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Tessa Greegor, Liz StenningUDP 422

June 1, 2007

WHITE CENTER PEDESTRIAN ACCESS NETWORK ANALYSIS

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WHITE CENTER PEDESTRIAN ACCESS NETWORK ANALYSIS

Project Summary

This purpose of this project is to examine pedestrian accessibility in White Center through a GIS spatial analysis. This analysis is a follow-up project from the We Create White Center: Neighborhood Action Plan produced by the University of Washington Spring �007 Studio.� The first section of the plan, the Pedestrian Element, identified the goal to enhance a sense of identity and create a walkable community. Pedestrian routes that linked neighborhood destinations were identified and assessed based on walkability. This element focused on the condition of the proposed route by evaluating safety and quality. Our project further assesses the walkability in White Center by using the GIS Network Analyst tool to examine access to key destinations, rather than the quality of the identified routes.

The community of White Center is located primarily in unincorporated King County with the northern portion in the City of Seattle, and the southern border touching the City of Burien. The northern border is SW Henderson Street/SW Barton Street, SW ��8th Street to the south, State Route 509 to the east, and 30th Avenue SW along the western border. The area is approximately 3.67 square miles.� White Center is a growing population estimated at �6,0743, with diverse communities, such as Asian/Pacific, Hispanic/Latino and African Americans. The population is also younger, on average, than the rest of King County.4

� “We Create White Center: Neighborhood Action Plan.” June �007 <http://courses.washington.edu/wcstudio/White_Center/White_Center_Neighbor-hood_Action_ Plan.pdf>� ibid3 Raster data sets provided at the official website for SEDAC: Socioeconomic Data and Applications Center < http://sedac.ciesin.columbia.edu/usgrid/>4 “A Profile of White Center.” 2001 < http://www.metrokc.gov/health/datamaps/2001-WhiteCenter-Profile.pdf>5 ibid

Downtown Seattle

White Center

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White Center is an area that has struggled with the perception of crime as well as real crime. Crime rates tend to be higher in White Center than the rest of King County.5 By focusing research on improving the walking environment in White Center, there will be long term benefits. Walking is important to improve the health of community residents, lessen environmental impacts, reduce costs associated with driving, and enhance neighborhood interactions. Research shows that people who live in walkable, mixed-use neighborhoods are more apt to be involved in their communities, knowing their neighbors and being involved both socially and politically.6

Project QuestionsTo assess walkability in White Center, we focused on access to key destinations. The following questions helped focus our data analysis.

• What are the key walking destinations in White Center?

• What percent of the population has access to key destinations within a ½ mile walking distance?7

• How many residents do not have access to destinations?

MethodologyThis process began with an initial analysis of the existing conditions in White Center. Through community meetings and interviews, the importance of pedestrian safety and accessibility was recognized, which led to our interest in pursuing this accessibility project. As a precursor to this project, the pedestrian environment was analyzed in the University of Washington Spring �007 studio course in which the We Create White Center: Neighborhood Action Plan was created. In this plan, our team focused on various approaches to enhancing pedestrian safety through the selection of pedestrian routes, however an in depth analysis of pedestrian accessibility was not assessed. Hence, our interest in this project was to pursue a further analysis of the pedestrian environment by addressing the element of pedestrian accessibility in White Center.

At community meetings and interviews, White Center residents identified places where they “live, shop, work and play,” which influenced our selection of pedestrian destinations that would be used for this analysis.8 Destinations were broken into three categories; schools, parks, and food access areas, extracted from King County parcel data.

� Leyden, Kevin. “Social Capital and the Built Environment: The Importance of Walkable Neighborhoods.” American Journal of Public Health September �003: �556-�55�<http://www.ajph.org/cgi/reprint/93/9/�546.pdf>7 A half mile walking distance is generally accepted as a walkable distance.8 February �007 White Center Community Meeting, Interview with Kathy Kiminski, Weed and Seed 4/5/�007, Interview with Vary Jackson, King County Sheriff’s Office 4/5/2007

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The next step was to create a Network Dataset through ArcView, using the King County Streets Network. Initially, this project was conducted using a selection of pedestrian routes. Upon analysis this method proved to be an unrealistic assessment of accessibility. The access areas only captured the population who lived along the identified pedestrian routes. Therefore, our maps appeared as though populations located adjacent to a park were not within a half mile distance. It was determined that the street network would be a more accurate data set. Once this network dataset was created, the GIS Network Analyst tool was used to assess the accessibility of these specific destinations. Through ArcGIS, the following steps were followed in conducting this analysis:

• Create a Network Dataset using the King County Street Network.• Use Network Analyst to create a new Service Area Layer.• Add Facilities (parks, schools, grocery stores) to the new service area layer.• Buffer facilities �00 M to capture surrounding area which does not intersect the existing street

network.• Use Solve tool to create polygons within �/� mile of facilities, accessed through the street

network.• Clip �/� mile polygons to the White Center border. • Through the Spatial Analyst tool, use Zonal Statistics to join the population raster with the �/�

mile polygons, producing the population within a 1/2 mile of an identified facility.• Erase �/� mile polygons from White Center border, producing the area NOT within a �/� mile

of a facility. • Through Spatial Analyst, use Zonal Statistics to join the population raster with the area not

within a �/� mile of a facility, producing the population not within an accessible distance of the destination.

