20240416 18183700 1.0 FALSE UpdateSchema "CIMDATA=<CIMStandardDataConnection xsi:type='typens:CIMStandardDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.2.0'><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original</WorkspaceConnectionString><WorkspaceFactory>Shapefile</WorkspaceFactory><Dataset>tl_2020_26_tract</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMStandardDataConnection>" <operationSequence><workflow><AddField><field_name>GEOID_Num</field_name><field_type>DOUBLE</field_type><field_is_nullable>False</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> CalculateField tl_2020_26_tract GEOID_Num !GEOID! Python # Text NO_ENFORCE_DOMAINS Clip tl_2020_26_tract co_outline E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\tl_2020_26_tract_clipped.shp # CopyFeatures E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\tl_2020_26_tract_clipped.shp E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\CensusTracts_No_Join.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\tl_2020_26_tract_clipped # # # # ExportFeatures tl_2020_26_tract_clipped E:\Nyaa_NSPEARS_Application\0_Michigan_Census_Tracts_Original\CensusTracts_No_Join.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\tl_2020_26_tract_clipped_with_ACS_data_join # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.NAMELSAD,0,19;MTFCC "MTFCC" true true false 5 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.MTFCC,0,4;FUNCSTAT "FUNCSTAT" true true false 1 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.FUNCSTAT,0,0;ALAND "ALAND" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.ALAND,-1,-1;AWATER "AWATER" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.AWATER,-1,-1;INTPTLAT "INTPTLAT" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.INTPTLAT,0,10;INTPTLON "INTPTLON" true true false 12 Text 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.INTPTLON,0,11;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.GEOID_Num,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped,tl_2020_26_tract_clipped.Shape_Area,-1,-1;JOIN_ID "JOIN_ID" true true false 8 BigInteger 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.JOIN_ID,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.JOIN_ID,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Label,0,7999,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Dataset,0,7999,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Population,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Male,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Female,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Female,-1,-1;Under_18 "Under_18" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Under_18,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Under_18,-1,-1;Over_60 "Over_60" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Over_60,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Over_60,-1,-1;MedianAge "MedianAge" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.MedianAge,0,7999,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.MedianAge,0,7999;One_Race "One_Race" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.One_Race,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.One_Race,-1,-1;TwoMoreRaces "TwoMoreRaces" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.TwoMoreRaces,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.White,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.White,-1,-1;Black "Black" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Black,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Black,-1,-1;Amer_Indian "Amer_Indian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Amer_Indian,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Asian,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Asian,-1,-1;Hawaiian "Hawaiian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hawaiian,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hawaiian,-1,-1;OtherRace "OtherRace" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.OtherRace,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.OtherRace,-1,-1;Hispanic "Hispanic" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hispanic,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.Hispanic,-1,-1;NotHispanic "NotHispanic" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.NotHispanic,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.NotHispanic,-1,-1;HousingUnits "HousingUnits" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.HousingUnits,-1,-1,tl_2020_26_tract_clipped,ACS_2021_Data_to_Join_NoHeaders_for_Join.csv.HousingUnits,-1,-1" # ExportFeatures tl_2020_26_tract_clipped_with_ACS_data_join E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Census_Tracts_with_ACS_Data_and_SNAP_Indicators # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.NAMELSAD,0,19;MTFCC "MTFCC" true true false 5 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.MTFCC,0,4;FUNCSTAT "FUNCSTAT" true true false 1 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.FUNCSTAT,0,0;ALAND "ALAND" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.ALAND,-1,-1;AWATER "AWATER" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.AWATER,-1,-1;INTPTLAT "INTPTLAT" true true false 11 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.INTPTLAT,0,10;INTPTLON "INTPTLON" true true false 12 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.INTPTLON,0,11;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.GEOID_Num,-1,-1;JOIN_ID "JOIN_ID" true true false 8 BigInteger 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.JOIN_ID,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.HousingUnits,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,tl_2020_26_tract_clipped_with_ACS_data_join.Shape_Area,-1,-1;CensusTract "CensusTract" true true false 8 BigInteger 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.CensusTract,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.CensusTract,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_001E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_001E,-1,-1;S2201_C01_002E "S2201_C01_002E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_002E,-1,-1;S2201_C01_009E "S2201_C01_009E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_009E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_021E,-1,-1;S2201_C01_023E "S2201_C01_023E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_023E,-1,-1;S2201_C01_034E "S2201_C01_034E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_034E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C01_034E,-1,-1;S2201_C02_002E "S2201_C02_002E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_002E,-1,-1;S2201_C02_009E "S2201_C02_009E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_009E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_021E,-1,-1;S2201_C02_023E "S2201_C02_023E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C02_023E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_001E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_001E,-1,-1;S2201_C03_002E "S2201_C03_002E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_002E,-1,-1;S2201_C03_009E "S2201_C03_009E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_009E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_021E,-1,-1;S2201_C03_023E "S2201_C03_023E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_023E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_034E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C03_034E,-1,-1;S2201_C04_002E "S2201_C04_002E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_002E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_002E,-1,-1;S2201_C04_009E "S2201_C04_009E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_009E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_009E,-1,-1;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_021E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_021E,-1,-1;S2201_C04_023E "S2201_C04_023E" true true false 4 Long 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_023E,-1,-1,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.S2201_C04_023E,-1,-1;Field22 "Field22" true true false 8000 Text 0 0,First,#,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.Field22,0,7999,tl_2020_26_tract_clipped_with_ACS_data_join,SNAP_Data_to_Join.csv.Field22,0,7999" # DeleteField "Number of Households Who Receive SNAP Benefits" Field22 "Delete Fields" UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>JoinID_2</field_name><field_type>DOUBLE</field_type><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>CensusTract2</field_name><field_type>DOUBLE</field_type><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> CalculateField "SNAP Households Median Household Income in the Last 12 Months" JoinID_2 !JOIN_ID! Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" CensusTract2 !CensusTract! Python # Text NO_ENFORCE_DOMAINS DeleteField "SNAP Households Median Household Income in the Last 12 Months" CensusTract "Delete Fields" DeleteField "SNAP Households Median Household Income in the Last 12 Months" JOIN_ID "Delete Fields" UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>S2201_C03_034E_TEXT</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT !S2201_C03_034E! Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT "$"+!S2201_C03_034E_TEXT! Python # Text NO_ENFORCE_DOMAINS UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.2.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\1_Data_Organization\SNAP_Indicators\N_PEARS_SNAP_Data.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Census_Tracts_with_ACS_Data_and_SNAP_Indicators</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>temp</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>temp2</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp Right([S2201_C03_034E_TEXT],3) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 Left([S2201_C03_034E_TEXT],2) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 Left([S2201_C03_034E_TEXT],3) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 Left([S2201_C03_034E_TEXT],4) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" temp2 "!temp2! + ","" Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT "!temp! + !temp2!" Python # Text NO_ENFORCE_DOMAINS CalculateField "SNAP Households Median Household Income in the Last 12 Months" S2201_C03_034E_TEXT "!temp2! + !temp!" Python # Text NO_ENFORCE_DOMAINS DeleteField "SNAP Households Median Household Income in the Last 12 Months" temp "Delete Fields" DeleteField "SNAP Households Median Household Income in the Last 12 Months" temp2 "Delete Fields" Rename E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\SNAP_Indicator_Layers.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Census_Tracts_with_ACS_Data_and_SNAP_Indicators E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\SNAP_Indicator_Layers.