Kyle Greaves
Final Project
12/10/2011
INTRODUCTION:
The majority of farmland preservation programs across the country typically employ
quantitative mapping techniques that help identify and prioritize farms that are
unprotected, and available for acquisition. It is common, that one of the various selection
criteria include a measure of farm vulnerability, which attempts to quantify the farm’s
susceptibility to conversion as a result of development pressure. Due to a large and dense
population, agricultural land in the Northeastern U.S. tends to be located closer to urban
centers than the rest of the county, which increases the chances farms will be converted to
non agricultural uses.
Currently, the Massachusetts Agricultural Preservation Restriction is the dominant tool to
preserve farmland across the Commonwealth. While vulnerability is one component of
their selection criteria, the APR program does not employ the use of quantitative mapping
techniques to prioritize potential acquisitions. While the APR has had great success, the
program has the potential to more strategically target the best, as well as the most
vulnerable farmland.
This GIS analysis will conduct a vulnerability analysis of active agricultural lands within
Middlesex County Massachusetts. As a result of data limitations, this analysis will
examine only a handful of vulnerability characteristics. These include proximity to nonagricultural development, proximity to major roads, proximity to existing conservation
land, the existence of supportive zoning and lastly, location within a floodplain. By better
understanding the multitude of factors that can impact vulnerability, the state and the
APR program may be able to more proactively target high value farms that are facing
strong development pressures.
This project coincides with my thesis, so I had already done a good deal of the leg work
in identifying the vulnerability criteria. Ultimately, I chose criteria based on easy access
to data.
CONTEXT:
Middlesex County
Middlesex County is situated directly to the North West of Metro Boston. According to
the U.S. Census, Middlesex County is home to over 1.5 million people in an area just
over 817 square miles. This means that nearly 23 percent of the states population and 22
percent of total housing units are located on only 10.4 percent of the total land area in the
Commonwealth. As a result, the population density of Middlesex County is over two
times the state’s average population density.
Middlesex Counties proximity to the Metropolitan Statistical Areas of Boston,
Cambridge and Quincy have made this one of the fastest growing counties in the
Northeast. As a result, According to the 2002 U.S. Census of Agriculture, total
agricultural land area in Middlesex County increased marginally from 33,160 acres to
33,893 acres.
The APR Program
Sine the early 20th century Massachusetts has lost hundreds of thousands of acres of
farmland to development. The Agricultural Preservation Restriction (APR) program
began in 1977 as
a tool to preserve the Commonwealths invaluable agricultural
resources. The APR program places permanent easements on farmland to keep it a part of
the working landscape in perpetuity. To date, the APR program has permanently
protected 803 farms for a total of 66,931 acres.
METHODOLOGY
For the farmland vulnerability analysis I used a variety of spatial analyst tools to develop
distance based raster layers for the five vulnerability criteria. I then reclassified the layers
using a 1-5 scoring system (1 – least vulnerable and 5 – most vulnerable). Ultimately, I
used the weighted overlay tool to compile farmland vulnerability layers. Since I was not
following the methodology of another study exactly, I played with the schemes until a
balanced distribution was achieved.
For the vulnerability component of my analysis I will be heavily reliant on the Euclidian
Distance tool found within the Spatial Analyst Extension. I will break down the steps I
will take for each vulnerability criteria. Since MassGIS data is meters, I will keep my
measurements in meters as well. For my reference, I have provided an easy conversion
chart from miles to meters:
1/8 mile = 200 meters
1.25 miles = 2000 meters
¼ mile = 400 meters
1.5 miles = 2400 meters
½ mile = 800 meters
1.75 meters = 2800 meters
¾ mile = 1200 meters
2 miles = 3200 meters
1 mile = 1600 meters
Criteria #1: Proximity to non-agricultural development: (residential, commercial,
industrial development etc…). Weight: ??
