Alek Bartrosouf
March 18, 2011
GIS 206A
Professor Leo Estrada
Community Gardens
Introduction
Ten years ago community gardens were seen as a thing of the past. They are rooted in what were called victory gardens from the world wars at a time when food supplies were in stress and individual support was needed to supplement production in time of need.
Though not under the same circumstances, the recent emergence of community gardens in the greater Los Angeles region certainly carries the tradition of a decentralized and local food source for many. While many community gardens today service those in need who are financially strapped, a lot of these projects are designed and meant to inspire community – a place where people can come together and establish relationships with people in their neighborhood that otherwise would not have.
Community gardens, now in particular, serve as a crucial component for those who are deeply affected by the economic downturn. With more people unemployed, restricted income, and additional burdens in sustaining a household, community gardens offer a place for people to meditate, relax, socialize, and create an experience that is unforgettable.
Unfortunately, with the economic downturn comes a limited pool of resources for what could arguably be unessential services for the community. Likewise, private grants and funding opportunities continue to be limited at a time when it is needed the most. Because many community gardens are established by local governments or grassroots organizations, these types of funding continue to be the lifeline for those that currently exist, and are the only option for those who would like to see new ones be established. In an effort to strategically allocate funding to areas that would benefit from new community gardens the most, research needs to be done on existing conditions and already established community gardens. Furthermore, the stunted growth of development in general creates an opportunity for cities to purchase land for discounted prices in areas that might benefit from a community garden. With so many vacant parcels available, there needs to be a selection protocol in order to effectively promote these community assets.
Policy Considerations
Given the limited data that is available on established community gardens, I found that it would be useful to create a database that includes additional information about them that may be useful to decision-makers in determining where to allocate their funding.
Contrasting that data with census data and demographics in a spatial setting would easily convey communities of interest based on numerous characteristics.

Methodology
In an effort to gather more information on existing community gardens in Los Angeles
County, I spoke with Yvonne Savio at the UC Cooperative Extension. When referred to a database she had on her website, I quickly started to call and email all of the gardens whom had a contact person listed in this database. Unfortunately I was not able to reach all of them, even with two waves of emails and dozens of phone calls, but did obtain information that some would find useful while on their search for community gardens. In doing so, I discovered through word of mouth that some newer gardens were not part of the existing database (including one that I established myself) and it slowly turned into an evolving document with gardens being added around the county on a very frequent basis. In total, I was able to create a map for 78 existing community gardens. Of the 78 existing gardens, I was able to find out the size of each garden (the number of plots they have or families they serve) and the year in which the garden was established for 45 of these gardens.
This data was juxtaposed with various census data to provide a meaningful assessment of Los Angeles County and determining where new community gardens should be established. The following diagrams and figures will help to explain the methodology that was used.
In addition, the following skills were used in ArcGIS in order to visually represent the information above.
Inset Map
Charts & Images
Aggregate Attribute Fields
Geoprocessing
Graduated Symbols
Attribute Sub-Sets Selections
Geocoding
Original Data
Buffer Extractions
Hotspot Analyses
Metadata
Measurement Analyses
Modeling
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Results
Layout One:
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This layout serves as a base map. Simply put, the inset map shows those who are unfamiliar with the territory where Los Angeles County is. The map itself indicates where existing community gardens are. The UC Cooperative Extension and myself obtained this data. City boundaries in black help to determine specifically which jurisdictions they lie.
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Layout 2:
Using data that I acquired on my own, I am able to display community gardens based on the year they were established (as seen on the left map) and the number of plots per garden (as seen on the right map). The relevance of this data can vary from one decision-maker to another, but simply knowing this information is useful in determining what neighborhoods are seeing an increasingly popular trend in community garden sites, and which are not. Also, the size of each garden may help one figure out the need for that community. One garden with twenty plots, for example, should not be seen as equal to a garden with 300 plots. Layout 1 does not make this differentiation.
This layout is displayed on a background of median household income, provided by the
American Community Survey of 2005-2009.
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Layout 3:
In order to make this information more accessible and easier to understand, a Google
Mash-up was made. Using basic code given to us during class, I was able to incorporate the data from the previous layouts on a website. This website is as follows: http://abartrosouf.bol.ucla.edu/maproom.html
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Layout 4:
This layout serves as a more in-depth look at what kinds of neighborhoods these community gardens serve. While Layout 2 shows you median income based on census tracts with a community garden layer on top of that, the use of buffer extractions does a better job of accurately portraying who is served by these gardens. Because Los
Angeles County is such a large area to look at, I used the Los
Angeles/Glendale/Burbank sub-section to serve an example of what GIS tools can help obtain.
On the left map and graph, we are given a visual representation of the gardens in that area and the census tracts that have varying degrees of single-person households that are over the age of 60. A lighter blue/green shade near most of the gardens tells us that many of these sites do not accommodate those neighborhoods with a high elderly population that live on their own.
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Furthermore, the map and graph on the right display census tracts that lie within the buffer of each community garden. You can see in this display that many of the gardens are situated in areas of Los Angeles that have high-income earners. These populations likely have their own space to garden and would probably be better utilized in areas that have somewhat lower income.
