Brynna Bolger
Intro to GIS
October 19, 2012
Assignment 4
For this assignment I began looking at data layers that relate to my final project –
the vulnerability of Massachusetts to invasive pests. I focused on potential introduction
sites of Middlesex County. I chose to look at an entire county rather than only a few blocks
because of the data layers I wished to examine. It would be unlikely to find a few blocks
with multiple areas of insect infestation, campgrounds, and plant nurseries. While it is
forested areas that have the highest number of potential host trees for pests, it is frequently
humans who facilitate the introduction. Middlesex County has both rural and urban areas
and therefore has the two primary ingredients for the introduction of invasive pests.
Researchers have found that urban areas with lots of people and high imports are potential
sources of introduction. It is important to look at where humans come in contact with
nature – for example, parks and recreation areas. Another source of invasives is
commercial plant nurseries. The suburbs of Boston are filled with flower boxes,
ornamental trees, and gardens that are supplied by small and large nurseries that bring
their stock in from all over the country and the world. Pests can arrive on the nursery
stock or in packaging.
Positional accuracy is not of highest priority for this project because it does not
really matter the exact location of the nursery, the campground, the park, etc. Rather a
general picture of an area – whether the campground is near a large forested area or if
there are many plant nurseries in a town – give an adequate idea of the potential risk. On
the other hand, time period is very important in this case. A forest can become a shopping
mall within a few months, a pest infestation can be discovered, a plant nursery can open or
close, etc. If the data layers are even a few years out of date, our knowledge of the factors
that determine infestation risk is incomplete or potentially incorrect. Connectivity is
somewhat important because the movement of humans across the landscape on roads can
be a factor in pest movement. Furthermore, the completeness of the data is important
because if nurseries, campgrounds, parks, etc. are left out then there is not a complete
picture of the areas of risk. Thus, outreach and monitoring are not done in the proper
areas.
I used the road centerlines data layer from MassGIS online. I chose to do so because
the data is the most up to date – it was last updated April 23, 2012 with information
received from MassDOT’s Road Inventory. The data was released by MassGIS September 6,
2012. The MassDOT roads layer utilizes linework from the 1:5,000 road and rail
centerlines data interpreted in the Black and White Digital Orthophoto project. The TIGER
2010 road data is, according to the metadata, accurate within 25 ft. They used a random
GPS collection test to determine the accuracy. From looking at several roads and taking
measurements to the centerline of the orthophoto, the TIGER 2010 roads positional
accuracy is approximately 7 ft. However, there are areas that are not very accurate at all as
can be seen in the two example pictures.
On the other hand, MassGIS is more accurate in certain areas as can be seen from the two
comparison pictures above. Judging from measurements for several roads, it seems that
the roads range from <3 ft to about 7 ft from the centerlines of the orthophoto.
In terms of this project I would choose the MassGIS data layer because it seems to be
somewhat more positionally accurate and is more up to date.
On the other hand, in terms of completeness, TIGER seems to be more complete, as shown
in the above picture. But, in terms of the attributes, MassGIS is superior because there is a
lot more information available. From the MassGIS layer I can determine road type, what
types of vehicles are allowed, shoulder type, etc. While not all of these attributes are
helpful, some details, such as vehicle type allowed might be helpful because private or
restricted roads would have less travel and thus lower chances for introduction of pests.
Both the MassGIS and the TIGER hydrography data are similar as far as attributes,
however, this project is not interested in hydrography for the most part. The only
possibility may be to focus on certain species, such as Black Ash, that grow near swampy
areas. Because the MassGIS attribute table specifies water type in great detail it would be
better for my purposes in order to identify habitat of particular tree species. It also
appears that the MassGIS data layer is more complete judging from the appearance of the
layers. There are many areas (as shown below) where TIGER 2010 data is not complete.
The TIGER hydrologic data is supposed to meet the USGS standards of approximately 40 ft
accuracy, however, I found areas ranging from 49 ft to 131 ft in positional accuracy. I found
similar positional accuracy for the MassGIS hydrologic data. The MassGIS data is also
supposed to meet the 40 ft positional accuracy requirements according to the metadata.
Therefore, based solely on positional accuracy it does not make a big difference for my
project which data layer I use. However, based on completeness, the MassGIS data is
superior. The TIGER data is from the 2010 census while the MassGIS data last major
update was in 2008. Therefore, based on currency, the TIGER data would be best to use. If
I am looking for certain habitat types based on hydrography it is important that the data
layer be very up to date.
One of the data layers I chose to examine is the MassGIS openspace layer. I chose
this because it has many helpful attributes, such as the primary purpose of the land,
whether it is open to the public, level of protection, size of the area, etc. All of these things
help in identifying whether there are many people on the land and thus the level of risk of
human caused infestations. The layer includes conservation land, recreation land, town
forests, forest land, and greenways – all areas that see an interaction of humans and nature.
