Diversity using site parcel data

corktown_offline_indicators.py

@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jun 12 11:21:39 2020
+
+@author: doorleyr
+"""
+
+from toolbox import Handler, Indicator, CompositeIndicator
+from proximity_indicator import ProxIndicator
+from innovation_indicator import InnoIndicator
+from mobility_indicator import MobilityIndicator
+from aggregate_indicator import AggregateIndicator
+from economic_indicator import EconomicIndicator
+from buildings_indicator import BuildingsIndicator
+from diversity_indicator import DiversityIndicator
+
+import urllib
+import json
+import pandas as pd
+
+
+I = InnoIndicator()    
+P = ProxIndicator(name='proximity',   indicator_type_in='numeric', table_name='corktown')
+#P_hm = ProxIndicator(name='proximity_heatmap',  indicator_type_in='heatmap', table_name='corktown')
+M = MobilityIndicator(name='mobility', table_name='corktown')
+B= BuildingsIndicator(name='buildings',  table_name='corktown')
+D= DiversityIndicator(name='diversity',  table_name='corktown')
+
+# 2nd order  individual indicators 
+E = EconomicIndicator(name='Economic',
+                      table_name='corktown')
+
+#H = Handler('corktown', quietly=False)
+#
+#H.add_indicators([
+#        I,
+#        P,
+##        P_hm,
+#        M,
+#        E,
+#        B,
+#        D
+#        ])
+
+geogrid=json.load(open('../CS_Grid_Maker/examples/results/corktown_geogrid.geojson'))
+
+types=json.load(open('../CS_Grid_Maker/examples/type_definitions/corktown_types.json'))
+
+static_types=json.load(open('../CS_Grid_Maker/examples/type_definitions/corktown_static_types.json'))
+
+types.update(static_types)
+
+for ind in [I, P, 
+#             P_hm, 
+            M, B, D, E]:
+    ind.types_def=types
+    ind.geogrid_header=geogrid['properties']['header']
+
+I.types_def
+
+geogrid_data_base=json.load(open('../Scenarios/12_Jun_20/ford_base.json'))
+geogrid_data_campus=json.load(open('../Scenarios/12_Jun_20/ford_campus.json'))
+geogrid_data_housing=json.load(open('../Scenarios/12_Jun_20/ford_housing.json'))
+geogrid_data_inno_com=json.load(open('../Scenarios/12_Jun_20/ford_inno_com.json'))
+
+cell_area=geogrid['properties']['header']['cellSize']**2
+
+updatable=[((feat['properties']['interactive'])or (feat['properties']['static_new'])
+           ) for feat in geogrid['features']]
+
+
+def get_type_stats(geogrid_data, type_name, updatable,cell_area, types_def=I.types_def):
+    sqm_pp=types_def[type_name]['sqm_pperson']
+    floors=0
+    for gi, cell in enumerate(geogrid_data):
+        if ((updatable[gi]) and (cell['name']==type_name)):
+            height=cell['height']
+            if isinstance(height, list):
+                height=height[-1]
+            floors+=height
+    return sqm_pp, floors*cell_area, floors*cell_area/sqm_pp 
+
+
+
+def get_all_indicators(geogrid_data, future_mobility):
+    all_ind=[]
+    print('Mobility')
+    all_ind.extend(M.return_indicator(geogrid_data, future_mobility=future_mobility))
+
+    print('Innovation')
+    all_ind.extend(I.return_indicator(geogrid_data))
+
+    print('Economic')
+    all_ind.extend(E.return_indicator(geogrid_data))
+
+    print('Proximity')
+    all_ind.extend(P.return_indicator(geogrid_data))
+
+    print('Diversity')
+    all_ind.extend(D.return_indicator(geogrid_data))
+
+    print('Buildings')
+    all_ind.extend(B.return_indicator(geogrid_data))
+    return all_ind
+
+def create_scenario_row(all_ind, scenario_name):
+    all_cols={'Scenario': scenario_name}
+    for ind in all_ind:
+        all_cols[ind['name']+' norm']=ind['value']
+        raw_name=ind['name']+' raw'
+        if not ind['units']==None:
+            raw_name+= ' ['+ind['units']+']'
+        all_cols[raw_name]=ind['raw_value']
+    return all_cols
+
+base_indicators=get_all_indicators(geogrid_data_base, False)
+campus_indicators=get_all_indicators(geogrid_data_campus, False)
+campus_mobility_indicators=get_all_indicators(geogrid_data_campus, True)
+housing_indicators=get_all_indicators(geogrid_data_housing, False)
+inno_com_indicators=get_all_indicators(geogrid_data_inno_com, True)
+
+all_scenarios=[]
+all_scenarios.append(create_scenario_row(base_indicators, scenario_name='BAU'))
+all_scenarios.append(create_scenario_row(campus_indicators, scenario_name='Campus Only'))
+all_scenarios.append(create_scenario_row(campus_mobility_indicators, scenario_name='Future Mobility'))
+all_scenarios.append(create_scenario_row(housing_indicators, scenario_name='Housing'))
+all_scenarios.append(create_scenario_row(inno_com_indicators, scenario_name='Innovation Community'))
+
+col_order = ['Scenario']+[col for col in all_scenarios[0] if 'norm' in col]+[col for col in all_scenarios[0] if 'raw' in col]
+
+output=pd.DataFrame(all_scenarios)
+output=output[col_order]
+
+output.to_csv('scenario_outputs.csv')

