David Quinn
Intro to GIS – Final Project
December 15, 2010
Project Description:
This project analyzes the impact of open space protection on development patterns in
surrounding areas. For my analysis, I looked specifically at the Balcones Canyonlands Preserve,
a network of preserves in Austin, TX that have been set aside specifically for endangered
wildlife habitat protection. In 1996, Travis County and the City of Austin developed a regional
habitat protection plan and applied for a federal permit, which called for the protection of 30,428
acres of endangered species habitat. Now 14 years into implementation, “27,906 acres of land
has been designated as part of the BCP, representing 91.7% of the goal.” Such an extensive area
of newly protected land provides an opportunity to answer the following key questions:
1. Are building permits for new construction more heavily focused in areas immediately
surrounding the BCP than elsewhere in Austin, TX?
2. Have the “hotspots” for new construction changed since land acquisition for the BCP
began in 1996?
3. Does the value of new construction increase or decrease related to proximity to the BCP?
The BCP provides an excellent case study because it covers a large area of land, and was set
aside for permanent preservation within the last fifteen years. This allowed me to compare
development data from both before and after the BCP plan was implemented. My analysis relies
on building permit data from the City of Austin from the years 1990 to 2007. Since I hypothesize
that the preservation of the BCP makes the areas more attractive for living, I am specifically
interested in residential building permits, not commercial building permits.
Data Sources:
This is a list of the original data layers that I used for my analysis. However, a number of
additional layers were created using the “selection” features in ArcMap in order to narrow down
the parameters of the project.
Map Layers
Layer
County Boundaries
Source
Date Created
1
CAPCOG Metadata not
available.
Downloaded from
CAPCOG website
in November 2010
Polylakes
CAPCOG Metadata not
available.
Downloaded from
CAPCOG website
in November 2010
Streets
CAPCOG Metadata not
available.
Downloaded from
CAPCOG website
in November 2010
Buildingpermits_90_thru_feb07 S: Drive
Building Permit
data from City of
Austin.
Usage Codes table
S: Drive
Updated_BCP_Parcels_PC
S: Drive
1
Capital Area Council of Governments (CAPCOG)
Metadata not
available.
Notes
Includes all counties in the
Austin area
Lakes and other water bodies
in Travis County
All Streets in Austin, TX
Contains all Austin building
permits for the years 1990
through 2007. This layer will
be used to create several new
layers for smaller time periods
and specific types of
construction.
Data table containing the
usage code definitions for the
building permits
Layer includes all parcels that
make up the Balcones
Canyonlands Preserve
Major Steps in Analysis
1.
I used the building permits layer and the “select by attribute” tool to create a layer for new
residential construction permits only.
o
The new layer includes only permits with the following usage codes: TI-1 (new onefamily house, attached), R6-1 (mobile homes), R3-7 (conversion from non-residential to
residential), R3-2 (new two-family building), R3-1 (new one family house, detached),
R1-4 (new five or more family buildings), R1-3 (new three or four family buildings), A25 (addition to increase housing units)

"USAGE" = 'T1-1' OR "USAGE" = 'R6-1' OR "USAGE" = 'R3-7' OR "USAGE"
= 'R3-2' OR "USAGE" = 'R3-1' OR "USAGE" = 'R1-4' OR "USAGE" = 'R1-3'
OR "USAGE" = 'R2-5'
2.
I used the “select by attribute” tool to create for separate layers for each of the following time
periods:
o
1991 – 1994 (before BCP was established)
o
1995 – 1998 (early years of BCP)
o
1999 – 2003 (5-8 years after BCP established)
o
2004 – 2007 (9-11 years after BCP establish)
This step was required to create building permit data that covers several equal time periods that
could be compared to one another.
3.
Using the Spatial Analyst toolbar, I created density raster maps for each of the four time periods.
o
I set the analysis mask to be Travis County_Poly, and set a cell size of 30.
o
I chose SQFT as the population field in order to display the total square footage of new
construction permits for each time period.
o
I chose to use a search radius of ½ mile, which was entered as 2640 feet based on the data
frame’s coordinate system unit.
4.
Clip “Streets” to create a new layer of “Streets” within Travis County only.
5.
In Properties - Symbology I manually created different ranges of square footage to display on
each of the raster density maps.
6.
I created a multi-level buffer around the BCP in an attempt to determine whether or not there is a
higher percentage of new construction around BCP than elsewhere in Austin.
o
Using the Analysis Tools – Proximity Tools – Multiple Ring Buffer, I created four
buffers around the BCP lands (1/8 mile, ¼ mile, ½ mile, and 1 mile)
7.
I attempted to create a spatial join of all new construction building permits to each of the different
buffer rings. However, I found that the spatial join only joined the building permits layer to the
entire buffer layer, not each individual ring. This was not helpful to me because I needed to be
able to separate the building permits based on their distance from the BCP.
8.
As a solution to the above problem, I decided to create two separate individual buffers, rather
than use the multi-buffer feature.
o
First, I created a ½ mile buffer around the BCP and saved it as a layer.
o
Next, I create another ½ mile buffer around the first buffer. This created a buffer layer
that included only the areas between ½ and 1 mile from BCP. For this second buffer, I
had to click the “OUTSIDE_ONLY” option so that it did not overlap with the first buffer.
9.
I used the spatial join feature to join building permits to the buffer ring that they are located
within.
10. In the attribute table for the newly joined features, I added a new field called “Val_SQFT,” and
used the Field Calculator to calculate the value per square foot for each of the building permits.
