Rachael Wolber
UEP 232
12/15/11
GIS Final Paper
Project Description:
I grew up in Santa Clara County, CA, sometimes referred to as the “Silicon Valley.” The
Silicon Valley is home to some of the world’s biggest names in the tech industry (Google,
Facebook, Adobe) but according to the Census Bureau, 9% of residents live below the poverty
line. Furthermore, tech wealth has not translated into funds for public services, especially in the
case of public transportation. This, combined with sprawling residential development, leads to an
area that, as a whole, seems distinctly unwalkable. This summer, I grew frustrated because it was
difficult to reach the train I needed to take to work without a car. In Santa Clara County, there
are many people who would not like to drive, either for health reasons (there is a strong biking
culture), environmental reasons, or they simply prefer not to. Furthermore, there are many that
cannot afford a car. So, for my final project, I measured how accessible it is to access essential
services in Santa Clara County on foot. I used the “Holbrow method” (using the network analysis
tool and rasterizing the resulting data) to do a weighted analysis of services and to create a
walkability raster surface. Then, I mapped this area to block groups to find the population of
each walkability “category” and the percent of the white and non-white population living in each
category.
The services I included in my analysis are:
 Open Space (including neighborhood parks)
 Schools
 Healthy food options/grocery stores
 Bus Stops
 Transit Nodes (includes light rail stations and train stations)
 Banks
 Places of Worship
 Health Care Facilities
 Libraries
 Shopping Centers & Malls
 Major Employers (over 500 employees)
Data Layers:
Source
US Census Bureau-Tiger
2010
US Census Bureau-Tiger
2010
US Census Bureau-Tiger
2010
California Protected Areas
Name
Tl_2010_06_county10
Description
California Counties
Tl_2010_06085_roads
All roads in Santa Clara
County
California Places
(incorporated areas)
All open space areas in
Tl_2010_06_place10
CPAD17_Units
Database (CPAD)
California Metropolitan
Transportation Commission
(MTC)
California Metropolitan
Transportation Commission
(MTC)
California Metropolitan
Transportation Commission
(MTC)
Reference USA
Bus_Stops
Reference USA
SCC_Grocery
Reference USA
Reference USA
Reference USA
SCC_Schools
SCC_Banks
SCC_Shopping_Centers
Reference USA
Reference USA
SCC_Libraries
SCC_Employers
US Census Bureau
tl_2010_06085_bg10
US Census Bureau
DEC_10_SF1_P3
US Census Bureau
tl_2010_06085_areawater
Transit_Stations
California, from small urban
parks to large national parks
and forests.
Bus Stops
Transit Stations (light rail
and train)
Facility_Publication_20101111 Health Care Facilities (both
acute and long-term care)
SCC_Worship
Places of Worship (866112
Synagogues and 866107
Churches)
“Healthy” food options
(541101 food markets,
541105 grocery stores &
markets, 543101 vegetables
& fruits)
Schools (821103)
Banks (602101)
Shopping Centers & Malls
(651201)
Public Libraries (823106)
All employers with 500+
employees
Santa Clara County Census
Block Groups 2010
2010 SF 1 Population data
(for Santa Clara County
Blockgroups)
2010 Tiger Water (Area and
Lines)
Analysis:
The steps I took to complete this project are
1. Collected the data listed above and imported it into ArcMap, using the plot xy data
feature to map the data from reference USA.
2. Made sure all data is projected into NAD_1983_StatePlane_California_III_FIPS_0403
(Meters) as this is what the California Department of Transportation recommends. Used
the “project” tool to change the projection when necessary.
3. Clipped all data to Santa Clara county, as what was not from ReferenceUSA was for the
whole state. Used the selection tool and “delete feature” function to delete the few points
that were not in Santa Clara County, even though their addresses indicated that they were.
4. I created a background raster from the Santa Clara County polygon, and reclassified all
cells to 0—this served as my extent, mask, and snap raster. I set my raster cell size to 10
meters.
5. I used the feature to point tool to create points from the centroids of the open space
polygons, so I could include them in my analysis.
6. I created a new Network Area from the streets layer. I had to do this step twice, as at first
the Tiger roads were improperly drawn so their intersections did not show up. Barbara
helped me to fix this, but I’m not sure what tool we used.
