Madeline Soule
Intro GIS
Parmenter
Final Project Short Paper
Project Purpose
I this project I looked at the feasibility of creating a community garden in Worcester, MA
near to schools and in low-income areas, prioritizing areas based on site suitability. Worcester is a
city greatly lacking in open spaces in comparison to nearly all other parts of Massachusetts.
Additionally, Worcester has large concentrations of lower income families, many with children
attending local schools. These schools would benefit by being near to a community garden for the
creation of some kind of school gardening program. This could help students to develop a positive
relationship with food, nature, and community from a young age. In this project I showed methods
that a community could use to determine the most suitable places in Worcester for the creation of
school-based community gardens. I used an overlay analysis with GIS software in order to assign
importance to variables and calculate the most suitable parcels of land based. Since Worcester is a
city, there is a large amount of impervious surface, which could be a problem for a community
garden. In my analysis I focus on finding suitable parcels of land that are semi-pervious and of a
large enough size for a community garden. In the end, I leave it open to the community on how to
use the methods I demonstrate, in order to allow the new garden to provide the greatest benefits to
the community, schools and the children it would serve.
Data Sources
My sources of data for this project were MassGIS, the US Census, and the City of Worcester’s GIS
section of Technical Services. I spoke with the GIS Analyst in Worcester, Claire Brill, who provided
me (free of charge) with data on parcels, streets, buildings, and elevation. As far as I know this data
is up to date, though it did not come with metadata so it is difficult to know. From the attributes of
the data tables, however, it does seem that their collection process is a work in progress.
I also found lists of the community gardens currently in Worcester from the Local Food Guide MA
(http://www.farmfresh.org/food/communitygardens.php?zip=01543), the Regional
Environmental Council of Central Massachusetts
(http://www.recworcester.org/UGROW/gardens.html), and the American Community Garden
Association (http://acga.localharvest.org/search.jsp?&lat=42.26366&lon=71.817505&scale=9&p=4)
Influencing Literature
One articles I read was an analysis for planning the creation of greenways, taking into
consideration the various benefits that greenways create, for the environment as well as the
community, including protection, recreation, and alternative transportation1. Greenways are
Conine, Ashley, Wei-Ning Xiang, Jeff Young, and David Whitley. 2004. Planning for multi-purpose
greenways in Concord, North Carolina. Landscape and Urban Planning 68, (2-3) (5/30): 271-87.
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unique because of their connectivity, which also makes planning especially important so that
options can be maximized and an integrated system can be created. Initially the authors considered
demand and needs of the community for greenways to determine where connectivity was
necessary for transportation. Next they took into consideration the physical resources that they
could use, which they termed “connectivity supplies,” including natural and man-made features that
would allow them to “connect the dots” in their greenways. Finally, for suitability analysis they
incorporated other measures of suitability using rating and weighting layers in an overlay. This
type of analysis leaves the final findings up for manipulation with changes in factor weighting and
rating, which utilizes an important feature in GIS.
This paper was relevant to my project in that it involved community planning of green
spaces. In a way their determination of where connectivity was necessary would be analogous to
what I assumed to be the communities demand for community gardens near schools. Their data
and variables were more extensive than mine, including more specifics of the locations
(connectivity supplies) along with general aspects of suitability, and it would be interesting if I had
the time and resources to do a more thorough analysis as well. In the end, they left the analysis
open for changing weighting and ranking, framing their methods as a model with room for
community involvement, as I have intended mine to be.
A second article is a case study of Sterling Forest, an area on the New York/New Jersey
border with demand for both development and conservation2. The authors use GIS analysis to
advocate a broader landscape approach to conservation. They created a GIS database with land
cover type, hydrography, roads, elevation, and soil type, and also catalogued critical environmental
characteristics under threat by development, which acted as constraints to development. These
were (1) Development limitations due to soil conditions/steep slopes/flooding; (2) non-point
source pollution potential due to proximity to wetlands/water; (3) habitat fragmentation potential;
(4) sensitive wildlife areas; and, (5) visibility from Appalachian, Sterling Ridge Trails (presumably a
reduction in recreation enjoyment). These constraints were classified for each grid cell at a level of
severity from 1 to 5. The authors used these constraints to create more simplified maps, which
were used by a coalition of land conservation trusts in order to negotiate with a land developer.
These maps were more simplified, but this study shows the practical usage of GIS products.
Additionally, it shows the way authors’ opinions can filter into systems of weighting, and ultimately
effect the outcome of negotiation situations, in this case positively for conservation.
