Nathalie Schils
Introduction to GIS
December 16, 2011
Final Paper
I. Project Description
My project is in collaboration with Groundwork Somerville, a community organization that is
working to improve the economic, social and environmental well-being of the city of Somerville
and its residents. To address inequality in terms of access to public transportation, Groundwork
Somerville is implementing a bike share program that will allow residents to rent bikes from
several stations around the city. Six hub sites will be located at major T-stops and bus
connections. I will use GIS analysis tools to locate the optimal four sites for the satellite rental
stations and to assess the population that is located within ¼ mile of each proposed site. Ideally,
these stations will address issues of injustice, targeting populations that satisfy the Groundwork
Somerville criteria of low income, high minority and long commute times.
II. Data Sources
Data
Metadata
Source scale Year
2000 Census block group level,
Income,
Race/ethnicity, CENS2K_BG_INCOME_HH,
Commute time, CENS2K_BG_POP_RACE,
http://www.mass.gov/mg
is/bg_fields.htm
1:100,000
2000
Population
CEN2K_BG_TRNS_COM_TIME
2010 Census block level,
CENSUS2010BLOCKS_P1
2010
Streets
2010 Tigerline
Block group
polygons
2000 Census
CENSUS2000BlOCKGROUPS_P
OLY
2010 Census,
CENSUS2010BlOCKGROUPS_P
OLY
http://www.census.gov/pr 1:100,000
od/cen2010/doc/pl94171.pdf
http://www.census.gov/pr 1:100,000
od/cen2010/doc/pl94171.pdf
http://www.mass.gov/mg 1:100,000
is/bg_fields.htm
http://www.census.gov/pr 1:100,000
od/cen2010/doc/pl94171.pdf
Unknown
2010
http://www.mass.gov/mg
is/mbta.htm
Jan.
2006
Block
polygons
Source
Aerial imagery
ArcGIS
MBTA stops
MassGIS, MBTA_NODE
1:100,000
2010
2000
N/A
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III. Data Preparation and Analysis Steps
1. Create criteria maps
a. I used the 2000 Census Data (MassGIS) and selected data for the town of
Somerville (TOWN ID= SOMERVILLE) to create three separate criteria maps to
depict income, race/ethnicity and commute time at the block group level.
b. Census data had to be joined to the 2000 Census block group polygons.
c. Census data had to be modified for each of the three criteria
i. Income: add all values with an annual income < $35,000
ii. Minority status: add attribute field
Minority Pop = Total Population – NH_White
iii. Commute time: add all values for commute time above 30 minutes
d. Each criteria map had five classifications
e. Below is an example of one of the criteria maps:
2. Create environmental justice ranking map
a. Join all criteria categories to main Block group data set (Inc_35, MinorPop
Com_Tim30)
b. For each classification, select the block groups that fall in the classification and
assign a ranking (1-5, 1 for the lowest classification)
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c. Add up the three separate rankings (Income Rank + Race Rank + Commute Time
Rank) to get an overall environmental justice ranking and create a map to show EJ
rankings for each block group
3. Create the population density map
a. Download and join 2010 Census data at block level – table P1
b. Convert features to points (Toolbox: Data Management  Features to Points) to
assign a point to each block that contains the data (population)
c. Use the Kernel Density tool (Toolbox: Spatial Analyst  Density) to determine the
population density with a search radius of 400 meters (~1/4 mile) and area unit of
km2
4. Site four rental stations
a. For this piece of my project, I had to use an aerial image of Somerville and look
within each high EJ ranking block group to see where the population density was
highest. Within that area, I sited the rental stations on/near a major road whenever
possible
b. Add features for each bike rental station and create a shapefile of these features
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Rental Station #1, Summer St & Bow St
5. Network analysis
a. Download 2010 Tigerline file
b. Select all Somerville non-arterial roads, based on the MAF/TIGER Feature Class
Codes of S1710 (walkway/pedestrian trail) and S1400 (local neighborhood road,
rural road, city street)
c. Use the Topology toolbar to split all Somerville non-arterial roads using the
Planarize Lines function
d. Build a new network and use the Network Analyst toolbar to create a ¼ mile
walkable network from each of the bike rental stations
e. Select the Census block polygons that fall within this ¼ mile walkable network
f. Use the Statistics tool in the Attribute Table to determine the total population that
has access to each of the bike rental stations
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IV. Challenges
A. Census data: Because the 2010 Census data has not been released in full, I had to use both 2000
data (income, race/ethnicity, commute time) and 2010 (population) for different pieces of the
analysis. This was problematic in terms of delineating boundaries between blocks, block groups,
and towns, because there are slight discrepancies between the data sets. Additionally, I had
trouble with the large tables given in the 2010 block data. Joining these tables took several
attempts because the program kept crashing. Eventually, I cut most of the table out and kept only
the necessary columns and this kept the software running more smoothly.
B. Site selection: Although the GIS software was very useful for determining which blocks were
most appropriate for the bike rental stations, the software was not able to actually select the sites.
Instead, I had to use the aerial map to balance between the population density and EJ ranking maps
and then locate the rental station along a main street or by a popular intersection. Future analysis
could look at additional factors in site selection such as primary bus routes, major work sites,
and/or continued education centers.
C. Network analysis: I ran into several unexpected problems with the network analysis portion of
the project. First, the 2010 Tigerline street data had to be modified before I could conduct the
network analysis. The lines had to be split using the Planarize Lines tool (Topology toolbar).
When I first ran the network analysis, the software was unable to differentiate between segments
of a street so the analysis showed that the ¼ walkable network was very very small! Once I split
the lines into individual sections and re-ran the network analysis, I was able to determine how long
¼ mile covered along the non-arterial roads. In order to analyze the population that was served by
the bike station, however, I had to manually select the blocks that fell within the ¼ mile network.
