2017 STAR Communities Update

Indicator 13: Equitable Access and Proximity

Background:This analysis has been developed in support of King County’s participation as STAR Community Rating System. The method is based the Leading STAR Community Indicators 2017 Methodology Guide, due to gaps in available data, it does not include all the community conditions that are included as outcomes for this outcome measure.

Data limitations: Community conditions that are not included in the analysis include emergency response times and digital access.

Geographic scope of analysis: This analysis and report covers all of King County.

Demographic Assessment

Demographic Characteristics:
A consolidated demographic score (ESJ Score) was calculated using the US Census Tracts of King County. The source layers for the ESJ Score were: People of Color (people who don’t identify as white and/or are Hispanic or Latino); English Proficiency; and Median Household Income. The 2015 ESJ Score source layers came from the 2010-2015American Community Survey 5 year average. Each demographic source is classified into quintiles. A score is assigned to each Quintile class ranging 1 - 5. The ESJ score for each tract is the sum of 33.3% of quintile score for each of the three source layers. A lower score indicates less diversity, higher income, & higher English proficiency. A higher score indicates more diversity, lower income, & lower English proficiency.

Distribute Race/Ethnicity and Household Income Characteristics into Quintiles:
Because there are only 13 discrete values possible for the ESJ Score, and the distribution of those values among the tracts in King County, it is not possible to create quintiles (five value ranges with equal quantities of geographic features in each range). Instead value ranges were created using the Jenks natural break classes which are based on groupings inherent in the data. We determined that natural breaks worked best with the 2015 data.

The overlay buffers outlined in Category Specific Guidance did not provide adequate descriptive detail. The buffers and methodology used will be detailed below.

Methodologies

Demographic Characteristics

Consolidated Demographics: People of Color, Income, & English Proficiency Census Tract, 2010-2015 American Community Survey

2015_ConsolidatedDemo.mxd

Data Input

  • 2010 Census Tracts for King County - Conflated to King County Parcel Boundaries
  • Location: KC GIS Spatial Data Warehouse: Census Subject Folder
  • Name: tracts10_shore_area
  • Source: U.S. Census Bureau
  • Race Ethnicity, Income, and English Proficiency
  • Location: Maint database, ullrichm schema
  • Name: demographic_index_2015_area
  • Source: U.S. Census Bureau 2010 – 2015 5-Year American Community Survey

Processing and Generated Data

  • Associate Race Demographics with Census Tracts:
  • add an RE_STAR_Scorefield to demographic_index_2015_areato collect Race/Ethnicity results
  • sort on “Percent People of Color” field into 5 Quantile classes
  • select records from “Percent People of Color”field that are in the first Quantile class
  • calculate RE_STAR_Score to 1
  • repeat selection and calculation for 4 remaining classes (class 2 gets a score of 2, class 3 gets a score of 3, class 4 gets a score of 4, class 5 gets a score of 5)
  • Associate English Proficiency Demographics with Census Tracts:
  • add an ESL_STAR_Score field to demographic_index_2015_areato collect English language proficiency results
  • sort on “Percent of ESL Speakers” field into 5 Quantile classes
  • select records from “Percent of ESL Speakers”field that are in the first Quantile class
  • calculate ESL_STAR_Score to 1
  • repeat selection and calculation for 4 remaining classes (class 2 gets a score of 2, class 3 gets a score of 3, class 4 gets a score of 4, class 5 gets a score of 5)
  • Associate Median Household Income Demographics with Census Tracts:
  • add an Income_STAR_Score field to demographic_index_2015_areato collect median income results
  • sort MedianHouseholdIncome field into 5 Quantile classes
  • select records from MedianHouseholdIncomefield that are in the first Quantile class
  • calculate Income_STAR_Score to 5 (reverse order to highlight people of lower income)
  • repeat selection and calculation for 4 remaining classes (class 2 gets a score of 4, class 3 gets a score of 3, class 4 gets a score of 2, class 5 gets a score of 1)
  • Calculate total score for consolidated demographics
  • Add a total score field to ESJScore_2015 to collect
  • Calculate score: (value in RE_STAR_Score + value in ESL_STAR_Score + value in Income_STAR_Score)*0.333 [same as 33.3%]
  • Organize consolidated demographic score in to classes
  • Add the field ESJ_Class_2015 to Consolidated_Demographics_2015_ACS
  • Sort the score into 5 classes using Natural Breaks (Jenks) algorithm
  • Select first class where the weighted score range is smallest
  • Calculate Class field to 1
  • repeat selection and calculation for 4 remaining classes (class 2 is calculated to 2, class 3 is calculated to 3, class 4 is calculated to 4, class 5 is calculated to 5)
  • Result
  • \\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\ Consolidated_Demographics_2015_ACS

