Gabriel J. Lopez-Bernal
Assignment 5
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 is meant to serve as a rudimentary walkability analysis for the Harvard Square
neighborhood in Cambridge. The layers included in the GIS analysis aside from roads and water
features are open space, and three geocoded layers displaying popular walking destinations: shoe
stores, post offices, and markets/convenience stores. While there really isn’t a serious
implication for a huge inaccuracy in the data – it could result in skewed walking times to various
destinations within the study area in an origin-destination projection. As you’ll see below the
inaccuracy of the data can lead to incorrect directions to stores – complicating matters for people
using this as an actual walking guide. The data doesn’t need to be precise for this application to
work, only accurate enough to not skew walking distance more than a reasonable distance. Any
significant inaccuracies would lead to exaggerated walking times/distances.
2. 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?
The image above shows the discrepancies between the StreetMap USA and the City of
Cambridge Road Centerline Data. The Streetmap USA (Orange, Blue, and Red) is typically
offset from the city data by 60+ feet. The third data set in the picture, the Road Centerlines
provided through MassGis, nearly coincides identically with City Data. The offset between the
city and MassGis data is often smaller than 3 ft.
The green circular areas of the image above depict regions where the StreetMap USA data file
blatantly omits certain detail – particularly around the complicated intersections of Harvard
Square.
3. Do the same as above for the two hydrography layers.
Just a bit further south of Harvard square we compare the Hydrography from the Census TIGER
Data set with the City of Cambridge Hydrography set. The attention to detail is obvious with the
census TIGER data; composed of a greater number of rigid lines rather than the smoother curves
of the city data. The offset of the Charles River varies between 130 – 30 ft.
4. Can you provide a quantitative assessment of positional accuracy for each of your data
layers (e.g., +/- 20 feet)? Why or why not?
The discrepancy of the roadway centerlines data remained fairly consistent throughout – varying
around a 60 – 70 foot offset. I would therefore quantify the positional accuracy of this data
around this range. The Hydro data varied far too much to provide an accurate quantifiable
assessment of the positional accuracy. The variation in hydro line segments combined with the
blatant linearization of the river would lead me to believe that the census TIGER source is too
inaccurate to begin with for the purposes of this task.
5. 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?)
The figure above of census TIGER centerlines place over the orthophotos of Harvard Square
illustrates the positional inaccuracy of the road network. The 60 – 70 ft offset described above
causes the roadways to be placed over buildings, sidewalks, and through parks.
The City of Cambridge Hydro Data (above) shows a much clearer quality outline of the river as
compared to the census Hydro Data (below). The city data nearly matches up identically with
the actual path of the Charles River – while the census Data swerves wildly across the landscape.
6. Are these optional layers appropriate for your project in terms of their positional
accuracy?
I would assume that the roadway centerline data would suffice for my analysis – leading
pedestrians within a reasonable distance of their destinations. As you will see in the images that
follow – the geocoded layers didn’t fare as well as anticipated by my hypothesis. The figure
below depicts the geocoded address of the post office in Harvard Square. This position is off by
hundreds of feet and is actually placed on the wrong side of the street. This would certainly not
work for my project – guiding pedestrians to the completely wrong location.
7. 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?)
In walking around Harvard Square, I took not of the locations of the various shoe stores. The
map presented above is accurate – illustrating all the known shoe stores of this location
reasonably close to their actual locations. Most of the points in the map above are display a store
with 50 feet of the position.
8. Currency: Are the data up to date? How do you know the answer to this?
The geocoded layer depicting markets/convenience stores had some very accurate points, some
points within 60 ft, and some points that were very far off. The major market in the area, Market
in the Square, was also missing from the dataset. A parking lot was also improperly marked the
location of a convenience store that does not exist.
9. 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?
The street attribute system for the Cambridge city data proved to be the most accurate of all the
roadway centerlines. Obviously, the census centerline data was skewed accordingly with the
offset of the data, however, the names appeared to be as accurate as the city data. All geocoded
points were found within the study area and positioned on the correct street, albeit sometimes off
by hundreds of feet. The open Space data (the image failed to load in this file) was also
extremely accurate – outlining the name and location of most open space. My only qualm with
the city open space layer was the lack of discrepancy between park space and open paved spaces.