In order for this buffer extraction to work, separate attribute layers were made for both variables, a ½ mile buffer was made and clipped, and the garden sites and census tracts were projected. This data was obtained by the UC Cooperative Extension, the
2000 US Census, and myself.
Layout 5:
Layout 5 is one of four layers incorporated into a model that will help determine where to seek out potential new community gardens. This particular variable deals with singleperson households over the age of 60. This variable was chosen because of my instinct that the elderly, most of which are retired, may benefit the most from a community garden compared to the other age categories. Furthermore, because they live alone, community gardens would create an opportunity for them to socialize with people from diverse backgrounds and create bonds while gardening that would otherwise not happen.
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This layer was created using US Census data from 2000. The original data told us the number of single-person households over the age of 60. I divided that numeric
(personsover) in my attribute table to the total number of households in that census tract
(households) in order to find the percentage of single-person households over the age of 60 in that census tract (percent60). This result allowed me to make five classifications (manually classified as I saw appropriate) and they were given certain points in order to make my index. For percent60 , the following attributes were given based on these rankings…
0 to 10% > 1 point
11 to 15% > 2 points
Layout 6:
16 to 25%
26 to 35%
36 to 46%
>
>
>
3 points
4 points
5 points
I believe median income and unemployment are two other categories worth considering when looking for a place to establish another community garden. Oftentimes those with low income have limited access to healthy foods and open space. Community gardens
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create an environment where families can work together in fostering healthy habits.
Furthermore, high unemployment rates leave many with little to do. Building community gardens where unemployment seems to be straining the neighborhood would give people a reason to wake up in the morning and stay productive as they ride out the economic downturn.
Parts two and three of the model includes variables for median income and unemployment. Both of these variables use data obtained from the 2000 US Census.
Much like the previous layout, median income and unemployment were indexed into five categories in order to be included in the model. These are manual classifications that I thought would be appropriate. The index works with the following scale…
Median Income
Up to $20,000 > 5 Points
20,001 to $30,000 >
$30,001 to $60,000 >
4 Points
3 Points
$60,001 to $90,000 > 2 Points
Unemployment
0 to 5% >
6 to 25% >
26 to 35% >
1 Point
2 Points
3 Points
36 to 45% > 4 Points
46% and up > 5 Points $90,001 and Up > 1 Point
Layout 7:
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Distance was the fourth variable to be used in my model. I figured that those that are close to existing community gardens have a greater opportunity than others to participate in one.
Unfortunately, as it is, the model does not do much for proximity to community gardens
– as you can see all of the lower Los Angeles County is given 1 point due to its closeness to the community gardens. The farther a census tract is from an existing community garden, the higher value they receive for this index.
Another complication was that the islands of Los Angeles County were included, greatly skewing the distance variable. If distance is to be seriously considered in this model, it should be tweaked so that this variable is far more sensitive to how close one is to a garden.
For this variable, census tracts were projected (tractsprojected) and centroids were created for each census tract. A layer was then made (tractsgardendistance) to determine the distance from each census tract to the closest existing community garden.
Layout 8:
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Layout eight is a product of layouts five, six, and seven combined. The formula is as follows…
Garden Index Need = Unemployment + Median Income + Over60 + Distance
These variables, given the values that were mentioned, create an index that identifies areas of interest to those who may have resources or funding and may want to specifically target rather than simply dispersing money to those who apply for it. A model like this one allows for a deeper analysis prior to making a decision, especially at a time when money is tight.
The following layouts will display the ways in which these four variables were classified and rasterized.
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Layout 9:
These models were used to help create the index mentioned in Layout 8. My spreadsheet (tractsjoined) included all the data necessary to create the index, including median income (medinc), unemployment rates (unemp), and single-person households over 60 (over60). Once these datasets were rasterized, they were then classified into the five categories that were mentioned before.
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Layout 10 & 11:
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These metadata files are created as an informational tool as people look for various shape files and maps related to community gardens. They are given tags and descriptions so that they are easy to look up. Information is also mentioned about how data was obtained and when it was obtained.
Conclusion:
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The most important observation made in these findings is where the existing community gardens are and who they currently serve. While it is difficult to obtain personal information from gardeners that participate in a garden, assumptions can be made about the people that they service and the distances they travel to get to the garden.
Given these parameters, one can look at characteristics of census tracts that fall within this buffer, as I have, in order to understand who the population is in these communities and how they interact with community gardens.
At first glance, it seems that many community gardens are situated in areas that do not cater to its population. I believe that further studies should be done to better integrate our knowledge about existing community gardens in an effort to enhance the success and strengthen the visibility of community gardens as a community-builder. At a time when resources are scarce and grants are limited, I believe non-profits and municipalities should use models like this one to help determine where to best allocate their resources. Using an index like this can create infinite opportunities and its variability is what allows these agencies to make the changes they see fit to cater to their needs.
I think that GIS serves as a great tool for information-gathering and spatially interpreting data like those relating to community gardens. It is one thing to see it in a spreadsheet, but once information is displayed in a more visually digestible manner, decision makers and the general public have a much easier time understanding what you are trying to portray.
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