1. Set work environment:
a. Processing extent: Middlesex County shape file
b. Mask: Middlesex County boundary shape file
c. Raster cell size: 10
d. Snap raster: N/A
2. Create a new raster using the Euclidian distance tool.
a. Input raster: Middlesex County Developed Land 2005
b. Save new Raster: Prox_Develop
3. Spatial Analyst Tools – Reclass – Reclassify
Land that is closer to urban determinants would be more vulnerable to
development pressures. Scoring system 1-5, (1 – least vulnerable, 5 – most
vulnerable)
a. Land within 200 meters would receive a score of 5;
b. Land between 200 meters and 400 meters would receive a score of 4;
c. Land between 400 meters and 800 meters would receive a score of 3,
d. Land between 800 meters and 1600 meters a would receive a score of 2,
e. Any Land greater than 1600 meters from would receive a score of 1.
4. Assign a new color ramp to the value field
5. Save the mapfile to a new name
Criteria #2: Proximity to major roads. Weight: TBD
1. Set work environment:
a. Processing extent: Middlesex County shape file
b. Mask: Middlesex County boundary shape file
c. Raster cell size: 10
d. Snap raster: Prox_NonAg_Develop
2. Select by attribute roads with classes 1-4
3. Select by location roads within Middlesex County
4. Create new map from selection (export selection)
5. Create a new raster using the Euclidian distance tool.
a. Input raster/source: Class 1-4 roads in Middlesex County
b. Save new Raster: Prox_Road
6. Spatial Analyst Tools – Reclass – Reclassify
Land that is closer to major roads would be more vulnerable to development
pressures. Scoring system 1-5, (1 – least vulnerable, 5 – most vulnerable)
a. Land within 400 meters would receive a score of 5;
b. Land between 400 meters and 800 meters would receive a score of 4;
c. Land between 800 meters and 1600 meters would receive a score of 3,
d. Land between 1600 meters and 2400 meters would receive a score of 2,
e. Any Land greater than 2400 meters would receive a score of 1.
7. Assign a new color ramp to the value field
8. Save the mapfile to a new name
Criteria #3: Proximity to existing protected open space, (includes forests, conservation
lands and protected farms) Weight: TBD
1. Set work environment:
a. Processing extent: Middlesex County shape file
b. Mask: Middlesex County boundary shape file
c. Raster cell size: 10
d. Snap raster: Prox_NonAg_Develop
2. Select by attribute:
a. Interest Code
i. CR – Conservation restriction
ii. APR – Agricultural preservation restriction
iii. CAPR – Conservation/Agricultural preservation restriction
iv. WRP – Wetlands Restriction
v. WR – Watershed Restriction
b. Level of Protection:
i. P – Perpetuity
3. Select by location open space within Middlesex County
4. Create new data set from selection (export selection)
a. Save selection: Middlesex Conservation Land
5. Create a new raster using the Euclidian distance tool.
a. Input raster: Middlesex Conservation Land
b. Save new Raster: Prox_Conserve
6. Spatial Analyst Tools – Reclass – Reclassify
Farmland that is closer to existing preserved open space would be less vulnerable.
Scoring system based on 1-5 scale (1 – least vulnerable, 5 – most vulnerable)
a. Land within 200 meters would receive a score of 5;
b. Land between 200 meters and 400 meters would receive a score of 4;
c. Land between 400 meters and 800 meters would receive a score of 3,
d. Land between 800 meters and 1200 meters would receive a score of 2,
e. Any land greater than 1200 meters from would receive a score of 1.