• Repeat preceding steps for each category of destination.• Union the three shapefiles produced in the above steps (1/2 mile polygons to specified

destinations) to create a layer of accessible area within White Center.• Erase the accessible area layer

from the White Center boundary to create the area within White Center that is not within ½ mile of any of the three identified destinations.

• Through Spatial Analyst, use Zonal Statistics to join the population raster with the area not accessible to any destinations, to produce the population not within a �/� mile of any identified destination.

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Upon completion of the above steps, calculations were conducted to determine the percentages of the White Center population that are not within a half mile of a park, major food access area, or school. GIS maps were produced in order to spatially display the areas of access and no access.

Analysis

Access to Parks

Twelve White Center parks were selected for this analysis. It was not appropriate to use a centroid to represent the parks; instead several points were

selected along park boundaries when delineating the service area. The results show that 84% of the population is within a ½ mile of an access point to a park.

The spatial representation of the ½ mile polygons indicates that a majority of the White Center population lies within ½ mile of a park. This statistic may surprise some visitors and even residents of White Center who are not aware of the abundance of parks in White Center.9

9 February �007 White Center Community Meeting

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Analysis

Access to Schools

Eighteen schools were selected for this analysis, both public and private. The results of the spatial analysis indicate that 80% of the population is within a half mile of a school.

The spatial representation of the half mile polygons indicates a majority of the White Center population lies within half mile of a school.

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Analysis

Food Access Areas

Six major food access areas were selected for this analysis. Corner stores, ethnic markets, and street vendors were not included. The results indicate that 33% of the population is within a half mile of a major food access area.

The spatial representation of the half mile polygons illustrates the significant portion of White Center not within an accessible area.

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Analysis

No Access to Destinations

The results show that residents have the least access to grocery stores. Most of the destinations are located along a diagonal through the center of White Center. The area with the least access is the Top Hat area, located in the east portion of White Center

Population without half mile access

Population of White Center

Percent of population without access

Parks 4,160 26,074 16%

Grocery Stores

17,581 26,074 67%

Schools 5,294 26,074 20%

All 1,545 26,074 6 %

No destinations within walking destinations

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Lessons Learned

There were several issues that we encountered in the beginning of this analysis. In our first attempt pedestrian routes were used as the network data set. The outcome created unrealistic polygons. For instance, it appeared that populations directly adjacent to parks did not have access to a park because they were not located along a pedestrian route.

A centroid for a polygon is best used for destinations with small areas or otherwise it will not accurately capture the actual access points. After first designating parks with a centroid, we decided instead to place points along the park borders.

Limitations

This study evaluated pedestrian access to six food access areas. However, this did not account for convenience stores, ethnic grocery stores, street vendors. Including more food access destinations may have generated additional access areas.

The analysis of these destinations does not represent the demographics of the population who use these specific sites. It may not be fair to assume that all the population within a half mile of a given access area will shop at a specific grocery store, use a park, or attend a school.

This analysis did not take in to account the quality of the street network or the identified destinations. For instance, while people may have access to a given destination, it may not indicate that it is a safe or walkable route.

The population data extracted from SEDAC may not render an accurate assessment of the population within the analyzed polygons. Zonal Statistics summarizes the population and makes assumptions based on values in the raster cells.

Further Analysis

• Assess quality of routes and destinations

• Look at additional food access areas, such as convenience stores, ethnic food markets and street vendors

• Evaluate the demographics of users to destinations

Survey who uses specific destinations to validate analysis•

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ConclusionsParks and schools are fairly accessible in White Center, specifically along the east/ west diagonal. However after site visits, it is evident that these park locations are not obvious due to lack of signage. Additionally, some parks are under utilized due to lack of trails, steep slopes, poor lighting and maintenance.

The largest area without access to the key destinations is the Top Hat region, located in the eastern portion of White Center. This may be a prime location for future development to occur along Myers Way South, a main arterial in the Top Hat neighborhood.

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Data Dictionary

File Name Type of Data Relevant Attributes Use Source

King County Street Network

County wide street network derived by TIGER data. Vector representation.

Transit connection, Pedestrian access,

Creation of Network Dataset

King County08/11/2005

King County Parks

Public parks in King County, vector spatial representation.

Site name, type, & shape.

Destination analysis & centroid creation.

King County03/14/2006

King County School Sites

Parcel based centroid for schools locations. Vector digital data.

School class, facility, district, city

Destination analysis.

King County09/08/2005

King County Tax Parcels

King County tax parcels and related features. Vector digital data

pres_use, ID, facility

Analysis of grocery stores and downtown destinations

King County5/10/2004

Population Data

Raster/ TIF representation of population statistics.

Area/ popu-lation

Population statistics

Socioeconomic Data and Applications Center- Columbia University