This paper was another example of an overlay analysis, this time for conservation land. It
used measures of physical land features, as well as environmental measures that represented the
level of threat to the landscape. This analysis further familiarized me with the capabilities of the
overlay function in GIS.
Lathrop, Richard G., and John A. Bognar. 1998. Applying GIS and landscape ecological principles to
evaluate land conservation alternatives. Landscape and Urban Planning 41, (1) (5/15): 27-41.
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A third paper I read in preparing for my project evaluated the ordered weighted averaging
(OWA) method using GIS to determine priority areas for conservation in a Brazilian river basin3.
The study took into account several factors that are important in increasing biodiversity, including
proximity to forest patches, proximity among forest patches with larger core area, proximity to
surface water, distance from roads, distance from urban areas, and vulnerability to erosion. The
authors created a range of alternatives using the analytic hierarchy process (AHP) along with a
Participatory Technique to determine the order and weight of the criteria, as well as using linear
weighting. They tested alternative settings with low-, medium- and high-risk by variably combining
and weighting the criteria. To show their results, the authors created maps displaying areas of low
to high priority. Once again, this model allowed for variability in defining importance of criteria,
giving it flexibility for use. This paper gives examples of the types of processes that can be used to
give meaning to the weighting of variables, given community input.
Another paper I read that helped me to understand the overlay process way an analysis of
land in the state of Delaware to determine land to be used for a statewide conservation network4.
The authors first found all land that met certain criteria, and then determining priority lands
through the use of ranking and several weighting schemes. This is essentially how I did my
analysis, though I created the suitability ranking first, to allow for alterations in the limiting factors
or impervious land and lot size. Results from this study were used to recommend changes to state
and local zoning and conservation plans.
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Major Steps
After gathering all of my data I imported it into ArcGIS.
I made sure all layers were projected in the
NAD_1983_StatePlane_Massachusetts_Mainland_FIPS_2001 coordinate system
To input the existing community gardens I created a table with the garden names and addresses,
which I input into the map file. I used the Tiger street lines to geocode the community gardens,
using the Geocoding in ArcGIS tutorial. First I created the address locator, using the US Streets style
and the Tiger street lines as the reference data. Then I used the Geocode Addresses feature to add
the community gardens table. Since the list was so short, only 27 gardens, I did not have any
problems with addresses and of the gardens coded fine.
Next, I used select by location to clip all of my data layers to only include Worcester. First I used the
MassGIS school district layer to select Worcester and make a Worcester School District layer. I
clipped the schools, openspace, streets, major roads, and land use.
For the impervious cover I had to do a bit more reworking, since the files from MassGIS had 1X1
meter cells, and were broken down rectangles of Massachusetts. The very western edge of
Worcester fell in a different file than the rest.
Valente, Roberta de Oliveira Averna, and Carlos Alberto Vettorazzi. 2008. Definition of priority
areas for forest conservation through the ordered weighted averaging method. Forest Ecology and
Management 256, (6) (9/5): 1408-17.
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Weber, Theodore C. 2007. Development and application of a statewide conservation network in
Delaware, U.S.A. Journal of Conservation Planning (3): 17-46
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6. In order to get these files to a manageable level of detail, I used the Resample function (Toolbox
Data ManagementRasterRaster ProcessingResample), making the new cell size 10X10
meters for both of the impervious layers.
7. To put both of the files into the same layer I used the mosaic tool (Toolbox Data
ManagementRasterRaster DatasetMosaic). Since the layers did not overlap I did not have to
change the mosaic method from the default, and just specified the two input rasters, with the first
one being the target raster (mosaic requires that the output/target raster already exist).
8. Then I followed the tip sheet on creating a subset shape file from raster data to limit the extent to
only the rectangle around the town of Worcester. To do this I exported the newly mosaiced
impervious cover layer, specifying the extent to be that of the data frame, which I set in the spatial
analyst options to be the same as the Worcester School District layer, and the cell size to be
10X10m.
9. After the impervious cover is formatted I used the Zonal Statistics function in the Spatial Analyst
extension and the Worcester parcel layer to determine the mean perviousness of each parcel. Since
the raster data was in binary for pervious or not, this measure was helpful in determining what
percentage of each parcel was pervious.
10. I appended the table created with the zonal stats to the Parcel layer, and created a map showing the
average perviousness of the parcels, which I used in my poster. Though in the final analysis I used a
binary measure of whether or not a parcel was “pervious enough,” using 50% perviousness as a
passing rate, I included in the map 5 levels of perviousness to give a better idea of the impervious
landscape of Worcester. This also leaves open the option of using impervious cover as a part of the
suitability score instead, as I might do if I continued with further analysis. I will come back to the
use of the impervious data later, as it was not included in the suitability function in this project.