7. Assign a new color ramp to the value field
8. Save the mapfile to a new name
Criteria #4: Municipal: Underlying Zoning / Percentage: TBD
1. Set work environment:
a. Processing extent: Middlesex County shape file
b. Mask: Middlesex County boundary shape file
c. Raster cell size: 10
d. Snap raster: Prox_NonAg_Develop
2. Select by attribute “primary use” code:
a. Select RA (Residential Agriculture) and CP (Conservation/Passive
Recreation)
3. Select by location RA and CP zones within Middlesex County
4. Swap selection so that all non CP and RA zones are selected
5. Create new map from selection (export selection)
6. Create a new raster using the polygon to raster tool for a base raster
a. Polygon to Raster: Set input features, value field, output raster
b. Save new Raster: Middle_Zone
7. Spatial Analyst Tools – Reclass – Reclassify
Land that is zoned for RA and CP would be less vulnerable to development
pressures. Scoring system 1 or 5, (1 – least vulnerable, 5 – most vulnerable)
a. CP or RA zone would receive a score of 1
b. All other zones would receive a 5
8. Assign a new color ramp to the value field
9. Save the mapfile to a new name
Criteria #5: Location within a Flood Plain/ Percentage: TBD
1. Set work environment:
a. Processing extent: Middlesex County shape file
b. Mask: Middlesex County boundary shape file
c. Raster cell size: 10
d. Snap raster: Prox_NonAg_Develop
2. Select by attribute “county”:
a. Select floodplains within Middlesex County
3. Swap selection so that all non CP and RA zones are selected
4. Create new map from selection (export selection)
5. Create a new raster using the polygon to raster tool for a base raster
a. Polygon to Raster: Set input features, value field, output raster
b. Save new Raster: Flood_Plain
6. Spatial Analyst Tools – Reclass – Reclassify
Land that is within a designated floodplain would be less vulnerable to
development. Scoring system 1 or 5, (1 – least vulnerable, 5 – most vulnerable)
a. All land within a floodplain zone would receive a 5
b. All other land not in a flood plain zone would receive a 1
7. Assign a new color ramp to the value field
8. Save the mapfile to a new name
Step #4: Use the Spatial Analyst tool: Map Algebra – Raster Calculator to assign weights
to the five categories above. (Out of 100%)
1. Proximity to non-agricultural development (25%)
2. Proximity to major roads or highway exits (25%)
3. Proximity to existing protected open space (25%)
4. Municipal: underlying zoning (15%)
5. Location within a floodplain (10%)
Step #5: Zonal Statistics
For the final step I used “Zonal Statistics as a Table” to create a table of the
1. Input Raster: Middlesex_County_Agricultural_Lands_2005
2. Zone Field: FID
3. Input Value Raster: Weighted_Overlay_Raster
I think joined the new table back to the Middlesex County Agricultural Land data layer.
From here I was able to map by a variety of scores, however the Max category proved to
be most useful because it provided the actual score.
DATA SOURCES:
Data Layer
Land Use
(2005)
Land Use
(1951-1999)
Floodplains
Source
MassGIS
Link to Data
http://www.mass.gov/mgis/lus2005.htm
Year
M-Drive
Updated
Yes
2005
MassGIS
http://www.mass.gov/mgis/lus.htm
Yes
1999
MassGIS
http://www.mass.gov/mgis/q3.htm
Yes
1997
Open Space
MassGIS
http://www.mass.gov/mgis/osp.htm
Yes
2011
Zoning
MassGIS
http://www.mass.gov/mgis/zn.htm
Yes
2007
MassDOT
Roads
Town
Boundaries
County
Boundaries
Census Block
Groups
MassGIS
http://www.mass.gov/mgis/eotroads.htm
Yes
2009
MassGIS
http://www.mass.gov/mgis/towns.htm
Yes
2009
MassGIS
http://www.mass.gov/mgis/counties.htm
Yes
1991
MassGIS
http://www.mass.gov/mgis/census2010.htm
Yes
2012
CONCLUDING THOUGHTS:
Overall I thought that my analysis accomplished what I was looking to do. There is a
dearth of literature and previous studies that have undertaken the type of vulnerability
analysis that I have conducted with respect to farmlang, so my process was a little trial
and error. At the end, the final map that conveyed the vulnerability of the agricultural
land area made spatial sense. The most vulnerable parcels were all located near dense
development, were close to roads and conservation lands and had unsupportive
underlying zoning. In hindsight I think there was one huge limitation to this type of study.
