In addition to the five layers used in the instructions, I am using a layers on MBTA bus stops, MBTA trolley stops, orthophotos, public buildings, and residential buildings.

  1. Provide one-paragraph description of the project you are using as a benchmark to assess the data and what positional accuracy it will require (or what is good enough - think about how far off the position could be and still work for the project needs)

This project involves looking at walking distance from residential buildings to various public sites, such as bus stops, trolley stops, town halls, and schools. As this project would be provided as information for those interested in real-life distances, this would require a high degree of positional accuracy. Although positional accuracy might not matter much in looking at one street layer for example, when comparing this layer to one containing data on residential buildings or the location of public buildings, positional accuracy will be extremely important in providing correct information.

  1. Briefly discuss the three different road centerline data sets in terms of their positional relation to each other (look at how far apart they are at different points using the measure tool in ArcGIS, and if there is consistency in the differences. Include some graphic examples to illustrate your points. Which data set would be best for your project?

At point A, the intersection of Kilsyth Rd and Lanark Rd

At point B, the intersection of Commonwealth Ave and Washington St

At point C, the intersection of Washington St and Faneuil St


As Streetmap (green) is an “enhanced” version of Tiger (blue), there are few differences between the two layers. Most often, the greatest differences come from positions of intersections in these two layers and differences with the EOT Road layer (red). Although it is tempting to say that the EOT layer would be better to use in this project as it best matches the roads in the building footprint layer, this is not strictly true, as this layer does not contain information on addresses. However, the building footprint layer does contain a “Parcel ID” field. It is possible that one could use metadata to find the addresses of these Parcel IDs and link them with the EOT Roads, thus making a layer that is p accurate and helpful for analysis.

  1. Do the same as above for the two hydrography layers.

At point D, the Northeast corner of the Chestnut Hill Reservoir

At point E, along the Charles River

Similar to the road layers, the shapefile made by the city, the BRA hydro (dark blue) is more positionally accurate than the census hydro (light blue). Since hydro layers do not have addresses (and for the purposes of this project, are merely detail or positional indicators), it makes more sense to use the BRA hydro file.

  1. Can you provide a quantitative assessment of positional accuracy for each of your data layers (e.g., +/- 20 feet)? Why or why not?

Because I have a reference point in terms of the building footprints, comments can be made on positional accuracy. For the road layers, the EOT Road layer is the most positionally accurate. Positional accuracy varies for the other two layers greatly, sometimes matching up fairly well with the EOT Road layer, and sometimes varying by as much as +/- 36 m. Similarly, the BRA hydro layer is the more positionally accurate of the two layers with regards to the building footprints, with the Tiger hydro layer varying by up to +/- 44 m away from it.

  1. Give a qualitative assessment of positional accuracy of each of the four optional layers relative to the other layers (e.g., do streets run through buildings? are schools in the correct location along a road?).

In this view, the intersection it can be seen that the orthophotos line up with the building footprints, as does the public school located at the top of the image (pink flag). Additionally, the MBTA trolley stop (green circle) is positioned where it is supposed to be. However, the bus stops do not always line up with their true locations, sometimes being located as far off as 22 m.

In this image, the bus stops are lined up much better, despite two stops being off by as much as 15 m. Although several of the public buildings are positionally accurate (including St. Elizabeth ’s Hospital and its libraries on the right side of the image and the two public schools on the left side of the image), the Brighton branch of the Boston Public Library has not been labeled (the large purple circle).

  1. Are these optional layers appropriate for your project in terms of their positional accuracy?

Although in some cases these layers are appropriate in terms of positional accuracy, such as when measuring walking distance between residences and most public buildings and trolley stops, in some cases they differ greatly. For instance, for a person with limited mobility, an extra 15 m to a bus stop can be a very large hurdle. If these features were being mapped onto a Streetmap or Tiger layer, the differences could potentially be up to 50 m.

  1. Completeness: Is each data set complete? (Does it cover the area question, are all relevant features present, and is the attribute information complete for all features?)

As brought up above, not all the appropriate features are labeled in the public buildings layer. Other data that would be useful as well – most notably the addresses on the EOT Roads data, the street layer most positionally accurate with respect to the five optional layers.

  1. Currency: Are the data up to date? How do you know the answer to this?

Using ArcCatalog to view the metadata on the M: drive as well as information from MassGIS, the layers were found to be current to the following years:

Orthophotos – 2005

Tiger – 2000

Streetmap – 2000

Trolley – 2006

Bus Stop – 2008

Public Buildings:

Hospitals – 2003

Schools – 2008

Police Stations – 2007

Universities – 2004

Fire Stations – 2006

Libraries – 2004

BRA Hydro – Unknown

Tiger Hydro – 2000
EOT Roads – 2007

Building footprints – 2003

Thus, we see that the data is mostly up to date. The layers most likely to be different are the Census 2000-derived layers: Tiger, Tiger Hydro, and Streetmap. Additionally, a date could not be found for the BRA Hydro layer. However, of any of the layers, this layer’s positional inaccuracy would likely affect the outcome of the project the least.

  1. Attribute accuracy: provide a qualitative assessment of attribute accuracy for critical attribute items (e.g., land use codes, street names and address ranges, school names, etc). How adequate is the attribute information for your project needs?

For the Tiger roads layers, despite its lack of positional accuracy, they typically have decent attribute accuracy. Some cases in which errors can be found however, are on very small streets like the one below.


Although the orthophotos and building footprints indicate only six houses on this street, the addresses are labeled 1-99. If one were doing a count of addresses, small roads like this would surely alter the outcome. The Streetmap layer is also missing addresses, which somewhat renders its usability in this project null, as it is not positionally accurate and a street-only layer could simply be made by selecting roads from the Tiger roads layer.

Other layers are typically accurate with respect to their attributes. With the exception of the one missing library in the Public Buildings layer, all other buildings are properly marked and labeled as far as I could tell. Additionally, the residential land use codes are mostly correct (although I’m pretty sure only one family lives in the huge house next door to me!) as are the name labels for the Trolley and Bus stops.