11. Next, I used the Summarize tool to calculate the total number of permits and the average square
footage per permit for all the permits within each of the buffers. This allowed me to compare the
differences in construction value between the two buffer rings. To my surprise, the average value
square foot was higher in the outer buffer (between ½ and 1 mile). However, the total number of
permits was significantly higher within the inner ½ buffer.
Difficulties / Problems
Data Quality Issues – There are many building permits with usages codes that don’t match any
usage code in the table (for example, 2-1, is an unknown code). I simply excluded all unknown
codes from my analysis.
Creating a spatial join between building permits and a multi-ring buffer – I was unable to create
a spatial join that matched up with the individual rings within the multi-ring buffer. While
attempting this step, I was only able to join building permit data to the entire buffer layer without
associating it with the individual rings. I was able to work around this issue by create two single
buffers, rather than one multi-ring buffer.
Lack of storage space – I ran out of space on my H: Drive which caused all sorts of problems in
ArcMap while attempting to create raster density maps. Often the error messages did not indicate
that a lack of space in the problem. Several times ArcMap simply crashed. Once I figured out
that storage space was the problem, I began using a flash drive, and the problems went away.
Corrupted Data – The building permits data on the S: Drive somehow became corrupted, which
caused by density maps to display as scrambled, see below
Thankfully, Barbara was able to run a tool on the data to fix the problem.
Concluding Thoughts
The results of my analysis are largely inconclusive. The raster density maps do appear to show
an increase in new construction around the BCP between 1999 and 2003, but this conclusion is
simply observational and not backed up by empirical evidence. Instead, the density maps should
be used for illustrative purposes only – they do not definitively answer any of my original
questions. The one-mile buffer analysis, however, did provide some empirical evidence about the
value of new construction surrounding the BCP. Building permit data showed a higher per
square foot value for construction in the outer buffer ring than the inner ring. On its face, this
would suggest that construction values increase with an increase in distance from the BCP.
However, in reality, there are simply too many additional factors that could impact the value of
new housing construction in the area to draw any meaningful conclusions.
A more conclusive analysis would be possible with detailed data at the parcel level. In the future,
if a student were able to obtain property value information for all parcels over an extended period
of time, they may be able to draw some definitive conclusions about the impact of the BCP.
Overall, despite the lack of clear conclusions, I thought this was a great project to help me
understand a variety of tools available in ArcMap and troubleshoot some basic problems.
Related Research
Koone, Emily. Proximity of Development to Balcones Canyonlands Preserves Travis
County, Texas. December 15, 2008. University of Texas at Austin. Community and
Regional Planning. http://soa.utexas.edu/files/gis/DevelopmentBalconesCanyonlands.pdf
This research project, conducted by a student at the University of Texas at Austin, is very similar
to the analysis that I conducted. Her project used GIS to try and determine if areas closer to the
BCP are more attractive to live in, whether or not growth is occurring closer to BCP lands, and
whether development has increased over time. The report includes a detailed outline of the
process that she followed in GIS and, therefore, was very helpful to me as I conceptualized what
features of ArcMap my project would utilize.
National Park Service. Economic Impacts of Protecting Rivers, Trails, and
Greenway Corridors: A Resource Book. 4th Edition. 1995
This is a resource book created by the National Park Service to help local planners advocate for
the protection of open space by highlighting the economic value and job creation that can be
achieved. A portion of the book looks at case studies from several cities around the country that
have measured property value increasing on lots abutting or nearby open space. For example, it
describes how in Boulder, Colorado “housing prices declined an average of $4.20 for each foot
of distance from a greenbelt up to 3,200 feet” and in Worcester, Mass “a house located 20 feet
from a park sold for $2,675 (1982 dollars) more than a similar house located 2,000 feet away (p.
1-3, 1-4).
Anderson S. T., West S. E., 2006, "Open space, residential property values, and spatial
context" Regional Science and Urban Economics 36. 773 – 789
This study looks at the impact of open space on property values in the Twin Cities metropolitan
area in Minnesota. Through a spatial analysis of housing transaction data based on “proximity to
open space, including neighborhood parks, special parks, golf courses, and cemeteries” they
determined that open space does effect housing pricing, but with some caveats. The impact of
open space was found to vary based on neighborhood density and income. Overall, higher
density and higher income neighborhoods showed greater increases in housing prices based on
proximity to open space.
King, Jonathon R. Anderson, Christopher M. Marginal Property Tax Effects of
Conservation Easements: A Vermont Case Study. Amer. J. Agr. Econ. 86(4)
(November 2004): 919–932
This study looks at the issue of open space protection through conservation easements from a
local tax perspective. When land is placed under a conservation easement it decreases the
assessed value of the property and thus reduces the tax revenue from that parcel to the town.
However, other nearby parcels may experience changes in assessed value as a result of the
conservation easement even through it is not located directly on their property. The study seeks
to determine how the changing property values resulting from conservation easements impact on
local government tax rates. It finds that local governments in Vermont will simply adjust the tax
rates in the short term to offset changes in property values in order to maintain government
services. In the long-run, the study determined that neighboring properties to those with
conservation easements will experience an increase in their property values.
Geoghegan J., The value of open spaces in residential land use, Land Use Policy 19 (2002),
pp. 91–98.
This study looks at two classifications of open space, “permanent” and “developable,” and
determines that permanent open space “increases nearby residential land values over three times
as much as an equivalent amount of developable open space. (p. 1).” The fact that a potential
buyer knows the nearby open space is permanent contributes to higher land values than if that
same open space faces the potential of future development.