11. To create the suitability calculation I needed all of my data to be raster, and I needed to calculate
distances from schools, openspaces, and community gardens. To do this I followed the Using
Spatial Analyst tutorial from ESRI that we used in class. In Spatial Analyst DistanceStraight line
I calculated the distance from schools. For schools, I used a maximum distance of 400 meters (¼
mile). This worked well, because it stipulated that all gardens would have to be within ¼ mile,
which is generally considered to be a walkable distance, I would say even for school children.
However, since this is a straight line calculation it does not take into account actual walking paths,
which would actually be more useful. In any further analysis I think it would be wise to switch this
step to use a network analysis. Additionally, it would need to be determined on a case by case basis
if there were any other impediments to school children making the walk, like a high traffic area
without sufficient protect, etc.
12. Calculating distances to openspaces and community gardens proved to be a bit more difficult
conceptually. Since I wanted to locate the new garden far from other openspaces (and gardens), to
offer access to those who do not have other resources, I had could not set a maximum distance
smaller than the farthest distance within the city from an openspace (garden). Initially I did not
capture the full city, as shown below.
It was necessary to go back and determine what the farthest distance possible was, and use that as
the maximum distance in the distance calculator.
13. After all of my distance rasters were created I used the reclassify function to organize them into 5
equal interval categories each. I used these to create my small maps, using schools, community
gardens, openspaces, and water as references.
14. For the income layer I used the MassGIS block groups and US Census data, joining a table with the
census data to the layer of block groups, which I cut to include only Worcester. I then used the
feature to raster tool (Spatial AnalysitConvertFeatures to Raster) to turn the layer into a raster
layer, which was necessary for the spatial analysis. I created five categories using Median Income
level, and I used quantiles to capture comparatively who was lower income. I reclassified these
categories to give them scores 1 – 5. This was another one of my small maps of the input variables.
15. For the land use layer, I initially used features to raster, and then I reclassified the land use
descriptions into 5 categories. This is one area where I felt that I might not have been completely
qualified, since I am not a soil expert, nor an urban planner, and I may not know which land uses
are “better” or “worse” for a community garden. Additionally, I had a hard time determining how
much “better” a forest might be than a wetland, since neither of them would truly be considered for
a community garden site. However, I classified the land uses as I saw reasonable, and created my
final input map.
16. Once I had raster scores for each of my variables, I used the raster calculator in spatial analyst to
calculate a final score of suitability.
17. This created a layer with suitability scores between 1 and 4.4. I classified these into 5 categories,
and created my intermediate map showing raster suitability.
18. Next, I wanted to determine which parcels would actually be usable depending on the raster
suitability. I used the zonal statistics again to find the mean suitability score for each parcel, and I
appended this table to the parcel table.
19. Within the parcels that had suitability scores, I wanted to restrict all parcels that were under .75
acres, which I decided was insufficient for the creation of a garden. I also wanted to remove parcels
with over 50% impervious land. To do this I used the select by attributes function.
20. This data layer comprised my final map, which showed the average suitability level of parcels equal
to or larger than .75 acres, with less than or equal to 50% impervious cover.
I think that for an initial analysis and a model for future work this project turned out well, and
as I expected it would. Of course, when I began on the project I had hopes that it would create more
relevant and useful results, but given the scope of the project I think that it works well as a
demonstration of the technique. The issues I had mainly concerned my lack of experience as an
urban planner, and my lack of knowledge of the realities of the area. I don’t know Worcester
personally, and I don’t know the preferences or demands of its citizens. Additionally, in the end
there really is not very much land open to this kind of project, and especially not land that could be
found using the data I had. I was hoping that the Worcester parcel data would have information on
the physical and land-use attributes of the parcels, which would have been helpful in determining if
there were any open lots available. Additionally, I felt that my ability to determine suitability of
land uses, as described by the 40 categories of the MassGIS land use layer, was lacking. I think that
in many cases most of the range of suitability would exist within several of the more feasible land
use layers, like open land and low-density residential. However, this would have been very
restrictive for this analysis, since it is only preliminary in nature, and it is better not to narrow
down possibilities too much
I think that my results may be helpful in some regards, such as in ruling out land that is likely
completely unusable, and pointing out which spaces should be researched further. Community
involvement is very important in this kind of process in bringing forward information and opinions.
My final conclusions are that this project should be repeated, using a similar model and making
changes I have recommended (network analysis for distances, inclusion of impervious data in
suitability calculation) as well as basing additional variables, values for reclassification of variables,
and final weightings on community input.