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Orig_Make_Copy FeatureClass ExportFeatures Orig_Make_Copy E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Demographic_Contextual_Variables # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.NAMELSAD,0,19;MTFCC "MTFCC" true true false 5 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.MTFCC,0,4;FUNCSTAT "FUNCSTAT" true true false 1 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.FUNCSTAT,0,0;ALAND "ALAND" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.ALAND,-1,-1;AWATER "AWATER" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.AWATER,-1,-1;INTPTLAT "INTPTLAT" true true false 11 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.INTPTLAT,0,10;INTPTLON "INTPTLON" true true false 12 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.INTPTLON,0,11;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.GEOID_Num,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.HousingUnits,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_001E,-1,-1;S2201_C01_002E "S2201_C01_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_002E,-1,-1;S2201_C01_009E "S2201_C01_009E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_009E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_021E,-1,-1;S2201_C01_023E "S2201_C01_023E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_023E,-1,-1;S2201_C01_034E "S2201_C01_034E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C01_034E,-1,-1;S2201_C02_002E "S2201_C02_002E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_002E,-1,-1;S2201_C02_009E "S2201_C02_009E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_009E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_021E,-1,-1;S2201_C02_023E "S2201_C02_023E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C02_023E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_001E,-1,-1;S2201_C03_002E "S2201_C03_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_002E,-1,-1;S2201_C03_009E "S2201_C03_009E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_009E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_021E,-1,-1;S2201_C03_023E "S2201_C03_023E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_023E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_034E,-1,-1;S2201_C04_002E "S2201_C04_002E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_002E,-1,-1;S2201_C04_009E "S2201_C04_009E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_009E,-1,-1;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_021E,-1,-1;S2201_C04_023E "S2201_C04_023E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C04_023E,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.Shape_Area,-1,-1;JoinID_2 "JoinID_2" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.JoinID_2,-1,-1;CensusTract2 "CensusTract2" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.CensusTract2,-1,-1;S2201_C03_034E_TEXT "S2201_C03_034E_TEXT" true true false 255 Text 0 0,First,#,Orig_Make_Copy,Orig_Make_Copy.S2201_C03_034E_TEXT,0,254;Tract "Tract" true true false 8 BigInteger 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.Tract,-1,-1;S1602_C02_002E "S1602_C02_002E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_002E,-1,-1;S1602_C02_003E "S1602_C02_003E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_003E,-1,-1;S1602_C02_004E "S1602_C02_004E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_004E,-1,-1;S1602_C02_005E "S1602_C02_005E" true true false 8 Double 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S1602_C02_005E,-1,-1;B28002_002E "B28002_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28002_002E,-1,-1;B28002_006E "B28002_006E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28002_006E,-1,-1;B28004_002E "B28004_002E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_002E,-1,-1;B28004_006E "B28004_006E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_006E,-1,-1;B28004_010E "B28004_010E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_010E,-1,-1;B28004_014E "B28004_014E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_014E,-1,-1;B28004_018E "B28004_018E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_018E,-1,-1;B28004_022E "B28004_022E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.B28004_022E,-1,-1;S2101_C01_027E "S2101_C01_027E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_027E,-1,-1;S2101_C01_028E "S2101_C01_028E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_028E,-1,-1;S2101_C01_029E "S2101_C01_029E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_029E,-1,-1;S2101_C01_030E "S2101_C01_030E" true true false 4 Long 0 0,First,#,Orig_Make_Copy,Demographic_Contextual_Variables.csv.S2101_C01_030E,-1,-1" # DeleteField Demographic_Contextual_Variables S2201_C01_001E;S2201_C01_002E;S2201_C01_009E;S2201_C01_021E;S2201_C01_023E;S2201_C01_034E;S2201_C02_002E;S2201_C02_009E;S2201_C02_021E;S2201_C02_023E;S2201_C03_001E;S2201_C03_002E;S2201_C03_009E;S2201_C03_021E;S2201_C03_023E;S2201_C03_034E;S2201_C04_002E;S2201_C04_009E;S2201_C04_021E;S2201_C04_023E;S2201_C03_034E_TEXT "Delete Fields" DeleteField Demographic_Contextual_Variables S2201_C03_034E_TEXT;S2201_C04_023E;S2201_C04_021E;S2201_C04_009E;S2201_C04_002E;S2201_C03_034E;S2201_C03_023E;S2201_C03_021E;S2201_C03_009E;S2201_C03_002E;S2201_C03_001E;S2201_C02_023E;S2201_C02_021E;S2201_C02_009E;S2201_C02_002E;S2201_C01_034E;S2201_C01_023E;S2201_C01_021E;S2201_C01_009E;S2201_C01_002E;S2201_C01_001E;MTFCC;FUNCSTAT;ALAND;AWATER;INTPTLAT;INTPTLON "Delete Fields" UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Contextual_Variables</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>S1602_C02_002E_TEXT</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>S1602_C02_003E_TEXT</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>S1602_C02_004E_TEXT</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>S1602_C02_005E_TEXT</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> CalculateField Demographic_Contextual_Variables S1602_C02_002E_TEXT !S1602_C02_002E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_002E_TEXT "!S1602_C02_002E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_003E_TEXT !S1602_C02_003E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_003E_TEXT "!S1602_C02_003E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_004E_TEXT !S1602_C02_004E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_004E_TEXT "!S1602_C02_004E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_005E_TEXT !S1602_C02_005E! Python # Text NO_ENFORCE_DOMAINS CalculateField Demographic_Contextual_Variables S1602_C02_005E_TEXT "!S1602_C02_005E_TEXT! + "%"" Python # Text NO_ENFORCE_DOMAINS ExportFeatures Demographic_Contextual_Variables E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\Demographic_Contextual_Variables_All # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.NAMELSAD,0,19;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.GEOID_Num,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.HousingUnits,-1,-1;JoinID_2 "JoinID_2" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.JoinID_2,-1,-1;CensusTract2 "CensusTract2" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.CensusTract2,-1,-1;Tract "Tract" true true false 8 BigInteger 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Tract,-1,-1;S1602_C02_002E "Percent of Spanish Speaking Households" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_002E,-1,-1;S1602_C02_002E_TEXT "Percent of Spanish Speaking Households_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_002E_TEXT,0,254;S1602_C02_003E "Percent of Households who Speak a Different Indo/European Language" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_003E,-1,-1;S1602_C02_003E_TEXT "Percent of Households who Speak a Different Indo/European Language_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_003E_TEXT,0,254;S1602_C02_004E "Percent of Households who Speak an AAPI Language" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_004E,-1,-1;S1602_C02_004E_TEXT "Percent of Households who Speak an AAPI Language_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_004E_TEXT,0,254;S1602_C02_005E "Percent of Households who Speak a Different Language than Indicated in Other Language Options" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_005E,-1,-1;S1602_C02_005E_TEXT "Percent of Households who Speak a Different Language than Indicated in Other Language Options_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S1602_C02_005E_TEXT,0,254;B28002_002E "Number of Households who have Internet Access" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28002_002E,-1,-1;B28002_006E "Number of Households who Only have Internet Access Through Cellular Data" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28002_006E,-1,-1;B28004_002E "Number of Households who Earn Less than $10,000 per Year" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_002E,-1,-1;B28004_006E "Number of Households who Earn between $10,000 and $19,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_006E,-1,-1;B28004_010E "Number of Households who Earn between $20,000 and $34,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_010E,-1,-1;B28004_014E "Number of Households who Earn between $35,000 and $49,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_014E,-1,-1;B28004_018E "Number of Households who Earn between $50,000 and $74,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_018E,-1,-1;B28004_022E "Number of Households who Earn $75,000 or More Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.B28004_022E,-1,-1;S2101_C01_027E "Number of Individuals over the Age of 25 who have Less than a High School Degree" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_027E,-1,-1;S2101_C01_028E "Number of Individuals over the Age of 25 with a High School Diploma or GED" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_028E,-1,-1;S2101_C01_029E "Number of Individuals over the Age of 25 who have an Associates Degree or have spend some time Enrolled in College" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_029E,-1,-1;S2101_C01_030E "Number of Individuals over the Age of 25 with a Bachelor's Degree of Higher" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.S2101_C01_030E,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,Demographic_Contextual_Variables,Demographic_Contextual_Variables.Shape_Area,-1,-1;Tract "Tract" true true false 8 BigInteger 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.Tract,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C01_001E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C01_021E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C02_021E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_001E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_021E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_034E,-1,-1;S2201_C03_034E_TEXT "S2201_C03_034E_TEXT" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C03_034E_TEXT,0,7999;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S2201_C04_021E,-1,-1;S1602_C01_002E "S1602_C01_002E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_002E,-1,-1;S1602_C01_003E "S1602_C01_003E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_003E,-1,-1;S1602_C01_004E "S1602_C01_004E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_004E,-1,-1;S1602_C01_005E "S1602_C01_005E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables,General_ACS_Stats_to_Join.csv.S1602_C01_005E,-1,-1" # UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\DemographicContextualVariables.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>Demographic_Contextual_Variables_All</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><DeleteField><field_name>Tract_1</field_name></DeleteField></workflow><workflow><AddField><field_name>S2201_C02_021E_TEXT</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>S2201_C04_021E_TEXT</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> ExportFeatures Demographic_Contextual_Variables_All E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\Baseline_Indicators.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\CensusTracts_before_data_Join # NOT_USE_ALIAS "STATEFP "STATEFP" true true false 2 Text 0 0,First,#,Demographic_Contextual_Variables_All,STATEFP,0,1;COUNTYFP "COUNTYFP" true true false 3 Text 0 0,First,#,Demographic_Contextual_Variables_All,COUNTYFP,0,2;TRACTCE "TRACTCE" true true false 6 Text 0 0,First,#,Demographic_Contextual_Variables_All,TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,Demographic_Contextual_Variables_All,GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,Demographic_Contextual_Variables_All,NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,Demographic_Contextual_Variables_All,NAMELSAD,0,19;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,GEOID_Num,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables_All,Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables_All,Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables_All,MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,HousingUnits,-1,-1;JoinID_2 "JoinID_2" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,JoinID_2,-1,-1;CensusTract2 "CensusTract2" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,CensusTract2,-1,-1;Tract "Tract" true true false 8 BigInteger 0 0,First,#,Demographic_Contextual_Variables_All,Tract,-1,-1;S1602_C02_002E "Percent of Spanish Speaking Households" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_002E,-1,-1;S1602_C02_002E_TEXT "Percent of Spanish Speaking Households_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_002E_TEXT,0,254;S1602_C02_003E "Percent of Households who Speak a Different Indo/European Language" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_003E,-1,-1;S1602_C02_003E_TEXT "Percent of Households who Speak a Different Indo/European Language_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_003E_TEXT,0,254;S1602_C02_004E "Percent of Households who Speak an AAPI Language" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_004E,-1,-1;S1602_C02_004E_TEXT "Percent of Households who Speak an AAPI Language_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_004E_TEXT,0,254;S1602_C02_005E "Percent of Households who Speak a Different Language than