It is possible to conduct the most thorough analysis of farmland vulnerability, but the one
fact that is very difficult or impossible to quantify, is landowner desire to sell. There may
be a farm that scores a 5 (most vulnerable) but the farmer has no desire to sell his farm
and actually plans to pass the land onto his son. On the flip side, there can easily be farms
that are scored as moderate or low vulnerability where the farmer has a strong desire to
sell. If a preservation program like the APR program was to rely entirely on vulnerability,
they may actually overlook these farms and lose out on opportunities to preserve key
agricultural resources. Therefore, I think any type of vulnerability analysis comes with
some caveats and needs to be looked at and utilized along side a broader selection and
acquisition methodology. Ultimately, I think using GIS to identify priority agricultural
land to select may be more efficient.
RESOURCES/EXAMPLE STUDIES:
As previously mentioned I have had a difficult time identifying existing studies that have
analyzed the vulnerability of farms in urbanized counties. The majority of farmland
preservation programs today spend less effort and money in acquiring or preserving
farms in more urbanized areas. Farmland preservation is expensive, so efforts are more
often taken to preserve farms that farther away from pressures and are deemed to have a
greater chance of long-term survival as an active farm. The literature is full of studies that
look at criteria farmland preservation programs use to prioritize potential acquisitions.
For the purposes of this analysis, and my thesis, at a high level I am attempting identify
criteria that could help to look at this problem thought a reverse lens, and identify which
farms are actually at the greatest risk of development pressure. The outcome of this
vulnerability analysis will be very general, and will not identify parcel level priorities, but
instead can be used to spur further discussion and research into more detailed local and
regional vulnerability analysis.
1) Bernard J. Niemann. Farmland Preservation and GIS: A Model for Deriving Farmland
Priority Zones. Land Information Bulletin. Technical Paper No. 3. University of
Wisconsin, Madison. 2000
- This is an extremely useful study that walks the reader through the basic steps when
thinking about farmland preservation in GIS. The study uses Dan County, WI as a case
study in creating farmland priority zone around urban areas that can be used prioritize
preservation acquisitions. This paper should be useful in helping me to recreate a similar
study.
2) David L. Tulloch. Integrating GIS into farmland preservation policy and decision
making. Landscape and Urban Planning. Volume 63, Issue 1, March 2003, Pages 33-48
- This paper uses Hunterdon County, New Jersey as a case study on using how to interate
GIS into existing or new preservation programs. The most important part of this analysis
for my purposes is that it discusses the point system (based of LESA) that was used to for
the preservation criteria. It also discussed the process using raster data and spatial
overlays. Similar to my project, this study created scores for each parcel so that they
could prioritize which farms should receive preservation funding first.
3) Montgomery County Agricultural Preservation Board. Agricultural Land
Preservation: Easement Purchase Program. September, 2008
- This paper was not a study, but was a plan put forth by the Montgomery County
Agricultural Preservation Board delineating the details for their farmland preservation
program, one of the most respected in the country. The component of this that is most
useful for me, is their integration of the Land Evaluation and Site Assessment system
(LESA). This is a widely used quantitative tool used to rank and prioritize farmland for
preservation efforts. The scoring categories and point system helped me to develop my
scoring system for the vulnerability criteria.
4) Richard E. Klosterman, Farmland Preservation Policies Studied with GIS. Department
of Geography and Planning, University of Akron
Marisol used this study for her project, and I think it will also be applicable for my
research. Researchers analyzed two different policies for accommodating growth around
Akron, Ohio. This study looks at the impact of various growth controls on farmland
preservation. This has applicability to my study are: Middlesex County, one of the
regions of the state experiencing significant pressures from urban development.