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_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 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
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_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 = {
if valfield == params.col_area_ac: continue else: val_density = dict_totals[valfield] / dict_totals[ params.col_area_ac] dict_out_key = "{}_{}".format(valfield, area_unit) dict_out[dict_out_key] = val_density return dict_out if __name__ == '__main__': arcpy.env.workspace = r'I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb' # input fc of parcel data--must be points! in_pcl_pt_fc = params.parcel_pt_fc_yr(2016) value_fields = [ 'POP_TOT', 'EMPTOT', 'EMPIND', 'PT_TOT_RES', 'SOV_TOT_RES', 'HOV_TOT_RES', 'TRN_TOT_RES', 'BIK_TOT_RES', 'WLK_TOT_RES' ] # input line project for basing spatial selection project_fc = r'I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb\Polylines' ptype = params.ptype_arterial # (self,fc_pclpt, fc_project, project_type, val_fields, buffdist, case_field=None, case_excs_list=[]) # lubuff_obj = LandUseBuffCalcs(in_pcl_pt_fc, project_fc, ptype, ['EMPTOT', 'DU_TOT', 'GISAc'], 2640) lubuff_obj = LandUseBuffCalcs(in_pcl_pt_fc, project_fc, ptype, ['EMPTOT', 'DU_TOT', 'GISAc'], 2640).buff_totals
csi_dict = complete_streets_idx(network_fc, geom, project_type, speedlim, transit_event_fc) csi = csi_dict['complete_street_score'] ins_row = [geom, stname, speedlim, csi] inscur.insertRow(ins_row) time_elapsed = dt.datetime.now() - start_time print("Finished! Processed {} rows in {}".format(i, time_elapsed)) if __name__ == '__main__': arcpy.env.workspace = r'I:\Projects\Darren\PPA_V2_GIS\PPA_V2.gdb' # input fc of parcel data--must be points! in_pcl_pt_fc = os.path.join(params.fgdb, params.parcel_pt_fc_yr(in_year=2016)) value_fields = [ params.col_area_ac, params.col_k12_enr, params.col_emptot, params.col_du ] posted_speedlimit = 30 # mph ptype = 'Arterial' # input line project for basing spatial selection input_network_fc = 'ArterialCollector_2019_sample' trnstops_fc = os.path.join(params.fgdb, params.trn_svc_fc) # output_dict = complete_streets_idx(in_pcl_pt_fc, project_fc, ptype, posted_speedlimit, trnstops_fc) make_fc_with_csi(input_network_fc, trnstops_fc, in_pcl_pt_fc, ptype)
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