Indicated in Other Language Options" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_005E,-1,-1;S1602_C02_005E_TEXT "Percent of Households who Speak a Different Language than Indicated in Other Language Options_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C02_005E_TEXT,0,254;B28002_002E "Number of Households who have Internet Access" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28002_002E,-1,-1;B28002_006E "Number of Households who Only have Internet Access Through Cellular Data" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28002_006E,-1,-1;B28004_002E "Number of Households who Earn Less than $10,000 per Year" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28004_002E,-1,-1;B28004_006E "Number of Households who Earn between $10,000 and $19,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28004_006E,-1,-1;B28004_010E "Number of Households who Earn between $20,000 and $34,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28004_010E,-1,-1;B28004_014E "Number of Households who Earn between $35,000 and $49,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28004_014E,-1,-1;B28004_018E "Number of Households who Earn between $50,000 and $74,999 Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28004_018E,-1,-1;B28004_022E "Number of Households who Earn $75,000 or More Annually " true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,B28004_022E,-1,-1;S2101_C01_027E "Number of Individuals over the Age of 25 who have Less than a High School Degree" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2101_C01_027E,-1,-1;S2101_C01_028E "Number of Individuals over the Age of 25 with a High School Diploma or GED" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2101_C01_028E,-1,-1;S2101_C01_029E "Number of Individuals over the Age of 25 who have an Associates Degree or have spend some time Enrolled in College" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2101_C01_029E,-1,-1;S2101_C01_030E "Number of Individuals over the Age of 25 with a Bachelor's Degree of Higher" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2101_C01_030E,-1,-1;S2201_C01_001E "S2201_C01_001E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C01_001E,-1,-1;S2201_C01_021E "S2201_C01_021E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C01_021E,-1,-1;S2201_C02_021E "S2201_C02_021E" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C02_021E,-1,-1;S2201_C03_001E "S2201_C03_001E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C03_001E,-1,-1;S2201_C03_021E "S2201_C03_021E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C03_021E,-1,-1;S2201_C03_034E "S2201_C03_034E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C03_034E,-1,-1;S2201_C03_034E_TEXT "S2201_C03_034E_TEXT" true true false 8000 Text 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C03_034E_TEXT,0,7999;S2201_C04_021E "S2201_C04_021E" true true false 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C04_021E,-1,-1;S1602_C01_002E "S1602_C01_002E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C01_002E,-1,-1;S1602_C01_003E "S1602_C01_003E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C01_003E,-1,-1;S1602_C01_004E "S1602_C01_004E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C01_004E,-1,-1;S1602_C01_005E "S1602_C01_005E" true true false 4 Long 0 0,First,#,Demographic_Contextual_Variables_All,S1602_C01_005E,-1,-1;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,Demographic_Contextual_Variables_All,Shape_Area,-1,-1;S2201_C02_021E_TEXT "S2201_C02_021E_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C02_021E_TEXT,0,254;S2201_C04_021E_TEXT "S2201_C04_021E_TEXT" true true false 255 Text 0 0,First,#,Demographic_Contextual_Variables_All,S2201_C04_021E_TEXT,0,254" # DeleteField CensusTracts_before_data_Join JoinID_2;CensusTract2;Tract;S1602_C02_002E;S1602_C02_002E_TEXT;S1602_C02_003E;S1602_C02_003E_TEXT;S1602_C02_004E;S1602_C02_004E_TEXT;S1602_C02_005E;S1602_C02_005E_TEXT;B28002_002E;B28002_006E;S2101_C01_027E;S2101_C01_028E;S2101_C01_029E;S2101_C01_030E "Delete Fields" UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\Baseline_Indicators.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>CensusTracts_before_data_Join</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><DeleteField><field_name>S2201_C03_021E</field_name><field_name>S2201_C04_021E</field_name><field_name>S1602_C01_002E</field_name><field_name>S1602_C01_003E</field_name><field_name>S1602_C01_004E</field_name><field_name>S1602_C01_005E</field_name><field_name>S2201_C04_021E_TEXT</field_name></DeleteField></workflow></operationSequence> ExportFeatures CensusTracts_before_data_Join E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\Veterans.gdb\NAD_1983_2011_Michigan_GeoRef_Meters\CensusTracts_with_Veteran_Data # NOT_USE_ALIAS "STATEFP "State" true true false 2 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.STATEFP,0,1;COUNTYFP "County" true true false 3 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.COUNTYFP,0,2;TRACTCE "Tract" true true false 6 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.TRACTCE,0,5;GEOID "GEOID" true true false 11 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.GEOID,0,10;NAME "NAME" true true false 7 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.NAME,0,6;NAMELSAD "NAMELSAD" true true false 20 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.NAMELSAD,0,19;GEOID_Num "GEOID_Num" true true false 8 Double 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.GEOID_Num,-1,-1;Label "Label" true true false 8000 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Label,0,7999;Dataset "Dataset" true true false 8000 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Dataset,0,7999;Population "Population" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Population,-1,-1;Male "Male" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Male,-1,-1;Female "Female" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Female,-1,-1;Under_18 "Under 18" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Under_18,-1,-1;Over_60 "Over 60" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Over_60,-1,-1;MedianAge "Median Age" true true false 8000 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.MedianAge,0,7999;One_Race "One Race" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.One_Race,-1,-1;TwoMoreRaces "Two or More Races" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.TwoMoreRaces,-1,-1;White "White" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.White,-1,-1;Black "Black of African American" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Black,-1,-1;Amer_Indian "American Indian and Alaska Native" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Amer_Indian,-1,-1;Asian "Asian" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Asian,-1,-1;Hawaiian "Native Hawaiian and Other Pacific Islander" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Hawaiian,-1,-1;OtherRace "Other Race" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.OtherRace,-1,-1;Hispanic "Hispanic or Latino" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Hispanic,-1,-1;NotHispanic "Not Hispanic or Latino" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.NotHispanic,-1,-1;HousingUnits "Housing Units" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.HousingUnits,-1,-1;B28004_002E "Number of Households who Earn Less than $10,000 per Year" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.B28004_002E,-1,-1;B28004_006E "Number of Households who Earn between $10,000 and $19,999 Annually " true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.B28004_006E,-1,-1;B28004_010E "Number of Households who Earn between $20,000 and $34,999 Annually " true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.B28004_010E,-1,-1;B28004_014E "Number of Households who Earn between $35,000 and $49,999 Annually " true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.B28004_014E,-1,-1;B28004_018E "Number of Households who Earn between $50,000 and $74,999 Annually " true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.B28004_018E,-1,-1;B28004_022E "Number of Households who Earn $75,000 or More Annually " true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.B28004_022E,-1,-1;S2201_C01_001E "Total Number of Households" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.S2201_C01_001E,-1,-1;S2201_C01_021E "Number of Households Below the Poverty Level within the last 12 months" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.S2201_C01_021E,-1,-1;S2201_C02_021E "Percent of Households Below the Poverty Level within the last 12 months" true true false 8 Double 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.S2201_C02_021E,-1,-1;S2201_C03_001E "Number of Households who receive SNAP Benefits" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.S2201_C03_001E,-1,-1;S2201_C03_034E "Median household income in the last 12 months for SNAP households" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.S2201_C03_034E,-1,-1;S2201_C03_034E_TEXT "Median household income in the last 12 months for SNAP households_TEXT" true true false 8000 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.S2201_C03_034E_TEXT,0,7999;S2201_C02_021E_TEXT "Percent of Households Below the Poverty Level within the last 12 months_TEXT" true true false 255 Text 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.S2201_C02_021E_TEXT,0,254;Shape_Length "Shape_Length" false true true 8 Double 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Shape_Length,-1,-1;Shape_Area "Shape_Area" false true true 8 Double 0 0,First,#,CensusTracts_before_data_Join,CensusTracts_before_data_Join.Shape_Area,-1,-1;Tract "Tract" true true false 8 BigInteger 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.Tract,-1,-1;S2101_C03_007E "S2101_C03_007E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_007E,-1,-1;S2101_C03_008E "S2101_C03_008E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_008E,-1,-1;S2101_C03_009E "S2101_C03_009E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_009E,-1,-1;S2101_C03_010E "S2101_C03_010E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_010E,-1,-1;S2101_C03_011E "S2101_C03_011E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_011E,-1,-1;S2101_C03_012E "S2101_C03_012E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_012E,-1,-1;S2101_C03_013E "S2101_C03_013E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_013E,-1,-1;S2101_C03_024E "S2101_C03_024E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_024E,-1,-1;S2101_C03_024E_TEXT "S2101_C03_024E_TEXT" true true false 8000 Text 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_024E_TEXT,0,7999;S2101_C03_027E "S2101_C03_027E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_027E,-1,-1;S2101_C03_028E "S2101_C03_028E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_028E,-1,-1;S2101_C03_029E "S2101_C03_029E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_029E,-1,-1;S2101_C03_30E "S2101_C03_30E" true true false 4 Long 0 0,First,#,CensusTracts_before_data_Join,Veterans_Data_to_Join.csv.S2101_C03_30E,-1,-1" # UpdateSchema "CIMDATA=<CIMFeatureDatasetDataConnection xsi:type='typens:CIMFeatureDatasetDataConnection' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><FeatureDataset>NAD_1983_2011_Michigan_GeoRef_Meters</FeatureDataset><WorkspaceConnectionString>DATABASE=E:\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\Veterans.gdb</WorkspaceConnectionString><WorkspaceFactory>FileGDB</WorkspaceFactory><Dataset>CensusTracts_with_Veteran_Data</Dataset><DatasetType>esriDTFeatureClass</DatasetType></CIMFeatureDatasetDataConnection>" <operationSequence><workflow><AddField><field_name>Temp1</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Temp2</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow><workflow><AddField><field_name>Temp3</field_name><field_type>TEXT</field_type><field_length>255</field_length><field_is_nullable>True</field_is_nullable><field_is_required>False</field_is_required></AddField></workflow></operationSequence> CalculateField "# of Male Veterans over the age of 18" Temp1 Left([S2101_C03_024E_TEXT],2) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "# of Male Veterans over the age of 18" Temp2 Right([S2101_C03_024E_TEXT],3) VBScript # Text NO_ENFORCE_DOMAINS CalculateField "# of Male Veterans over the age of 18" Temp3 [Temp1]&","&[Temp2] VBScript # Text NO_ENFORCE_DOMAINS CalculateField "# of Male Veterans over the age of 18" S2101_C03_024E_TEXT [Temp3] VBScript # Text NO_ENFORCE_DOMAINS CalculateField "# of