Although the attributes are quite comprehensive, there are gaps in the data due to the fact
that most of the information comes from individual municipalities and is therefore better
for some than others. However, in 2005, data was updated for polygons in over 80 towns.
The data was first compiled in 1988 and has been updated continually since the fall of
1989. The last major revision of the data for this layer was January 2005. Although this
was not too long ago, due to the nature of the project, most recent data is best.
Furthermore, the data is continually being updated for land protected by EOEEA agencies,
by volunteers at the local level, regional planning commission staff, and land trust staff.
However, updates can be limited by funding and volunteer efforts. During early
development of this data layer, data development was done without an ortho basemap or
digital parcel data. Openspace polygons were first positioned on a basemap and then
digitized relative to a MassHighway Department roads dataset (USGS 1:100,000 roads
digital line graph data) and a USGS 1:25,000 hydrologic dataset. Therefore the postions
may be +/- 166.67 feet in accuracy or about +/- 40.00 feet. Since it is digitized, the
positional accuracy is most likely lower than this. For this project this is acceptable
because positional accuracy is not absolutely necessary in determining areas of
vulnerability. However, for future monitoring a certain level of accuracy is necessary but
due to the spreading of pests naturally this positional inaccuracy should be ok. Most
current incoming data is positioned using an orthophoto, although some is digitized from a
map if the map meets digitizing requirements. The openspace data layer is limited by the
fact that in general the parcel representations are not based on property surveys.
The next layer I examined is the insect infestation layer from 1997. Obviously, time
period is the biggest cause of concern in using this data. Unfortunately, MassGIS reports
that there have been problems with getting up-to-date data for this layer. This limits the
layer’s usefulness because insect infestations from 15 years ago are likely to be completely
inaccurate now. The area could have been turned into a shopping mall in the decade that
has elapsed – or it could be completely recovered. The data is acquired by flyovers by
trained observers at an altitude of approximately 2000 feet above the forest canopy.
Abnormal areas are sketched on to USGS topographic maps and then visited on the ground
for further assessment. The maps are at a scale of 1:24,000 or 1:25,000 – meaning about
40 feet positional accuracy. However, when the maps are digitized to GIS they most likely
become even less accurate. Although it is not absolutely necessary that positional accuracy
be perfect for this project, it would help with future monitoring. Data is as complete as the
observers are. The fact that they both fly over the area and visit it on foot gives some
confidence that the data is complete and the attribute table is accurate. The attribute table
includes the insect name and the approximate area defoliated as well as several details
about location. It would also be helpful if the tree types or at least land cover type was
included.
The third layer I chose to include is a geocoded layer of campgrounds of Middlesex
County. All of the data points matched successfully. However, it is not clear how accurate
or complete they are. For example, the picture below shows a point where a campground
supposedly is – however, it seems to just be an empty lot in a neighborhood. Because of the
uncertainty of the validity of the data, I cannot tell the positional accuracy.
The problem with the data pulled for this layer is that campground is apparently a loose
term – a real estate agency and a campground company were also included. It also
included day camps, which are not necessarily of interest if they are not nature related.
The attribute table includes addresses and longitude and latitude which would be useful for
monitoring and outreach. Furthermore, online Reference USA has other details about the
businesses, such as phone number or email which would be useful for contacting owners to
ask about their observations of tree quality. Contact with the campground would also open
a dialogue about initiating outreach regarding the “don’t move firewood” campaign. The
information is fairly up to date as Reference USA updates their data eight to twelve weeks
after the phone book is updated. Furthermore, the file is processed through the U.S. and
Canadian National Change of Address records on a monthly basis. Therefore, the data is as
complete and up to date as the phone book. This is most likely adequate for the purposes
of this project. However, further research on campgrounds in the area would be helpful.
My final layer was also geocoded with data from Reference USA – nursery stock
wholesalers in Middlesex County. Therefore, the completeness and time period is the same
as for the campgrounds – it depends on the completeness of the most recent phonebook
(within eight to twelve weeks). It is important that this layer be complete because insects
are commonly moved on nursery stock or shipping material so it is vital that we know all
potential points of introduction. The nursery data seems to be more useful than the
campground data layer. The points can be connected with a plant nursery, but are
positioned on the road in front – varying from 70 to 300 ft away from the actual nursery.
This is not a big deal at all as far as analyzing the potential for insect spread. However, in
the case of in-person monitoring or outreach, we would need better positional accuracy.
The attribute table does not give any details other than location. It would be helpful to
know the types of plants the nursery stocks, how much stock, and where it comes from;
because this would give a better idea of the level of risk of accidentally importing invasive
pests.