Jay Monty
12/17/10
Intro to GIS
Final Project
Putting Back the Tracks: A suitability analysis for commuter rail expansion north of Boston.
Project Description
The purpose of this project was to see whether GIS tools could use census and
infrastructure data to identify abandoned rail corridors which may be suitable for new commuter
rail service. The basic methodology was to use a raster calculation that would assign values to
land parcels in a manner which would highlight corridors that may be suitable for new rail
service. Suitability for this exercise was based primarily on a land parcel’s housing unit density,
proximity to an abandoned rail corridor, and distance from other public transportation facilities
(other commuter rail and rapid transit stops). I began this exercise with the belief that there are
in fact many abandoned rail corridors that share land use characteristics of existing commuter
rail corridors and would therefore be good candidates for re-instated train service. The intent of
the GIS exercise was to prove or disprove these beliefs.
The primary benchmark which is used to determine a neighborhood or town’s ability to
support transit is its density, either in persons per square mile or housing units per acre. For this
project, I chose to use housing units per residential acre. This method isolated the measurements
of housing density strictly to residential areas, discounting areas with other land uses. This
methodology was more critical in lower density areas with large census block groups which may
have had dense residential development on one end near a downtown, but open space or
industrial uses in outlying areas which would have given the block group a lower overall housing
density. The downside of this approach is that it can overstate the total population of a census
block group. If for example, a census block contains only 10% residential area, its density per
residential acre could be very high, but overall density acre could be low.
The other primary criteria used for the analysis was geographic location in relation to
other transportation infrastructure including abandoned rail lines and existing rail lines and
stations. In order to indentify suitable corridors, land parcels adjacent to abandoned corridors
were ranked very high, so long as they were not within the catchment areas of other commuter
rail or transit stations. As will be discussed later, the wide variations in geographic locations and
densities created conflicts in the ranking system.
Data Sources
All of the data layers were obtained from MassGIS. Census data is based on the year 2000 US
Census while data layers regarding physical infrastructure and land use was compiled and
published solely by MassGIS and updated between 2005 and 2007. Metadata for each of these
data layers is available on the MassGIS website.
Data Layer
File Name
Accuracy
Date
(feet)
Census Block
CENSUS2000BLOCKGROUPS_POLY.shp
2000
CEN2K_BG_HOUSING_UNITS.dbf
2000
Groups
Total Housing
Units
Major Roads
EOTMAJROADS_RTE_MAJOR.shp
50
2007
MBTA Stations
MBTA_NODE.shp
50
2006
Railroad Lines
TRAINS_RTE_TRAIN.shp
50
2008
MBTA Transit
TRANSLINES_ARC.shp
50
2007
Lines
Land Use
LANDUSE2005_POLY.shp
2005
In addition to the data layers themselves, this project also took advantage of several layer
files which isolated various data subsets.
Layer File
File Name
Purpose
Railroads by Service Type
Railroads_by_Service_Type.lyr
Isolates railroad lines by type of service
provided: Passenger and Freight, Freightonly, Abandoned.
Commuter Rail Lines and
Commuter_Rail_Lines_and_Stops.lyr
Stops
MBTA Rapid Transit
Isolates only rail lines which have active
commuter rail service.
MBTA_Rapid_Transit.lyr
Isolates the MBTA’s rapid transit lines:
Green, Red, Blue, Orange and Silver
Methodology
The focus area of this suitability analysis was the area of Massachusetts within the
MBTA commuter rail service area north of Boston. This includes all of Essex County and most
of Middlesex County, as well as a small portion of Suffolk County. Thus the first task was to
isolate these three counties by selecting them by attribute. This would later allow the large landuse and census data-sets to be clipped to these areas.
Calculating Housing Unit Density:
Housing density in the units Housing Units per Residential Acre was calculated using the
following steps.
1. The Land-use and Census Block Group data layers were clipped to the 3-county area
described above.
2. Using the ‘intersect’ command, Census Block Groups and Land-use were combined in a
table which isolated land parcels based on land-use in each census block group. Looking
at Figure 1, we see how for block group 718 there are multiple parcels for each type of
land use (low density residential, forested wetland, etc).