As shown in the picture above (Step 5e), this was not a perfect system because the blocks did not
match up even with the network. I did my best to capture the blocks as accurately as possible but
a different methodology may be better applied if Groundwork Somerville is interested in
furthering its assessment of the population surrounding the individual sites.
V. Concluding Remarks
Overall, I am very happy with how this project turned out. I think this site selection methodology
can be applied for other projects, such as parks, libraries and grocery stores. Any criteria can be
listed and ranked as the three criteria were for this project. This method is very helpful in
narrowing down the overall area to the block or the block group level so that planners have a
better sense of where their target population is located.
I think the methodology was at its strongest in isolating which blocks were most appropriate for
the bike rental stations. Additionally, the network analysis could yield interesting comparisons
between the rental stations if they were assessed in terms of how accurately they met the needs of
the target populations. In addition to calculating the total poulation within the ¼ mile walkable
network, I could also have added the other Census 2010 block data tables and looked at the racial
make-up of the population. After Census 2010 data is completely released, it will be possible to
look at the communities served by these stations in greater depth. Groundwork Somerville is
interested in conducting interviews on the ground, which could yield helpful information when
added to the site selection methodology.
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One potential issue with my analysis could be my use of total households for the three criteria
map. I ranked each block group based on the number of households within it that met the given
criteria – income, ethnicity, commute time – as opposed to the density of those households based
on the overall size of the block groups. Because the block groups vary in size and shape, this may
have affected my results. I think that the addition of the population density corrected for this
problem for the most part.
VI. Annotated Citations
A. Malczewski, Jacek. “GIS-based land-use suitability analysis: a critical overview”. Progress in
Planning , Volume 62, Issue 1, July 2004, pages 3-65.
http://www.sciencedirect.com.ezproxy.library.tufts.edu/
The author covers the use of GIS to rank different sites to identify the most suitable site out of a
host of potential candidates for a particular amenity. In site selection analysis, the location, size,
attributes, etc of the site are known and then GIS is used to compare the sites and identify the best
candidate. Site search analysis, on the other hand, can be used to explicitly define the boundary of
the best site. The author also covers the history and evolution of GIS technology and its use in
urban and environmental planning. An overview of the different methods for GIS-based land-use
suitability analysis covers multi-objective and multi-attribute methods. Several case studies are
offered to demonstrate the potential use of GIS for land-use suitability analysis, including a case
study involving the siting of transmission lines in West Virginia.
B. Rogalsky, Jennifer. “The working poor and what GIS reveals about the possibilities of public
transit”. Journal of Transport Geography , Volume 18, Issue 2, March 2010, pages 226237. http://www.sciencedirect.com.ezproxy.library.tufts.edu/
A study using GIS to identify gaps in transit service, especially as it impacts working poor women
in Knoxville, Tennessee. Based on data gathered in interviews on the mode, distance, and purpose
of travel as well as employment, family status (marriage and/or children), and vehicle ownership.
The analysis also addresses problems of urban inequality and land sprawl. The study used GIS to
analyze transportation, childcare, and employment opportunities in relations to travel habits and
access to public transportation.
C. Sister, Chona; Wolch, Jennifer and Wilson, John. “Got green? Addressing environmental
justice in park provision”. GeoJournal , Volume 76, Number 3, September 2009, pages
229-248. http://www.springerlink.com.ezproxy.library.tufts.edu
A study was done to assess equity in park provision in Los Angeles. Park quality was looked at in
terms of an environmental justice risk in which urban low-income populations are
disproportionately exposed to undesirable land uses, pollution and limited access to clean and safe
green spaces. GIS was used to determine a “park service area” around park spaces. Census data
was used to identify regions with increased “park pressure” or congestion because of greater
population density. Several shortcomings were identified with this strategy but it was applied in
an analysis of Los Angeles’ parks.
D. Leigh, Courtney, Peterson, Jim, and Chandra, Shobhit. “Campus Bicycle-Parking Facility Site
Selection: Exemplifying Provision of an Interactive Facility Map”. Surveying & Spatial
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Sciences Institute, Adelaide, Australia, 2009.
http://www.spatial.adelaide.edu.au/SSC2009/papers/Leigh.pdf
GIS was used to create an inventory map and to identify sites for parking facilities for bikes on
Monash University’s campus in Victoria, Australia. Several criteria were given for how facilities
should be located, including slope, lighting, and clearance from roadways, curbs, and pedestrian
walkways. A valuable flow chart diagram was included that depicted how GIS could best identify
the most suitable sites for bike parking facilities. Several articles that may offer more help on
siting bike-parking (or rental) stations are listed at the end of the article.
E. Maroko, A.R., Maantay, J.A., Sohler, N.L., Grady, K.L., Arno, P.S. “The complexities of
measuring access to parks and physical activity sites in New York City: A quantitative and
qualitative approach. International Journal of Health Geographics , Volume 8, Issue 1,
March 2009, http://www.ij-healthgeographics.com/content/8/1/34.
An analysis was conducted in which GIS was used to study access to parks. Access was measured
both in terms of density of park acreage or access to physical activity sites within a certain
neighborhood. A social justice component was added to the analysis with data on socio-economic
status, race, education, and population density. The study did not conclusively find a strong
relationship between distribution of parks and race, ethnicity or socio-economic status. Several
challenges were identified in this type of analysis, including that certain parks may be more
inaccessible than revealed on a map because of obstacles such as highways or even race relations
tension that prevents certain people from feeling comfortable with using a park. The report also
identified potential shortcomings in the census data, which has been shown to undercount low
income, high minority populations.
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