Laura R. Crossley
UEP 232, Fall 2012
Bikability for the Cambridge Health Alliance Food Network
Project Description
For this project I am working with the Cambridge Health Alliance (CHA) on the
Somerville Community Health Agenda in order to visualize the current food network in
Somerville and surrounding communities. In particular, this project focuses on a food saving
network that includes places where food can be rescued from disposal by Donor Agencies,
given to Recipient Agencies who disseminate the food to people who are in need, and best
routes for volunteers to follow in order to connect the two locations. These volunteers could
be driving or bicycling, but for this project the focus will be on bicyclist.
My goal is to map a food network for Cambridge Health Alliance, because we would like
to better understand where excess food is coming from, where it goes and the best routes to
take in order to transport food between the agencies. This type of organization has three main
components.

Creating a database and visual representation of Donation Agencies, Recipient
Agencies, and Transporters of food.

Mapping the roads that link Donator Agencies and the Recipient Agencies of our
designated communities: Somerville, Cambridge, Medford, Everett, and Malden.

Creating a bikability index by scoring roads according to their bike “friendliness”.
Mapping bike connections is intriguing, because there are many factors that signal to
riders if a situation as safe for bicycling. It seems that a bikability index could help people select
the best route to take between places donating and receiving food. Features that could
influence the scoring of roads for bikability include the speed limit, the condition of the road,
marking indicating the presence of a bike lane, how busy a road is at a certain time of day, the
width of the road and the road shoulder, what type of surface material is used in the road,
separate lanes for bicycles and how hilly the road is. For the purpose of this project, the road
features selected were based on the literature reviewed and by what was available in the
selected datasets.
Literature Reviewed
Kutz, Myer. Handbook of Transportation Engineering McGraw-Hill, 2004.
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Laura R. Crossley
UEP 232, Fall 2012
The Handbook of Transportation Engineering includes two sections relevant to this
project. One is a section on bicycle transportation planning and the other covers GIS methods
for general transportation planning. The chapter on bikes validates bicycles as valid and an
inexpensive mode of transportation that is both better for the environment and individual
health. The bicycle is also listed as an important time-conserving and traffic congestionreleasing mode of transportation. The handbook outlines width dimensions needed for
creating safer bicycle lanes and different designs for different intersections. Accordingly, some
aspects of the road to consider are drainage grates, actuated traffic lights, railroad crossings
and road surface quality.
The GIS section categorizes five types of GIS analysis that is often utilized by
transportation planners; display/query analysis, spatial analysis, network analysis, cell-based
modeling and dynamic segmentation. Although the intention of this project was to perform a
deeper analysis of the connections between the different food network areas, I was able to use
the display and query analysis to create the bikability index.
Inouye, Daniel K. and Kate A. Berry. "Assessing Bikeway Networks Around Public Schools: A Tool
for Transportation Planning in Washoe County, Nevada." Planning Practice & Research 23,
no. 2 (2008): 229-247.
The article demonstrates one way to assess the current bikeway and relates that
assessment of bike usage to middle-school children. The authors found that shorter distances
between a child’s school and home as well as the perceived level of safety on bike routes all
played a significant role in whether students rode their bikes to school. To assess that level of
safety, the authors looked at types of bikeways (paths, lanes and routes) in combination with
speed of roadways and continuity of paths. They found that modes of transportation available
had an impact on bike ridership. Also, the number of miles of bikeways was significant in
bikeway accessibility. Some future recommendations for assessing bikeways were to include
information such as number and width of travel lanes, posted speed limits, traffic volumes,
signalized intersections, road surface condition, slope, ‘commonly accepted’ bikeways and a
bicycle compatibility index. Several of these factors were included in the CHA bikability index.
Office of Transportation Planning. (2012). Bicycle Facility Inventory Year-End Report 2011.
Massachusetts Department of Transportation.
This report provides detailed information about the process and purpose of creating and
maintaining all of the bike inventory data in Massachusetts. Most importantly for this project
are the descriptions of existing mileage for each of the individual towns in Massachusetts. My
hope was to be able to compare this mileage to the total mileage in the data. The towns in the
Cambridge Health Alliance food network has about 61.17 miles of bike roads and paths. The
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Laura R. Crossley
UEP 232, Fall 2012
mileage for the individual towns is as follows; Everett 1.43, Cambridge 35.17, Malden 0.32,
Medford 6.93, Somerville 17.32
Johnston, S. (2009). Engineering Design Standards: Appendix C-5.0 Bike Paths. City of Red Bluff,
California. http://www.healcitiescampaign.org/document.html?id=9 (Accessed: December
3, 2012).
There was variability in the preferred and allowed grade of the road according to
different reports. This information was needed in order to determine how I would score slope
in the bikability index. According to Johnston’s report, a regular bike trail should not exceed 8%
grade. As a bicyclist, this seems to be a very gentle grade for short distances of about one
block. I was not able to find a better source of information, but I did find an unsourced online
mountain biking trail guide that confirmed 8%-10% as the maximum grade for an easy to
moderate level trail. According to this second guide, the maximum grade of a difficult trail
should be no more than 25%.
Flintsch, Gerardo W.; Diefenderfer, Brian K; Nunez, Orlando. (2008). Composit Pavement
Systems: Synthesis of Design and Construction Practices. Virginia Transportation Research
Council. http://www.virginiadot.org/vtrc/main/online_reports/pdf/09-cr2.pdf. Accessed
December 6, 2012.
This article gives an economic and physical analysis of composit pavement structures.
The details include information about the durability, types of materials used for different types
of composit, and the descriptions of the resulting road surface. The information was used to
score road surface types based on the composit layer they were made from. In the bikability
index, I had to understand the difference between a “block road” and “bitumous concrete.”
Along with Wikipedia, the Composit Pavement article helped to define what those terms meant.
Data Layers & Sources:




