The Green Index: A GIS-based approach for
assessing the quality of a community’s green
infrastructure
Center for Land Use Education
www.uwsp.edu/cnr/landcenter
Across the country many
communities are taking a new look at
their parks, trails, active recreation,
and conservation areas to see how
these elements can help to contribute
to their community’s overall physical,
economic, and environmental health.
While these elements aren’t new to
community planning, increasingly they
are being viewed more holistically as
part of a system rather than as individual
projects. This system is now widely
referred to as green infrastructure. The
term green infrastructure acknowledges
our community’s open space network
as being similair to that of the gray
infrastructure system of roads, sewers,
and utilities. This comparison with other
public investments is based in large part
on the many benefits provided by green
infrastructure. It provides ecological
benefits such as protection from flooding,
Very High (60+)
essential habitat for organisms, erosion
High (45-60)
control, and improved air quality; social
Moderate (30-45)
benefits, serving as a vital gathering place
Low (15-30)
for for community events and recreational
Very Low (0-15)
opportunities, and improving the health
and happiness of those living in close
proximity; and economic beneftis such as
increasing property values, and helping
businesses to recruit employees who may
otherwise look elsewhere. The benefits are clear, but
Green Index Score
how can these benefits be quantified
This tool can help idenand compared within and between
tify relative difference in
municipalities? This report highlights
green infrastructure sera process within GIS which allows for
vice. Figure 1 shows the
identification of green infrastructure
results of the GIS analysis
service levels for individual
identifying areas well
municipalities, and can also for
served and underserved
comparisons with other communities.
by green infrastructure.
July 2012
Figure 1: Results of spatial analysis
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Green Infrastructure- Definition and Methodology
This analysis uses common GIS data layers, available to almost anyone, to analyze and compare the
green infrastructure of two adjacent municipalities: the city of Stevens Point, Wisconsin and the village of Plover,
Wisconsin. We focused the analysis on four key areas: water filtration and retention, recreation, native habitat, and
connectivity. These areas provided a fairly comprehensive assessment of what most professionals consider green
infrastructure to be, and meet either the ecological or human criteria of the definition laid out by Benedict (below).
What is Green
Infrastructure?
“An interconnected
network of green
space that conserves
natural ecosystem
values and functions
and provides
associated benefits to
human populations.”
(Benedict, 2002)
Figure 2: Diagram of essential green services
Since green infrastructure focuses on the green space in our communities, the analysis would apply these
key areas on a parcel by parcel basis. First, a weighted value was assigned based on whether each of the four areas
provides for either ecological services (10), human services (5), or both (10+5=15). A higher score was assigned for
ecological services, because after a site has been developed, it loses much of its ecological integrity and habitat value
permanently. An example of a score of 15 would be water filtration and retention services. It provides for human
needs, as we require clean drinking water and flood retention, and for ecological needs as organisms rely heavily on
water for survival while wetlands and lowlands provide unique habitat to a diverse array of organisms.
Human and Ecological Services
A. Water Filtration and Retention (15): Water filtration and retention is essential to the health of ecosystems and also
to decreased sewer and flooding repair costs. Wetland areas are vital ecosystems and also serve as essential drainage
and filtration basins. Upland areas provide these same services, but to a lesser degree.
B. Recreation (5): Recreation was assigned only a score for human services. Without open spaces to use and enjoy,
people would be very limited in their recreational opportunities. These provide a space for people to come and enjoy
the outdoors, and provide a convenient meeting place for neighborhood citizens.
C. Native Habitat (10): For a healthy ecosystem, native vegetation is essential. Organisms rely on native species to
survive, and invasive replacements or unnatural habitats make for poor substitutes.
D. Connectivity (15): Connectivity is essential for the diversity of organisms, and for access and transportation needs
of people. A connected network of green space is far more valuable than a scattered, disconnected array of open
spaces.
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Each of these four key areas were added together. Next, preservation and size were used as multipliers to
determine a final score for each parcel (figure 3). These two factors were used as multipliers as they are relevant to
both human and ecological needs, and a multiplying effect emphasizes their importance, as highlighted in the key
multiplier box. The green index value (GIV) identifies which parcels best serve their cities. With a scoring system
now completed, common GIS data layers were identified that would address all variables within the equation.