3. The next step was to summarize the table based on land use. This was done using the
summarize command and the attributes LU_05DESC and LOGRECNO.
Figure 1
Figure 2
As we see in Figure 2, a total land area is given for each land use by the category
LOGRECNO.
4. Finally, as shown in Figure 3, a pivot table is created which creates one row for each
LOGRECNO and lists the area of each land use within that LOGRECNO.
Figure 3
This table is then joined back to the census block groups such that the land uses are
summarized by block groups. A new attribute was created to combine all residential land
types into one area.
5. Finally, the Join command was used to join the layer for total housing units to the pivot
table. In Figure 4, the layer “blockgroupden” reflects the total number of housing units
divided by the total number of residential acres.
Figure 4
Station and Corridor Catchment Areas
A catchment area is defined as the land area around a transit station from which transit riders are
drawn. Catchment areas vary based on several criteria, mainly travel time, travel distance, and
travel mode used to access the station. For example, a commuter rail station in a rural area where
there is little congestion and most passengers drive to the station may have a catchment area of 5
miles or more. However, a rapid transit station located in a dense urban area where most
passengers access the station by foot may only have a catchment area of ½ mile. Since it was
impractical to calculate the appropriate catchment area for each existing station, a distance of 2miles was chosen for this analysis. This seeks a middle ground corresponding to areas in
moderately dense suburban areas and also reflects the typical distance at which commuter rail
stations are spaced (3-4 miles) along a route.
Two methods were used to define the catchment areas, a network analysis using the
Network Analyst tool and a straight line analysis using the Spatial Analyst tool. Figure 5 shows
the network analysis overlaid on the straight line analysis as to show the difference between the
two methods. The network analysis gives a much clearer picture as to the densities and total
populations around each station by the density of the street network.
Figure 5
A similar analysis was also done for the MBTA rapid transit stations closer to Boston
using the straight line distance method only. This applied a smaller catchment area of 1-mile due
to the density of the physical network and the population it serves.
Next, a similar 2-mile catchment area was applied to the abandoned rail corridors.
Because stations no longer exist along these lines, a straight line distance was used to the actual
rail line giving the graphic shown in Figure 6. It is interesting to note how if the catchment areas
of Figures 5 and 6 are combined, nearly the entire land area would be within 2 miles of a rail line
or station, demonstrating the density of the historic rail network.
Figure 6
Finally, in Figure 7, a straight line analysis was used to generate distances from North
Station in Boston, where all of the northern commuter rail lines terminate. This was necessary in
order to give preference to abandoned rail corridors close to Boston.
Figure 7
Raster Analysis
The next step was to reclassify each of the data layers described above into 5 categories
with 1 being the least preferable and 5 being the most preferable. Land parcels were ranked
higher if they were near an abandoned rail corridor, and outside of an existing station catchment
area. An exception to this was a lower ranking the farther a parcel was from North Station. Land
parcels were generally ranked higher proportional to their housing unit density however, as will
be discussed later, it became necessary to rank the highest densities below the more moderate
densities. This can be seen in Figure 8 where the core of the cities of Boston, Lawrence and
Lowell rank lower than their immediate suburbs.
Figure 8
Raster Calculations and Final Analysis
The final analysis was done using the Raster Calculator tool and the rubric shown below
with the resulting suitability scores shown in Figure 9.
Data Layer
Coefficient
Distance from Abandoned Rail Line
0.25
Housing Unit Density
0.25
Distance from North Station
0.15
Distance from Existing Commuter Rail Station
0.25
Distance from Existing Rapid Transit Station
0.1
Sum
1.0
There were several caveats to the raster calculator giving the desired results. The biggest issue
was dealing with a very large range of values both in terms of density and proximity to
infrastructure. For example, during an initial attempt when the highest housing densities were
given the highest ranking, all of the downtowns of Boston, Lawrence and Lowell were given the
highest suitability scores for ability to support new commuter rail, even though they were
crisscrossed by rapid transit and existing commuter rail. The first attempt at solving this problem
was to give more weight to the existing rapid transit stations however this skewed the results by
allowing less flexibility with the other criteria, and did not affect Lawrence and Lowell which do
not have rapid transit. The most feasible solution to this problem became to rank the highest
densities as a 3 out of a total score of 5.