7. For each service category, I used the “Service Area analyst” from the Network Analyst
toolbar to create walkability polygons. The “facilities” were the data layer that
corresponded to each service type. As a default break, I used 400 meters (approximately a
quarter mile) as that is typically used as a measure of what is “walkable.” However, as I
used the centroids of open space polygons, I set the break to 800 meters to correct for the
fact that the point is not at the edge of the park. This worked as most of the parks were
small, and there were only a few problems with some of the larger open space preserves
where the midpoint was nowhere near a road. In the “polygon generation” tab, I merged
the buffers into one polygon for each service type.
8. I exported the service area results as separate data layers. Note that I did this step twice,
too, as I first did it using the improperly drawn streets. But I knew that something was
wrong when the polygons showed up as slightly larger straight lines!
9. I converted these polygons to raster cells using the polygon to raster tool. I then
reclassified the raster so that cells within the buffer polygons received a value of 1, and
cells outside these polygons received a value of 0.
10. I weighted these services based on what I considered to be “essential.” The weights
added up to a maximum score of 10.
Point Type
Weight
Health Care
2
Grocery Store
2
Major Employer
1.5
Open Space
1
School
1
Transit Station
.75
Bus Stop
.5
Place of Worship
.5
Banks
.25
Library
.25
Shopping Center
.25
Total
10
Then, I used the raster calculator in the spatial analyst toolbar to add up these raster cells,
creating a walkability raster surface.
11. I joined the population statistics from the SF1 data to my block groups layer.
12. Using the zonal statistics as table tool, I calculated the average walkability of raster cells
within each census block. I joined this table to Census block polygons, to show
walkability by Census block.
13. I gave each level of walkability a category and selected out each category using the
select by feature category and Boolean logic (for example, select where meanwalk <1
AND NOT 0)
13. I created new layers from each level of walkability.
14. I added new columns and used the field calculator to determine the percent of total
population, percent of white population, and percent of non-white population in each
walkability level.
Block Group
Walkability
Score
Not Walkable
(0)
Low (0-1)
Medium (1-3)
High (3-6)
Exceptional (69)
Total
Number of
Residents
Percent White
Population
Percent Non-White
Population
2277
Percent of Santa
Clara County
Population
.04
.18
.08
411879
1228118
139368
0
23.12
68.93
7.82
0
25.44
66.42
8.0
0
21.07
71.15
7.7
0
178`642
100
100
100
Similar Analyses:
1. Martens, Karel et al.“Public Transport versus private car GIS-based estimated of accessibility
applied to the Tel Aviv metropolitan area” Springerlink. April 1, 2010.
http://radboud.academia.edu/KMartens/Papers/550771/Public_transport_versus_private_car_
GIS-based_estimation_of_accessibility_applied_to_the_Tel_Aviv_metropolitan_area
This paper presents “Urban Access”, an ArcGIS extension that makes it possible to compare
accessibility levels by transport modes. In creating this tool, Martens concludes that “detailed
calculations are necessary at the address level to calculate transit accessibility” as a few minutes
in the car do not matter much, but if you are coming from one side of the neighborhood or the
other really matters. Furthermore, the study argues for simpler accessibility analysis, as most
studies are comparative in nature anyways. This study focuses on the Tel Aviv Bus System, as it
is the most prominent public transit system in the region.
2. Handy, Susan and Clifton, Kelly. “Evaluating Neighborhood Accessibility: Possibility and
Practicalities” Journal of Transportation and Statistics. September/December 2001.
http://stuff.mit.edu/afs/athena/course/4/4.293/Phoenix/JTS_paper.pdf
This paper identifies the factors that contribute to accessibility at the neighborhood level and
explores different ways that planners can evaluate neighborhood accessibility. It outlines the 3
basic types of accessibility measures: cumulative opportunities measures, which count the
number of opportunities within a given distance of travel time; gravity based measures, which
weigh the amount of activity at different destinations by cost, time or distance; and random
utility theory inspired measures, which reflects the utility of a particular choice relative to the
utility of all other choices. The article also explains the merits of neighborhoods as the primary
unit of analysis. It also lists commonly studied types of activity such as grocery shopping, health
care services, restaurants, and convenience stores. Lastly, it explains the importance of
transportation data (how satisfied are people with public transportation?) as well as land use data.