GIS Analyst: Mary Ullrich & Paul McCombs–June 20, 2017

Demographic Assessment Guidance, Step 5: Walk Distance

King County does not have a pedestrian network to use for computing walk distance. Instead, Cartesian buffers as indicated by population density as described in the technical document were used to identify areas served by foundational community assets. After initial analysis, we determined that prescribed buffers did not provided significant quantitative differences in our demographic classes in several cases.

After testing several options we used the described buffer distances, different graduated buffer sizes, and one buffer size depending on the community asset. These are described below.

Community density categories as described in the documentation were not used. Because low and intermediate low classes were never handled separately in the prescribed analysis, low and intermediate low are combined in to one population density class. This new class is called low and is less than or equal to 7.5 people per acre. The population density data sets were created by joining total population with 2010 census block groups. The area of the block groups were converted to acres and then the population density was calculated by using the formula total pop/area of census block group in acres. Total population and census block groups were provided by the U.S. Census Bureau 2010 – 2015 5-Year American Community Survey.

Public Transit Facilities: A ¼ mile buffer service area was created for all Transit Facilities (aka Bus Stops) regardless of block group population density. The analysis determined the average percentage of tract area served. This analysis assumes an even distribution of population across census tracts. The percentage of tracts served is determined by comparing the area of the service area with the area of the tract that the service area intersects.

Transit Service Levels: transit trips through each Census Tract were used instead of transit service hours. This analysis assumes an even distribution of population across census tracts. The number of tracts served is determined by whether a transit trip touches the census tract.

Public Libraries: Buffer service areas were created ½ mile from libraries in high density areas, ¾ mile from libraries in intermediate density areas, 1 mile from libraries in low density areas as prescribed by the document. The analysis determined the average percentage of tract area served. This analysis assumes an even distribution of population across census tracts. The percentage of tracts served is determined by comparing the area of the service area with the area of the tract that the service area intersects.

Public Schools: ½ mile buffer for high density, ¾ mile buffer for intermediate density, 1 mile buffer for low density as prescribed by the document. The analysis determined the average percentage of tract area served. This analysis assumes an even distribution of population across census tracts. The percentage of tracts served is determined by comparing the area of the service area with the area of the tract that the service area intersects.

Public Spaces: 1,000 foot buffer for high density, 2,000 foot buffer for intermediate density, 3,000 foot buffer for low density to show differences. The analysis determined the average percentage of tract area served. This analysis assumes an even distribution of population across census tracts. The percentage of tracts served is determined by comparing the area of the service area with the area of the tract that the service area intersects.

Healthful Food: ¼ mile buffer for high, 1/3 mile buffer for intermediate, ½ mile buffer for low density. The analysis determined the average percentage of tract area served. This analysis assumes an even distribution of population across census tracts. The percentage of tracts served is determined by comparing the area of the service area with the area of the tract that the service area intersects.

Health and Human Services: ½ mile buffer for high density, ¾ mile buffer for intermediate density, 1 mile buffer for low density as prescribed by the document. The analysis determined the average percentage of tract area served. This analysis assumes an even distribution of population across census tracts. The percentage of tracts served is determined by comparing the area of the service area with the area of the tract that the service area intersects.