diversity_indicator.py

@@ -1,7 +1,7 @@
 import pandas as pd
 import json
 from toolbox import Handler, Indicator
-from indicator_tools import EconomicIndicatorBase, shannon_equitability_score
+from indicator_tools import EconomicIndicatorBase, shannon_equitability_score, flatten_grid_cell_attributes, collect_grid_cell_counts
 
 class DiversityIndicator(EconomicIndicatorBase):
     def setup(self ,*args,**kwargs):
@@ -11,41 +11,115 @@ def setup(self ,*args,**kwargs):
                                       '42', '44', '45', '48', '49', '51', '52', 
                                       '53', '54', '55', '56', '61', '62', '71', 
                                       '72', '81', '92']
-        self.third_place_naics_codes=['7224', '7225', '7211', 
-                                    '4451', '4452', '4453' ]
+#        self.third_place_naics_codes=['7224', '7225', '7211', 
+#                                    '4451', '4452', '4453' ]
         self.education_naics_codes=['6111', '6113', '6115', 
                                     '6116' ]
+        parcel_data_loc='./tables/{}/geometry/{}_site_parcels_cs_types.geojson'.format(self.table_name, self.table_name)
+        self.parcel_data=json.load(open(parcel_data_loc))
+        self.school_type_to_NAICS={'School': '6111'}
+        self.prepare_base_populations()
+
+
 
-
+
+    def prepare_base_populations(self):
+        # create dict of housing {'R2', 'R3', 'R5', 'R6'}
+        self.housing_counts={'R1': 0, 'R2':0, 'R3':0, 'R4': 0,'R5':0, 'R6':0}
+        self.education_counts={'6111':0, '6113':0, '6115':0, 
+                                    '6116':0}
+        self.third_place_counts={'2200':0, '2100':0, '7240':0}
+
+        for feat in self.parcel_data['features']:
+            area=feat['properties']['area_sqm']
+            floor_area=feat['properties']['floor_area_sqm']
+            if feat['properties']['CS_LU']=='Institutional':
+                school_naics=self.school_type_to_NAICS[feat['properties']['school_type']]
+                self.education_counts[school_naics]+=floor_area
+            elif 'Residential' in feat['properties']['CS_LU']:
+                if feat['properties']['ZONING'] in self.housing_counts:
+                    self.housing_counts[feat['properties']['ZONING']]+=floor_area
+            elif feat['properties']['CS_LU']=='Retail':
+                self.third_place_counts['2200']+=0.9*floor_area
+                self.third_place_counts['2100']+=0.1*floor_area
+            elif feat['properties']['CS_LU']=='Park':
+                self.third_place_counts['7240']+=area
+
     def return_indicator(self,geogrid_data):
         industry_composition=self.grid_to_industries(geogrid_data)
         industry_species_counts={}
-        third_place_species_counts={}
-        education_species_counts={}
+#        third_place_species_counts={}
+#        education_species_counts={}
         for td_code in self.two_digit_naics_species:
             industry_species_counts[td_code]=sum([industry_composition[code] for 
                                  code in industry_composition if code[:2]==td_code])
-        for tp_code in self.third_place_naics_codes:
-            third_place_species_counts[tp_code]=sum([industry_composition[code] for 
-                                 code in industry_composition if code==tp_code])
-        for ed_code in self.education_naics_codes:
-            education_species_counts[ed_code]=sum([industry_composition[code] for 
-                                 code in industry_composition if code==ed_code])
+#        for tp_code in self.third_place_naics_codes:
+#            third_place_species_counts[tp_code]=sum([industry_composition[code] for 
+#                                 code in industry_composition if code==tp_code])
+#        for ed_code in self.education_naics_codes:
+#            education_species_counts[ed_code]=sum([industry_composition[code] for 
+#                                 code in industry_composition if code==ed_code])
 