Figure 9
Other Difficulties
Initially, the intent had been to use a network analysis for both the catchment areas of the
existing commuter rail stations, but also as a means of weighting various rail corridors based on
their rail mileage from Boston. There were two issues with using the Network Analyst which
prevented using it for either of these purposes in the Raster Calculator. The first was in regard to
the existing station catchment areas. Unlike the straight line analysis using the Spatial Analyst
which calculates a maximum distance based on the extents of the map area, the Network Analyst
only calculates buffer distances defined by the user. The result in the Raster Calculator is that a
suitability score will only be given to land parcels within those buffers. Thus, if the existing
station catchment area is defined by a 2 mile radius, only these catchment areas will be scored by
the Raster Calculator.
The second issue with regard to the Network Analyst was that there were no junctions
defined for the rail network, making it impossible to analyze the rail network in the same manner
as the street network. However this may have been a moot point. Even if these junction points
were present, they would not necessarily reflect the realistic movements of a train at a junction
since most rail junctions do not permit a train to turn in any direction where two lines converge.
Final Thoughts
To my surprise, the analysis actually worked fairly well. It showed with relatively clarity,
several rail corridors which have surrounding densities similar to existing commuter rail
corridors and are in areas under-served by transit. It took little imagination to take the suitability
analysis and create a new map, by hand, showing possible extensions of the existing system.
That said, the analysis is not necessarily what one would use to determine which rail lines are
best suited for new service. It favors areas which are already built-out and does not address the
issue of creating inter-city connections which would connect isolated urban areas with rural
lands in between.
It is important to note as well that I started this analysis with a desired result. I suspected
beforehand that most of the rail lines identified by the GIS analysis were in fact suitable for
commuter rail service. In that regard, it could be argued that I structured the analysis to favor a
certain result. Not that this is totally unrealistic. Many if not most planning studies and
transportation models are created with a certain desired result in mind whether it is smart growth,
transit expansion or economic development. Everything from the densities needed to support
transit, to catchment areas vary based on local conditions such as other available travel options,
culture and other behavioral and economic patterns. They are not set in stone and relatively
impossible to calculate with certainty.
Annotated Bibliography
Matisziw, Timothy C.; Murray, Alan T.; Changjoo, Kim. 2006. Strategic Route Extension in
Transit Networks. European Journal of Operations Research. 171 (2). June 2006. pp. 661-673
This article discusses how GIS analysis can be used to determine the best routing for new
transit. It is focused primarily on bus transit, which provides an added level of complexity
since bus routes are flexible and can be placed anywhere or moved at any time (some
would argue this is a disadvantage not an advantage). However, many of the criteria and
methods used in evaluating the best corridors on which to extend service are the same
ones I chose to use in this project including: extracting existing service locations, demand
(though oddly, they did not use density as one of their criteria), and network structure.
The article discusses finding the best route based on capturing the most riders using the
shortest route distance, which is the ideology I attempted to use in this project.
Cervero, Robert. 2007. Transit-oriented development’s ridership bonus: a product of selfselection and public policies. Environment and Planning 39, pp. 2068-2085.
Unlike the previous article, this article discusses the physical attributes required to make
transit successful including transit oriented development and the “catchment area” of transit
stations. It discusses how physical attributes of a neighborhood or development influence a
person’s decision to use transit. While I could not evaluate physical attributes of all of the
communities north of Boston, I would argue that the network analysis performed on the
street network surrounding existing stations is one measure of the physical attributes of a
neighborhood.
McGuckin, Nacy A.; Srinivasan, Nanda; 2003. Journey to Work in the United States and its
Major Metropolitan Areas – 1960-2000. U.S. Department of Transportation.
This lengthy report details the commuting characteristics of several dozen major US cities
including data on transit availability and ridership.
Urban Transportation Planning, 2nd Edition. Michael Meyer and Eric Miller. McGraw-Hill,
New York, 2001.
This is a text book used by several transportation planning courses. For the purposes of
this project, it provides hard data on what population densities are required to support different
modes of transit. I used this reading primarily to determine what an appropriate catchment area
would be for the existing commuter rail stations, existing rapid transit stations and potential new
corridors.