Data Input

  • 2015 population density
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name: PopulationDensity_BlockGroup_2015
  • Source: U.S. Census Bureau 2010 – 2015 5-Year American Community Survey

Processing and Generated Data

  • Prepare PopulationDensity_BlockGroup_2015 to create variable size buffers based on population density
  • Add PopDensityClass field (string 20 chars)
  • Add WalkDist_feet field (float)
  • Add WalkDist_food_feet field (float)
  • Select block groups that have a low density (<= 7.5 people per acre)
  • Calc PopDensityClass = ‘Low’
  • Calc WalkDist_feet = 5280
  • Calc WalkDist_food_feet = 2640
  • Select block groups that have an intermediate density (> 7.5 and <= 10.8 people per acre)
  • Calc PopDensityClass = ‘Intermediate’
  • Calc WalkDist_feet = 3960
  • Calc WalkDist_food_feet = 1742.4
  • Select block groups that have a high density (> 10.8 people per acre)
  • Calc PopDensityClass = ‘High’
  • Calc WalkDist_feet = 2640
  • Calc WalkDist_food_feet = 1320

GIS Analyst: Paul McCombs–June 20, 2017

Categories of foundational community assets

2015 Public Transit Facilities and Service Levels

2015_Bus_stops.mxd

Data Input

  • 2017 bus stops
  • Location: KC GIS Spatial Data Warehouse: Transportation Folder
  • Name: Busstop_point
  • Source: King County Metro
  • 2015 consolidated demographics
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name:Consolidated_Demographics_2015_ACS
  • Source: King County GIS Center

Processing and Generated Data

  • Buffer bus stops to ¼ mile
  • Dissolve buffer to make one feature => busstops_buffer_poly
  • Add BusStopService field to Consolidated_Demographics_2015_ACS
  • Intersect buffer with Consolidated_Demographics_2015_ACS => busstops_intersect
  • Join busstops_intersect to Consolidated_Demographics_2015_ACS
  • Calculate the percent of area served:
  • BusStopService =busstops_intersect.Shape_Area /Shape_Area
  • Calculate null values to zero
  • Remove Join
  • Calculate average percent of tract area served
  • Summary Statistics geoprocessing tool
  • Input Table: Consolidated_Demographics_2015_ACS
  • Output Table: busstops_sum
  • Statistics field:BusStopService
  • Statistics type: Mean
  • Case field: ESJ_Class_2015

Number of Tracts Served / ESJ Score: Natural Breaks / Average Percent of Tract Areas Served
92 / 1: 1.0 - 1.7 / 56.39%
54 / 2: 1.8 - 2.4 / 51.72%
82 / 3: 2.4 - 3.0 / 64.22%
80 / 4: 3.1 - 4.0 / 76.60%
89 / 5: 4.1 - 5.0 / 81.17%
Community Norm: / 66.02%

GIS Analyst: Paul Mccombs – June 21, 2017

2015 transit trips

2015_Transit_Trips.mxd

Data Input

  • 2017 bus routes
  • Location: KC GIS Spatial Data Warehouse: Transportation Folder
  • Name: Routes_line
  • Source: King County Metro
  • Number of bus trips in and out bound
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\TransitData\
  • Name: RoutesTrips171
  • Source: King County Metro
  • 2015 consolidated demographics
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name:Consolidated_Demographics_2015_ACS
  • Source: King County GIS Center