Male Veterans over the age of 18" S2101_C03_024E_TEXT "$"&[S2101_C03_024E_TEXT] VBScript # Text NO_ENFORCE_DOMAINS CalculateField "# of Male Veterans over the age of 18" S2101_C03_024E_TEXT "No Data" VBScript # Text NO_ENFORCE_DOMAINS DeleteField "# of Male Veterans over the age of 18" S2201_C02_021E_TEXT "Delete Fields" CensusTracts_with_Veteran_Data 002 0.000 file://\\DESKTOP-J9TVK45\E$\Nyaa_NSPEARS_Application\2_Data_Layers_Uploaded_to_Data_Repository\Veterans.gdb Local Area Network Projected GCS_NAD_1983_2011 Linear Unit: Meter (1.000000) <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.3.0'><WKT>PROJCS[&quot;NAD_1983_2011_Michigan_GeoRef_Meters&quot;,GEOGCS[&quot;GCS_NAD_1983_2011&quot;,DATUM[&quot;D_NAD_1983_2011&quot;,SPHEROID[&quot;GRS_1980&quot;,6378137.0,298.257222101]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Hotine_Oblique_Mercator_Azimuth_Natural_Origin&quot;],PARAMETER[&quot;False_Easting&quot;,2546731.496],PARAMETER[&quot;False_Northing&quot;,-4354009.816],PARAMETER[&quot;Scale_Factor&quot;,0.9996],PARAMETER[&quot;Azimuth&quot;,337.25556],PARAMETER[&quot;Longitude_Of_Center&quot;,-86.0],PARAMETER[&quot;Latitude_Of_Center&quot;,45.30916666666666],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,6497]]</WKT><XOrigin>-28810000</XOrigin><YOrigin>-30359300</YOrigin><XYScale>10000</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102968</WKID><LatestWKID>6497</LatestWKID></ProjectedCoordinateSystem> NAD_1983_2011_Michigan_GeoRef_Meters 20240830 14150500 20240830 14150500 Microsoft Windows 10 Version 10.0 (Build 19045) ; Esri ArcGIS 13.3.0.52636 Number of Veterans Aged 65_74 Veterans Data Source: American Community Survey (ACS) 5-Year Estimates (2017-2021). Highlighted Variable: # of Veterans aged of 65-74 S2101_C03_012E) Fields/Data: OBJECTID (OBJECTID) Shape (Shape) State (STATEFP) County (COUNTYFP) Tract (TRACTCE) GEOID (GEOID) GEOID_Num (GEOID_Num) Label (Label) Dataset (Dataset) Population (Population) Median Age (MedianAge) Total Number of Households (S2201_C01_001E) Number of Households who receive SNAP Benefits (S2201_C03_001E) # of Male Veterans over the age of 18 (S2101_C03_007E) # of Female Veterans over the age of 18 (S2101_C03_008E) # of Veterans aged 18-34 (S2101_C03_009E) # of Veterans aged 35-54 (S2101_C03_010E) # of Veterans aged 55-64 (S2101_C03_011E) # of Veterans aged 65-74 (S2101_C03_012E) # of Veterans over the age of 75 (S2101_C03_013E) Median Income of Veterans over the age of 18 (S2101_C03_024E) Median Income of Veterans over the age of 18_TEXT (S2101_C03_024E_TEXT) # of Veterans who have less than a HS degree (S2101_C03_027E) # of Veterans over the age of 25 with a HS degree or GED (S2101_C03_028E) # of Veterans over the age of 25 with an Associates Degree or who have spent some time in College (S2101_C03_029E) # of Veterans over the age of 25 with a Bachelor's Degree or Higher (S2101_C03_30E) Shape_Length (Shape_Length) Shape_Area (Shape_Area) File Geodatabase Feature Class 0.000 dataset EPSG 8.2.10(10.3.1) 0 Simple FALSE 0 TRUE FALSE CensusTracts_with_Veteran_Data Feature Class 0 OBJECTID OBJECTID OID 4 0 0 Internal feature number. Esri Sequential unique whole numbers that are automatically generated. Shape Shape Geometry 0 0 0 Feature geometry. Esri Coordinates defining the features. STATEFP State String 2 0 0 COUNTYFP County String 3 0 0 TRACTCE Tract String 6 0 0 GEOID GEOID String 11 0 0 NAME NAME String 7 0 0 NAMELSAD NAMELSAD String 20 0 0 GEOID_Num GEOID_Num Double 8 0 0 Label Label String 8000 0 0 Dataset Dataset String 8000 0 0 Population Population Integer 4 0 0 Male Male Integer 4 0 0 Female Female Integer 4 0 0 Under_18 Under 18 Integer 4 0 0 Over_60 Over 60 Integer 4 0 0 MedianAge Median Age String 8000 0 0 One_Race One Race Integer 4 0 0 TwoMoreRaces Two or More Races Integer 4 0 0 White White Integer 4 0 0 Black Black of African American Integer 4 0 0 Amer_Indian American Indian and Alaska Native Integer 4 0 0 Asian Asian Integer 4 0 0 Hawaiian Native Hawaiian and Other Pacific Islander Integer 4 0 0 OtherRace Other Race Integer 4 0 0 Hispanic Hispanic or Latino Integer 4 0 0 NotHispanic Not Hispanic or Latino Integer 4 0 0 HousingUnits Housing Units Integer 4 0 0 S2201_C01_001E Total Number of Households Integer 4 0 0 S2201_C01_021E Number of Households Below the Poverty Level within the last 12 months Integer 4 0 0 S2201_C02_021E Percent of Households Below the Poverty Level within the last 12 months Double 8 0 0 S2201_C03_001E Number of Households who receive SNAP Benefits Integer 4 0 0 S2201_C03_034E Median household income in the last 12 months for SNAP households Integer 4 0 0 S2201_C03_034E_TEXT Median household income in the last 12 months for SNAP households_TEXT String 8000 0 0 Tract Tract BigInteger 8 0 0 B28004_002E Number of Households who Earn Less than $10,000 per Year Integer 4 0 0 B28004_006E Number of Households who Earn between $10,000 and $19,999 Annually Integer 4 0 0 B28004_010E Number of Households who Earn between $20,000 and $34,999 Annually Integer 4 0 0 B28004_014E Number of Households who Earn between $35,000 and $49,999 Annually Integer 4 0 0 B28004_018E Number of Households who Earn between $50,000 and $74,999 Annually Integer 4 0 0 B28004_022E Number of Households who Earn $75,000 or More Annually Integer 4 0 0 S2201_C02_021E_TEXT Percent of Households Below the Poverty Level within the last 12 months_TEXT String 255 0 0 S2101_C03_007E S2101_C03_007E Integer 4 0 0 S2101_C03_008E S2101_C03_008E Integer 4 0 0 S2101_C03_009E S2101_C03_009E Integer 4 0 0 S2101_C03_010E S2101_C03_010E Integer 4 0 0 S2101_C03_011E S2101_C03_011E Integer 4 0 0 S2101_C03_012E S2101_C03_012E Integer 4 0 0 S2101_C03_013E S2101_C03_013E Integer 4 0 0 S2101_C03_024E S2101_C03_024E Integer 4 0 0 S2101_C03_024E_TEXT S2101_C03_024E_TEXT String 8000 0 0 S2101_C03_027E S2101_C03_027E Integer 4 0 0 S2101_C03_028E S2101_C03_028E Integer 4 0 0 S2101_C03_029E S2101_C03_029E Integer 4 0 0 S2101_C03_30E S2101_C03_30E Integer 4 0 0 Shape_Length Shape_Length Double 8 0 0 Length of feature in internal units. Esri Positive real numbers that are automatically generated. Shape_Area Shape_Area Double 8 0 0 Area of feature in internal units squared. Esri Positive real numbers that are automatically generated. 20240830 iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO xAAADsQBlSsOGwAAIABJREFUeJzsvVlvJFmWJnZ83510Oncy9si9sqq6qpfqbvUMRtPdGuhBMy+C nvSr9DoQIOhFkNAYQdJgRo1ujKar0aWqUm2ZWZkZGRGMlfvm+74I33fudTc3ujudpLvTGcGTYAbp i9k1s3vPPct3vuM/PT1ty4xJu92WRqMhXq9XxCPSbrUEv+Bvj8cjHg8+5RnpOPjhpz0e/t5qt0Ta +Fuk1cIv/CCPh9fwKz5Xr9ek3W5JMBjizyjnqtZqUq1WpYJ/a3VpcdwqPq9H/H6fNBsN8Xi80my1 9FzmfZw7FonI/NwcrzOTyUoun5c2r3f4teJ9r0fE7/NKq9WUVtsjTVzbDInf75eFuaREI5GJn8vn 80mz2ZSbJi0zX70jPPN3WRqNOu9DIBA8855fZlDwsAKBQPcFr+/Sx3E+eC5s8Xb+9g05LJRGsViQ cqUiPp+ff0MB4QfHwQLETbWKsFKtSaFYpNLi54yixOTz+TzSbjVF2l6BHoEiNG9T4tGozCUTEg6H xef18jjFchmDGEEtq7JsiUcaTTM2n0c80pa2eKiUZ0F1QYHki0U+14B/JqfdtYpzvrzPyqor/e/B 7cwZsksHQyGpVMpUXFBQ1WpN/AGf1Gt1CYaCUq83JBgIcDHWGw2pN+rGcmqL16tWDzQT1Eez7ZFW s2tVQQHGohFaHPFYjP/ie1BW+UJRGs3mhSYuvts0liPOQUXp9UjDKrRr1lpqgdYlly/I/FySinmS 57qJygpP+1ZZWen/DG8V1gDBxAkZV7BRV0UEJRbwB3kvYUWVymWp1+vSgOtnzHkfZ53ecC9cPyiS lioMuLbhUEii0ahEwiEqKSg8nKtWq0mhVJJ8Xq26y05czH2cs+2FVadD8fq84jXjuM61jHsGyzEQ 8PPa/cNM3PdEOiGLW2XlklsL68ICKygcCksrEKBrBYWF13x+v+QLBbph1Uazu6M74g9th6KCRCNh iYTVoorFouLz+qRaq0oml5NGXa2zUrlCl3IcgvG2qDQ94jOPHxYXhqpuafvalBYsSEgsGp2opXUT xIYVbi0rp9A1kX5ya2GdI5hIiGHBGMBiq1SrUq5UpVAsdUx5p2jkCH4ZokgIhPslGo3IfDIpyUSC x6tUKrSkoPTyxYI0my1NMEwqkNtsG4XlFQ9Ow7iWKtXrUFtwnxHPggUKSxMbwUjXAXcb99j822uh eDjP8W8wqFbrrLuGTMrAkr6NWfWIvR1WmTvlVmGNsFAQo8JPra5WEJTWILFZRgiUECyr1aUliZjs GL57eHQsuUJB410miD9J6WQjYQEyvtUWj9crDQS9bJJ0yoL4X7ZQYKwuFAzQ4vR4HZaGDrjzKyxP ZI7p8jKBoZtD2yQVrGWLnxRiZDPublLxYtwj5bvfM/FoIPZWYV3UpWq3GFuyrhoW18i7NoPtolmx QKAD1cjn81KqlDuQByiOaYm6ieoa+jxwFzE3oLg02DVtxYX4X6Zep2IPmuxhx9J0WH82XtjPou2X XcZzmpTFOg6xlu1t3Kq/aI67f9ji1sLqbOaq0ZlRa7VoTZWrNVpEUDQXFU5IZup8HVcyl8vRskK2 7DpFY2tQWl5mM32qxeiaXoe1Ze/PMMt1FPH5vISIhILBmXYHLTRmlpXqdQqtbLj0fQIW763CshMa /8eODKUE2AIsKSgrJ+jzsjcdxz08PpJMLiu1emOmFhEtPiYGdKEH/T6pAz8G7NY1xbauInAJoaiS 8dhM3We3YF7Nurs6EwrLhlZct+q9VFhwgZChg5IC4BPo9PPcjcsKMnLXbVENE1XYLVpdgGQE/AC3 thnfmtQ9mYQAJ4esowX4zqrcWlUjCkMUZ5/je6OwEOCuVKpSqlQUO4XSDWNJzPKOPF2Ly2Q5200J oMyn7SXGbFK3B0omGPArDq1eZyD+soLniThXNBwe6xhv5fqECRZbYmWyvu+8wsJuW65WOZnh8uHv 60rn3xTFTogAN7eW+IHdAlKfoNPx3DXAKxBrAtwD7hGCz/VGk+444liXsXhtvNBmCmfZyrqVEaTd xyJF2Zm8o2Lr+4CsxkJoNhta7Hwr5987KC6T5YSbCJUFKARAXFBoV5VgMMgfCxrVALSCBZ0Yq1El YNxBlDrZ493KzRZCVt4XHFYVeKlSiQoLIMXb3fby2C3gnBDQJs6JNBd4XQPzlxW45MiWqpIyTBot xM2anVrMiwisNATcobicDB23cjOF6xXx9j6kB++UwrJxDCDRgZ8ahzXwvgtLebjbaTE3lAw2vWbL ZBMvoRuYlR0j/Yu6g7cQgXdFSAElKIXzv5sKCztqrVaXQrnEwuFG4+ZxIc2+tWUQ5WIUF5UXXETg 1trXXDrlE5//FirwroiNMffLqPrflewfYlVQVrfuwOTEMkEAdAqFRcS8V5UXjNnrhEHcPvd3T965 GJa6gBVySCEtfivTEcu9BWcR84nxLS/gEHATp688tHyqLIlY7Dbg/g4I3HvAGcCe63YL/TdaWZXK kisWL1U6cytXF7iDpJqm+d5WNghjgWnwe0rhgHpdiqWyBPwBCYfO0ureys0Sr9dn6JmUxBJ/32iF BQUFFxAUL++bstJaR2WPIH+7+YGArsVryj58voD4/UqzMtnxWNYEj7Q9LVpbwG6h5GeaJT5QWtl8 TtrtuAQDQfLn38pNV1q61gMB7eVwIxWWcoOXyKf0vsEVgCWr1SpSLGSlWMxKpVqSaqUklUqRmiMQ CEkkmqBSS84tymJ6TYKhyFTcJOsmthHTMrgtZBTxhKbBK29xd7XaqdYUgiM/GLx1EW+oaDLFy/Kc RrNB6/nGKSxynpdKUiyV3itlBQsKO83J8a4cHryWXO5IGo2zzKSVikfy+SPuTPhcvfqJrG88Eq9p ouE0radCYYOfdks8PouUn/jpadWhsqFyVJPVpcUOBfWt3DwB9REywGDjVVV1w5DuiFPgZ5wYnlkX tAXb3dmS/f2XUiplO+7f2d0oIMFQjEqpWilIPJGSQChKy6ZQyEooFJFIJDZlChuNa/mN1dUSw701 pc3t4OhY1paXWLN4KzdVkJFGQbta7DfmSRYQYC8UiJJ+H4TB5GpFvv3m51IonNAd7Fe9DvH7Q7K8 cl/u3f+Uf7daDdOaDLV1XtNXcfpWBmMQTbRGU2AnIBDsIDQlCAQgL6h6QFwPsbVbuYGiLIcdbqyZ V1ja+kpjViizeV+KLuDyvXz5leRyh32tKivaIzEg4XDUEWR3Z8qud7EqHztgD4rZCvoBOLVW2GTP fZrJSjVSk2Qiftul56aKo+Owf9aVVTaf77iB7ws4sF6vyv7uazncfytNdOUxT6vb9VolFIrKfGpV Fhc3JJFcmOlYjXLdszdH5wU0fIX1xbZkE3q2mDeIeWqBdOSWj+qGiXtGz6zCQlAdLiCC7Lpo331B BrRUzMnx8Y5kTvclGo3J40d/JE+ffSX5Qran9RGU1dLyPVlZvc/YVL+6q1kUbSLR7XTj8bTFh/gW KWHaE3MNbyKL6q1AAEqGRY4wh2+2FJaCxVrkrQIvEsj2OgRe77A0GnU5OHgre3uvpNUAFY5SNIfD EfrveFhapqBWVjAYleWVe7IKZRXV1mE3Saw1ZRUX41u4Pi/euxoTRD+JhMMElN4WSN88IXCUDYkb 1AUzpbBgvqPJJpQV41XvgQsIZfXq1bfy9de/lEL+VFLzaQmHw+L1gCqlIc+3vpFKpWQ+7ZH04h1J pVZkPrUk0WhSbrJYi8o2y2ZgHL8ou8hYYBBQUolYlPCGW7nZvUGReJoJhaXlFUCvl+gCvi8Yq0zm SHZ2Xsi33/5KtrefyVwyxYwes2stWFkNNl31+VCeYLKBy3clvbj+TsVibImPZYIAfouWJTKMV1Ra kUiYQNKbZoXeiiux5PMJalquXWHBzAMjaLFcIdvC+6CscM0nJ3vy5MmvZWvr91IoZCQeS8rqyl1Z TK9KtQr0epnWl7UyAFFYXNqQWCz5TimrM0wQRmHhCskEIYrBuYybiIkej8Xeyfv1/gmUlv96FRZ2 ULAt5N8ztoWTk335zW/+QZ4//1LK5byEgmEqrJWVDVlfvyuZzIkcHe0xduVFt2avTxKJRdnY/ECi sYS8y6LYLdvkVQQlgWRn9lycCQKKCtnBW+vq3ZFrUViwohBch0UFjNX7pKyw4L777jeytfUVlRWk WqvIzt5rCYUjkkzOST6fkVw+IwHTUSYanZPNOx8yM/i+CC0qo6A80mRHZ2xwzQswQaAA+n2Ig75P MnWFBUWlNMYVZgOvSmNsu6RoNkGbl87SJAU6ncW/BqVeLhfl9esnWrDsEGQGd3ZRJ5iVXO6EC/TR w4/F6/VLenFD5uaXJBB4/6hT1BX0CnqQedotKiEkjpW2ebh1NZ9M3mYG3zGZiMIi4kV7tVN5QJEQ stBsSjZfIDXMOGJVqC0ClUg0Eha/18easZNsljGxWVBaqAMslwv8qVbLVFoIsiPY3g+9Xi7pZ7VC PSDZXEaWlzdkYWH1xuCsJtp+zOMVTwvRDLQfQ9/ELjTCKYR+BAK3wfZ3UPyTdPm0aLFJxVUuVxmr Ggd/lZJ6eamo5hKJnpKLlcW07BwcXKkp57juQSZ7KM+++7VUKur6QcA9j+zfWTEpfQNvJF1KpSqP P/iRxBPzPYHjftSx74PguutNLajGJgjAKYwvSxjoFFhi798devdlIgqLi4mpeYC9WsRWgcb2KoBA BE+xa3p9KKL1EQiI3nbuola0hW8ZRgDrKk679ROUFUCg3z35tTQblR4yueHjUHZFMCvE40n54IMf ytLSRocWBpYXAvawxObm0izHscfU2/B+LFHLBKFkgayoFvQdsUF5/KBzUr5UYhQfwFHMn1u5+eKf hDuohG1aCoE6rsu6aFA2kVBIYpGIBIIBWhlUQg5l1HNuciFVOJnxHr6HD4PYbRrMpAC2HR/vybNn X8jW1pdSKhVkYX5RFhdXOjWARO73vReGUdEfkPTCsqwsr8vdux92lBVcyrdvv5NKuSiJxAIpYxAP SyZTEonE9frVD5f3pv0YLC5TUI2Gr8Cr4fZCoTH8kMtp4B6ajLiupngAmTAuYzgUei8t1ZssY1dY HodrgwU86nywSgiCmiEA/sLBEHdG/G2bbg4Su6uiUBouYjgcovuFIPyV42WGX7oOK7HVJIsnxlOt VqRer1GRVsoF2d7ekhcvfi/7B2+oTKik222JRuMSi8U6SsXjAd2Jz/Rfs8pXXT4cFwF3KKHUwjLx WDjnq5dfUWk16jUpFbPSajdlLpmWleUlefzwAc+FJEYmm6GVCRqe64/iTVYUDa/Bd9QiQmmB6dQX 8EmtDhppvb+FclkB9OwqrUodbmU0GpFkPH5LPfO+KCz37oS/m5ZvHHU/RnFgh4MycS8gmvR+P4Oj 2PGgmDANoQDwXfw9yg6o9LhVyeRy5rteKiss2qvWpYGC+PR0XzKnB1KrIXDelmBQLTfQFSMehTHC gjs42JHDw20pFnM9NDFQ3N2xorOxLhxVUlg8wFnNE9KA8cdiCaLaEWgHwJQso9kjvtZoVPmDccRj CQn6fWzTDoHrA0XPsRwd07qdheTD1BRX0/RLNM0L7LVb69r+bd9D+dethfWeKKx+7hgmRoOFuwiM ImsXEL/Xz9+x+Gx8AdMGfM2wnvADBCv+viwiWTOQLeK5oOTardZY4A2IFR0cvKbCQPbOBsutm+bO 9IFnvVavnh2bw8LTXd62YwfGCNYVLMoI6whtx1tYUCDw2999IdnsoYFHaMMJe11sOuGgPca1RyOR zn0O+H3aqGPGoB4TbdCB+8CkDFzAbgIDAqsK1mvIhBdIcHjNY76VKSgsp7Kyk8F2c8GfqoB8nZhT GBMFCsvxebwHRXaeqzfqeKyyg3V3ZX6HdltK5YLs772Sg/3XUql0LSZIP0gCS4wqZbqIPYcyBc5W cG8S8XnJ5k609AafMF2UobQhpNHwBeTw8K0h8IOixALEv13Fo/cciqzVo+xxPxLxOBdnPFaRk0xG yoCSvMNKy5k5BecW2o25BQkbZJajYXXp39278e7KhRXWoMwbfzcuHhSRnTwdUOeE6rlsXzq4hM4x dsZ0QcHiz+dO5OjwjRwdbdOyGiUOhywoEOtOZQagZzKRkghoYoyEQiHWC5bLtkVZ/zFCOe3tvnC4 