Brian DeChambeau
Assignment 4
The project for which these data are to be used is the development of a market-rate apartment
building near the Alewife T-stop. It is important that accurate large-scale data is available for the
assessment of suitable sites for construction, especially when considering physical features (as opposed
to community features like libraries).This is especially important when talking about things like roads
and wetlands because these features determine in large part which sites are most suitable for building.
Additionally, these two datasets will ideally not contain any extra features, but also include all features
that are relevant. More concretely, there should be no roads that do not exist or cannot properly be
considered roads. Parking lots are not roads as far as this project is concerned, so they should not
appear as such in the data. For certain characteristics, like the location of buildings, currency is perhaps
the most important standard, as the existence of a building is more important at this stage in the
development process than its exact size or position.
Roads:
The two datasets used for roads are
the 2010 Census TIGER Roads and the
CambridgeRoadCtrlines9506available through
MassGIS. Each of them appears to have some
fairly serious inaccuracies. The TIGER dataset
clearly shows some areas as being roads that
are not public rights-of-way. The image to the
left illustrates this well, with a parking lot and
two private rights-of-way clearly being shown
as roads. The dataset from MassGIS does not
have such glaring faults. Apart from a few
minor glitches like a cul-de-sac being shown as
a straight line (see image below), this dataset
seems to be positionally accurate to the
necessary level. The metadata from these do
datasets do not provide specific numbers for the positional accuracy of the data, so they were each
compared to aerial imagery to determine accuracy. In terms of currency, this dataset is also superior to
the Census dataset. According to the metadata, the city’s data is automatically updated periodically, and
was last updated in 2012. The city’s data also provides slightly more information in the metadata about
the road type, though it is not formatted in as intuitive a way. The TIGER data does not distinguish
between two-way and one-way roads, but this is a minor difference. The attribute information for the
two datasets is basically equal. In terms of completeness, the data published by the city of Cambridge is
superior for the purposes of this project because it is both more accurate and more complete.
Hydrology:
There seems to be much more difference
between the two hydrology datasets than
between the two road centerline datasets. The
two datasets being used are the National
Hydrography Data for the Charles River
watershed and the CambridgeHydro95 created
by the City of Cambridge. The NHD dataset is
estimated to be accurate within .02 inches at
map scale, which is to say that it is not very
accurate at the scale of Cambridge. On the
other hand, the City of Cambridge data is
purported to meet ASPRS Class 1 accuracy
standards at the scale of 1” = 100’, or 1:1200.
This means that the limiting error is one foot at
the 1:1200 scale. The Cambridge dataset is
quantitatively superior.
The images at left show, from top to bottom, aerial imagery
of the hydrology near the Alewife station in the first images, the
City of Cambridge’s data overlaid on this in the second image, and
the NHD and City data overlaid on the aerial imagery in the third. It
can clearly ben seen that the NHD data does not show the full
extent of some water bodies. The data from the city of Cambridge is
clearly more accurate than the National Hydrography Data in terms
of position, and is thus more appropriate for the project at hand.
The Cambridge data is also more current, having last been updated
in 2012, compared to the NHD data, which was last updated in
2007. The data from the City of Cambridge also offer more
information through its metadata including the type of water body
being displayed. This could be relevant to the project, so the City’s
dataset is clearly superior.
While both datasets are complete to the best of my
knowledge, the dataset from the City is superior based on the
above criteria, and thus should be used for the project.
Optional Layer 1: Buildings
To determine the location of existing structures in the area,
I have used the dataset CambridgeBuildings10 published by the city.
The metadata for this dataset does not offer a quantitative analysis
of the position accuracy of its features, but a visual comparison with aerial imagery proves it to be at
least moderately accurate for the area in question. As far as completeness goes, there is an exact
correspondence between this data layer and each building shown in the aerial imagery. While this may
not be completely current, it is as complete as one might expect from an analysis of data like this. This
dataset was last updated in July of 2012, so it is safe to say that it is current enough for current
purposes. The attributes of the dataset do provide general information on building type, but not use.
This is a major failing, and any analysis of the built form in the area would necessarily include at least a
zoning map in additional to the data at hand.
Optional Layer 2: Schools
To determine the location of schools in the area, I have used the dataset CambridgeSchools04
published by the city. There is no qualification of the accuracy of this dataset in the metadata, but this is
not very important. The exact position of a school for the purpose of our project is unimportant
compared with its simple existence and basic attributes like type. To my knowledge, this dataset is
complete, and this can be verified through a google search or through data published on the City’s
website. This data was updated in 2012 and can be considered current enough for this project. The
metadata for this dataset includes the school name, square footage, and type. I have verified that this
information is accurate for the area near the Alewife station. This is nearly all of the data that could be
reasonably expected from a dataset like this, and is certainly adequate for project needs.
Optional Layer 3:
To determine the location of hospitals in the area, I have used the dataset
CambridgeHospitals05 published by the city. The metadata does not specify the positional accuracy of
the elements of this datasets, and they are not shapes so this is somewhat irrelevant as long as they are
in the right area. I cannot verify the accuracy of the data any more than verifying if the hospitals do in
fact exist. The data was updated in 2012, so is current. Unfortunately, the metadata provides very little
additional information about the hospitals outside of whether or not they exist and what they are
named. It would be useful to know if they are specialized in a certain area of medicine, provide
emergency services, etc.
Optional Layer 4: Open Space
To determine the location and size of public open spaces in the area, I have used the dataset
CambridgeOpenSpace08 published by the city. As with the previous datasets, there is no qualification of
the accuracy of the data. However, this is more important for open space because the size and position
of different parcels can make a large difference as to how much an amenity they actually are. This
dataset is complete to my knowledge, but my knowledge may be imperfect. The only way to verify its
completeness with 100% accuracy would be to physically go to each open space to verify its existence
and character. As with the other data from the City of Cambridge, this data was updated in 2012 and can
be considered current. The metadata for this dataset is also somewhat lacking. It provides accurate
name and ownership information about the parks, but nothing outside of that. It would be useful to
know the area of the open spaces in the project areas.