Key Multipliers
E. Preservation: This was dependent on whether the parcel was designated to be preserved perpetually into the future,
in which case it received a score of 2, as opposed to a score of .5 for parcels which may be developed in the future.
Cities proactive in the preservation of their open spaces see a huge benefit as compared to those that haven’t as the
preserved parcels will continue to provide benefits well into the future, whereas unpreserved parcels will lose those
benefits after development..
F. Size: Large areas provide far greater service, both for humans and especially for ecological needs, than do smaller
ones. It was determined that subsequently larger areas with desired attributes receive higher scores than smaller
parcels. ”Large core areas” as defined later in this document, were given a score of 3.5, with areas seeing no core
area receiving a 1.
Figure 3: Equation applied to all “green” parcels
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Process within ArcGIS
The following data layers and steps were used in the GIS process. For more information on the full process used,
contact the Center for Land Use Education at the University of Wisconsin- Stevens Point.
1. Parcel Identification
Open space parcels, including
parks, vacant land, and
undeveloped land from a county
land use layer were identified first.
All parcels within ¼ mile of the
municipal boundary, or 5 minutes
easy walking distance, were
included in the analysis as these
areas still provide essential services
to the municipalities. Figure 4
highlights all the vacant parcels and
parks included within the analysis.
GIS Processes:
Select by Attribute and Select by
Location
2. Water Filtration and Retention
Analysis
Soils data from the NRCS and
wetlands data obtained from the
Wisconsin Department of Natural
Resources were used in this step.
This data provides key insight into
where water storage and infiltration
occurs in upland and lowland areas.
Each soil type was assigned a score
based on infiltration and retention
quality of the soil. If the area had
Figure 4: Parcels included in analysis
wetland properties, it was
it was assigned the maximum possible score of 15 points. Non-wetland areas were assigned
a score based on general ability to hold pollutants and allow for water percolation. Soils
such as loam, which do well in filtering pollutants, but also have adequate percolating
properties, were provided with the highest score of 10 for upland soils. Other soils, such as
sand, which are primarily valuable for only filtration, received the lowest score (2). Soils
with a mixture of sand clay, and silt were given a score between 2 and 10 depending on their
balance of the two properties. After assigning a value to each soil type, an average of the all
the values was calculated to find a final parcel score. Figure 5 depicts the final output for a
portion of Stevens Point.
GIS Processes: Soil and Wetland to Raster > Extract by Mask >
Cell Statistics (Maximum)> Reclassify to Rank > Zonal Statistics
(Average)
Figure 5: Filtration/
Retention Services
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3. Recreation Analysis
Using the initial land use layer, the recreation analysis identified areas
that were open for recreational use by citizens. Park property received
an additional 5 points, as it is readily available for public use, while
vacant land recieved no points as it is not as available for public use.
Processes: Select > Feature to Raster > Reclassify > Zonal Statistics
(Majority)
Figure 6: The basic process used to obtain a habitat score
5. Connectivity Analysis
Two different factors contributed to the connectivity score.
Riparian corridors were identified using stream, river, and
water body data within GIS. As water provides increased
habitat potential, and likely a buffer from other land uses,
organisms are more likely to frequent these areas and use them
as a travel corridor to other areas. All parcels adjacent to these
water bodies were assigned 10 additional points. The second
factor identified was connectivity from a human standpoint.
Using similar methods to the riparian corridors, we identified
parcels that had a “regional” recreational trail either passing
through or running adjacent to them. In the case of Stevens
Point and Plover, this trail is known as the “Green Circle
Trail.” Parcels along this trail were assigned an additional 5
points. Map 3 highlights a portion of the green circle trail as
an example of the final output for human connectivity.
Factor Calculations
Some data were restricted to a 30 meter
cell size. Therefore all raster layers
were made uniform to that size. This
limited the accuracy of calculatons for
small parcels, and all parcels below
2700 meters (3 pixels) were eliminated
from the analysis. It is likely that
parcels of this size would have minimal
recreational or ecological value to begin
with.