k2c/h6D+7t4bSaeXZWlptXcz8QitLfyAxL9Wr0nrmqEeVxWN05WlXqvSkmfc0+ul4sd1JxMLvOf6 2M/eL4VDAL/VEk/b2+F6fx+sz/fawuoHE9ASCtMFeIqZF7g7YHiA68PasYBfgpyIHiqx89whdSfr DKjXG1Vm4Q4P30g2c0CXjDLC9UBZQbnAzYBLB9aFRHxO0gtLymtllCEEf7stIuc9g9IrFjNUaMP0 fLGUk1evnrIrzlrmiOdNxJO0pHLZE1laWpO5OSjMMGOE141Nu6woJq1E+h1UAVSrRWnA9TYbIWKB iURaEvFUh5YGBdQgB3Q+eli3mA+1Yl1CoaDETZ3hrcJ6BxVWPzewz6emnlanK1atcUEGgwFaFCHD fQQlBsoaUNc4x8y2WXVkEOsMqpdKOUWZVwoah6qVGAez3xj1isCrHgyGiaNCZhBBcYvBwvmLpSLd RhxXWxc5Y1G9wFB1AwfDFOzlwHV99uwr+e6737F3G5QU4nn7+2/lB9//iXzvez/mPYlHY5qIuObG s7rZgaSxrBX4fiRboDj0mhRO5uko+FoVZVx5OT05oNWL7Ctjf47bklpYk9TCigTYbENhDcwYsuW5 I0Ps0fuOLCpdwvcUgPvOK6xRAJjODMy0xE44ZsfCIQkFgavpvp+Ixxi3ANQBC1WVW5kYpnz+RMql PC0prevrV+LR5SK3fw+SWDQhkXBMs5t9ug5jrCcnR3Jyum8yhY7zEVt2Nokx6F7qmPT7tVqVLhLw RVABufxp53M7O6/k0aOPSbWcmp+jW3ia7Y3HTVKwMQCWgXseCoWZVQUUBP8eHe1KJBwljiwUjlJp a72lh11ucD/q9YqcnuyTMhqUO8wA+wNU5rY2E9ZVOBSTWGyu99y0sgB3cCp3tZidXsCtdfWOWljn o8XVGpimwsK5AInAz6D3Y5EoOwCj3VOhkJfDgwN5+eJrqVQKPXCDAWfgdSHraZHkgy4PrqjirqDY +ys3nq/PfXS70VZJDnQHjTLrOxarYD0i3z37vdy995gKy+8PEpaBmBwsQYv7mpQgroSayRcvviY+ bX39nuztIS4I5VPmJnHvzmOZn09rLMlsiFBU2p4MSQbwgXUtpIXUssTj83Jw+FZKJS13svHRMxAb tjrvTY7AYkNWOhGLSTymUBDWuqIu8T3gYbusaEiwv0EybcV/rsIadUCDFul1CzmVAJ3Aj9cjhfmU 5FJL0mymZG//9Vlrx/Vdq6SsVTNMIWOx0ZE0/E1uYRZ10InOTITB1zQMz072TvM8Do925X/7m38r /+a/+e+ZmfzPP/0PUm+05Ec//i9Z+jOJzYX4pnpV3r7dki+++Km8fPkNr3tr64s+n9WroVIyNwb/ 9OPxx6bxz//Zf81//+lnf9tVWMg006XsvRZASfzk09LmrRrX8ko8GpVYLNpxwW9dwvOFuMp2yzTF 9ffVDbif0yiBG6qw3jWzGW7Jowcfy93Nh3JwuMuYx/7+K7otEOB2OvO+2wptdEWsBtkAQf0bdvH+ 7ALOhaPU5h5pNXs7vWhW7PzBeBHAEZHlpTV583ZL/qf/+X/g8eGKRaMJCYdj8oMf/JmETUnPOAXs qV988U/y9e9/LvlC1z3tKxfQl4g5/ea3P5eNjfsEJ3eEuL6z9xSKrenzCsA0ZAthiABxxdgZyhln RvtdmevjEsxZ3F8WmxOG1FVMTqvUKi1n+MgpTvjTVeS9rAhF6nttdVNSqUV5/ea5/OIXf8+b71AZ 7ODi1A0s/2CNmt5wWA2nmRP+q51sQhKj69W/2QG+ZYuy3aLgUdfq9XQVT89xUPgLRk7U/Lred1q5 +AyoDDY3H8rOzkvivsjm2WhIsZCTYiF/KYWF660xnuSToCEUBPbs6HBXXr76hjWUKNAGwn9sYmhk Xr76Tvb330i1VjqTQHFj0SAIuCOGqSFFrwQDClDufNdgBwPil0q1Ii9ePOGcSCbm6Da/79TKHhNb tZhJKHq3onLKoNfdECenIeT+znmbhv99sa7cggWHoO+dzYfy9LuvqLhggcD8LVeKRKMDlgCYAACY HlNc6/w+YjHZ7BF3/FgUZHHrEo/pZ88Ii3UHP9BRXRMW747wOTy15eU1oufXVu/Kl1/9Qg6P9jST GQgwiI0Ykc+nCtZOHLzWrGvJERSSPxDumEI2e9looDbPy/cRl/v1r/8fsqjm86csG2pZ7qoxeZxk ZvCib2FNCkVgrrpzEp2DknPpzkR338cGXUOveNqAipQY3A8G/cTNFYvIDJfZpejps9/Lf/y//xfx +QPy5z/5a/nJn/wLlki9b+JxKBVaTWgN4rFhht7SplFlkHXVD58JKBKSZHDpMU8RG0YiCxYeN5/z Bv+uKqxu0D4kkWhc9g6AaNfaO1v+AqpiYqnSy4QouL8LBdRoIvvY6ABGoVAGLVRAJQaNAwvpomPv ew4bcSdQ0i+BQIgWkGWJyOWO5Zsnv5L9o7fy8MFnzG4CfAllDS23uXmHChp/AOuENqe0GolXaxgi Pb+0Ww1+55e/+Dv59smvqKygqACKTZK/63xtBcrn84L/mvDo3lObSbR/V2tlyWYOJRgIK3h0Lm1g EqZeEJnJVsMASlHT6pVqTfm0gqEQGV7BsPF3f/83cpo55LGruF/vWRDe64LVuJUMHW/HhnAZpWU/ zuST6YRTbzSppGD94lmTjbiqtbLoGMX1RCvPw43lXAtrdLk6tYnyeKsvPC2BpfT48afyD//4f3IB Oh8CF2gTFkVdKnNpRax7uiwNKHK2lgkWHm7qoNvGxZRckEYjbh6+BvuhxMDMACrkns7HjtvZPaa1 dNyvO6TTZccjhWJG8oUMrUcoKmTNwGJQq5eNBZmUUjTO42ig2ytrq8sSDNhayaq0ay1ptyL8TL0G yxPn9zJ4//VvfirffPNLxq3UevRIOBSRRAJF3KMUrZ/NjjqvaxQ4SbNRk9OTHSkWTqlYFxfXZWFh RXz+kLSaoDWqY6eQVrMix0d5KZVBIlmXNlzbek0ODnaZdTw63tX75vHK0uIa453vw4Zt5UzVygCx Susy9wZzjA2Sy2VTigZWjboCpOGqswFJl12F/zfhSowUyZOhFtaoA7JlOYMmVj8/1elydtKmU0bJ Q3Dj1lY2ZXlpU4rF/JlaQExuWFpQUn64hgJmhjrdQGCgunigs5k+p+D9hYUl0fBv95px/bAIYAG4 74mbnHCUDcIivfGbuqwZyWQOzxRl8xOI/zScr7uC0ch5tmrSrJOXWKSt5TCNRkt2d7flq69+RoXo hB5oXeRopVhOBe3MwjpfP28+sDSrptgusGjUqkVpt6qkp8b1WfcRcxDu+9HRPisaMGbs4IdH+8wW W8FzmJ9LE4T7LorHYSGNMxg+wok5N8jmgu7utZphdzFz55wO71qTO6CR6mXMPSdC2W0xuV/nonIq MbPYnZxY0xTspn/xX/wrWVu7S7Dl4eGOFIrZTtEybjLiWmIqdfoJLIxqtdqpo+SPi3AwEgr3DaSf FRuPGfDukNfts8vlwItVkVKpSJodJ2sp3ETUOKrrd85IWN4CK0WPj10ynzuVnZ0tqRgwpyYirjbh nfPNnTEdRYKBkKTmF0nRwxYVYOxoNLjRkCEDBe2oNDAlVN1zeox1iwWjQeB3qSzaMzR0ML0Ytcfy 3KGruddHS6sTdDfZdRIXOPWCNWbMdZDZZWwWlsl28W/7msN8dH++bcBNowSc7fEmqdC+99kfyubG Q9k/2Jb9vbeyf7hNxXV8sk8Ly215uQXWzMnpgeTyCLojJN1VWOS88vlkZWl9bOM97z4UCjnJ5I6l 2QCbQ4Nocri0UMLx+JzcvfNY6nWFczgln89R8UKxaalRF5SLyYVFf3C4I81mlddTrVWV/bRS1EXP bGpfaNmI13Xx7/B75EHzs6g8n8+aovgan4tmZxG/KvdslFg4sVhcFlsrcnJ6yMxmo1mTtzsv2SMS 71lcmMfEXG4KbsvTB+A5bpzURQ0bZXoN8j6C4kcz79rzQd1B0FOZWDAqHRrgtGswswt3H+zDZxTW ZR6I3am6GCbj6gxwC6zVcd7s7Ab8Jo9MhWWEQmX8fPTB5+wLuLf/lpAA/Ht8vE/kdi532te9sm7J WdEx44YDFzXJWkvrCrJcJRIVfzEn9boyn8ICWUgtmWAnJktbcvkskwodKESrLYeHu1z8UE7IkiEm ZQWTC7WLUOBIVgQXlvlaLBqXfEEtUtRR6l50sVKt7oI6/xr7HRIbyvHpocgpgKtKyW2tpt4TdV1t 8JAl43OEo+C4iMXBakTM7+HDT8WL+yQ4njLnoqICShGBYDI/mPvUaxEOt2imIZ4+MaZxKqvLZgv1 O2ppKVnn2eN2judBaZbWfYLXDd4AFN1AC+tiA7HxlK4Wv+qytMfhkSdIT9NPcDMBB8APlFexVGBZ yZs3W/J2e0uOT/a42GFZOONYA67EIq0utBlc2tIwscT5uRQxW6BrrhplCmsI2c5SuSQ7e2/4jNBx uhvPaJEBwgqKwvsr2K47gecyNzfPQDu+r8R4XkdM6iLX238BOI/Rfb83WA/3r9kcgQqo24FOj+Fp swpidWVdToIh2d17xYJ4G6e054TSQvE4ed4c5JPIciHDpZlerYN0xh77eRfjFk+fi5406vwqSmuQ uL2tfmV3YwGOatC91xW8ioIhyNJ0RRkeeJ684DqQTUs8/kweP/qUCgocVAAx4gflL8VCltknvNeP 3A+i7uH55xvl2WPRIA5l+Zx0ESLjqM/BWjhAtCPtD7cWTBSwEsF2gNIZZDjh8iHGgwWI7Bgc2TZj cD2SsYAJAAAgAElEQVQjGjgOfM5mAxWjdrFn3uWtOt9C6b5vlWV3DM7xnHvOToBf6WfsObGDp+YX eI+yuVMJ+LWMx1pRSLUr+4f21MR3YGUh5oJmFsoDB9cF5Iv6u0Jcunxw5+pSh5JxbtIkaXTG+ETn pTuA7j7GpMVuWPb3aUiPwrpIPZBtlqqMl03GA5zB97P74AVkwvGqK+G2QmG5f+8D/jSa/5Xs7+/I i5ffytbWN7K3/8ZkFMtKmGexWXRDhi9m3nJjUZ53+8EKgQaryeS8oxbSlkt0kfh4NpFITL748ueM r5XKSOt3eyRinLt7b/ncgUmKhZOSSALmMKriudrzsdfZb74Nq9vUMJTTeruE5dru92xDMpdMSavZ YKxO+c2CyrMfidAlQSNgBIxxb228BV3MnWKpmAF6JHssUviwyHx+A33B/dVWZQrI1KQNjgdL0SpQ 8Loxo8YO6TiXWjMYB2KLUI7Y0K+zcBvnxjX2q/+cKQI/3EAEM/FQ6V+60sAWYwTL4qLHbg2Jf82S YCfdWL/Lnz/9yb+UTOaEVteT776QV6+/k0zmmNYMH+aQNWX0TU98ZZjAIgL3Fxp8nCewoAa1uIfb c5o56PwdDsflceRjLtxxybDQI8uL+sBhumwV/b7YfY3t5C4R2tQ8SP942erqBv/d399mezZkHq0V AeQ1FJdbQbkFSqZZrZ5hwaW7bOiQmF3lpqLzHc+i0UBwH+fRpiw5kA02GozlWPF6PJKam6N732z6 6Ja6ySAhPfErG7BxuG/jNAbs5jINK8t/kSwC9n82mgAJHDQ9dhGv7XbTK/aGYNegeTzCDbLK6rrd wKsG7f/gh3/Ksg/EvH735c/liy9/NtAKoEVk3ZvO/4aLAUqMNC4shEElQfZols4Frcd6rb32gBZu DiuQBOrDY1KTtpStggO8gkrOXXjb90uOjzn2Rutmv37zhB/69JMfsdIBfGoAOSKO1QPBGBHQwU3e 9BuoFwpDr6XiUnZOabXbcpzJ8HesOzQUZiLA9FpEgJqYJWOtMf7WaFBJgtgSrq0CfINjay5r6ziv RWH1E5txIVtlG12YA1rTRr98OFjS+vmDlJYzA+jcCW6ywP1DcPujD78n9+89lj/68V/IP/3s7xXP NGB62+JqrwNPNVCGgHQvKrCON9cfSSwe57PE5NeypJwcIfZVLjncWgPR8PrYeXppaYULCPpwGMp/ UtKds133UMMSvUq0r3r39hIhugWew+s3zyS9uEqSQcz901PQM5dJZwNlkMscEVcXj4OEMMYeSNOU ZhNZ2661Nyjzbmvx0IHdZugRSvCZGN24ldYkFddwHJZ5cHhYaJ0ERdWPLG2YWKVlfV0rLfeFmZ34 XamQ53X7/IwhId6FXfrt9gv57rsvGPDuIuRNi3X2Y7TpaMM04HCJXLdqZAG2ZVANoxmpxkuItHee o0V3FnTRbkHBdLzVrRe0DBZOse7a5boiWVdP23B5vH4WZZtije7Iz1PsV5RmsyZv3jxjXAsJjLdv n8r29vPO/EVZkM5Zvzx+/H1SNWOs05R2H9cPlhYwS3DtCZ5F890Kys6cQXuPVP3+TvdrBXOrVU0X lP03NX43qkzDLRx6dxF8ZIwKwUKTSbqMWNS3DQ7aG+t2/W66ZdVPCDj0B1juAXYIFAd/9/QLwiSU 1xwZJ6OsO8kKlOtYgrmzcSC1Vkd7FnYSDh/k2Y3ZFrv2n4AKBTjnkH2PO4ogAzqfWpW1tYecf7h/ aA4CjNTx0bZUKvkOen0Uh2zQQsJiRAH23NwCj3N4uCetVjdeBDk9PZCnT7+k23xw8JaMrb0qQgW0 Og/bHplfWKUFdp3iYfUGir1bfE4VdizvFi9jg1LXs0bFCzeXhIfIPgOq0VQIxxySMWQqMfevY531 ZjN7SsquS2HZmh/tAnN1hG93MXbN83dRQQ0SXCtgBMtLIYIx72w+IsSAGK/d15Iv5CQeT5BXCxkq dM3pToqzx4LYeCL+RGC9327I1qJD5tCgDOZgXaDAUzCWnnPBlypxgbWSnFuUzTsfsXyImTT0WUyk 6AKhsBmsGiw+L5yyySzYR0EXY61WyzBrr2Nl9QGtTEA7KuUCLUduCl4fXffVFTTuaMrx8YHJQHat WIWxaK0hO24PuCaM4dXrb8Tj88rc/DKv4yLSL1g+6HN+ZA8dRcLO94AJA6sBFA4gGJgT8JA6lSJ2 3iAhU68zgYB1TogCvQKftkHzgNWiRqWGz5PmxZwX1j/R58gOtlqEf0xL/IMUB28GAuuIV41JsbxP CmooKDW1SKAlJgJQ8+mFZSouEMdBYYF7HiR44K/CQstkj84UGAP3tb39UnZ2XytWLDFHSwFu6Mry hkSjUcOLrrTMvRaZrUxQWpt+zwU7a79kCs4FxRsOgServzgLmS8mHjaTgIKBgoJVbkcAeAES0aFw WOJNhdLUqsuykF5jR51Xr5/I9vYz3q+AQfVDuWBTePDwBzKfAiq/IYXcqZRLuQ6uiS4REkeCcpEu z7vWkdoOPxXj+g3ARJjvgGIHmDxY0RinMhGcTfdbi8VaJwBHKji1TqcXiiFglAGOYT8bi0b4XPDM coWC1B31gIA4RNmMReEXTBA4sovmxB2cZD/FiHN3lJfXS9yZkhy2xFftIvvt+7ZUiR2raLVNPlvY 2Qac5pwdVLcW7laGSU8hqS18Pu/G+xUKgh6FsLigWGzMABMXtWzr2RO+/vr1Uy6gYinP+BcWEHic dve35enz36trEwyzlAZI66X0CnFaSMuDjdO9vny+ID8L6wI7Zb8iaJs5dAusEuDAhsWmdBKHqDxZ xoRyDMahwBumhce4diihcCTG+BSQ+LAS04sbkkqtDImdqBWg4NmgRGMJicXmGTvC/XnxAvfD16EK mptbkkAQVDFeiYQTEg5Gpd5YMkylYHVAuze4SRHZvPOh1E2DVii6KrspYaxB8QUC0kKQmy3hNAbJ OKU/yM+x/CkYlrnkvMwldeNBWQksGDxPfbYeBskBjYAygWKCsoL1UiyVpFZr0yWDYkLpEz7TzOeZ 4AqhKW88rpYj3DgwF7BBrlpP6GaNLlH4voUduZXHIMvMKU4dUHN87sx3mpqEg2K1MS/MIxvjnpTS 6lhYXcZJuIINZlowOW9lsHSBf1d7OJjc7r/xQxBjS3mr8FywILPZU/K0s77xRFuGwURHjR9+IEdH eyy9WUyv0MUB2r3b9E+tvEQsyW7R7sA1Cp+hZPBvD3e6ESih4e4gAJhRSaWWaS1hMsPSgdWD4mK4 ZLn8iSwsrEkkkmBSAokBdPQJ+IOSTKYH4sb6ns3jpdK6d/8jXl82eyKnp0Crn/D9Dz/6Q4nFkh22 ikAoLMFQRJMKjSoVOmoGcc54It2xPuE2okSHY4fC8gdIWYPSpVIRJUtqTSABgVZxrXaTccq11TsS jwF8qxlXu/mjUw82B1hHUCxQNLCKADUgcZ2n0rOv6PdaEotG9XNBxWaZi5Z4NCKtVkgKpTIt9ZAL pgDFgc5AAJ0yaeaFwg7RxYP7PA6hJUqqmDqvaxqMPD2ONnxXi1hFb713JWM3CelJIEyQxwvPYHFR 29BDoACwSIGuRxlJP4Elc3JyKKenR+L3h5jtspUJHDPjkw3thIJYRRUKSktOsrksy40WFlZpicG6 UKWmBGuwItA2DIL38B1nuzQoBlg1q2sP2ZKL5PhMIvhpoSBwfnK8KwvpdYlEnIyjV6qN4Lg2Nh7J D3/4F/L8+Rfy+vV3dA9xHco6gU2h9znBGg0EEI8523UHRdz46X1RyE7bTkPR25KUlszNLYrX06Li DIajxEAp7U6zyz5gDh+LaBdu0m537hmqF/Cc1GopV6pS8QAYqvEhWFcdZWXE/g2Xsw5kvSPQb6lY 5pNJOc1meUy4vnhtXMrKCsuWKhUN2DfVXZ2kU9ajsCyNBh7grbIaLNaiuo57hImOBhpra/cYDLbt rtxiaW1g1biLs2HRoAC6Ui7TVUGD10z2mG7nyekJu93cufMxGUrhOiFeRdYGQ7GC62acKL1BbvjM 6WGnByBcPQTNE8n0mfsDBYGuPWBY1XiH8/2rz3J02/7ssz+hAqlVq3J4tE3ljEzp6ekhoQn4TGc8 7AJzMS9CN6fud/A7Cs3JOd8WKZZQaK7WEwqjsZhxZYg5YTEz+2vrC+lS+vkv4k/waiq1qmm6gThd W2sX2y1WNvQTWwKEH6fY4+NYsK6gsKA8MXXxDG1sahwChVgoFU3cE7GubrfzcUvPVWJHYjX6rWU1 VNyYsmkKJgEm6eICykaWmDGz+DVS/3a4ybx0U/oFiDVgXe4Ek+E+ocYQrsVp5kh++IM/kwcPPpZX r77pMD/Ayouw0YY23wBY8v6Dz+Xw4LUU8qdcpDhr0Fgmw+bQJC1SjG9pcV3+5Cf/Sn73u59qyUuz Ifv7byWVWmLMUMc2vvMjnEdcVqtNPi1k19wCq8kK4lgsloYCB0+5X+OFWOywqKDIGWODRVpRS2uQ hAYoMgprFBVPBYXXrrUlGgnzeEC8j7MGkWM0t9TZwWjcSqtXYfHYt0H2WRYsvpOTY1LbhENRmZ9b 4syEy3N0vHeh9lp2kdhJlc+DIroqP/7RPyfP+eHhNoPSqFsD/fDKyn2Zn18mV3woFGPcB63kbVMB HA/WE5TWZaVbz6wWggUYj1oCg0RRLJ6UB/GkrCzfUVclGJKHDz+jNYj7pgF/DdwDonHRetezJ/WI h9UcSu/spObvJyybcRROW24tmx20vyO+ZbOFlxyWoCQHsTecA3E0uv35At3zcQqeE+JptL7HVPLT T3qOrJueyRaaZp63clZsevdaXEI2WliRR48+k42NB+ShR0ofsSEwRYyqsOBmAMtUq2EK6HVgEa+v PZD51BItLlhyillSVw+WE+I0iONoK/pDyefR3AIWJzKeMYnH04wnXVYsRRFq4mBtoOWTZtSwcPun 492v2RIVKGNcJ36A+q7XK7Kz/VzyuWOOMZlclMWlDYnFQat8eak3bVdkjzQ9LfG0TPXCCNaFsp2o QnJaZnbh48fCHsRJkjmiIDjP+kGTuSuWK0S9j7srEI9dKnGDQLDfWtHjzhj2KCzsOHALWu1aZ/e5 dQ/Pin0ICqw9n+do3DI/v8Afxf5kCYFA1hCWl5OAb5ggxgMMVxvluCih8fhlbi4pP/j8n7G7D1pf IagOCQTCkkqvEfMEAQxhd+eFHB9vGyR6UFILG3L33kekX76MOIGT+B2EeZlsrkNRjPnYb+ITywYe Mga6kSzya1so1PvVcrK7+1bmUqsyP7/EJMDa2gPCGQ4PX0suf8wM6ocf/ViuIiQQJDYMmXXEiLS8 qgHFdZ65NUCgxKDA8IMuM5hrEcu75WgGe54gK4hMISmE/H4ej+QFExA8HyRwsLnAsnOW5o1LabkU FoLtGptRBkfNdtzKWbFZoElTNw8TRYDPEVO1vnaXG8zf/t3fSDZ7PMS1d9rNjhILtMz0+Dsc7oqn 0VgKLCdYI1om1CJGCQu+VMwwwH3/4fcJHcD8ucq1wP2xWVek/rFjI/aDRToo3mJjQRhXo16Rei3b sbC8Xiygkjx/9muJxVOysnqfLvSde59IwlxPaqGbgb2qMJDdFPFxAxDxY+l4PVdSXPa42XzeBO41 AxiLgaM/PJCGyVo8mZxayshOIoCvFQGTA3aiDIjWW6c+VgP+46KL8rtpIToshvTH1Vy9VVr9hWUM nb6E16e0MA6U9Xz/8z+SZrsl/+5//7cGauB8rtiInJGg3vEiyxdPzMvG5mPWPMJq+vDDP6BCWl29 S3YG1p9VynJ8tEPwZDq9KWsbj2lVXUZZuWl2GdfxCLFH83NzHKkzWD3sHjB21AQeqHP0znuIw2VO 91jKs7xyX1ZXH8jq2n2+f15N5GUEritmBduddRSXV+rNy9faWWB3y6DR6+hQXq7Q/bIFzM7PAliK WBXwWXOJBJVcrlBUxpUJiqWzQWzO9qrEowAYdhzuIZ9Wv4Mg26Ktkuo9PfNupSuWKncW7g2sIexo z5591b/5AsUWDFtuKx03lM3jxz+U73//z4mQ9weCsrn5SDY2HjJuZesUoQQLhVM5PHhFq2t55R6p Vy5rWbkXWZPNMgISjWgJCjBiowjmKPoRSgvF5N2qA+fxcU9gbTXQKahdl1w2J3t720qA50NpU4o1 gLaB6lWFrixibkweKLWz34fXvexkdJVl22bpUINuMJIwQMWD3wrWMUaOOJV1+6isAgHJFwp0B6fB WYVzMG5mMtrOuWjjv5cV/6CHowtRU+NahnCLenc+kA5J6AwoKytAZN+7+wEJAy0uSsWS2rkrmpW8 D6Uwf/zHf2nAovqcFc3uAlqyjjAm0dg8F8v8/OKV3MB+gvgMqG4wDqbiz6lJRKIB0IpSISORSITo /X7Pysm+gDZlKDCfSy7Qpc4cHbJ7tH97ixZmenFtbNelWc4u/z0KwrXJMZqIXk15tNjKDAXwTSoj Cw6FdYN/k4k4/4XyQhzMZianIU6l5C7svoqldS4fls1O3Ip5EJbe0qTdZ+nOwL25d/cxS2JADWQF w0VtHTBCbgEg9Hvf+1O2eHcqhn70NWwfFo7J0vI9abcaLFcZt1hwZd0ssmHKChnM05Nd2d97yUB6 rZaUVApZSrhIlma5F3kNWEMunyMxIRbzxvomM52oEWw2stJqa2ccFEuPS2kZAlcCWD2mYUcH30+s 1BWO3VbLtIW6QwfCnjWD6GLk8TKuhGvx+9Uym1bDCDs+ZwMNZ9G3/fsi4h/MMorAmcmCzZAVcR3S UdzO4uYZuyda7V+Tk5MDWV3eoIVln2wmg16KZ3nIgVZfXXsgH37wg5GvR2sJ5yRnavXGLYAwnGQy XGiFohYfD5JyqSD53EmHZBAKGW6rz9fFgSEWawkRAbJFezYotNWVDbJb0JV2hPUK+RPZ3dmSaCxJ TNk4hVMIv4Ch1SjnNrjQjNK6iiJp2xigsaIQ54K7iGyrBt1Re6jdsK+FFbXdJrQEsAdbuoSxuEuO Ls+H1VQ/2/JFv69idwjegxloOzZMsKOioWkymeypPURXY3f7MTxbNFt9/Oj7DLafJ05zXnfr8dEO OQWWVTbXv9zILbZjU1dUOTEAz/iiZT7Vn0z2lBYWGGAfPfqYDBe4PwBWOiWbPSBhILBatsONDZGM QzgmdgdXamc24zCtx6wLOa7zVGs14rjQRVlLc6ZnXVnFqZxb2uHZEwoxe0mF2miQgQKu7JUUFrt5 GHPyfc8QOnNqs6y4rSJxd7ypoOVYtXJGYcHtAd7qzt0PRjq+dnWps5AYrieI9MYX69C6wove31A4 IqGwghRBmAfkP2JyUEJKf2N7AupxgbnSVLtPqpUyP9cwXaKd6xg4s8PDN1Iq54wFpowN8USKVtdY gvJ05TS2BYS80hZDcWnG07qR45AKmTe6ANVpC6/VxNk006xNNuw1EuFvaHEu5RJ2GBuuqV5ulmTU kpDrFuxWmcyR5LKnPa/DeujXNScaTcq9ex9TaY0i5PavV0kHM85Au7WScMyLzjfQ2KCmMRxOMBOH +BrGSX6oKChklMq5A9Ux/z843GWFAPBrKBpWjFRvxnJvb8vRRQjFxxFZXNokuWAsmrh6OU/nXHBl