Cambridge Health Alliance collected the information about Transporters, Recipient and
Donor Agencies they are working with in 2012. This was provided in an excel table.
MassGIS Tiger Roads were downloaded as geodatabase files and provided vector data.
It was last updated in April, 2012. Metadata can be found at
http://www.mass.gov/anf/research-and-tech/it-serv-and-support/applicationserv/office-of-geographic-information-massgis/datalayers/census2010.html
The Bicycle Inventory data was vector data provided by MassDOT at
http://www.massdot.state.ma.us/planning/Main/MapsDataandReports/Data/GISData.a
spx. The data is current as of May 2012.
MassDOT also provided the overall road data, which was downloaded as a geodatabase
vector format. The data is current as of 2011.
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Laura R. Crossley
UEP 232, Fall 2012


http://www.massdot.state.ma.us/planning/Main/MapsDataandReports/Data/GISData/
RoadInventory.aspx.
ESRI vector roads came from the Tufts shared data source and were visually compared
to the MassDOT vector road data for quality and accuracy. This information was used to
gather speed limit data.
USGS provided the Digital Elevation Model as rastor data for the area.
http://ned.usgs.gov/downloads/documents/NED_Release_Notes_Oct12.pdf. The data
was last updated in June 2012.
Data Preparation and Analysis:
Area of Study
1. Projected all data sources into
NAD_1983_StatePlane_Massachusetts_Mainland_FIPS_2001 (meters)
2. Selected towns to be studied and create layer called “CHA towns”.
3. Clipped Tiger Roads and ESRI Data streets to CHA towns to minimize data set.
Geocoding for Community Inventory
1. The excel spreadsheets from CHA listing Donor Agencies, Recipient Agencies and
Transporters were each geocoded using MassGIS Tiger Roads streets and zipcodes for
address location.
2. All of the unmatched records were matched/rematched
a. Used Googlemaps to find locations that lacked a match opportunity and then
matched by hand.
b. For addresses that had a match location which fell within the 80% range, used
googlemaps to verify locations but selected match provided when appropriate.
Creating Bikeability Index-using MassDOT Bike Accomodations Inventory
1. Joined MassDOT road inventory to MassDOT bike inventory using common id field of
“Road Segment”.
2. Selected Bike Facility Status by attribute under “FAC_S_DESC”= “Constructed (existing)”,
which eliminated any proposed, but not yet existing bike roads and paths.
3. Set Workspace Environment Settings with the Snap Raster based on Cha_slope and used
the outline of my area for the Mask.
4. Established best and worst attributes. Note that where there are blanks in the table,
features were not present that would be scored accordingly:
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Laura R. Crossley
UEP 232, Fall 2012
Score
Bike Road
(best=5, Facility Type
worst=1,
0=No
Data)
5
On-road
divided/separate
bike lane
4
On-road marked
bike lane
3
On-road marked
shared lane
2
Off-road shared
use path=2
Bike Road
Surface
Type
Bituminous 1-10
concrete
road
Surfacetreated
road
10-25
Block road
(bricks in a
block)
1
0
Speed Road
Limit Structural
mph
Condition
NULL
Unknown
Slope(percent)
Good
0-2
Fair
2.1-5
25-35
5.1-7
35-45
Deficient
7.1-10
45-55
Intolerable >10%
No
Data
No Data
5. Rasterized each road data feature using Polyline to Raster. Below is an example of the bike
roads’ surface type feature being rasterized. This was repeated for Bike Road Facility type,
Speed Limit, and Road Structural Condition.
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Laura R. Crossley
UEP 232, Fall 2012
6. Used Spatial Analyst Tools to reclassify each feature. An example using speed limit is
demonstrated below:
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Laura R. Crossley
UEP 232, Fall 2012
7. Slope was a little trickier, because it has continuous and not discrete values. I needed to
restrict the slope to display only where there were roads. First I created a “Roadgrid”,
by using one of the road layers. Wherever there was data for a road segment, the data
was given a value of 1. Then I multiplied the Roadgrid by the existing slope feature
using the raster calculator. This gave value to slope only where road data existed and
created a “Road Slope” feature based on percent rise of the road.
8. Next I added the reclassed Speed Limit, Road Conditions, and Road Slope with a 60%
