, p.accdata_fc, projtyp, get_ej=False)
collision_data = coll.get_collision_data(fc_project, projtyp, p.collisions_fc, adt)
complete_street_score = {'complete_street_score': -1} if projtyp == p.ptype_fwy else \
cs.complete_streets_idx(pcl_pt_fc, fc_project, projtyp, posted_speedlim, p.trn_svc_fc)
truck_route_pct = {'pct_proj_STAATruckRoutes': 1} if projtyp == p.ptype_fwy else \
linex.get_line_overlap(fc_project, p.freight_route_fc, p.freight_route_fc) # all freeways are STAA truck routes
ag_acres = luac.get_lutype_acreage(fc_project, projtyp, pcl_poly_fc, p.lutype_ag)
pct_adt_truck = {"pct_truck_aadt": -1} if projtyp != p.ptype_fwy else truck_fwy.get_tmc_truck_data(fc_project, projtyp)
intersxn_data = intsxn.intersection_density(fc_project, p.intersections_base_fc, projtyp)
npmrds_data = npmrds.get_npmrds_data(fc_project, projtyp)
transit_data = trnsvc.transit_svc_density(fc_project, p.trn_svc_fc, projtyp)
bikeway_data = bnmi.get_bikeway_mileage_share(fc_project, p.ptype_sgr)
infill_status = urbn.projarea_infill_status(fc_project, p.comm_types_fc)
# total job + du density (base year only, for state-of-good-repair proj eval only)
job_du_dens = lu_pt_buff.point_sum_density(pcl_pt_fc, fc_project, projtyp,
[p.col_emptot, p.col_du], p.ilut_sum_buffdist)
comb_du_dens = sum(list(job_du_dens.values()))
job_du_dens['job_du_perNetAcre'] = comb_du_dens
# get EJ data
ej_data = lu_pt_buff.point_sum(pcl_pt_fc, fc_project, projtyp, [p.col_pop_ilut],
p.ilut_sum_buffdist, p.col_ej_ind, case_excs_list=[])
ej_flag_dict = {0: "Pop_NonEJArea", 1: "Pop_EJArea"} # rename keys from 0/1 to more human-readable names
ej_data = utils.rename_dict_keys(ej_data, ej_flag_dict)
ej_data["Pct_PopEJArea"] = ej_data["Pop_EJArea"] / sum(list(ej_data.values()))
accdata_ej = acc.get_acc_data(fc_project, p.accdata_fc, projtyp, get_ej=True) # EJ accessibility data
ej_data.update(accdata_ej)
# for base dict, add items that only have a base year value (no future year values)
for d in [accdata, collision_data, complete_street_score, truck_route_pct, pct_adt_truck, ag_acres, intersxn_data,
npmrds_data, transit_data, bikeway_data, infill_status, job_du_dens, ej_data]:
out_dict_base.update(d)
outdf = pd.DataFrame.from_dict(out_dict_base, orient='index')
return outdf
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
for row in cur:
pclarea_inbuff_ft2 += row[0]
if row[1] == lutype:
lutype_intersect_ft2 += row[0]
# get share of on-parcel land within buffer that is of specified land use type
pct_lutype = lutype_intersect_ft2 / pclarea_inbuff_ft2 if pclarea_inbuff_ft2 > 0 else 0
# convert to acres
buff_acre = pclarea_inbuff_ft2 / params.ft2acre
lutype_intersect_acres = lutype_intersect_ft2 / params.ft2acre
return {
'total_net_pcl_acres': buff_acre,
'net_{}_acres'.format(lutype): lutype_intersect_acres,
'pct_{}_inbuff'.format(lutype): pct_lutype
}
if __name__ == '__main__':
arcpy.env.workspace = r'I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb'
parcel_featclass = params.parcel_poly_fc_yr(
in_year=2016) # 'parcel_data_polys_2016'
project_featclass = r'I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb\Polylines_1'
lutype_in = 'Agriculture'
out_pcl_data = GetLandUseArea(project_featclass, params.ptype_sgr,
parcel_featclass).get_lu_acres(lutype_in)
print(out_pcl_data)
pct_adt_truck, ag_acres, intersxn_data, npmrds_data, transit_data,
bikeway_data, infill_status, job_du_dens, ej_data
]:
out_dict_base.update(d)
outdf_base = pd.DataFrame.from_dict(out_dict_base, orient='index')
# ---------------------------------------------------------------------------------------------------------
# outputs that use both base year and future year values
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
for year in analysis_years:
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):
nat_resource_ac = 0
# pdb.set_trace()
pcl_buff_intersect = GetLandUseArea(fc_project, projtyp, fc_pcl_poly)
for lutype in params.lutypes_nat_resources:
lutype_ac_dict = pcl_buff_intersect.get_lu_acres(lutype)
lutype_acres = lutype_ac_dict['net_{}_acres'.format(lutype)]
nat_resource_ac += lutype_acres
return {"nat_resource_acres": nat_resource_ac}
if __name__ == '__main__':
arcpy.env.workspace = r'I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb'
# input fc of parcel data--must be polygons!
in_pcl_base_fc = params.parcel_poly_fc_yr(in_year=2016)
# in_pcl_future_tbl =
# in_ctypes_fc =
# input line project for basing spatial selection
project_fc = r'I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb\Polylines_1'
# infill_status_dict = projarea_infill_status(project_fc, params.comm_types_fc)
# print(infill_status_dict)
nat_resources_dict = nat_resources(project_fc, params.ptype_arterial,
in_pcl_base_fc)
print(nat_resources_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