wfbZhUF03Ngx1eLV3ER+1yC0tMooDK/TLcTGCsPIkhxCYY0S1+qrsKw1McsWxTTFuoOzLJVKUZ4+ +70cHe/0vA4LAeR5TlYCXM/CwrI8fjx67Ioso+HxUeFCCQDTBbyXOzA+quB7QLCT3LBwykVxfHwk /oBPsjkALINEwXe5zNXF66LmLesGmFnV8gJKu9uWq9HhpcJxSqUCiRPv30fvxgiVFpTkONYJdGbL qbhgddEKs+33rnyKaxUbY2MxdiBAYDDJDeGOm07l1ptz8vO55cxWyQd0mxfs3mi92zOtvG1JBChU bFYXrASwiLAIY9EksVS2kBhYo9XVe0SnDxMyexrczLhT4mjCu7/3mm20gIG6rCA4vhoISbGYlkI+ I/v7r+SLX/xMypU8OeGXlzfFY7rtJOJz4gfnuaF3dlIVQ0GA+iaRSPBvWKzgGwP8AckMKsViQV6/ 2ZJc9kjm5rVm8s7dj5SB1cR6bT/GS98XxLKabWmRa18kQAC3SMMFz7iMTKOrzajjgMC6AkgYJUMQ Jwh7EF7rjMLqkNXP8AKdlth07LjM/0kJFlehkGMWDBkwWFSfffZHsvXiWyaNgIK3jxMWBsjzQA0D xTVI8PyT8RizS4VSiajpcU521PwBdIpM5UXFBtFh+VhGT1Ayw1pDTArbDChwDqtlOTk95sJHZhMK C1YTeMB0DKh5i1KZw3VMzadkff1Op7/e4eEu2V1RCG4Fygl1lq1WjeDbXPZQNjY/JIsF7ifL266Y qLJZaZtRRLbeZ0p8LitOJXCdSgvnR9AdGUxsiGCkuMi9OqOwropEfadkAm29JyFAas+n0qSFgfWA hbu8vCbVSoWKDAhvK3BhUqnVkYqUEW+AgAJ3EpN8IX3xOj6WnRCxjp+qHBzsycHBDgPlsIQQD0vN pyWTPdTPtxpSN4wNmazl/ap1OxmForK6clfS6aUzJWpwAclJ7zy/+c9aU9VqUbae/0bSC2uysXFX QmGwjibI9HpV0TgW2Fp94vegmPry61JdXCXovG5LC647oA4oG8JmiJpIct73afvnHmdfC0t/ZnuR Tlo6N2rGlZUto0om5olOt1BWLDYwksItdFpXKPZdXrkz0vWDhgQlFrPgRkAQR0JXGk+7JoEAMtht Safhmvlk/2AbLUCl3fZJuHW2bg3MrAiiO5vK4n3Q8uwdvJF4PH7G5YKSO7Ng0Bw3jOP3fjabQ8G5 yNrapm5wCPjTerza/CFMo9mWBkPwXrmqkHKZPQs1LmbLfLR9GBhafJIvljSLN+Hn3qg3JF9XSmhA LxDXst13BiosBL2YSekULqKAVAtq32u5AYrqDKzBUT713dOvJF9Ab8Nu7AnUxehObGlj+gnKYcLB oJTRvMC0mJoFgatXr0JZVbR1WdtCFrykPUZrLQz14YOPpNGsy907H7Cxxdu3L+X122dE+kPcl6Px v7Jsb7+SZCItG+uqzNFqDS62Ew4CNgxkJvtBfZQf7EBCYaDsCQtlJ59xZFS1iYdfWg3gy9pjYctl Vs7wazmhOyj1sa3FpmFp2VZ07BAE1tLAKLWEBsHdDUDeots9fYCjs+4aWsGOubv7skO+Z11BpOVR /zfoOrC7xaMxCYfB/52behnHMKlV86i+48JVwkAwc2KyA3IQogsMRomHDz5k3AqpciDgETTf2X0p 1SHXAaVUKGWlwPZd6mH89nf/L7sPOfnwYcGuLK+fwbp1s2ANuqdwpVfXQnQNxyEYOhDrXq/fOKSX F1jMMFKwMcUjyPyGzjAtTDMkhLkYj8XULXTx0vcTP2lkTNdXq7xmY4pev3RwISYATybwGVdamGzo egNENxYrdi9aIIt3GGc517oKBaVUUlDldQkUCMZt233hmkrFrIlAq0Jm3z7geAwYEch79CcslopS KpXoLiIuAigGaiv7NZuAJYTvaSMHtFVbIN4Krucpy5nQoUi/h6cO0OjK8iYXOXt2ipfnerb1lbpQ CGjDggk8lbWDXdLzrK7e79v38cKChhkekeYVgYG23yDcXbi3gWDAwVWFpq6YA2gHNp1QgN0gbHMT t5zprWhftKRnM1XNOwPipkCZdYXV6S1YqzGugwf6vbWHsrRyl1ZWP9Hgc5DNODFpMFmvo4xDx15h +3k0yQBvld9kkQD2ZM2gYXoNmKA2XDEwiqAqA8BZ1FImk3MdV255aV1i0fgZpH/nuiNRKiH0EgRV 8txcSn76j3/LYmjb9BWCWwGoCNggtKu2Li4E+g+OtuVw6xvGZLTxR4sxRCg/AHNh+V1dPBp/G9Mj 0V6WysGObDDPABgF24Ml5OikMXHurG4NbN3EC7s4rEFzr2ODddHAvcW99suz4hpch1gVdRNypwoK XSSX1ZNnX3LRr6+jR+BcX3gGdleY5AjEInYFSl2NEY33eYNE7/h4l9zvC+mVvnV5iCWhqUU2e0RM FKx/e01Qwmg/hkWLOA4spmAgbLjpldUBgXXEnR4+/LBDzJdIJGVhAS3H+m80sJoe3P9Y5ua0nhLX /er1UzatdbdHU3qdCPspWoF7yJ6LLTQzDWtHolqZHbpfv35CFoxxKCzdLGVsYt0/KC2EArBhQXAf EQBH5tnTnE42EWSD4O5XdlLtwejWP1b81l/tuIS8mt4DjmJVvMsKzWJiboJ1RfxUYl4+/fTHsrP3 Wu7c+ZAKop9LhGeOBYcd1hb3TsqygiUEHnicZ25+Ufyk4eyVYjHLrs0AZKbT6z0BazS+KBROdCdm k4emNL2Nnpo7xJCgaPb3d6SKILtBV0N5kVo4GCIXPiyuQWIZSvvdA8V9de+PnQ+wpmyxNbBlpVKA 2UcwWhwevpUPP/zhlZNYiLV7jRU0rsdDiEi9TlYMKHQ3c8K0ytKwQQLeoL9HpBU27mrgLJ4N8I7O 4LtkZ12Lyv1wLrJoLzLxO1YcEb4zCNR0lA7cBFleWpU//9O/FvEExDMgU8UMG3jBzWJCYHdSgXYt jQnS3esX1IVCy+dOpQgivqVNCUd63VftwmysffMaWs1bsWNGNnB755W8ebOlysV0/MHzQ8efzz75 oUthnfUeoOARd8KYABlhvIo7PQgAj2nFaVcen9RrNamU0RsSWUG0oZ+XhdSqeP0BKRQydHHz+dMO mv+yistetxegVoI/ZSyCZ4EQACANtvaSjVdhZYlHqnDXhpDxjUts/TLKdaBEEUdL+mJn1pwfmsw5 +E4syyVuBea+APfFDFJsfXcuQSaupV1osNOjjmrGrBmi3m8Qvz3GeWfzoRydntKdavdVIEjTd4Pw 2OXQqGCcLaCUgwkWi8j6xuO+liogC2icUSxmpNmssS4S4E5nu7Buk1htLqHX2H+O4Lug1AE/Fst2 TGwAACAASURBVF3k1Aotr1K5KPlCrtOFh8LH2nu9dzYeyPHxp5LP5zpgVNvtem9/R1kVTDb98PBA TjOHDKXMk/e+TRf87r2PCV49Odmne4hiczIzXMHS8kwoxmyBnIA0oC0YmFvRw7AarIuXBcs1Wqra SUv5rCbFqwU3VWNnbbqHbs79nr9sVf9FeDSdCmiYMhv0Hf7d0kCq+RLH4cQUXbcotEFjJwFYLDOm TAcJHjzjUa7XMX6kkNEYABPQfhYKy+50YxtDrcL+hWwVtrBMvnj3ooWyOth/JZ52k12b/cHomXGU S/kOFY2dPoxlDVjEcCebLWAMA3L/3oesC6xUK1TeIDQEUBQxp3oDQfNdKZcRs1JXE12v5+fTsr+/ TUgEYmMQ3bPqHUsPWdg3b5+THJFMn42GHOX2ZG39MV3tBw8+kfX1B6YvwtXnTZstpscXeHe7horJ 1BZdxGayNVhAimXdyPA7OxhVzpJBXlXsBgCxtauW9tkZx2IMy7IRcCfxaNvsq0q/QL0by0RF0IJl Zehs0I13xiwYi8O6CTG6Dh2tcR8AAu43btxjNHpAnaCtjM+hG/AE8DdQCIViVuaSumC7JUFqLSEu lMnsS6WcY4NYlOv4g3EJBCIMwlO5BsPS6Cmkw+6LwDeKlGE9Yg5rt2TMoVagJUtL61KuFFgrCBro u3cesz0ZAuKvXz+VdHqF2K1S6Uh++o9/x5gTeOrBmfXZp38oPm9AMtkjWntOsbMXma3dvW3jlmIe N+XweE/BlzXwp6sFAqU4rGbzItIGUynXqqUuHa+Qk8sRjsE8wWvIHIaDIZ4SSmQSdDXYOLGBQlBh YXnqrdLqfM5OWGUlUC6eSVkQVolZxQVFVTf8RZYZYDaJ8Xx8mjCDrWk8a8yjnQCqsZJgfbC3ZB8l hN1ey0tMe61GU/Ln0BFfVoCBevjw8545hbo/nQc+xpygFLhxNYE6r4vHC5ejIM+f/56cVWgzD/fO GU/9/ud/zCX29NmXbB6LrOLK8oZpjOGTTz4pM3OHz25tPaGlFQ6npVDU2srjkwPyuheKeXn6DDgq YNZUySBLeP8uCpoXzliDlslEN1tgxUKsV9RXNayRyR4TB4cGF+NiKYW0Wi3xoyQJNYYT2EC5yZmY lb1uWD1gVMAPLDBsbIh5jUtwfKyluUScmyjKdLwe1HpCYWE+lzvIfMbY7IJkDAnth6YEFrNiFw0n rKfJHXLmBIur7YJ4zJjCgiJF00wELYcJGjwAHGhRxdw8zCY1qWC7W7Knh4LTlytg9twmJbEteWkT NFrndbx88bX85V/+d2yoAVwWrDE2ciAVTIzIc8SP0u0V+fTTH8m9u4+ojGFZ/va3P6f1hWUI9xBx JGezV1QBZFln6ZVPPv4Ddt958vS3nTGeZ1XD5fvg8Sfsquxc4HtwI7efSzyWJGh1YWFpbGRNbaNQ LBvEuJ8X3dxymbAGQF3cMUKcGxbPOAVWfjQS5jktzQ8sKigxKEZkMBFDW1tZ7o1hwRREiYNy/yBb MP6OKE4hYRfMeJ9Iy1hcsxrQZlLAVOgHZpjj3o0b6id0ux1WA6yxU7SEn/BGBfT43u5LWly5zD55 59++ecoONrBMohG4gQECOZHZPD19TVcSwNGTkz3Gh3jXSW7nk+OTQ8ajkokFWVxcY8mMRZQr3XB3 Lt3ZvE/r4DRz3EMVA4G7uLZ2n5k8p8JyFkkPEmKW6K50k0ybG3e5yF6+/L1s72zJ55//qXz66R+P rWGr3x/oZPInITgqrHTURAY93bmOoDyKor1o2hsMsPyIhHxXDCMwXu3TMJQCmEN8diwTi0VpZTkh Fj13EbsNUs90fUxR9KSFvPEAqZkBzxq1jcaEhIpqVpHutuIeWZ5+wkp8n68TC2CRqTPYOuEyHLKL lgvy5vXXxgVAp+qYJJIpDYRXyixxyefzEounJRRBvMurYFePR/Z2X5gtQy1xuGLffvsbBoWXl+AG pkmuh+A2BPeiXC7T9cNnid2SNhlOoeRsATGeJNxhvl6rMZOHZ41FGQiCYlmZSN33up9u196dSFch PhgV3+KaUro0UEReEb9/PB142maT0TF4Jpepq9XN5qyc8LBaLdMDCQ7rDckVCgzGX0VIm1xvSCQU YmYS2CviwYy95IxfMUvpPoBSyPp5U6altKxYhTArAW6OwxaGO3bRWRKMEe4TCPbcyl5R7NqYAS4g 3aW6xrXY7rxep3U1+QabahUBsgDrfTG9ZgqWV4lpUlejQgAmqU5qSKVX5NGjz6XZqMra6hoXPRgV Do93TEpdn83u3ls5Pj5QkkLjwtgMnlo+3fsE74G9lx2uDoLm+wdvaeXdv/fYjBaLI0D32Z0k0ms5 Ow+QN7Kv+0lJrRZes1GRXPZ4bC3DWshU+31SB3PDhNYJN7JGXSJtKAu1yC1ez15X29fboAaWZsBQ T9umFyNdj6unYtwb7WSu3VApxrD6HcSS+GkR6nTYG5zB+FlRWJBZZ2mgNVGp9s3cQFlhV4TFYCsZ YNEi2YHYC+IV02pQoMpUDXuAQDGnsBmiiBmp9Fq1weA7lFYUrATtNpvCthoVxqvg1u3uvmamD0pX M6FQtOBuB7I81VEYttUXedEdjw5DcNPzc3HWqmxuikRE97Ndkr6u6Pj7TocO55j9V3/BGnKyZlz5 Prbb5MeyaI5JLRUnHMYqEOc6wDhgiVm2BeC3qMDabTnJqtK6yDrG/NCMYLBHYblloPlEon5BaUNd vG3rY2pEf5JZxJlSWo5xWG7zWRJ16Rp9SfZgSiPr4uwaA8EiBu8E0u7nBejHN84W4Q3qiraZQYNF heA6XEG4b+UKWnBpPWAwHOuAMHP5E6lUCnJycsjvJ+Mpgj8BHVDrEpm+HJuiRiMJCYaQtWvTvbSZ RVxz99EpOeW4p7Ceo1eRaBmNu3fi1aRNuLvBStrU/gTEB+S++d293tUt1b6CkbCWdiGLZwUbkKd2 8TWsm0n/B2NfZ5aw34GZPTQZO5pjhD4oxao7cDtOGcY2OG3p3LoZxWHBOkIMwY06thX4ziLSs+jz 5lgR7YMEYwPzARo32AYNoG3OFXIElCJzh7gS4AWlaEICftC71Djp87lDqVbyki/k5eWrJxKLzjHr FonG6ErCUgwGgpJeWJGlxVVJJudZnIzrq9dfSbGEFD3mbpeiziorfZy98SiWKkW0CUWpXFImiBHv Uc9txqZrDgw3tFzW+Nr4ElmeiSoryLCaUugCbHgW8uBUVhDEo+oOIsBR146NwLjF6RYOVFgQ6woy ndyhTlZA4qSyek4ry/59ncLrn4Fx9JPagDSz7fs2aLeyoLxJCkCfqA1EPR1KbsolNCXV95qtupye HMjx6b5pWY5sU5MLAZgqdfU0DtVuN6jQUGoTCcclHA6zdKTRmKcCgCW2mF6WWDxB/itYlNquHl6B fX69sazegLXOY9wzcIWtrm7y7929HUIdRsm8Mpxmj2bdSPWOqKjoEo5x+rQd/5+owhrwnnbTFgI9 +3Vt1tc81BN1EinW6D6et4b0/cGf6SisYaIPoNeaalY1nTmp+M4sWVluwOCsiE2KuC0AuOzAtgwC tjIrBr6sCceuctkTefP6Wyor1PY540EYdxEMB01shOASD8h8OCbRSMzUN4apvE6rR6zHgyWlisO4 5wAZI70em5OV5TVJpdKdgDvJ/krA7jjiRlrAqL+a6aR7LbKnfonF5yUWjfG7aBRr42u2e3TnMIYD 3S1kpD1TgqvxLq83IKFghF2CxiVty64yQbJNi4vqJ7jXIHv0DwB7q6UaRj0C4SgIWaDbs7XaGIdz daKm5+bvT+Jn3+8E3S+qGBDkxC5mraxJKa2ZiWc52n1d91hoLXD3UhiDHQ+eAyYP3MBYTNu3n/ku Mji1KmNXk74OQApgXaH2TuEqWLywCmuSOT3pVSjipQJGQB3A8ER8Q1uABYNSLGXleP+NufZetwHf ISyiVqVFycBttcIC5VK5YJSSMoCo+rAbYVe5QFmiy87c3BxrDEGLjE9jLCNZV2ZMXl9vBa51O6F8 QQw4zjXSQomO11iNE3iM6upFBlae4HXvCLWReBeKPw5ITTBkyBbRvadFDiy4lYBHkJAxgOYemoAZ pgv6dn4+T7C7obCUnXHbqmUnYW1pwPT6e6k5d9HrFpttw25lC4TxPBA3CJufQVkWxBWw000jMwiU N8pl8ugmwxuHXVUkk8nI7t6rTvwMKPZGEwXJR/zc4sKKLC4sy4cffi73738oC6kl+Xf/x/+oBzUW jp0LYF9oHu5qa69UmqU2aEHPhrLYwc0T00RD//gI5nAul5FqDfxVGcabtPKiX5awv6hy6oW9WNcT bmYw1J/p9bLSNsfuNJEY49rANQDCMCj+6fzcRY4JhYQfK9bCAnEfKGygrCJBINz7K0l8lpav07W7 yIWjcSXwM8j0tFqKs0EwfpxxrYsE7KYCzkRshAWh16O6bAkTU8pmZyLaOhiQ1Fzy3ERIBdxNY6wD GybsopxMSaGg7a+wvBjsr9eMpaPPNZM9ZaYvmzuRZqPJVvAws8CYCjpiNj6tVjiRYfHYNu4qAMzW +F1kCvP5bKdLENwWQBUA/LTZu35TCXGWo+M9fvayYpthOGEUneotnHeMWUIr9tgekDeMcYlg/aLd 1qShPAQQe70yl0woS4wpfRsktMyAC3VaMvb3UYXk/RJQ0w7xFLI7BsbqJs6ClaX4JQ8LtTE7SOp2 DdgsZ30b/H2SrrVQGxg8d6PQzJmWU0xDUO8GKhl7n2yGUGmau/fu7c4WqV8Ac7DX9vzF1/Kf//Hf d+JAeBndrEE9A/rn3utqsE6wn/B7DkXUQbiP8uwGBC0tvKd7CPsLFmC3VT0UJ8YGhQpo0Lil2Rq/ S8jSmGAvRfE0hFbVkNOxYgAhg0qli8NymrQXVQwIXOJHA8GIWaAucXB3lptgZbnPqy6hBj9sK/Np C6w7NApFEFoLmDU2MgpzBNzASZGu9RPQDDdRqAsQqOnSglmJdvFHx7s96TXF94ERo3tP67WqYfq0 HEm2bk0VxiDBewrDqSshpM97BqnegTOYP9Vy7R6DcTIoBJfSwnNHkTaArN0uL17x+dXq1jb1+vrh wZ5svXwi+UJJ1tY/GOu9hZBOxwcQcItA0nEI5hJcs1kUTHGUBfUorMtaWla6NCxA96I773iU1mXd 1suKzWRo5sfTDTTy3lw/NsxpTVFhmcYhw4Q1g1Oyrqz1vbKyLkuLWuf34uVTluag48xHH3xPU+No 7ppIy8npUad9vBWU7qwsbUpyzvJnCZujriyvDp5Xhl4bc++X/98/yt7Btmys35MQmUt7Y1Kc705w gyv+RCXkuqUY09LSCumnEehHzaCbuMNaWHNJNP2Ay5uj0h63tEAxA6iAvfArCDY8xD7nEoCGzCBB pYGnINnXt1X9VQTuUturXEHjFGsBWutmUoqLXVgaDU58W0XudgeuyzXldYN0sqkKfFReLq2ja3CC T09a4kMcyQt2AcwHWN5Kb+P1dndxZPewSCztsRW4JdhRE/FuM1KAFNGxxt3nz3J6QRDYZZCd3XSq tHwQC8tmTzuEgOpK2TIb/XuUNQqX+uhoXw6P9sgZr9AHnzy4/6Gsr90z4+oeF9TMwWB8IvW4bawH IMqZuLj8etAYaJAxUHdVxKwIrVfDrd9XYV3VmnG6luO8AU58lvPvcYrNvmDCs1LfkUi4bpgFr9vQ AjNoaVuymR120LhssfM0Rw0LCkqy3a7TKAWtDBDvltWgKwPmh6lx6bV8FMPVi43qFqh3M9X6Psp3 QHFMJlJHQqKDpxpwasT08XYykSLnlt24cG4g8o+OdhnoR0YRr6Nx7Z/9yV/J6qp2hVaXtU0KHY/X NzFXvOkI9F9mSqrV4icQd1aV1RmYRN83rqgQrCUCK8vZqmkc4hzTpJQHrADLbO9+iNf1UIllosuu u6pii0yJhkV0D1JYBOxNtw4St0lbeQXZGxCxpBcvnnSobXrbkqUkHAqTnhiWEOtX2aOuW4wMwXss O2IJTthY2/qe1uz1Xj8YF3L5jGy9+EbW1+8RmKofNu4jLbF+YwegNEDKZpzL3ldkFJkpBtgUTSWM RQ682fOtr8kLn04vE2Kh1Mgh8fqC4vV3+xiOUzymAfJlZ6RSZYclFgnPvLKCDGRruCrS3JrZ2FnG rbDc4xuni8i4lbVYbDfsKUkH1Oiwls5MIldcbVRR+Mn04leEXpDcDVgxRbUD0AmF4lQquAy0h8e/ cKfsZ0AxjCC8s2sOBJ1vEAdDgfTS0prEYnGH1as9CezxoQQRvyoW8szu9rjOHpFatUYKG2Qzw8EI qn173rcdp1FWghIdXFMwFCbR4OPHn8qdOw8J0UBN5Bdf/JzPL1/IUtkCHIvekOilGAolxB8cLw7L SudOXsGwmGRd8LUorMsoLWthASSidV2T4dQat3XFpMM10cnYQH8HONdb8n/GHXZ/1/me+74AEd9w WTaTlBY527USAiwM6D6D7LHlTHdeA2JStm0WFg6KmGFZ+fzgo/IzNtUFR7akCPrkcom1hQBlxmMJ Fjw7k0b6r0+Wl9akGE0yCI+stSUqxGeg/MCjhQA+wKlAunPsRuFBUR0e7jLrh76DsKTQ+AI876vL G47mHTl+DgF4WGwoyt7beysnJyeysflQ1hMrVIqTkHbHur72XNDUxD9KGchlhKhUQQtvuCN1Tr53 oYRnUuewoFAUzdrONxByWAIsSezP4Ps37njhZYXc/Mj4tZVkD1zsQJ9D2XgMbOEMuNLMMdYHxqGA NPuG6wYjJVrXQ2HMJdNSq1dpiYGiBi4fAupzSW0zb49k6U9SiUWJx+b5OWQh0VwCsSfMTZTeFIoZ CfiDPB8UliMcRuUGd7IHb4XC/3qiU68IxYlsIawp29w0FIpLMAgOsoD4A1HxByYHE2gz02ljdxef myznuiHW1WgKy9Hi6qKWh+6YhnURSqup9BqTcLTGwfBw/RuUoYg2iF6bcsd9R6ZtlHvvtDCcAneL jUamQCdTLhUkm9mTVrNCJZPNZTp319YUOsd7FhaAYmgl8FMke530MqBIxn2A24U6QVRYgEJZFUX3 uto9YMO6BEJBxpLwA2UHVLu7szmOARcUr/iDIBUMmLIabTkPTBhcRHTwAYIf34HS3N17Q6vRFmYD vrC0dFfSSxsKqp6gsrJC6pxLBN7tfXa3p7/xCovZJSgEZEourGxM6t3rJZJZfebJW1r276t8f5BM yorpxM7MOHxmLApduFq5Eyh1EWCddAUhFu7e/mvZev6lNJtVulAoS7FMCvwM1raBMOi9HP6cSJsb n2ezUuC5AE2wkBmAlYdZCLDC8AOJhGNapUA3s4v5whgQm0KwnOwNgRAbieqCRjOMI7KgwnUFSSCU JBZ6NKaUx8pEoWZjIBiWxaU1mU/1ovEnKW2zsV10VpLjn3Q874iFdQbpDfAmK9Iv6yL6Nc3tV9Nf CzfHXywNuah7qIWxXdDs9Uh37BZFfdl75L7+aVULFPI52Xr+lTx79gXDCcikga8KWUJ7GWQ1cFzS sGFZKwBdn/PFDLFT6Mxs7xWC8u7A/CApV8AEoeBDrcbQw7ALTCwhd+8+OvMdJI1SjG+laTFZy4+F uB4vXUlYcXas4G0fJ43MKOIxzKAXIZohR3sELenH0+B1muIfvuN7xePgIbpsnITHAohQQJ6PgKya 4IohGS89jVU8F1Fa7Ct3zS3GOluBQWtf9p7YZ2SVFN2XWq3TXXtSgvNsbX0lOzsvaG3EowlaKFAw SPHjWSMrB1eqVyz3VO/9d8a3YFGh2B7zUTkY9LlC+WgcBsrExdnGphBmvhIkCuBpQpLJBSoaZV2t s94RXsTh4R5/t7zk2pEcxdpIHiAI7zXWFzJ+ykMGjBY46QFoRJA/Gp2XQGC6SqBla4w6ZUcj1AuG QuRMG8adPqsydDuwKXTWSPFBXj7ojJZB4tPFCL+/AQeFDS60iPeqbo9TLmpRzAIjhLrLGn+6qvpG 7KiBFH+jybbfxRLofiersAA1ePnyaznNHMja6h12Zl5fv9OhJ0YrrW+ffCnfPfvaUG93lQjcMbhc Z5+h9v0DkhzXpE0knJAWMKtGZDG9IunF5d7vt6Coq4zRAFOFz2Oz1O+C5uaESgq4qkIhL//+P/6v srn+QObnU51mre49A/0PU/MpsyH4ZX1t03lGaXtCZ1D4k5Y2NmhUDwBT3Dh/3mOOoV4Q1QU3UUa0 XzVDMqjo172jDz2h36+mNYhwwenEHohoXTSDNUzTzBLif6Zm8krHQdyxrR1IUMfGDjMTFlgbX375 Mzk4fEuGAhuIBt+6Y2R0RYA+dwoyfMEgYlG9iHabJYR4zeLqx1GFzS4UDkvcxJT0+23JZjPM5M2t rndKY3x1LdvBdwBzQCt5jH1v/608f/GVgV0Mqs4z9DgNBeEWCgWCV3Vcyk0WT2oc7KICS0+ZJS5H z9RoNLX6weOR8+qgnUy1s9q4+PIWFmIIDvdqGPjQLjRnGYvbRYHYTBUcIARNIVRcTaDiwURgOsFe 4aLswxh37GYSpUa2PtCO0++io77MMUmFQ7Do5LOCWOT/9E//Qb755hdSLGqAu7+opd7zisfLhhLD iuQ1I2c4vECnHIAb2E3ld6G2XQGb6d//p/+L8akf/eAnfI2ZxUaDWT4oGtvTDy7q0dGBpFMrkkoB xOqRtsdZGO1hsB2xuGgUQFUFnaJT9MtX+/wELL1YbF5CkYUL3TuQF6LJRqMBwGxTxAOSOzQrjV4o FuYxGXk2fiUf/mDBdbEBSfMdU1jOhTlKOtyZJh4mNhPnVHB1T10a9Tp7+/K19mgxHHdWz61QnEpr Ftw+t2CsnSzsmJD1dJV82thhYk3rHPLmzTN5sfUVXcJB9xgv99vREduCCzXsWWMhv3j1TBqNKjPM 9+99QKCoFZbHkBpZW51ZgCgKn0PeqBwc7tDS2t17zTje6sqGpOYWyHZgjw8LB9m/bsaxdzzxWFLS 6aVObLRp3D5LE37v3ueyuLSh/RYNlMfWD/YrfNZi7Zo0amWp18sibW3AAXe33SxSIfqaUXafHmmD 9ChcRLOnI8BfABKZwfUwigytJZwUmtx9XGK8DEkfu6iYFkKMew0Ylz3GoNpCp/JyAkvHcT3jBKni 