         job_diversity=shannon_equitability_score([industry_species_counts[code] for code in industry_species_counts])
-        third_diversity=shannon_equitability_score([third_place_species_counts[code] for code in third_place_species_counts])
-        edu_diversity=shannon_equitability_score([education_species_counts[code] for code in education_species_counts])
+#        third_diversity=shannon_equitability_score([third_place_species_counts[code] for code in third_place_species_counts])
+#        edu_diversity=shannon_equitability_score([education_species_counts[code] for code in education_species_counts])
+
+        # make copies of the baseline counts
+        housing_counts={k:self.housing_counts[k] for k in self.housing_counts}
+        education_counts={k:self.education_counts[k] for k in self.education_counts}
+        third_place_counts={k:self.third_place_counts[k] for k in self.third_place_counts}
+
+        all_new_LBCS=[]
+        all_new_NAICS=[]
+        for cell in geogrid_data:
+            if cell['interactive']:
+                lbcs_this_cell=flatten_grid_cell_attributes(
+                        type_def=self.types_def[cell['name']], height=cell['height'],
+                        attribute_name='LBCS', area_per_floor=self.geogrid_header['cellSize']**2,
+                        return_units='floors')
+                naics_this_cell=flatten_grid_cell_attributes(
+                        type_def=self.types_def[cell['name']], height=cell['height'],
+                        attribute_name='NAICS', area_per_floor=self.geogrid_header['cellSize']**2,
+                        return_units='floors')
+                all_new_LBCS.append(lbcs_this_cell)
+                all_new_NAICS.append(naics_this_cell)
+
+        all_new_LBCS_agg=collect_grid_cell_counts(all_new_LBCS)
+        all_new_NAICS_agg=collect_grid_cell_counts(all_new_NAICS)
+
+        area_one_floor=self.geogrid_header['cellSize']**2
+        # for each education lbcs, add new
+        # for each third_place lbcs add new
+        for lbcs in third_place_counts:
+            if lbcs in all_new_LBCS_agg:
+                third_place_counts[lbcs]+=all_new_LBCS_agg[lbcs]*area_one_floor
+        for naics in education_counts:
+            if naics in all_new_NAICS_agg:
+                education_counts[naics]+=all_new_NAICS_agg[naics]*area_one_floor
+
+        # new_R5 = 80% of new housing
+        # new_R6 = 20% of new housing        
+        new_housing=all_new_LBCS_agg['1100']*area_one_floor
+        housing_counts['R5']+=new_housing*0.8
+        housing_counts['R6']+=new_housing*0.2
+
+        third_diversity=shannon_equitability_score([third_place_counts[code] for code in third_place_counts])
+        housing_diversity=shannon_equitability_score([housing_counts[code] for code in housing_counts])
+        edu_diversity=shannon_equitability_score([education_counts[code] for code in education_counts])
+
 
         return [{'name': 'Diversity Jobs', 'value': job_diversity,'raw_value': job_diversity, 
                  'viz_type': self.viz_type, 'units': None},
                 {'name': 'Diveristy Third Places', 'value': third_diversity, 'raw_value': third_diversity, 
                  'viz_type': self.viz_type, 'units': None},
                  {'name': 'Diveristy Education', 'value': edu_diversity, 'raw_value': edu_diversity,  
+                 'viz_type': self.viz_type, 'units': None},
+                  {'name': 'Diveristy Housing', 'value': housing_diversity, 'raw_value': housing_diversity,  
                  'viz_type': self.viz_type, 'units': None}]
 
 
 def main():
-    D = Diversity_Indicator()
+    D = DiversityIndicator()
     H = Handler('corktown', quietly=False)
     H.add_indicator(D)
 #    geogrid_data=H.get_geogrid_data()