weighting on Road Slope and 20% weighting for Speed Limit and Road Conditions.
9. Finally, the reclassed and weighted road features were added to the reclassed Bike
Surface and Facility Type features. This final provided a bikability score of 4-24, where
higher is better.
Difficulties and Limitations
One difficulty I ran into occurred while trying to add the data together was that at first,
only the bike paths and roads would show. I had to reclass the Null Data in Bike Road Facilities
and Surface Type to zero using the following raster calculator method:
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Laura R. Crossley
UEP 232, Fall 2012
Most other difficulties I think were related to my brain capacity and its apparent
inability to take in more new information or map memory pathways to the simplest of steps.
Also, finding a meaningful and visually engaging way to display road line information
was challenging. The roads are simultaneously busy/crowded but thin and difficult to
distinguish the changing colors along the lines. I debated whether or not to include road
names, but chose not to in order to keep the visual information simple. Furthermore, there
were only five total factors being included in the analysis. This might explain why there was not
as much variability in the how the roads appeared. It is possible that including other factors
mentioned in the literature (ie. the width of the travel lanes and the road shoulder, the
presence of drainage grates or railroad crossings, actuated traffic lights, number of travel lanes,
busy times of day and traffic volumes) could all be factored in as well. Some of these factors
are in the bike and road inventory for MassDOT. They were not included because of time
constraints. Others would have to be drawn from different sources, which creates the issue of
how to get the data to match from different sources.
Conclusions
The lack of variables made certain elements weigh more than others. The maps present
are close to showing just a slope map or just a bike lane map. In reality, some of those bike
lanes could be in horrible need of maintenance, in which case it would be better to take a road
with a higher grade or slope. The power of GIS is that it gives us the ability to make a plan for
action, in this case about which roads to bike on. Those attributes have to be tested in real life
to know for sure what is best at a given time of day for a given person.
As for the food network, I wish that I had gotten into more detail about the distances
between localities. From looking at the map, it is obvious that more of the Donor Agencies
were in Somerville and more of the Recipient Agencies are located in Cambridge. Interestingly,
many of the agencies are located on or very close to the bike pathways, which happen to be in
areas that are more hilly.
Future Research
There was so much more I wanted to do with this project that became limited by time.
One idea is to add to my bikability index the features described above and then overlay the
resulting index on a density map. Meaghan Overton’s density map of places that could use
more bike infrastructure might be an interesting combination of information. Maybe the two
together would be useful in figuring out where to add and improve upon bike lanes!
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Laura R. Crossley
UEP 232, Fall 2012
For the Cambridge Health Alliance, assessing the access of low-income households to
Food Network could help direct the agency in where to focus the direction of the network’s
growth. A map showing buffer zones and point data of food network or raster map showing
zones that have good access to food network and those that don’t could be rather illuminating.
In particular, I would want to know if low-income households have access to Recipient Agencies
using a proximity analysis. Then analyzing the distances between the Recipient and Donor
Agencies would be interesting.
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Laura R. Crossley
UEP 232, Fall 2012
Appendices:
Road Slope Score (Green is good and red has greater than 10% slope)
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Laura R. Crossley
UEP 232, Fall 2012
Speed Limit Score Map
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Laura R. Crossley
UEP 232, Fall 2012
Road Conditions Score Map
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