2t1UNlcdW6MOrvPaVCZlOr0qm5sPOf6MAXjC4oArin6DWt5Sld29bTk4sDxYRtBy69wzaNa0XK5J Xeos+XFit3C+/YNd+ebJFyyVefjwE9neecUi5aX0qqwsbxALtrP3SmrViiozukTdeQIlB/fVNl/l ceFb8dc26W9wHtDUgGceFlAonJD5+RWZm1+S5NyihMLRHoXshltAuVUrJcaa0MXa64WCAryAnxZ6 o0Stwx9uSKtRlobHS6jEudLu1pOOkiy0GLebKP5pKqtBx6VVgJ0L7ap9Gt9yo9f74av6jW/Y++NQ XG4X96qirAveK1UVuAXlMOOm9xkk6Cu4ufkBCfsCyJiJhywFqL/b2vq2wxaLUhhkCc9K/1IjKhX2 DGh1XEbOAU/vc4BrA/Q6UOxQCqeZY+1Z2KzR2nn2/FspFLOmqUQXrKrHs6ykfg1ag6PLupqe7vv4 LiwiFgvH5mV947GsrT+S5WaDCgVBfXf7euc6ggV3crIvRwdv5M7GXdYbYhyWBYJJJx/uk85LKK1W uy7tRoWZyfM2srbR4LZh7Hlzk11rbqCFhbngn7ay6i+GLsWL5graU045vHsVy3ljGuySDE4UOH8f 1Q0dp3SsQFLajOeel8qgG5apCJ7bwsKqHB687XCuIwsIlww/HYul1SKcYGXlDjcnuExoH4dFauOX VqB48rmMnGYPeR1UdMYqoDEBXi2vn8yf8cQ8EehoJwbLCg0oVOPA8tomhz0sGuCl/P6Q45nb8Stj A7KCFjDfQUIYsBbcNaDdmz6/JHwBiccXJORwSwcJahiLBdQabsv2zpaEkTzYvKPU1iap0Gx0QwIY c0eZMiUP1taa+IPDEfNto4RwB5mFH2GdzGKIZJhoF/pmV2Fdn7Ky50ebKp9U6zUDmjzfmrqouK01 K9YKoGt6gePMomDiotnENMcIpQE2zloNpVfdLJ/zNkMhAb7wwQefGMuoKQcHe3JwsEP0ujPwjk0r l89KPg+udiwu9AuM0yXTfoRJgzpPMg6GYwH+gFIYWFrWksqiY0/b2wM27cY0dXyqsLpKzL0fYVyg dYay9fmDMje/PJKyskqIoY2AX9KpeQb8ERx3zj9YiL1hEh0PXNdaDWDXiqTSa6MRCHi47Y0wttme w/3EAnY7jVTHGeO5jFiGh2mQujjLcHpwT+5VNuT7syqwXKZJJQMJmtiO7WzDu+PCMeLZgg7m7p2H ndewKKFg0ESih9HAwSAKsCiUxlJayfFQrByLJ8lVpR2gNUW/tnaHixqZwP3DHVp3JAt0LmB3w1Mm A2DhNSRfQHsvJe+DhMMxbRaKJq/zadOsNSCBUHKke0JoR6tF1tTFdFrSqSSfi11vuHbL+GCtLZ1+ uHa0HsvSWoSSRPB9bv6cch/jx2qHnvNdyFmew27LCrg4QHVgUPQ0Up2Fi/A6AZQTxGW5lVbHyhri 9k2yBpK/O3qe459R6I97j3Vd1rGn090YrpfH8LWfN3psTsBtoWzG/bpSUTfZOTmVWiatC6wToNoj YWXItMI4lNdPAOkPvv8n8nbnJeNYaIoKCyaXP1Gsk2mqaq0qG+8Bf9X+/k7PGEBz02qBNcLDAD7O FwiCfaGL9zoX71SvoIOkeJH98wIw3c22QyFr1yMiEnt43zEPcU9OM4e0GqPR3fMVVhturEX2jzJR 5EYIM6qMZeoa9c+KorLjUMT3dEoGnEpLIwjny6SUFsS2MlMCQZzsovfherpl83zollwpk04GPFWI L4FFtFvjhnhNs5sp9IjkCzkuYrdVjSwjCqej8RhLuDY373d6++E90ruUi+KtIANZ5+/IDe6v+gAA IABJREFUAJIw0u9lJ+dGc04K+QJZTPVW6D0BJQwapkJBwI1VpQG6mEJPPZ4/EyAtDiy/bPZEgqGQ LC5uyocfzUkk2uWZHyyYz00qLBu8t26N/otsp6LvkflstvS6LN4RMbNuo9ejUc4mxLpBMcLKHfL4 r3+1X0AcuhX3ZrqVmucqrJbpWzfd89pFTtyWBbZOGXVvFbbFvAH5f+FjmGmLOM80W3rhzKBt2d5+ yXjU/PySLKZXyXFurwsWzt7ervz8l//Ab+D2AjuFkh132y4Ex1dWNuWTT37A7wLf9Ob1c3YbRwB+ /2BH/CeK3wKDw6vXz+Xt9pZiqgzKHvehXkVPwIbMJVOkR0YG8PT0WDKZY8aTlhbXJB6Pm7hbs0dh 5XIn5jj6GmNInoB8NEIJmc4pWMgtKlooDyhmBOGpGNtN8nwh0F+rVlV5wc4y7jyUFKw+PRYwZqUz iYn+WDfNMCrifbBamhUjZRSxBozCZfo0obg+MbxbE2JnHHpm8/CwOzXOAclO6kFbKpmLZCx7xmXu H+mG6Zo1pzYpoaSODrclXwCVMBZcnfElZ9NTKCdgsd5uPz/3eDCw44mE3Nm8z79xHTs7L6XdUApk VXJlunbgxwKlzdPnX/GzHQCuI/uKsYTDIcJHoMjBrRVoBNUCgmLxobwnQktHi/PbtBJthhPPJZFI yfr6A0kmU0PHDmXDprVVgEKR3dRyK0I7igV58/YZtct57AqwvpxF/FDU4XB0+Lww0AZ97IOPr93D p8vxf1mx7CUej5/AcrqE/T/I/09zaDypkqFNv4q8kykaYuG5417jFgStgce5zLGVmRQARNAhT09Z 4Xm9eQ0kel0WUsvafnxugZisy4oi18F/VWQMR2sTu/gpp0WB3TcSiZEzCxMb1pitybMst+Bht2SS zgMoOhyZwJA8evAJ6wN3dl/xfACcotYQmxhQ8Kurm7K4fGfoZsLMZAb8WXkJBf0SCaPztbfLHsFn 27XgFWhqqjVsq3ZiErFAdfwI/AdDkZHa3bdZR2orUwavXXU7UUx+veU5g7L2brGKG9aoHxNDQYvd xpHum+kup5mEcIK1lX7mMu7QOIRgPiLtm5cKeo9DrqIMbejepuenobOAlzo5fivLSyvy6OEntKqu WsNIZPzuW4I+72w+YkbNdsUhXbRZY1RYvoCkU0vy8P4HVFz37nwgsVgv88PO7lu6rG43mXgu8nMh E6ldbgDwBMgVCg7KAsovAKURDCJFIJVyQcKROC1JZBCdAiT7zvZ3cnq6L+kF1CYumoJpFPg3TBMN fShQhumFRcbRcA1sEop2ZOg16GBmBSSC67APe8QgkiLU/9bOqYJm67drriekNWuYXAaNw74Oj4HM GygE1XCdyRQa14KLxhEkdO9wVrpKToGfFp6gr11QyY3Y0HLiMq3V3keuYhmpO4TSHFgkMhXZ2vpC qtVibwKj7zO8QDlIu02ow5PvfstGEkCvWxpiuEpnU5Dm2CYO6pbz6jRhmYLnClnOjz76kTx9+oWU SmCVQFsxn+TzBVpf8/OHEk+kyNkO2EN6cbWDj8L1v379hLEvKDMoyOMTLY7m2iFljSpICGJqwKU5 6WicawXK0an4NUgvQ9cHY6DImJqczbC7Tde91ZQ+vZSnshHrWPVvJAk8KNNyXr8xmqz+sa8hktjT rruToRpx8TjrkrqBYzXh+ZoJ8/eUnnT+sTUQ5oLYHur6fWvsULb5qK3xm1YQ/ioWFiZAoYgOL9PR Vtj15pPzEo8qIV4me0qeKV1YHloLthcArKR8Pk9LBhMR1lDQH2RQHIh31gtW0Bgi2+Fpx++///pX BI1qIw5Xq3lzHicsRIPWpogePO3tFmsaiYESkYUF9BpM0YVFjeHh8b4kYnPyV3/5ryUaAZNDXDbW H8gvf/WfWDDdDRV4ZHlpQ+7e+ZAddRAIh+ubXtyU+w8+lqODt3JyvEv8l46vm6mzGUJvu9sHEeen xYfXOoF95YaDIPO5u7vNOBuuf+POB+c+D1tSZNtVDhN1h1tTxVLhP8t7h2cGi8ltCDmfp1NZ2ffO pZc5T7qfcVtgjrDiwLvX/Q4ryGcg5WofIvmFTFaGjSGM8pplpYVFWSiVZFoCxQNQJZg9ce5Xr59J IZ/tkF866+sYyPb55PHDj/kecFMWD2UVDywpLNRmSzNkOCY617AhqVeVW7uBAHTVcFep61Qz7btU NPAMKx+9CUOhmOTyeVoUeA1KBpTNYHDI5jNsgookBcCpcM/u3LnPTjy7e1uSyRxQqaANGFD1wWBE SqWSnJwcyjdPfkXrCiGVv/6r/1Z83pZUKkXNMhvzxpYRdeY43TzdoGHdwO30B3w92Cxww8E1xPUh FqZ1h/juaFlf9pwesQDa1Xd2ImKZW23j35Y0O0ynBBobY8UZfumXxWRRv7M05+ridgUcZvg5K73H VJyS1j9PrItjvQ/EtqxyxgOYRHzrKu4glCpqCKeJcucuGYqKR4DaVqsa0AMbf+kpCDb1esBndV7i QtSNTYn1tBchu9gwmB4hrYtSxjTZdh7QAEv3DNQ7LDZN+QeoBLtbnofxtERikeOKx6NUdDgWFYXf T5xYvRajhffixXcMvu/tv+G4wC768MGnxGKBzsa2oK9U8nKa3RefH5oI5/XIyfGOxOOxTqzJhlYs J39PVyAzQiRHurFiK4h1IVbXZFZV7yWgGri/iIXVmL0cakiQX23UuTkFjWUYL5xofrqARilAeY1S M9wpzZEZkJ4CYBf6+zrFbY5OUy5iZXFsrRZdlWlLIBiTem0YcZ8KFjDAneWKWoCwWBj/seYYmR/i srF2lwoIChhKAg1KIWBfAJgUSg33BRYR+NSxgPH3QirNGBRQ8CotOTk9IDgVYFEqKu3YynfxN4Le aEUP1xHWHRYUAuYY0erqHcaYUHakDRuitLYyciqxaFwkvSq5/ClrGYnhIhK7E1zRsh+QD5bKtJQI JQBtNV3FtiTiKXn06KOeewQam9evt0gOiDHg+qBU4T4Cud9o+iXBTtlns+ieDk2TZiRnQRgWYMZT YR02VGGJDTRLaX384ceyMIyZUFgzF3TvI7prqqU1yezhZaETBEqazsrTFDRuaNQwjYY3EWO5Sbkk +4fbVDrAFJFx1uy6uFY0mggkAYdQyIETioDYzsLCkgkciKPFuq2FdW8qbcnlTs25zQbYoSjSeBGU kKo3xcBByQGwibEA66UWHz4XZ1svIOTx90JqkWBUgFu1t19YO/o4ey4aFxBA0YPDXRZg2+A7roGN QVyaBfcDSvD51tdkN91Yu0+lXS4V5Ze//AeJJxblD//oX/LeucVacoxNjeISOnozTloYt8Kj5MBM Jt7UgNoOROeOty28ZzOlsGwQXksswFHkmSllNe3guxXGAOwO2uf8LcMfNV10uwrxQgYSsL52V+aS C1zslWqZ2CH8i0A83JtiqcDAOuiIia0xLeoRjMfrUFigitE6Qcs7puexreE6RHWOwLuOo6swbNzI Ygm7vRC7GcbuYvUQOrC8vE6X8fh4j+/lshm6d1A4+EypXOb7Pl9QwqEWlef3vveHvI7f/e4XtOY6 8SszBuWnwj2AVdk9n3bhqdJyUsiGlurArVUiwWbHIoPUGzV58t0XEovNyfd/8Od9FZbd7EeNqEyi zd5QD4VlQw7MmXWXR4kMm3Fifs+WwjJob2QKW/U6swjX6RpaRWVn/zTH4nzYWrqBGJCa127BTl0s IRN2PfE/xJHARoAMWTSSpHJSEj1lTvj/yzvP30jT7LrfCgzFKubMZufu6cmSV9CMVv4gwYuF4CDB X2wYgr/7X/G/INgw9NEwrJUBf5JtyBa83rU2zMxqOkzoyO5mM+dQrGIVjd+5z1P1VrEYms1k+wEa 3U1WeON97z333HO+e/xQAn+okHqmU38vWM3S8rytrC5ZrrPL+ndhyZN9oeyJ0Sd/2vR5sYMcGf0x M0tqiXlwqku11KfswmtigIuCEqng2Dw0qoC6vDyn3xCAYJeXpRpBt7NN+0ZwW16ZFY/q009+V93A p8++k5TNbmC1+3LpB0rGnu4BcceipZ0kZ7JwvhY0WK3gBMu+XFbJ7MGX/3uJH+lCG5urAvlbET5d P+v4BDx1JM/Z5is+8OVxKr+I1tdzY5IQzpWUYc+LhHHMxcZDoKukKkrNGQi9KO80sha/GeoH+rzC Vbz5Ig9FwTIyoRMrZhU7OyVlABe2UmnZyb969Uw4WqHQbZYCU9sRnwpuEsELrIjSaHFpxvr7hsRB gnJARhYVOii7uClhlzPnNzg4Kh0syiqUP5Ot+JhB1XWtXLEgKm/WAe89BUG+n892ENsfRI6hMPy8 acXt7VrWVhTTfk1yyDQKRobHNH/IGBIZE1nv48cPFVh8PjBme5HG44tycnzMHZadse18RV7APvn3 R1qQjzlFhx7uAVchZf/a1CkkiO3voNVv6uM8syKl4Dxt6pthjqOqlYhl69+BE0o391IFrLjELt5r s4pkad02/DxXPECtSrDzG3mpY1lu8tpiW4LYHcHqvDWwkovtevjoK5t5MyVlA240WvxgSNAUuMka CJK15kqQiE5n5K7c092rMiniGpRIBD9eD/gNBhYTiBDHD8E8G3/R2ZF34b31ZatsNeJtBNPp6ZfK DD1LpavYK3BdLPhsxtQfMGYNS9pOsKj/+t9+4kqYuz6TqDKWfQsxSHOdbYzX0M1kTMjxTzLlOH7U TAeiU1nI9/pxSWW0PTLYIOh25sNc3f7jr4DNf46oCXktFvWdHcE09pyD1XHunyTNgQzWJ09QVE1Z VjozlwQriivJuL+o72/1BNiX+ZzTdrQCJWPmxcls5CFdzGKWD+PSR99+Za9eP1cWQvZBdoRyQ1tb b8ig2y1H8NDYCxGHY1rVTU3HLlnqwaBHF56V7CrJXSZkULy/kXjYevso8Vj+/saqie1kcJvtAWfh Be1GgOVPyvr6B0WRWFyat2fPHynADQ2NSU7H0QLUctHlp2XveJQ0nCTYB1dtyCbGr2ofKW/B6ja3 Nu3F1HcaFIfKEImm3YU+6y70KIjH64ysC3edfN4JtM0rJTD/aHkkjlcHfoyFgrqP57FiUDwoWNWR vfo2xmpG7YlwnmmySMBP9eQ7+gCe5vK01q2XuLgvAsPaN/mR/F0ipT3rgHrQd/GQwdmZbdSc2iXo YzOy8mLqqT2f+l4X19joVY2fRL1+sub2tpyNjIw5BSGMtIBLxYZGMjBxoTqIXicgk3HETNMv5mqt ++YlF40awHwikgd0LxXdQosVwd5YQtbMWoO3n7PzM+pIqfyq7qlLt7A4Z0uL87awOKuxm+dTj22w f0jbpuHrPDI1XtYuLy8Ik4MNz+9u3Xrf/uSP/7Qh03j16oWEBQlGkFGxMZMJiwJVsvBJ1eSji8Ut 4WqNYHYcDg5OPwcsXkOw6ukmWGEWcvarsTmy1/L3kQDsPg77X+8GsXVYKMuTIYJ4lyFosaHchJyk aLR64Gtb0PdbrVjaHRb85CQSsqYkSfSgbZTp6zkECuf31J9S4R+17spx9v88Vnd3n3300e9KYpib CrIlZWCyc5fv6rM7t9+rjdA4Lt1mlSoseOSNK8pawIQYjF1fW7ZyBRzHL+7bNz+0ri68+pyXs7K6 YktLsNFL1t8/bFcnb9nggCtGYD/Psenu7tY2AOwTHADB+3r7gykq5aifa0pOzimztQSx16+nbGHh je2UigLU+QMoz2cwelYqVeyNSKZVBWcCQaRh8J6t7fVQzriGV/Im5G+24+7tD1RiErzmF960vJ5i I4pVKm1rmqCvf1R4mn4fAjLHUHOWLRbYT3c+b/murv2qFecUsJI/a3SQx5szlHyKQYdHIdd0d63Y CydBxfauGK1HBaswPBk9DY/zWjerrOslJcu6mAUcdQRihyPZQSTbcX7W2VwMkXSn4BURVsY7KmHs 4ZKsDz74HWtvy9jiwkwwb2CcpUPlFtkBfKWbNxtn4sBnCi8LtrKyoAcVr52bfy286vsnD2qtfTp0 N2/ctb09iJpQX8pSR4gie3Qoe3v77erV67rxHz9+pBGc8fEJ/X87OCz3dPfYyMi4yqu4OK4ol8bM q/nhVi/Nw+/1U2/Jg0c55slNGDlfGRFDkVmmHFxbW7M/+zf/WqV7HjONjg7tK8RRysQYNA+69nxE zK/XudnXte6hfhe3Vffw/vf2dXdbIU+gOrgjdxYr7lP8d3LFwByrqYywveP5OSgqiEoQyw+7uBUD y1HW5TFYHDcrdMZtACUTT7mkguNxPicgayrB4pPCOzlB4yjyTE5h7SVObOygaR4uqC86hnW4ftd5 L0TurkzctkIXoHFGQWpxEUnkMKYSmgf7NMDCTUdnjm4duNfoyDUr5Pvtl1/8D72OzIYOYkc7hqVe HjLzNzIyqiHmYnFHGRqBAHrAwtK85QHslUUFSgGwbcCGWp0nrgc6rrwWXImshVMQwX22a2x00kaG J0Rr+OHv/dju3/+1yjkCE3ZeAOk+m9htV6/etPGxa/b8xWP79Zd/o9Gh0ZFJGxqEAOvOQToGCiZs ZLg8a96XoTGRYoyoS8dPnoVB7ywKBtR9yRojFtdNLtd57sGquRQ8qAIQphk0y467Gmy+4s5f6FgM J6IJWIwtznelGcTMyv86wT4Gwhs3GKzyzJ5f+Fygp0nEq+MzVV10KvvCtvNEqmVbZIUhSF6GspDt 6Sr02jzmD+VtddOAgglcMoIol+zbb7/Wa2N2TEYCWfT69XsKNjdvvK8jQMYz0PfGfv+HP5IV2Fd/ 93NhQ+A96uLle6x/YNBynRiZZm1jY86++Oqn9uCbXwl32thYkxY8i9IUtx63ls+0fCDG0ssxrZSG kr2DmXhNOq3AgSouBE8Co9NKdrw0BY9R0KFM19NEpR8ZFRy0aob9QrYZa/qEhr9GVeoNvqRiKNsD /+jWzfe0jdeu3pVuPd9RM4BtMcjMddPXC2/sfLmMR4Hszettt60esPzdDUDvRaykdGzMuOL2RFDu 3GkOMYAErI8/bndeFZflqFLwqBNXK1lrapQubxPLz4bSdd9Uu12qlct1q4P2ZvqZPXvxjebiKGEo wZjJ29hcqStwKqlI2eDgkDpnexVm7HqtVC4qm+rtc3Lm1Mun9v2Tr4MhasX6egeVPSV5RJSI4EaW HGtMXDvwsPx1kYLQ+DLK6zgpoFlFSST7efXnuJ8f2Y8Fg9iZmZfqbBIUcl15ZTNv3ry0jc0N2ykW 7eXUU304AXlkaEL8Mx5Cethm3F5Mmb7giMDPqm1T3Tsxuf3t7e22F3Tr6Q7WNWXqFRKTAj2FguVz uVNhALRSTjgJyH4aa5/zc41hahe1PNOrhAwqiTldxDZ5MEFMrTGzaezauSRt3N7DnjrxGCezIn2+ BNqiTZPjeC1B9VrLN/z3nDqWx13cLGhOwRhffvbQNjZW9TM4UIjeMZPHAhQnAMWLm5ue7EM/3y0J BN8KBhP8HAljjtHy8pL1dmMqAWjuGRqfOz5+rUG8j+NSLpVFRYguTJ6xZtUQ4HxCWIWMyudubcLx 2ksMapetXCqFzMt/jmIEs35kjIwhLS7O2scff2YLC3N25cp1cc8whyUL43Ssri5a9k3Gblx/z/Jd 3bawOO02aIHwGufp6iYcjn9xvCipvUPYViOcZlVqetPA0tlwr9adFyMhFH0ymhOnwbOStFLgQfm1 5iX8Pqb9OQQr1j5k+yKDVQQ9Yw1/UAA4342qkZe1FLQShph6yPm/GjLUw5QWk79jkaGh2xYDov/s aA/fiKPFsvGyLEomPy4pOdOANfX09IoQee/eJ8Kpvvvuvs3NvawZilI68TdcLso5eFwvXz2zmdlX dv36HfuHf/TPxGH62f/+a2Uv7LdrRVU0iHzt2h217dWV29mRtjtmpPPzjqGxOLaFPBlRh2XSqJpu 2PzCtGtqKTjFIx75P3U2fXw/7H22n0wLJQjmJOGgEUDZZzIzH4hmtCgXWPM70mVHqRRm//zCrM5X rrPbVtdgz+/oc/kegilTAPzNeykHKUV3yzjtbNnmxro9ePAr6+0ftbHRK9aZxQoNcqtLOuc6OqTy 4GTj1qvVxMRBgUYPakES8brEfS7T8DnJa/ysH5yXjukudi8GmhcdqMKq8XZ4mid+zhxUxNaS7ibN 66ATWMumak2BjKWC9IjekknVx4OS0rFciFKvjNlZvWy+LMuzkmzwJywIqO7r65N9153bH0irXThl YIfH/XTA2ZnnMObhO4HRTU7etA8//G0FgMdPHqnsqtnP75kwosGBYd3kBLvFHSRiCC7bVt7FAbp+ fDB8VSamLqtnWTFAQRwFI3PnHyKubxefD9EyPiAIilAqCJSUrrnOHmVWDm6nlfGx73QtMYAVWz1Q G8jOZueYB6xYV1evZgej/DPYGrwtykw6nDWzVUi0xZKC6puZl/Zm5pVNTNyy/t5+OVKjmCGgP4s2 fOatqAbJazD58/jvSOKuvV+a+vWmRXM39azXpQpYzlyuq35elhWdiNFsi50cLs7mp1Krk37Y2peN hSe5j1l4JpckVe5/f6A2XALiaHIRrHp6B2xs/JrkpsVfKm1rPIasBp4VWZCcW/b2bH19LXS50iJs Ms5DpuWMb+SVS/bFlz/T5y6vLAjjUpCp4JC8bVvFTduS5hWCfDtW3i2qtML5hoDh5VUyYyBwFBvE 5eJaWJzX5zOwjI4VmR6Ko2ROES/iOyh5KfNExVhfVuBhlIdMiYdue1vahoaHbXzsiuzN+CwCKPtI yeslK56E+YRxSFoZJ0EwUhcYEYpBq1AoSNWCjinuPPC7GBBnaPwo7mByHZXxJ3/ulU6odpTN12Wv j+oC/j8fsFgRobloC6Lkqs/zNZ7UGGBZrbCtk36Xf2Cdf5VkSDfjYVLPxDj1kgUtunjgPDvFTZEi wXegJNx/AA1gV3OCMMMJHouL87a2uiqVT1QMCDaYTyDex+FYXV22v/jLP9fxQNYlL9VSP06l8o7E 92bn3kgjnoxucHBQ3cm7dz+29+5+pBIwrlevntv3jx8Ku6rxn5Rt+Y3HwDNBoad7UEFzm+Cah3wa Bbxdz0pW9HDBdrZsXmz1TZW+BK6N9RW9Zs+gWBRtdXVF5SxBnA7m6tqStoUgDulVdmQyD3HJGW2b yuScMsK2NjqTaeuUw05W2ZmyKZx8ghjeaQSO5pE0/iZAVGUQUQ+I5xmk4uRDdPm5fAErKDaAT7Sn T+5td9rrwM5IAvFLBtl3PqFSIEiLcxP5ZslsK34PpQDYxe725QpYXOCYnM7OPFdWwWZzg3755c+U 8XR3o5xZP3ZprK7aOnVjwxwfHh6xBw9/GdQQFuVAg0a7mjKhOQZ/CbwHXKpaLSvr9VI0tvK9w0qJ GTty4s0py6rYrrSonIYgSWIwMRE/TRmUMsWefgUNVsTlAO4j5tZd6NZIEpkfdIPV1QVbX19RCfjy 9TMN8FPqoToxNHhbJejKqlvPsw1uW+YPI5X3evDs6mfsDxlbe0d7UJOtqjTF//HGjfdsZGjcx2xO ET5pLhV1TIErgjzPWVY/ERaIjS43yqjYTqmsrjzZ576AddHIkeMEGbfqvhh7wndazR3Aky75ywm8 doKrTiI3WoIl7CJ49dGNy7QA3smiooUWZY1Y+dI1cm4SgYy5OcBjZFwwmuCGIKPC6TmdgVzpPCVk Wj795HOVkpREfFY243N2tPoJNu4V6MGBRfnJ2AsBCXt6ggjlKRkQAcpFBhE/rNji4pyCond8nXIw MTGpsi+eSvG4wkQEWvP8vrRblPyMf8eSzc5NK8CwKNkY42E+0RK69X29A+HhRiDIKKskMyuxPeq0 ug8izHhkk/EzVAlrPiB9/fpdu37ttg9Dn8F0RXMGpQdnwFfPannGytC4D46XyrsazyJoJYUp9wes yKq+hCoO/98FLU3XdwjX8c/y1nIDrnXJAPe4wDrc6KHDxsau2MDAoDSzfvKf/lx7MDQwLnY42QKe f8vLK3KlIRgQAAgGyytztont117Vcl1dwr4wbwAM994Dci/cyL4c8O6ya1dv6v8EpZXVOd3sz59/ q4wvrkjOJChmKHnQ0ArNnlSaYAtWE4aKA7VE2UWQxGHk6NNPPrMb1+/YV7/5W3v69JGyIFQdAP7B oHq6Ac/7XKUhQ1BF5iZnf/yP/6XoG0+ePlKBSYB9/OShPZ/6Tlkd5fBQ57CCsBoPNGAq7qYDfcPj fuhQnPE6jy49D5/ltTWZqByVwbXMsHQwLjhYXQzr6t3XaRFvIx4WW8qUftGJJQryx+7aYS3si1o8 +MggyGoYPO7vp7TqtB/94T8VHsOsHWA7GQR8JX5H5gWGQ4AC8wIDo6s2/ealpGYo+3x2MlBfGNFp uGedbCvfQOSMxVUKvwnW9Nq2UC6627k7SYOv5bt63GtQooF0E92tmYDKrKKboaJEQTMha19//Su7 /+ALm56esk8/+R1lPM58L9rc/LSA+rEx+GG7Njv7WkoPDFd//tkfKhObnXul/SQLi5e71GUTVvMu TijzLuvrw1Oxz2ZnZyQhHhUvLouO1UnX6tq6bR8jWLFa7vHFEkcTc12lohjQF87Fesv1LtvbfJFE