4. Native Habitat Analysis
Percent canopy cover was used to evaluate native
habitat for the area. Canopy cover was appropriate
because in pre-settlement times, the Portage County
area was traditionally almost entirely forested. The
canopy cover layer lists what percent of the area is
covered by forest canopy. These percentages were
split into categories of ten, with 90-100% recieving
a value of 10, and 80-90% a 9, etc. The canopy
cover for the entire site was averaged to find a score.
Figure 6 highlights the general process used to
obtain this score. It highlights how areas with high
canopy cover (dark areas on bottom layer) received
higher scores than those with lower percent canopy
cover.
Processes: Zonal Statistics (Average) > Reclassify
to Rank > Zonal Statistics (Majority)
Figure 7: A portion
of the green circle
trail running
through Stevens
Point; increasing
connectivity for
recreational
purposes. The
water connectivty
used the same
methods.
Processes: Create Buffer > Convert to Raster > Zonal Statistics (Mean) > Reclassify to Rank > Zonal Statistics
(Majority)
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6. Preservation Analysis
The preservation analysis assigned a score of 1.25 to those parcels
that were preserved perpetually as green space, such as parks
and public lands and 0.75 to those that were not preserved, such
as vacant parcels. Vacant parcels were assigned a value below 1
because the possibility of development in the future detracts from
their score. These values were simply assigned manually after
export into Microsoft Excel since it made use of the same data as
in the recreation analysis.
Process: Same as step 3
Vacant lot for sale in Plover, WI
7. Size Analysis
A landscape fragmentation tool was used for this step
which makes use of the national landcover database
obtained from the U.S. Geological Survey. The tool
identifies large, medium, and small core habitats along
with patch and edge environments. Landcover types
that were seen as “un-fragmented” were classified into
a different category from those that were considered
“fragmented.” These were then put into the fragmentation
tool which classified them into habitat areas. The
following scores were assigned to each average habitat
area: Large core area (2.5), medium core area (2.0), small
core area (1.75), perforated (1.5), edge (1.25), patch and
other (1). Next, an average of the different habitat types
was taken on a parcel by parcel basis to determine the
final assigned score. It should be noted that the use of this
tool skews the analysis to areas with better habitat and
subsequently more passive recreational uses.
Process: Reclassify > Landscape Fragmentation Tool >
Zonal Statistics (Mean) > Reclassify > Zonal Statistics
(Majority) > Final Size Rank
Figure 8: Simplified diagram portraying the fragmentation analysis
8. Parcel Calculations and Results
We exported the table from ArcGIS into Microsoft Excel and we applied the
equation listed in figure 3 to calculate a final score for each parcel. While it is
highly unlikely, a possible theoretical score of 140.625 could be achieved. Without
the two multipliers, a score of only 45 would be possible.
The maximum score for any parcel within the Stevens Point/Plover area was 85.31
with an average of 23.06. The lowest score was 0.94. When comparing the two
adjacent municipalities, Stevens Point recieved a much higher average score than
did Plover: 24.12 compared with 15.59. It should be noted that these calculations
do not account for parcels outside of the municipal boundary, which could change
the scores to some degree.
See figure 1- Kriging Results
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9. Kriging Analysis and Results
It is clear that some parcels score far better than others, but how did these differences in score affect the surrounding
community? We answered this question through the use of a “kriging analysis.” Using each parcel score, a Kriging
analysis interpolates values through an averaging technique applied to the entire map surface. This allowed us to
identify areas within the city that are underserved and well served by their green infrastructure. An equal interval
classification scheme was applied, as seen in the legend of figure 1, in order to identify well served and underserved
areas. This analysis accounted for all parcels, even those outside of the municipal boundary.
Kriging
Kriging interpolates a surface based off of the values assigned to points, the number of points in
a given area, and the distance between points. The following diagram shows how two points, in
this case representing two different green infrastracture values which are an equal distance apart
provide the same weighted area. Adding a third point (at right) of similair value creates a larger
area for that value. The diagram below is only a general explanation whereas the entire kriging
process is more complex.
