Andrew Smyth
Introduction to GIS
Final project
May 9, 2011
Seattle on Foot: A Walkability Analysis
Overview
This project was done to describe the walkability of Seattle. I identified
zones with exceptionally high walkability. I also looked at the walkability of those
areas designated as Urban Centers by King County, as well as finding an
average rating for each major Seattle neighborhood. By “walkable” I refer to the
quality of having easy pedestrian access. In a completely walkable
neighborhood, a resident would be able meet all of his basic needs without using
a car. This means that a person could walk from his house to a grocery store, a
movie theater, a shopping center, or any number of other destinations. I used a
radius of 3,000 feet as the outer limit of what is considered walkable. I also
incorporated speed limits into the design, since low-speed roads are safer and
more comfortable to walk along. Housing density was a factor as well. All of
these factors are described in detail below.
Walkability Factors
Each of the following factors was represented in the final walkability map as a
raster layer. Each layer was then assigned a weight and added using raster
calculator to create a single map.
Weight
14
14
12
12
10
10
8
8
6
3
3
Raster Layer
Grocery Stores
Bus Stops
Shopping Centers
Housing Density
Average Speed Limit
Parks and Recreation
Restaurants
Movie Theaters
Public Libraries
Elementary Schools
Middle and High Schools
Table 1: Walkability factors and weights

Grocery stores were deemed to be the most essential, and were weighted
highest accordingly.

Bus stops were ranked highly as well because public transportation
enormously enhances a person’s ability to live with a car.

Shopping centers were ranked somewhat high as well, though better data
would incorporate other types of shopping or commercial density in
addition to major shopping centers.

The speed limit factor expresses the average speed limit of roads in each
census block, using a spatial join. This is because slower roads are
generally friendlier to pedestrians, have more crosswalks, sidewalks, and
can be crossed more easily. Lower speed limits were given a higher rank.

Housing density was included in the analysis because neighborhoods are
only truly walkable if people can live there. One paper which assessed the
value of walkability factors says of that denser neighborhoods “include
mixed-use development,…improve accessibility to [a] variety of
complementary activities, and thus, increase[s] utility.” (Leslie et. al, 93).

“Parks and recreation” included points for parks, as well as playgrounds,
community centers, locations like the zoo and the aquarium, landmarks
like the space needle, and other points of interest.

Restaurants, movie theaters and libraries were deemed to be less than
essential, but still worthy of inclusion.