3. Carr, Lucas et al. “Validation of Walk Score for Estimating Access to Walkable Amenities”
British Journal of Sports Medicine, April 2010.
http://bjsm.bmj.com/content/early/2010/05/12/bjsm.2009.069609.short
This article presents “Walk Score”, a publically available website that estimates neighborhood
walkability based on proximity to 13 amenity categories (grocery stores, coffee shops,
restaurants, bars, movie theatres, schools, parks, libraries, book stores, fitness centers, drug
stores, hardware stores, clothing/music stores). This article presents walkability from a public
health perspective rather than an environmental sustainability or social justice view. The article
focuses on testing the validity of the “Walk Score” program, which uses Google Map data by
comparing it to objectively measured GIS data. In order to compare the two, researchers mapped
a 1-mile buffer zone was created around each point address. They then created an algorithm to
award points based on the distance to the closest amenity in each category, up to one mile.
4. Schlossberg, Marc. “From TIGER to Audit Instruments: Measuring Neighborhood
Walkability with Street Data Based on Geographic Information Systems.” Transportation
Research Record: Journal of the Transportation Research Board. February 2007.
http://trb.metapress.com/content/w665h53164237126/
This article focuses on how GIS can be used to enhance the effectiveness of TOD (Transit
Oriented Development),that is, making sure there is pedestrian access between transit stops and
the immediately surrounding area. Schlossberg measures “the full picture” of walkability by
including metrics that measure quality, proximity, and connectivity. With regards to my project,
I think I will have to narrow my focus to only proximity and connectivity. He uses a technique
called “pedestrian catchment areas” to measure the proximity of start points to destinations. This
seems to be a similar method to a buffer analysis. He has a very detailed description of the steps
he took in GIS to conduct his analyses, which will be a useful reference point for me.
Other sources:


“Walk Score” Website
Gabe Holbrow’s GIS Project
Difficulties:
First, just finding and importing the data required for this project was pretty difficult. California
has very little GIS data publically available, so obtaining most of the point data sets involved
downloading reference USA datasets 50 at a time (for up to 800 records per point type) and then
pasting them all into one excel spreadsheet. I also had difficulties at first making sure all my data
was in the same geographic and projected coordinate system, as it came from a lot of different
sources.
Finding out that the TIGER roads were not drawn properly was frustrating, but once I
found the function to fix them it was not too difficult to redo. Calculating the percentage
population for each block group was also difficult as it involved creating separate layers with
Boolean logic, adding multiple new columns, and then calculating what I wanted using the field
calculator and the statistics function.
Conclusions:
Overall, I think my initial walkability raster is pretty accurate and could be useful for the
county, especially in addressing transit deserts (there are plans for a high speed train, if the state
ever gets the funding, and this could be important in siting the stations.) The data was a bit
skewed by the weight of open space, as there is a lot of open space around Santa Clara County in
the form of large nature preserves, but no one lives there. I was also surprised that no area scored
a 10, meaning that it had access to all services. I’m not sure if this is because I considered a
relatively small area “walkable,” or if it is just because SCC is so unwalkable. I was also
surprised to see that race (at least between white/nonwhite) did not have much of a correlation
with living in a walkable area, although it did have a slight inverse correlation (that is, non-white
people tended to live in more walkable areas.) This could be because many of the super-rich and
predominately white areas of SCC are also very unwalkable, because everyone has a car so they
don’t need to be.
In terms of analysis, what I really wanted to do was measure walkability by income
levels, to see if those who can’t afford a car can still access the services they need, but the 2010
data at the block group level has not been released yet. The analysis I did do would have been
more accurate if I had done the population analyses by block, but as I was originally planning to
use different or more data to analyze it, I chose to stick with block groups. Although I know it
isn’t perfect, I think this is an important analysis for this area, as there is so little available GIS
data for Santa Clara County. I also wonder how this analysis could be adapted using data from
Google Maps, as there is abundant data for this area available (including street view, which
would give a more accurate picture of how easy or hard it is to walk somewhere) through
Google.