An equal interval classification scheme was applied to identify well served and under-served areas of the city. Areas
exceeding 60 points were considered to be well served, as this far surpasses the possible 45 points if recreation,
habitat, water filtration and retention, and connectivity are all present. We compared the two cities based on the
percent area that lies within each of the designated classes. Figure 9 highlights the differences in areas well served
and poorly served by green space.
Plover has no areas within its municipal
boundary that are highly or very highly
served by Green Infrastructure. Furthermore,
only five percent of the village is aquately
served. Stevens Point on the other hand, has
about twenty five percent of its area within
those three categories.
Figure 9: Service comparison between Stevens Point and Plover
Discussion
Differences between each municipality can be explained with further investigation of the data. Plover does not have
the canopy cover found in Stevens Point, especially in its vacant parcels. The area directly to the east of interstate 39
in Plover is largely unmaintained agricultural fields with little or no ecological value. Stevens Point also has more
wetlands, providing higher values for water fitration and retention services. Plover can take measures that may not
be reflected in the score, but would still help improve drainage for the village (eg bioswales, rain gardens, etc.). The
most apparent difference between the two municipalities is the number of vacant parcels and parks along major waterwarys, along with the trails connecting them. Each town has a river running through it; Stevens Point with the
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Plover River, and Plover with the Little Plover
River. Both of these water bodies drain into
the Wisconsin River, which provides a corridor
for both municipalities. Stevens Point has
a much larger, connected area of preserved
land surrounding the Plover River. If Plover
had a more extensive park system in place
along the Little Plover River, they would see
a large improvement in service to that area.
One area that both Stevens Point and Plover
could improve is the park system along the
Wisconsin River. This area sees surprisingly
low values, primarily due to the lack of
ecological services the area provides and the
fact that much of the land is not preserved.
The area also suffers due to its industrial past,
but could be improved upon in the future.
Wisconsin
River
Corridor
Implications
The green index tool can go a long way in
helping municipalities in their management
of green infrastructure. It allows them to
Wisconsin
River
identify where their open space parcel
Corridor
stands in comparison to others, making
for better management decisions for each
individual parcel. The tool also allows for
the identification of areas well served and
Figure 10: Differences in preserved land surrounding the two
underserved by green infrastructure in the
rivers is quite apparent in aerial photographs
areas surrounding each parcel. Comparison
across municipalities is also possible. Obviously it will not always be possible for a city to reach extremely high
levels of service, especially in large cities where the ecological landscape has been heavily degraded, but it allows
for a fairly efficient process to compare municipalities of similar population and size and identify areas in need. The
UW- Stevens Point Center for Land Use education is looking to automate this tool so that municipalities can plug in
the neccessary data to understand their green infrastructure needs, and also to compare between cities.
ACKNOWLEDGEMENTS
Analysis and document prepared by William Risse, Research Assistant and Aaron Thompson, Ph.D., Assistant
Professor and Land Use Specialist, UW-Extension Center for Land Use Education, 2012. We gratefully
acknowledge the the thoughtful review of Dan Mcfarlane and Rebecca Roberts, UW- Extension Center for
Land Use Education, and Jeff Hartman, Portage County GIS specialist.
Benedict, Mark A. “Green Infrastructure- Smart Conservation for the 21st
Century.” Renewable
Resource Journal Fall.2002: 12-17. Web.
Benedict, Mark A. The Green Infrastructure Approach: Principles from Past to
Present. Wash
ington D.C.: Sprawl Watch Clearinghouse, 2001. Web.
Forman, Richard T. “Some General Principle of Landscape Ecology.” Landscape Ecology 10.3
(1995): 133-42. Web.
Parent, J. (2009) Landscape Fragmentation Analysis (version 2). Developed with
the support of the Center for Land Use Education and Research (www.clear.uconn.edu), and the
Department of Natural
Resources and the Environment at the University of Connecticut (http://www.
nrme.uconn.edu).
A special thanks to Andrew Weidner, the UW- Stevens Point Schmeekle Reserve
staff, and William Risse for providing photographs for use in this publication.