Processing and Generated Data

  • Add BusTripsService to Consolidated_Demographics_2015_ACS
  • Determine the number of trips each bus route makes in and out bound:
  • Append Inbound and Outbound pages from RoutesTrips171 to produce BusTripsRaw
  • Frequency forBusTripsRaw on RTE with summary of Count_of_IB_Trips
  • New Table: BusTrips_freq
  • Add RTE_string (Text 3 chars) to BusTrips_freq
  • Clac RTE_string = RTE
  • Intersect Routes_line with Consolidated_Demographics_2015_ACSto create bustrips_intersect
  • Determine total trips in each census tract:
  • Frequency for bustrips_intersecton GEO_ID_TRT & RTE_NUMto create bustrips_intersect_freq
  • Join : BusTrips_freq to bustrips_intersect_freq on RTE_NUM/RTE_string
  • Frequency for joined tables onGEO_ID_TRT with summaryof Count_of_IB_Trips to create bustrips_count_by_tract
  • New table: census_ID_and_number_of_trips
  • Join bustrips_count_by_tract to Consolidated_Demographics_2015_ACSon GEO_ID_TRT
  • Calculate BusTripsService = BusTrips_freq_Count_of_IB_Trips
  • If BusTripsService is Null calc = 0
  • Remove Join
  • Summary Statistics geoprocessing tool
  • Input Table: Consolidated_Demographics_2015_ACS
  • Output Table: bustrips_sum2
  • Statistics field:BusTripsService
  • Statistics type: Mean
  • Case field: ESJ_Class_2015

Number of Tracts Served / ESJ Score: Natural Breaks / Average Number of Trips
92 / 1: 1.0 - 1.7 / 333
54 / 2: 1.8 - 2.4 / 376
82 / 3: 2.4 - 3.0 / 575
80 / 4: 3.1 - 4.0 / 560
89 / 5: 4.1 - 5.0 / 697
Community Norm: / 508

GIS Analyst: Paul Mccombs – June 21, 2017

2015 Public Libraries

2015_Libraries.mxd

Data Input

  • 2017 libraries
  • Location: KC GIS Spatial Data Warehouse: Admin Subject Folder
  • Name: common_interest_point
  • Definition Query: ‘CODE’ = 390
  • Source: King County GIS Center
  • 2015 consolidated demographics
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name:Consolidated_Demographics_2015_ACS
  • Source: King County GIS Center
  • 2015 population density
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name: PopulationDensity_BlockGroup_2015
  • Source: U.S. Census Bureau 2010 – 2015 5-Year American Community Survey

Processing and Generated Data

  • Turn off all PopulationDensity_BlockGroup_2015 fields except:
  • OBJECTID
  • GEO_ID_GRP
  • PopDensityClass
  • WalkDist_feet
  • Overlay libraries on population density
  • Identity geoprocessing tool
  • Input Features: common_interest_point (defined to libraries)
  • Identity Features: PopulationDensity_BlockGroup_2015
  • Output Feature Class: libraries_point
  • Create Library Buffers
  • Buffer geoprocessing tool
  • Input Features: libraries_point
  • Output Feature Class: Libraries_buffer_poly3
  • Distance:
  • Field = WalkDist_feet
  • Dissolve Type: ALL
  • Add LibraryService field (float) to Consolidated_Demographics_2015_ACS
  • Intersect Libraries_buffer_poly3 with Consolidated_Demographics_2015_ACS to produce Libraries_Intersect
  • Join Libraries_Intersect table to Consolidated_Demographics_2015_ACS on GEO_ID_TRT
  • Calculate the percent of area served:
  • LibraryService =libraries_intersect.Shape_Area /Shape_Area
  • Calculate null values to zero
  • Remove Join
  • Calculate average percent of tract area served
  • Summary Statistics geoprocessing tool
  • Input Table: Consolidated_Demographics_2015_ACS
  • Output Table: libraries_sum
  • Statistics field:LibraryService
  • Statistics type: Mean
  • Case field: ESJ_Class_2015

Number of Tracts Served / ESJ Score: Natural Breaks / Average Percent of Tract Areas Served
92 / 1: 1.0 - 1.7 / 24.28%
54 / 2: 1.8 - 2.4 / 20.60%
82 / 3: 2.4 - 3.0 / 25.76%
80 / 4: 3.1 - 4.0 / 30.92%
89 / 5: 4.1 - 5.0 / 46.94%
Community Norm: / 29.70%