XbCS5HbaGp1IlAWnNebB0/ciNN0PWpQ1gMal0q49fvyNTb18ZqPDV+xH/+BP1O7nxv73/+HP7NG3 X9qtG/dsfPyKaA+zs9M2/eaFWeqGAHTKQ2VVYcXhZILSwvycra/XsyaORT7fE0qxqo2PXbeOjjH9 Djea6TdTwrwG+kdtdHRCmRjlGqXezAwBZcA++vBz++jDH6g0+dnP/8q++s3PbWFhRuUJ39vXO2TD Q8MKYrtz08KyKCfjsD6lbyrVKQInDYKx0QkNc0NXICBS2lKq8h4nju7Z8sqy7VX9s/mcwcExzRzS fKB5cfv2+9IY+/a7v1PGSOl0vfjemeJJ5xWs1je3XIDymPuyL2C52uPFzhOqOwFjdOFoAAAgAElE QVTwThemtKObkRLj/6bA9a4lYS1TE8jr+97codQoscqTNst35WSpflk4Wd5BZZauyyYnB+zq5E0F XJQLwJn6+wdsdGTcKtWyKATz87NKJyiH7t37SIoKr1+/8OBUhija+PnsJ4FJ4zjJQeoMg8vBiDWY j5Kh8G/KR5jjsO2zbW3CsLaWN1XesU1RNK+3t1esdjIYUS/IzCKrnAyqVLLllSW7Onnbrl+7q2xx eXXZFp/OiTDqFmZtMtcgA6PrRwY5eeWWtbV3CK8BNP+7r/9WZR+dxK6uTsvlJvSg5rhMTFxViUtD wT0aO3RfogzCE4ru5Fk9oM4jWPG5mP4SsN5GceTSZlgxaKVE4nNBfik7qhPk+MJlXcnZwzgWcpwV A1Lzk7MVxyZeTGQCDjb7calekiwryhyTVRBaoS5wE4LxsDinf//3fyxawqyym1kB9ZBMUefkfE8b AYsAxDVQrlmex+Fo+E5kLLWREUTqOnK10m9rc91WVpaV3WBDPzJyzYaGJmQ5j9oCw9gwz/mzurZs Uy+fS+cq2oOR5fyLf/6vxLj/9Rf/06ZePqboVNZ/dZLPGlKW9umnn9lf/OW/U2YIbuUqq6MKXPDE 2K7unj7r7Ru0fN69FVdXllSyomsF74ptbmtz4qkHSW+6LC+jw4U0TlkPAF63sUmQ7WyQnT6PYBWv 6QgZRQqShrmlKuFYYBL/O+jaoAzcDJnV2wTFlqM5oHr0oiKz92L5T77ze3vZmqyKlBnDbNVl4WrF 5d08Luu3a1ocRDptZrrv+y6OSWIm8bIsn/PDPWdD4nrv3f1YwHJ3IS/8hbk6qVyQNeyWBbSrjQ+v af61HJdfTz9XVw8qwMzsVFAX5dPB7bLCm4aGR8JF63wqyqh4vDa31iy1mLLR0Rs2MDBiXV09YTQH +kSHgGzMHeBFQeyUVZfmGr1hcO3abbt75yOVfY+f3Le9KW5iH56GVU8GxmDzxPikjY9esWqlLNB/ dPSKtmF7i2DldnWA7D09g7o2FhcXhHH1940Iw0PEsLu3X3w6MsvJyVsqhZ8+/1aNiNGRsaDa0C6p ZIItfw6TYTqtYOVaYjtWDD6Kyd/te8CGcTpJGTM/2oF2fEeDkQwBF3Cd7OokWm4tMixEtVzaRLKx Rjl2sUGrxj1CJC3tKbpklCu71p4+3ZN2KtmVROUoRY6XXbUci0hkaZz8lk0IDUr7P/21l6McZEWV iZ0S0jKL9mLqW1tZmbO1gUEN5pIhYczguuVu/1UzZkhV3cMQxjut/j24WOu1Eo8FGXQgA4nUZ/hi YHcBPX8N2BTWWJU3zwTYA5wTKCKDvK9vUN1HuSp35jTreOvmvaCz7gROMDLwMrp8dPx6e4ZUProh RJstLc3rO8DOrl97z+cHrWrLy/PqTqLRTuaIZtfwMETTsq1vzArHGhgctZm56aCu647WYH6vp6dC OZy2sfFb1lXo17YvLMzpWE5eueMuQKd4X+7DToPyL4GFgOVZ1PFLxHQZOIcMtuj2XLqGvSMoS7oT 4m8tM6w4O6R0zXZhBF2KTMYPKjiD2V7Zg9ZlYsTXVhAMPIr5fhQml2S2t3ovv9Hg867LnlwWxQaW 5vF2kQfGQWdelAS2GfWEzU0UN72M5WfxyU2A7+kZkHAfWdCNG2hkORcJbEplUBZVB6y6MLhoflih PFqyhcXFfS7MfT2DIh7yh/Kvd23ZCoW85fN5kVRL01vK4AgyWc1gmeSI6WRubW4qg7t7+2NhbADw lH5kPGRnCwvTtltxSRrZculh5Y7QBDdxxdrYdvdZJBBNrz1XQIDmANGUQW0nvC4FOKTdJiZvS/mC Y7O6xjHctI0Nx9y8AfPu56l5HIc/8rnc3lbAKe7sd6g+zlK3tloV4ZPP4fxF8um7TGXsC1gqY4IN kQqbatV2rWJtFy3s17R8W/zgXqaA5SB5kHw9QL2h+f8HPblEIjwkS4tPRc4VAnGXaWnebXVRQ8yU Tk4UTanjRqDyciw42ATUlHKqv3/ERkauaO8pmSgTUTdAMwvZFkqkra013bwrK57dJBcBDsqAZ1zp 8N056+osKHvjeiHTI2tDNmZ0FCWJHnszk9ZYzosX39u1azeFDwFHgEPRrWRcB2VRAiqzgBhsaFi5 s0scL44/34WCw/jYValUkFVxWvl+un3OqN8WQEywouwjMBHMKHt5LYEc9jzZF07PZGeUx+trK9oW juPVK7esU/pYqVMNVkALZESa5yz6sTqNcR8FqHccHYtSYS0CFuzpMFNUdc6Plzl7yhouekXGrXOQ 3JcvqkQmpUAOCg5nvfQdia7FYdtw1KjDYY2FGORcS+vyDUBzA8IcX1lbErM7coZ2ittWhghZjQHL 9c+cvsErqsEIgn2HCNoRDEbpBpKxgl0i3VKpydhEXNMF9ry8TIUASLBB+oUhZjIrzo3wp2xGKqGd uc6aB6U0tpQVO7EU7EqmsKg8dHTaxuaaD2qnTKarBKLhOxOWyTI6g9UaSqertraWF5jONlC6xf3n d1B1CLyM/FByUhry2ky2TdlhBNfZNzLJrrx/PyM5Q0M4Dg2J5uCORCdbrRo43E9kl6vr65fCNi4u 5U+RiwY80vpFoaTR0x+/tb1G8bgLLD2kUBAwtvCTxE3vw8iJkFWDeQINLynO2HrV3p8MfPX/NwfF g9x3SIDT2bqwfpL9fpzj55laYtg07HwtqwoC/pfJhCK5IhkSxQXIk3dugQ1VJA/jfvLchF26oSkT scWCCU8w4qamfAIo7+vrt5XVRdEewLAoiciEKoxthIcXZR6ZjY+2pGxwgNGclPwQI0UH+zG6hmBX w8PjUlFgJGbmzWvvdO2WlRm5hLLPsMLNYqSH70CLCkqChrHXVjQUzbaiCDE8OG5vyi90n7Btr6ZL Ntg/qmYDnoZ0KWUxDy4cOpkEUgQCeSi1d3RKIob3SkkUGks6q/ex7bnOnCYF8l1dmhoAS/OZyoMb MsfBhON9I/manZKtbWxemmDlgSro44Ubtipw6tA3RQ+4+hP91IZ7T7j8qZxRFxPxFVrbmnZvKL/q +kp18mVNxq3Gfk61onGEgBT/36j70yp7aySBeDOAciNQEcI2HCcwtlpSEpAaqf9fDZCovpr87ItP fhsW+45DTVfHPXv//d+WNjs3/dTUE2lUWQWl0C6bmLgmJjxywwxHP3n60Ox/mQDxH37+I9EGUDt4 9RrgvKIgMr/4RvQGmPBDQyMCpAG5NzZWdJO/f++3NL5yZfymzcy9dFA/jXdij42OTNjtW/fs6uQN W1pasO++eyiQnBJwbGxS4zxkP8VtCI0bVtzZrHW3GLaGE0Zp6OKBLs5Hx25uPqNGwW6lZD0dAzY8 fEXBmE7hbpmGCD6SKRFX/ZQhJeOD0T5IjV58p85td8+AgrXMKXhlW9YK3X3WPzAq8HqnvGslFC/g 4FG6HnOWtFUX0BsjJVvf2NS+XNSq5QFiTECPCJmVuGdh4mPvmPIy/mGpfaTSiwhaUVAvlfEM0Icm /cSfFpYV9yvZMfHAB6YXf1d7dW3LWP7Ur1oFukHFj1djQGzs9zU8IffJQMeM0Es/mZKiGxXNQsLX R/nay7SQSint1Amf8WYhm3CP4l3xnRgidvlf5xQJ55FOFLhcSZkJf0f+T2A1NIrKxWog/AxMCNBb 9IOOTmVmha4eu3r1jrAl2u1gYHy2PwCd4Hr75kcy0MCJZ2Fh1rcjaJKhmkDHkCDC5wO4o1CKXM3S 8htde7nOgn368Q0bHb0qLhUSzwQ63w/EBEvqUjIGREDiUlonW9yrWkdbR8jsgoxQcLpRlq0xpA53 8ZFygeN27HNfD1QN13s/SbDaLhZtbX3Ddi4gs4qXfqMUus+Jstjf3aah6+MPIyV2MsnPuqhMy7Wg XPuapxfdKLonp4FTNWZVb7e8/evHiJX0V0wGwnqmlwiA4YasdWvCTSrT1qD5vmtl+bhJBymWwxBU Mci8HNm8ltttlTwj2EGrfUMZDUanZCj8X/IqmYwoAaPDk5Yv9Og9ZFlpy9j8PGMoaKuXpVzufLNw vGQcmhCZCxkwDy7kkvleVk9vr14HWZTsi7W4OCPA25VFo3xPyvIFjCRu0soJ+k/Bug0S6va6BpEh qk6M3RAATikKEXR1LVhy4Z/YlVfGB1MeHAp2PYGOa4IuJ0C6E0UrAuzZPzTqCaoEUAK3Z0yOq2XT ZrtVmhT10r+m9x8y+qNKw5b8qmpVIzFrmpM8v4ZN3EqGzpU9Iq2T8EXds7QyqogjNuM3xw5YEdcS ZhIO2EUHrbhdXPSc4ujsWxf1P98V+IshM4omc43bmvz70M/iIpTxRVUEy3TVnaajlxs3JOeCm0YZ nEDfy7GkNSVp4rJl0m3Yi+vJCRF0bGxcr1lcWpA+OXIxtOjJtMYChkQLHPlhsqC1tSVJzNQcZbSz MXAljm94QvMQA/dqdjgmkHC9Qq+gQ1jd8+Mm3bFMKL0r5VpAWFtfUVblw8dOafjh5z9WQCSwPH78 0Ed6Ms62h/rA9ffk6bcyUIVRH514KH0jQ59mBKRXgjhYFH88sLfZ4AB2YCV3ZqLDyTOoAk558PWy FwLlQa+oO/9UG66tzS2aEOvngoHqUQIDPnT296oVTQu4ZA6E8BBMwzE6bL31uHckkbqTRt159kKD VtCO4iSDQURxtfPfjjquFLfpbVcyw5IO1m61AQitCb2JKOoB7XyERo6/6H4BSkuATTfHpoaPIW/G y4SLE6CdGzgGYoDne3c/tc9+9w+Ucfz0p38layxdW0FtAb8/2Onczdxr5dKuKBLM7klLPdMuZ53m sRWuC55j/I0gHEGQYAKuRvaz1b5txe0dncT7D7+wBw+/UFbz8Uc/kF4X3cGv7/9SJFOCojIp+grp rDSzAN+n3zzXjB9nA34VmND29oZuToIWWN3o6KTNzr5Rp9LlbTKWVoO3qtcqSysUpPWln+65UkOr lQqVTqtOdDJQNd+bInRiPHvGw9OOlzOVUMdi1dm1lG3v8MBwesvbrBPrU8SuVdRmuuigxYrGBl6S vZuoXZRyOdE7gxns2x6TWOJxIcVvbjXMzImPr/d/nK0S5NsuHmKUO1cmGIkZVtbwi1/8d4HqBBNG bzCiZei5v2+ghl+9evVUVuxwjIYG4GNNCE/6/vG30kMn0yETo2PGYaGD9/jpI1ERenv67MrEpP49 9fK7fTcx2U7k7mnGM5MSfiOJl51NlYufpf5APKdH3/7G7j/8peYgf/CD35M219Nn39vs/CuRPCGh dncX9Bl8JIYRAOzxYc7+MP8HsE65S5Bj0bWEOlHIF1QKEsTAtTqVoa1LxG9gcEjD1y4rdPj1097m phPJBlHc74NoLgQrmOZk5qff1SMz1NPItdu4/tXR9qJMUsu18aqTfMcRXcKDVq1mToDvlyFoRcrF u26B40fBRjyI7h/vPfXhUAUeHQ8fAj5Oxueu0mnbSznZLoKRzYKAnoHVW9K7lctFHF1ZnpNUsN/I eACWdQPzNyXZ6rrjR12ddQMGVqVSsq1duEglZShopEOrITNTNiLFTjTNnYbA0dDxroZSOczWicvU tHxkJ3L1whKre9dxsnJJNANm34RBwfJOb+tvOFYaTs7AMO+0dugYnQWVjbz/2rVbyrqwpCdzI2sr Ft1bkayP7A9zVcZ5YMmzndAr+D0ZMvs7OMSsY5d1wEHLwOYnC9z/EMrKndoDI8a0MZNsRVdoXts7 Oxqc5lo5jbtU8s70gHYrGr/RNajr13l2tViQsMk7SXjQuc1mrben+2QBy9v1flNGloQDaAfbXZ3P 8o6PiIjHDBIHfIxWDaQ8xghN5ETVgkp4kitggZy+5cB3LA1d7cGPsIJoCMhJc4o4onQZFgoItPbd DirjFu3VPZEj3baesRb0sKrW20umAt7kc2bsi8ZPNjetu3vHisUdAeQoPTBk7FLRfAuDwDkbHppQ FgSdIRpaJBeUFzIySk1KUqzunY7gRTTXCtpYmeyEAgjZFZ+R63C8iyCGpRacqXQqK0kYAjF6VXdu fWh//Tf/WTgX7HC4XWBxwtpSKe2bY1Uu/BfVRtq2swLsccAhIPX39Vsel+Z8Qd8fTSXcUcj/JK3m 0pIa8v3jJnZrsYMdbMikgBR4qNH15N/JhsXbLO/keUca0Jz7jGNsMkGh/PcHA+u0QgDHkxGsnnxB JOITl4TNWUeSUHqRQYs0WeluuPFPErSU6YRAcZyysM68bwLZ1WFyAf/jrshej1mV6v7gRcj+xCDY 8J5LNPRMNtSdz1t7dkyZBeA3eA0KBpQv2Qw3peNvzMmNj03qdb5PKUm97O5WZUcP0xt6AUoNN67f 00gP56S3Z1ABixlDDi0qnP67kEnphoItnhE7HEwTB2nMH/jMmIN35XpsYgxy6qC9//5vSYJmbGzC JsavC2vyw+zsdbn5QF3I5ZQRgVd1BnNUcLXtrS39nu8H70KBgZKP7I2f0UQge5Lw4OKs89R6+jXL SJMAfS6JDcQOQKJjHFv+qUNUOxoG54O3I6VfsVS3U0viWW9zf0YFBk/sQkd0j1LPM6no8OR4jJ3K 4ponm+xo5zh2aCKBkvvEASt58JJ4i7KCCywPCTTZbJw2dyeV46omNHxOE1H0OFmRTlygNMQ/UBCa 10HHJTlq09CCVnlYH6ZubudfFu19l/cti+dEoIqNA7axkO9W2bC5uWUbm93BxcaDr4/iUOpVLZfr UVBTqVZh+Nbt5GOQlkrDINlLu83MvrSVlVh69ilTY5SmkM+LQsF5J7Pzbp5/PqNcOpZBO57AhKIC Lj5LSzN269Y9aXYNDY5Yaaekhx/mEvUMpmodnV1i6dPh85I/o8ywr3fU0qn2IDvTEzqlFc0qErwY DQJkXlyaU6lJoCKIMSYUnZGSvMf490FrrxW+iVfjTlGgOtndu9q/pZPdvVoQjRndKaZSiaVA1dGu zIoHEWWvT93sna7NVw1DipjLBRGw3bstamf5ze+p9NlsTVROqIYZNj2NagJnrV/f6oJL4mDJ19a5 M/uHUWVVD61DqpoXWxaurS5auUSHcE0Du7muvByZHWfyeT+yLW5mwGts5MlGyHAIaIyspFLe8sdk dH1jWZ6GkbTMvlOCTb18Im4WgPna+rIyHi/vBjXSMzFxQ1IvrrPVrS6du/JkaqC4S8Ug2+JBCZkY gguMer6DAFdOo+ywpBlCBcxwww72j6iZQPAjKFHewbVC6oZmA98DsRR9LbqGmLn29PTa02ffKIvj WPCZdCnFu4LKcQIT1H3qCOWyAhVYlduCnXxFjlR8brP7lQO4Uae1RCxu80F5Apb00gIzIe73majY q7MVfMWio+15ZwHisnATy5POaf3xaX8mK/B1SFrdX7X13GVDZpoIXJoNbGGllMTEDrpO2C9uELpe F7XgIM3NTelpvLg4K8UERl2QYRFDX/UEA8k5GaYyh7eysioCJyVhT3evsCK6iK9fT6mcfvUav78Z fX4M4rhCo+yJPlY8IBvVXbkLgQeVytsKiJ98/DsKmi9fPRUj3bOYVO14gYnxB/oEnKwnT+9LhO/R o680loO8DcoJBK9q1akKcZVRFe3otO5Cr4InphUMUrMNqDkQmFGRAFtDrSKakALmZ7vpZjKOhIP1 Th37TTzc3rYyKUPPIFAVi8qq3lUm2x+2QBAufkCSdkbJVENW1R7KvzhuFKGQ5D1z6gHLOzeNYl+Z NHyTi/FsVba154OyCACeWZaV0F4/quw7aGI+XixqCr+NbKzstNpP5WI9yWJb6Qyury/JlZmsBaxo p7iloCE8oi1rqbJjHsinMNALaZQAhMYULs0EN4iXyAILg0pRAjIC4wEvmouim17RiE847pls6BD6 A5LgzVMaPIynM505RnQAwqN+u2ddneJPQUmA1Io6KuUhSqajO5PKgrhpCXb4ZBJ8GFB23fdB0RQe P/nGfvmrv7H1jRW7c/sjyxe69XAh8yJAMcg9M4uC6prGlZZX5u3unU/0eWRfGhJ/h1qkjMje5paI oKfh/s0xh+jrHD9wLx+0P6slXCqok+Y6O0L5Fxy53Qep4fCcbkkosLBe0lw0zSEuV7V0CZOzXsed s9yHQ3FDMifIRRhcio/9NBTg7Iai5z3AKqwQi/XytrVl2sRe5uFAYKCc+u7x/dprscbq6R20G9ed BtDfP6TumIPTYFZgjm6qAQjN79s7OmoGpCw6cQP9ePZ55saCc0fwCRtUw7vAP4aHJ7SNyBIT0Hl6 89lQDwhiCvadOfvwg9+WhPPVq7dVnlLOriwvBE/FYXUYv37wa3v67KEs4sfHJ5UpUab/x5/8W6lF TF65qSAVzSz4o8Bd8llXysaZx48k9w3OFkmzx2hEt1yViqt3nkawcry1LudSDdryZ3HHOObrUAY4 lcq/tqwmOOJxi9SgZomlM0t7nO0dyF4XlF3FVUu3Lz52Hhy41FV0h107oiPUUs4mnZbzCk/c89TG Aj8qbm1oVm9zY0W4EmU4uuiUhWhixUWQunXzQ7t9647KJJQ+uWgpvfAdXF7GBQdDzTWrbFQUzHp6 XA8KfhNHiWsJm7Dk4YmqHPq3shs/BvyIYCOn5jfDNj3dZTdvvi9C6tzc65p2FgRQunUSMEmnRN6c nXttyzUCZDRFQUW0w2wvpSyQwKYB4u1NdQxZYG9wkcDWZuZeK0jxPWSa4GkjI2O2vDxrm1ttdufO PQX2o1arRsveHtro21IGfZdglRyb4WEBngf96yyyqtgg4kHSleuyrhwqrNl90lWHUXTOIJLUO2TS I4dTdALdntNcbvPkYyw+dXg5l5oEocvYSnmiWT0i+XNWDtJjbldSIee1UMLc2Vq37a20bly/sdvU zZPuUy5fa88TbMg04pSEu8O4vLN38jOSVU42bvYdg1pzJ/GzmpSQD13T1Zubf2M7xZIVurv1vXCQ aEowB0g5isHE2uqKvZ5+ITVRyjvws5/+9L+EuUInV/IAYO6RbIhANT52Q9ncl1/+zO7f/5Vcnq9d vaOguL21aU+efSOw/1df/NS++e430ua6fesDa8+22euVWZFFR4YmbWT0pigXBAeXi24kf8b9kmoF rlEldw0iI6FsUhDcfjfGugPargghZyrhqJFZeXor7hc4FdBFZ0ebgn1yX1uNEJ1LwJI8cCJTkOZ4 k8LDea+6xfjlaP8fGMBVtLvs875fJUpHVvNTKE4e5HIoYzIfd/bpJN8xO/vSXjz/RrpU+Okhbxwz AG56AG86VhAXSfvhpVV262oWMzPTtryyKBkXzg83ohyWA1u6+ft0E1eUxITjoN/41H/oIt5/8At7 8PAXZntp+/DDvyeMam6OALalnz989Eu9lxIR+gMl4j/5R39qU68eq1MITSHSU15PP3OlhbYOgeve WSzb2jpgumd+lDScPG8yuWZcw4wf1x5Vxh4lYtmmZ17YzdufKFDxcFnf2rZ8rtPyCeY6GQ7bx7mk 61fDgzMZG+zvOzH5s5mlnlJnGuGk/cTb01gcB7Io8CkRPxnal7v3ycbJzqRW87Z+psZBcoayt+yT fJPzWpgL+NT9xWdXnr47CXRf0DpI7C9ZFoTWeqQzeGCoBzg+kZT7rEd1uNjgLa3LZj4rM1DKQFL9 +MBiG7gxcrnQsm5vE2WBCzfue3//oEsPFzGp8M8WpS3geZRr+4+F76mfU8+5Gh9GkSNUL+ki4ZEA E6fK4jaAtaF6Subkw8u+6PlqpjRobRGo6MaBU/X29iXmJguSONZ3SGNq065N3hajH8E+aBaco0ff fKkS+sqVO3bz5oeSUiHAEdhR+9zY2nZlDjqKwvT201gqaNKvrmmE6ERs9Rq3ysswGH4+AnS6wQp6 Apgq28mDimsVjMqriJMrvJwbuBS5StK+OeegFUjUF7qErXABVp1M2YyyxnLwKOwAnMsSQYrAJwul gAEQprxkOFsXHbpIr19+L2Y75R+l1tLSkm13FrWNerK20abOqX0Pz0kd0L2UdRcKtWABxsPgL9rm ZEdQHDBswLtwbw/HlviNHpicUxeHmOOMYILdHbCi8fFr+o5vvn2grIksL0rFcA5cgolgmtc2vnz5 RKROMkCkYd67+6FoC+BwfGbMcOlQ+oB9AKjTGbtz5wP9n+yJYebZ2Vc2NDQqAiolIO/ZKm/KUqxS abfPPv+x43BpHureFRatRaa49RnRg1aZ8ZqQALw92O1BNgbv08Sq4swfM44ELAXGbFYlebIBl5yP PeyzWh2DMwlYBw1DK80PWcJ5YVrVWmZ3fq4/rfY9ZkK1MYvE6yMP66g6PsmgjyvOEUZAU0+ybMa2 z2zvfHtRE93YWJbkMDQGSJLbRcTgmFdDRwpN9VHRC5A//vKrn6uFn8/3yTWZG12BA2mX7Q2B8wwT A97fvfuJQPDHjx/UMid/zlF2xeNQDxox4yIAMdaD4QQkUkrBr37zc1EOYKbDXpc5RJDgbZOLeEGf v7W1GkaiKirx6AL29faHQer65EGc9WP7kUbmc5l5jFmdjH8JRJlscMZZsdJO2QrdvfbxR5/b5NX3 NIvodBuXzHG11OPr/e/VhCKPH2wA1BlpVaAKsj+nFat4YFLSumlqVpQUTQBQBYSqpmFbdc4OjwEH 7duZZlitomSUDI432VkGkdqYy9Hy9SdecfsP+jsu2MwdbV6LROZu8onjdvTBYOOQJ2eNr5XUJYtA fDieuc5O29jYssoZZVhkMgwB37p5V9kVulYEEzIlbmgvwdM2Pz8jCZgFSseN1SCQh2ZZUYE1Kii4 zLUbnbLvyLFsbrpdfJOCdIv/e7ZQ006GSLq+JvLqjRt3vYtZ3NQoDIE1X+i1noJ7CvJ6ymfeH0X0 dGOracBYSN3cFEMJ4Ve7RUnEZMJoD+99MUWmWbdrZ/88qO9ov7Cpv3H9Pcu2d9ng0ERC+BJYKzyE Km9Hutk75rkV2K1AxXUVpV7eneATQfSOzg7rUCbdadmI2+kk1fHqfeTpI+Ta99IAAANISURBVGRz DlvZw8w+36WkiKAahybJpYhKDzVpmjOs1pTV7DpN910D40HvT07KN393q9c2p8YN/z/mkUhmsM2N jCTHxY//6S7tK5lSZ66mhEmWMTI8qUCGxArBwiVU8PTr8la/v7sBKyH7QCOLGwgZFgIB7HPGdlxS Jjn9H0tAiQPoaFHOEVQoNyFn8nSHRc8xIWh4yeYMezJR2PF8HyROV8QA6BdgpteMjUyI4EoWFo8r JR7ZI/sGGx+JZ/aJ8rG9o10/J8ASjJMSNhwZsiqysPHx69bXP2rZ9ty+88XusSXplJe2UX3zNFZG w/eeDZKRkVm960c7DcS7feK0aaA9a+2RnlC7J6LUU+v74KB9POo+zb7tG49DBq11cyImo7q5KWi1 4JecOqFx11nEpKzNkrnvUt41f09z2Rt/ftDrk69tSI2PACNrgeqA74nbGgMWwO1pzlPoKV0pW1s2 ZQNXrtqr1090A0L8ZJ5uu4hbcFEgdm/vkDpjKoMZQpY3YCPdgvEV9M0JbnTgcNkhM0MX66BMlUWQ wuuPGcTiTlESMJSUYE7MLjr3ChytESvkWJHF+XXB/ysNn4lKAw0BGVio64dzMZb1WzqOmGZ4ACYT Q0qG7AwJ6w4FQIICOBzOOhhVEKz6+oasQEYnXaz9Wb4Cchzr4vyFwKwb/oTnKRXIn0ADHH8vAd/9 UkgOJUcgXSVwi6mNw2PE4dSJw+63AwPWQeugzCH5u/oN2LhhrTCt2PWKu3Fay0mBbgJx2ODzUYEy uc3N+/euT8IGjCs8i5IZb9yy2vFJZKXO6N5rLK1r7GA0hNrEsD6tp7XLi2StYhXbLW9Zuew8K6ci VFRSTU+/EC+JAV86ZB3tnQo+2Qx65SPyF4wOOZwTcX/EDKdmicKHlRqhk02HDDs8PClaAhrx/Bym +/DImM4tM3mMzKjcTO1ZZy4nQDuWfGR/BEu61ig0SA8+McQbO4g81BjLwYjCbeOcjkMDgNEh8DcC LgPbgPQ9PYOW6/KMCaFB/mYUiDlH+FzsL9tHwyeVdgWHg5aSSL3OydZkRmyfyre3OH+pSFcITjux I/euWmnebPA5P4KVA+phfIZE5JjYW/ysyNE8bEVct3kG+f8AuXnjroZ6RI0AAAAASUVORK5CYII=