Schools were included, but since they apply only to families, and because
bus service is available, they were ranked lowest. Elementary schools
were included separately from others in order to give children a smaller
walking radius (1,500 feet as opposed to 3,000 feet).
About the Data:
Layer
Common points
of interest
(narrowed to
parks and
recreation, also
shopping centers,
libraries)
Bus stops
Grocery Stores
Restaurants
Movie Theaters
Schools
Source
(links to
metadata)
King County
Website
Source scale
http://www5.king
county.gov/gisda
taportal/
Not available
Year
represen
ted
2011
King County
http://www5.king
county.gov/gisda
taportal/
www.referenceu
sa.com
Not available
2011
www.referenceu
sa.com
n/a
www.referenceu
sa.com
n/a
http://www5.king
county.gov/gisda
taportal/
“aligned to school
2011
buildings using digital
air photography,”
scale not specified.
Not available
2006
Geocoded
from
Reference
USA data
Geocoded
from
Reference
USA data
Geocoded
from
Reference
USA data
King County
Transportation
network (roads)
King County
Census Block
Groups 2010
King County
Census Blocks
King County
http://www5.king
county.gov/gisda
taportal/
http://www5.king
county.gov/gisda
taportal/
http://www5.king
county.gov/gisda
taportal/
n/a
U.S. Geological
Survey (USGS)
1:100,000-scale
Digital Line Graph
(DLG), USGS
1:24,000-scale
quadrangles
U.S. Geological
Survey (USGS)
1:100,000-scale
Digital Line Graph
(DLG), USGS
2010
2010
Urban Centers
King County
City Boundaries
King County
Hydro
King County
Address Points
(used for
geocoding
reference USA
data)
King County
http://www5.king
county.gov/gisda
taportal/
http://www5.king
county.gov/gisda
taportal/
http://www5.king
county.gov/gisda
taportal/
http://www5.king
county.gov/gisda
taportal/
1:24,000-scale
quadrangles
Not available
2010
Not available
2011
Not available
2007
Not available
2011
Table 2: GIS data used
Preparing the data:
1. Removed freeways, highways, on-ramps from transportation layer using
an attribute query.
2. King County datasets were narrowed to the Seattle boundary using a
location query.
3. Created address locator based on address point layer
4. Geocoded Reference USA, corrected unmatched addresses
Creation of the Walkability Map
5. Created a network dataset from the transportation layer.
6. Used Network Analyst to create service areas around destination points
(theaters, grocery stores etc.) with a search radius of 3,000 feet, and
breaks every 600 feet for a total of five zones.
Here is a buffer zone created around a public library
7. Used Spatial Analyst to convert the buffer zones to raster layers
Library data after being converted to raster form.
8. Reclassified raster layers so that the nearest part of the buffer zone
scored highest, and the farthest parts scored lowest.
9. Weighted each layer according to Table 1.
10. Used raster calculator to add all layers together with this equation:
outraster4 = ([Reclass_groc2] * 14 + [Reclass_bus_2] * 14 + [Reclass_spee3] * 10 +
[Reclassified Housing Density] * 12 + [Reclass_shop2] * 12 + [Reclass_rest2] * 8 +
[Reclass_park1] * 10 + [Reclass_movi2] * 8 + [Reclass_libr4] * 6 + [Reclass_elem2]
* 3 + [Reclass_hsch2] * 3)
11. Used zonal statistics to calculate average walkability scores for Urban
Centers and for Neighborhoods.
12. Prepared maps from results of the analysis.
Difficulties Encountered
Initially, I had wanted to use parcel data to create a commercial density
raster layer. Unfortunately, after spending much time attempting to join the
assessor’s data to the parcel shapefile, the data turned out to be incomplete, and
could not be used. Shopping center points were substituted.
The overlay tool in Spatial Analyst also failed to work. The tool worked
only when adding a handful of layers, and could not seem to handle all eleven at
once. Raster calculator was successfully used instead.
Besides these two problems, the analysis went smoothly, with only very
minor and easily solved issues besides the aforementioned.
Success of the Approach
This approach to walkability seems to have worked well. The final map meets my
expectations, and agrees with my personal knowledge of Seattle for the most
part. It is hard to say, of course, whether appropriate weights were given to each
of the factors. I feel confident, however, that the results are substantially correct.
There are some factors not taken into account here which might have
improved results. Elevation data was not used. Seattle is a very hilly city, and this
surely limits walking range for many people. If I could do this again, I would
definitely include that data. It would also be ideal to have a network dataset that
had sidewalk data. Unfortunately, this was not available. Also, banks and ATMs,
street junction density, location of major workplaces, bike routes, and other
commonly used factors were not chosen, and may have affected results.
Related Reading
Julian D. Marshall, Michael Brauer and Lawrence D. Frank “Healthy
Neighborhoods: Walkability and Air Pollution.” Environmental Health
Perspectives. 2009. vol 117, no 11. pages 1752-1759.
<http://www.jstor.org/stable/40382462>
This was a study done in Vancouver BC to assess the relationship of walkability
of a neighborhood, which entails greater physical activity and exposure to the
outdoors, and air pollution and its health impacts. Unfortunately, the authors do
not go into much depth in terms of discussing their GIS methodology for
determining walkability, but the factors they incorporated gave me some ideas.
Robert David Stevens. Walkability Around Neighborhood Parks: An
Assessment of Four Parks in Springfield, Oregon. MA Terminal Project.
University of Oregon, Eugene, OR, 2005.
<https://scholarsbank.uoregon.edu/xmlui/bitstream/handle/1794/1288/steve
ns_thesis.pdf?sequence=1>
This was an extensive study conducted in Springfield Oregon. The author
investigated the walkability of four different parks. It provided some good
background on what has been done in researching walkability questions (as of
2005). The research question is a little different than what I am doing, but some
of the factors he used for his scoring system may be applicable to my project.
Prasanta Bhattarai, A GIS Based Walkability Analysis. Independent Study
Project, University at Buffalo, 2007.
<http://www.vattarai.com/mup_project/other_projects/Independent_study_
report.pdf>
This is an independent study project of a student at the University at Buffalo. The
paper itself has some issues with writing and reasoning, but it did help me think
through what sorts of variables I ought to consider. The author considers
population density, housing density, General Commercial Area, Vacant land,
Restaurant and Ding area, Parks and Parkways, Tree shade densities, Bike
paths, bus stops, speed limits, street junction density, and traffic volume. The
maps are sometimes unclear, but the methodology seems sound. It prompted me
to create a housing density map.
Eva Leslie et al. “Objectively Assessing Walkability of Local Communities:
Using GIS to Identify the Relevant Environmental Attributes.” GIS for
Health and the Environment. (Springer Berlin Heidelberg, 2007).
<http://www.springerlink.com/content/l134ng3736002458/fulltext.pdf>
This is a chapter of a book (available online) that attempts to objectively
determine factors which would be relevant to a GIS walkability analysis. This
proved very useful, since most other attempts to find factors seem to be based
more or less on personal opinion, with little explanation. This chapter gives clear
reasons for why each variable should be considered a contributor to walkability.