GIS Analyst: Paul mccombs–JuNE 21, 2017

2015 Public Schools

2015_Schools.mxd

Data Input

  • 2017 schools
  • Location: KC GIS Spatial Data Warehouse: Admin Subject Folder
  • Name: schsite_point
  • Definition Query: CODE = 660 AND SCH_CLASS = 10
  • Source: King County GIS Center
  • 2015 consolidated demographics
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name:Consolidated_Demographics_2015_ACS
  • Source: King County GIS Center
  • 2015 population density
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name: PopulationDensity_BlockGroup_2015
  • Source: U.S. Census Bureau 2010 – 2015 5-Year American Community Survey

Processing and Generated Data

  • Turn off all PopulationDensity_BlockGroup_2015 fields except:
  • OBJECTID
  • GEO_ID_GRP
  • PopDensityClass
  • WalkDist_feet
  • Overlay libraries on population density
  • Identity geoprocessing tool
  • Input Features: schsite_point (defined to elementary schools)
  • Identity Features: PopulationDensity_BlockGroup_2015
  • Output Feature Class: schools_point
  • Create School Buffers
  • Buffer geoprocessing tool
  • Input Features: schools_point
  • Output Feature Class: schools_buffer_poly
  • Distance:
  • Field = WalkDist_feet
  • Dissolve Type: ALL
  • Add SchoolService field (float) to Consolidated_Demographics_2015_ACS
  • Intersect schools_buffer_poly with Consolidated_Demographics_2015_ACS to produce schools_Intersect2
  • Join schools_Intersect2 table to Consolidated_Demographics_2015_ACS on GEO_ID_TRT
  • Calculate the percent of area served:
  • SchoolService =schools_intersect.Shape_Area /Shape_Area
  • Calculate null values to zero
  • Remove Join
  • Calculate average percent of tract area served
  • Summary Statistics geoprocessing tool
  • Input Table: Consolidated_Demographics_2015_ACS
  • Output Table: schools_sum
  • Statistics field:SchoolService
  • Statistics type: Mean
  • Case field: ESJ_Class_2015

Number of Tracts Served / ESJ Score: Natural Breaks / Average Percent of Tract Areas Served
92 / 1: 1.0 - 1.7 / 61.25%
54 / 2: 1.8 - 2.4 / 63.19%
82 / 3: 2.4 - 3.0 / 73.80%
80 / 4: 3.1 - 4.0 / 75.30%
89 / 5: 4.1 - 5.0 / 76.17%
Community Norm: / 69.94%

GIS Analyst: Paul McCombs–June 21, 2017

2015 Public Spaces

2015_PublicSpace.mxd

Data Input

  • 2017 Park Service Area
  • Location: \\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb
  • Name: ParkServiceArea_2015
  • Source: King County GIS Center
  • 2015 consolidated demographics
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name:Consolidated_Demographics_2015_ACS
  • Source: King County GIS Center

Processing and Generated Data

  • Overlay parks on population density
  • Select block groups from population density that have a low density <= 7.5 people per acre
  • Select parks that intersect the selected block groups
  • Buffer parks to 3,000 feet
  • Repeat steps to create 2,000 foot buffers for intermediate population density of 7.6 – 10.8 people per acre and 1,000 foot buffers for high population density of > 10.8 people per acre
  • Merge to combine all buffers together into one feature class
  • Dissolve to create a final buffer feature class with one feature
  • Add ParkService field to Consolidated_Demographics_2015_ACS
  • Intersect ParkServiceArea_2015with Consolidated_Demographics_2015_ACS to create parkservice_intersect
  • Join parkservice_intersectto Consolidated_Demographics_2015_ACS on GEO_ID_TRT
  • Calculate ParkService = [parkservice_intersect.Shape_Area] / [Consolidated_Demographics_2015_ACS.Shape_Area]
  • calculate null ParkService values to zero
  • Remove Join
  • Calculate average percent of tract area served
  • Summary Statistics geoprocessing tool
  • Input Table: Consolidated_Demographics_2015_ACS
  • Output Table: parkservice_sum2
  • Statistics field:ParkService
  • Statistics type: Mean
  • Case field: ESJ_Class_2015

