Ian Adelman
Assignment 5 Basic Queries
Goal of my analysis
I am interested in determining the least cost paths for pedestrian commuters. I want to determine which roads will see the most pedestrian traffic as commuters head to employment destinations. My assumptions are as follows: Roads within high-density residential and commercial areas will be trafficked the most. Roads within low-density residential, open space or industrial areas will be trafficked the least. Roads in close proximity to subway stations, commuter rail stations, bus stations, and bike paths will have high pedestrian traffic. Roads with sidewalk coverage will be trafficked more than those without.
Queries I performed:
My first query was a select by attribute. From my MassGIS Zoning Layer I selected all of the parcels where the “Prim_Use” = Multi-family, medium density; Mutli-family, high density; Mixed Use;
Institutional; Health Care; Office Park; Limited Business; General Business and; Central Business.

For my second query I wanted to select by attribute all of the road segments that are walking friendly. In order to that, I first selected by location all of the road segments that intersect with the zoning areas I highlighted in my first query. Then I removed from the selection, by attribute, the road segments with a value in the left and right sidewalk field. Theoretically, I have created a map of the road segments in the most desireable places to walk that have a sidewalk on at least one side that is greater than one foot wide. Here is the map:

Similar to the last map, it is difficult to see any trends in the urban core areas. For the most part these are heavily walked places with adequate sidewalk coverage. There are however, some things that stand out with a visual analysis. In the more periphery, paths of least cost/resistance stand out. Also, there appear to be noticeable barriers in other places where grids of walk friendly streets are cut off from other grids. In most places this appears to be caused by a major road, but in some other places that does not seem to be the case. Perhaps these areas are being divided by an industrial area?

Maps and summary table
I wanted to compare to see which municipality has more walkable streets. To find this out I used the
Summarize function to sum the “To Measure” field of the street segments (from my table of street segments that met my query criteria) by the “City” field. This has given me a table with to total distance of “walkable” street segments for each City. I am assuming that the distances are in km because
MassGIS layer is in meters? The metadata did not say what units the lengths were in.
According to this table Boston (35) has the most walkable streets—637.28 km. Second is Cambridge (49) with over 96 miles. Thirds is Somerville (274) with 77.67 km. Then Brookline (46), 43.3 km; Chelsea (57),
42 miles; Revere (248), 30 km and so on. What is interesting is that Brookline(26) is ranked as one of the lowest with only 7.6 km of walkable streets. However, this does not tell us anything about which town is more walkable because each town has a different total distance of street segments. In order to make this comparison, I summarize made a summary table of the total length of street segments by town:

Then I joined this table to my previous summary table by the “City” field. I then added a field for the percent walkable streets. I used the field calculator to divide the walkable street segment lengths by the total street segment lengths for each town:

Why my results might be incorrect:
Unfortunately I used the MassGIS zoning layer instead of current use for my analysis. This layer is showing the allowed use, but not necessarily what is actually there. This will have affected my analysis by showing incorrect areas for various uses. Most likely there are vacant property or “under” utilized land that are not as walkable as my analysis has shown. Areas zoned “high density” residential may not be built out yet. Therefore, my analysis shows them to be more walkable than they are.