John Taylor
Intro to GIS
Assignment 4
Project Description
I am working for a private firm looking to build a mixed-use housing and retail complex at the
new Assembly Square T station in Somerville, Massachusetts. In conducting its feasibility and
market analysis, the firm is interested in learning what amenities and safety features are in a onemile radius from the T station that might attract prospective tenants. Because the firm’s designs
typically cater to young families, it is specifically interested in the quantity and location of
schools, bike lanes/trails, parks, and fire stations to see whether this would be a good location to
develop. The firm is also interested in the road configuration in the neighborhood to gauge
amenity accessibility for future tenants. Because the potential site is so close to the Mystic River,
the firm is interested in hydrological features to determine potential flooding issues in later
studies. While future studies may look at amenities in other surrounding cities, the firm is
interested right now only in what exists in the City of Somerville.
Data Quality Requirements
Positional accuracy is most important for roads and water, and should be within +/- 1 meter. It is
not as crucial for the other features, which can be within +/- 15 meters. The firm is more
concerned that these features exist within the one-mile radius. Required attributes vary by layer,
but should have names (of schools, parks, etc.) and additional identifying information important
to prospective tenants (i.e. whether a school is elementary or high school). Completeness is
probably the most important data quality point; there will only be a handful of schools, parks,
fire stations and bike trails in the neighborhood, so the dataset needs to be 100% complete. The
time period is also important; the data would ideally be from the past one to two years,
particularly with roads and bike lanes as new development has begun to occur around the T
station. Connectivity is really only an issue for roads and bike trails.
Roads
I used two datasets of road centerlines in this analysis. The Census TIGER 2012 dataset road
centerlines appear to have very little error as compared to the USGS orthography. This can be
seen in Figure 1, which is a key access road to the Assembly Square T station. Most of the road
centerline is exactly over the ortho centerline; the most error I could notice was .17 meters. In the
road centerline dataset from the City of Somerville, the error also appeared to be small, although
as high as .62 meters at the location in Figure 3. In terms of currency, the TIGER dataset is more
up to date, from the 2010 Census, while the Somerville road data is from 2002. Both have
acceptable street name attribute information, but the Somerville dataset has additional
information such as one-way streets and trash pickup that could be helpful. The most important
difference I found between the two was in connectivity. While the TIGER data has strong
connectivity, road centerlines from the Somerville dataset often do not connect, as shown in
Figure 4 (TIGER roads in red, Somerville roads in blue). Based on these factors, I would
probably select the TIGER dataset as the more complete of the two.
Figure 2 (TIGER data)
Figure 3 (Somerville data)
Figure 4 (red = TIGER data; blue = Somerville data)
Hydrology
Both the TIGER hydrology layer and the MassGIS hydrology layer analyzed have some
positional accuracy issues. Looking specifically at the Mystic River near the Assembly Square T
station, both layers fail to actually reach the shoreline visible in the ortho, by 9.5 meters in each
(Figure 5). There is no clearly better dataset in terms of attribute information, as they contain
roughly the same information. In terms of currency, both datasets are from 2010. The MassGIS
dataset is clearly more complete. The TIGER data is missing entire parts of the Mystic River, as
shown in Figure 6. The light blue parts of the Mystic River are from the TIGER data; the
MassGIS data includes all the same light blue parts of the river, plus the darker blue sections. I
would use the MassGIS data for my project, though, almost solely on the completeness of that
data compared to the TIGER data.
Figure 5 (light blue = TIGER; dark blue=MassGIS)
Figure 6 (light blue = TIGER and MassGIS data; dark blue = MassGIS data only)
Schools
Like the road data, there is virtually no positional error with school data from MassGIS in the
one-mile radius surrounding the Assembly Square T station. The marker for each of the 4
schools in the area was placed right in the middle of the school building, as shown in Figure 7, or
at the front entrance. I confirmed each of the locations using Google Street View. For
completeness, I checked the Somerville Schools website, which has a list of school addresses and
a Google Map of all school locations. The MassGIS set appears to be complete as compared to
that listing. The data is from October 2012, which is more than adequate for purposes of this
project. The attribute table contains the school name, principal name, address, grade level, and
whether it is public or private, which is adequate for the developer’s needs.
Figure 7: Arthur D. Healy School
Bike Lanes/Trails
At first glance, it appears that this neighborhood has a few good bike paths that would add to the
neighborhood amenities for the development. When I looked closer at the MassGIS metadata for
the bike trails layer, however, I noticed that it mentions that “this layer represents trails which all
permit bicycle travel or corridors with conversion potential.” Comparing the data to the ortho,
however, revealed that there is no actual bicycle infrastructure where the layer says it is; these
are clearly corridors with conversion potential along rail tracks and parkland. In terms of
completeness, the metadata is still vague on whether this data should include bike paths on roads
as well. I know there are bike lanes on Broadway and elsewhere in the one-mile radius, but they
do not appear in the data. I am not sure whether this is incompleteness, though, or whether they
are simply not supposed to be in the dataset. Further detracting from the data’s quality is that it is
from 2004, before Somerville significantly expanded its bicycle infrastructure. Based on all of
these characteristics, I would not use this data to map bike infrastructure in the target area.
Figure 8: Potential bike trails
Parks
The park data from the City of Somerville has strong positional accuracy; the layer’s parks lay
right on top of the actual parks as viewed in the ortho. A few of the layer’s parks, however, do
extend into the roadway. This is most visible with Foss Park (Figure 9), which extends 14 meters
into McGrath Highway. For the purposes of this project, this is acceptable error. The dataset also
appears to be complete, but it is important to note that the City of Somerville includes
playgrounds, basketball courts, and school recreation areas in the parks layer. Again, for
purposes of this project, this is acceptable, but helpful to know so that the developer does not
think this is all open green space. The data is from 2005; while it would be better to have more
current data, it appears that it is still correct compared to the current ortho. The attribute table
contains the park name, but no address or cross street information; this would be helpful, but is
not essential.
Figure 9: Foss Park
Fire Stations
There is little positional error in this dataset from MassGIS. The marker for the lone fire station
in this neighborhood is right on the parcel, although it is tucked away from Broadway (by 15
meters), which is the given street name for the fire station address (Figure 10). Unlike with the
Somerville schools data, I could not find a listing of fire stations on the city’s website to crossreference against this dataset to look at completeness. The data is from 2007, which is again
acceptable for purposes of this project. The attribute table contains the station address and name,
although it might be helpful to know the size of the force or number of trucks at a station as well.
Figure 10