Number of Tracts Served / ESJ Score: Natural Breaks / Average Percent of Tract Areas Served
92 / 1: 1.0 - 1.7 / 81.55%
54 / 2: 1.8 - 2.4 / 86.09%
82 / 3: 2.4 - 3.0 / 88.32%
80 / 4: 3.1 - 4.0 / 88.38 %
89 / 5: 4.1 - 5.0 / 85.72%
Community Norm: / 86.01%

GIS Analyst: Paul McCombs – June 21, 2017

2015 Healthful Food

2015_HealthyFood.mxd

Data Input

  • 2017 farmers markets
  • Location: KC GIS Spatial Data Warehouse: Natres Subject Folder
  • Name: FarmersMarkets_2017_update
  • Source: King County GIS Center
  • 2014 grocery stores
  • Location: KC GIS Spatial Data Warehouse: Admin Subject Folder
  • Name: food_facilities_point
  • Definition Query: SEAT_CAP = 'Grocery'
  • Source: Public Heatlh & King County GIS Center
  • 2015 consolidated demographics
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name:Consolidated_Demographics_2015_ACS
  • Source: King County GIS Center
  • 2015 population density
  • Location: \\\gisnas1.dnrp.kingcounty.lcl\Projects\kcgis\client_services\Budget\SMARTUpdate-2017\SMARTData.gdb\
  • Name: PopulationDensity_BlockGroup_2015
  • Source: U.S. Census Bureau 2010 – 2015 5-Year American Community Survey

Processing and Generated Data

  • Merge 2017 farmers markets and food_facilities_point (selected for grocery stores) to healthyfood_point
  • Overlay food facilities on population density
  • Identity geoprocessing tool
  • Input Features: healthyfood_point
  • Identity Features: PopulationDensity_BlockGroup_2015
  • Output Feature Class: food_point
  • Create food facility Buffers
  • Buffer geoprocessing tool
  • Input Features: food_point
  • Output Feature Class: food_buffer_poly
  • Distance:
  • Field = WalkDist_food_feet
  • Dissolve Type: ALL
  • Select features from food_point where WalkDist_Food_feet is 0 and calculate to the same value as the nearest Block Group.
  • Note: one is located in the sound (Coleman Dock Grocery) and one is located in Snohomish county (Bothell Farmers Market)
  • Add HealthyFoodService field (float) to Consolidated_Demographics_2015_ACS
  • Intersect food_buffer_poly with Consolidated_Demographics_2015_ACS to produce food_Intersect
  • Join schools_Intersect2 table to Consolidated_Demographics_2015_ACS on GEO_ID_TRT
  • Calculate the percent of area served:
  • HealthyFoodService =[food_intersect.Shape_Area] / [Consolidated_Demographics_2015_ACS.Shape_Area]
  • Calculate null values to zero
  • Remove Join
  • Calculate average percent of tract area served
  • Summary Statistics geoprocessing tool
  • Input Table: Consolidated_Demographics_2015_ACS
  • Output Table: food_sum
  • Statistics field:HealthyFoodService
  • Statistics type: Mean
  • Case field: ESJ_Class_2015

Number of Tracts Served / ESJ Score: Natural Breaks / Average Percent of Tract Areas Served
92 / 1: 1.0 - 1.7 / 7.53%
54 / 2: 1.8 - 2.4 / 11.51%
82 / 3: 2.4 - 3.0 / 19.89%
80 / 4: 3.1 - 4.0 / 23.39%
89 / 5: 4.1 - 5.0 / 27.93%
Community Norm: / 18.05%

GIS Analyst: Paul McCombs – June 22, 2017