Kasia Hart
April 1, 2014
GIS Assignment #5
Assignment #5: Basic Data Preparation
For the final assignment, I want to compare MassGIS’s Environmental Justice data layer with
other methods of measuring environmental justice—including proximity to major highways,
polluting facilities, and impaired waters. MassGIS measures environmental justice using three
indicators—English isolation, income, and minority status. Including exposure to environmental
harms will add a new dimension to MassGIS’s demographic assessment of environmental
justice, and will of these communities are most vulnerable to environmental burdens.
Data
Since all data has been gathered from the MassGIS data portal, each data set was already
projected into the following coordinate system:
Name: AD_1983_StatePlane_Massachusetts_Mainland_FIPS_2001.
Projection Type: Lambert_Conformal_Conic
Linear Units: Meters
MassGIS’s TOWNS_POLY will serve as a basemap for this project. However, in order to create
an assessment of metro Boston, I first had to determine which towns would fall under this
category. Using MAPC’s Inner Core Community and MassGIS’s Metro Boston map of
environmental justice, I came up with a list of pertinent towns:
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Arlington
Belmont
Boston
Brookline
Cambridge
Chelsea
Dedham
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Everett
Malden
Medford
Milton
Newton
Quincy
Revere
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Somerville
Waltham
Watertown
Winchester
Winthrop
Data set 1: Environmental Justice Populations (EJ_POLY)
Projection:
Data selected:
Data set 2: Bureau of Waste Prevention Major Facilities (BWPMAJOR_PT)
Projection:
Data selected:
Data set 3: MassDEP Integrated List of Waters (305(b)/303(d))
Projection:
This data set had to be clipped multiple times. First, I selected only category 5 waters, as
MassGIS states that those waters constitute the 303(d) list. The 303(d) list is made up of waters
that are contaminated by pollutants, whereas the entire list contains waters impaired for other
reasons. I also included the HYDRO25K_POLY for context. The darker areas represent
MassGIS’s full list of impaired waters, while the selection is comprised of only category 5.
Category 5 selection:
Next, I had to clip just to the area I defined as metro Boston. After creating a new metro Boston
layer from TOWNS_POLY, I used the select by location tool to only include 303(d) waters
within my specified region.
Metro Boston 303(d) waters:
Data set 4: Major Roads (EOTMAJROADS_ARC)
Projection:
I used the select by location tool to clip the data to only metro Boston. This inevitably included
roads that extended outside of the region, which is fine for the purpose of this project.
Metro Boston Major Roads
Geometric information about data
Data set 1: Environmental Justice Populations (EJ_POLY)
For this data set, area of the polygons has already been calculated. A sum of the area of all EJ
communities in metro Boston is provided below:
Area Sum = 207,692,868.6 m2
Data set 3: MassDEP Integrated List of Waters (303(d))
Again, area for this data set has already been calculated, so I’ve included a summation below:
Area sum = 137,344,326.1 m2
Data set 4: Major Roads
Length has already been calculated, so here’s the total length of all major roads in metro Boston:
Road Length Sum: 1,886,246.9 m2
Using GIS Tools to Evaluate Data
In order to determine what environmental justice communities face the most environmental
burdens, it is important to look at the relationship between these communities and presence of
highways, polluted waters, and proximity to Bureau of Waste Prevention (BWP) defined toxic
and hazardous waste facilities. Using the MassGIS definition of environmental justice, we can
evaluate which of these communities are most proximate to environmental harms.
Main Question: Where are the most vulnerable environmental justice communities in
metro Boston?
Select by attribute: What environmental justice communities contain a major highway
(classes 1-3)?
After reevaluating how MassGIS defined major highways, I thought that class 4 roads were not
as likely to contribute to air pollution as much classes 1-3, since some of the streets included in
this class are fairly small. By selecting by attribute (‘CLASS’ = 1 OR ‘CLASS’ = 2 OR
‘CLASS’ = 3), this eliminates some of the smaller roads that are subject to less traffic, and thus
fewer air pollutants. The data itself is likely accurate as the data set is from 2012, but it is
possible that some of these smaller roads are in fact heavy traffic and more destructive than
larger ones. Nevertheless, eliminating smaller roads brings the dataset down to a size that is a
little more useable for the purpose of this assignment.
Spatial Join: Of the EJ communities that contain a major highway, which also serve as
hosts to BWP facilities?
Next, I used a spatial join to highlight which of the selected EJ communities are hosts to a BWP
facility. Selecting these communities will help filter out which ones are most vulnerable to harms
from toxics, but it is important to acknowledge that the harms of these facilities are not
necessarily confined to these communities. Additionally, the range of what the BWP defines as a
hazardous facility is quite broad, meaning that the impact of each facility on their host
community likely varies greatly.
Types of facilities:
Select by location: Of the remaining communities, which are within 1500 meters of polluted
water?
After determining which EJ communities host both a major road and a BWP facilities, I next
wanted to see which ones were within close proximity to polluted water. I chose 1,500 m as a
buffer because I wanted to include coastal communities, but not so far out polluted water may
not be a viable harm. A problem with including a 1500m buffer without specifying what type of
water is that the impact of each body of water may be different (ie: water that contributes to
groundwater supply should be more impactful that a small contaminated reservoir). There is a
“Class” attribute within the dataset, but it is difficult to locate on MassGIS’s what that means.
Final most vulnerable communities:
Summarize data in attribute table: Where are the remaining communities?
Now that we know where the most vulnerable EJ communities are, we can use the summarize
data feature to see what towns these communities are in. It’s not surprising to see that Boston is
well represented given the size and diversity of the area. Winchester was surprising, given its
reputation as a white, middle-upper class town. However, looking at the map, we can see that the
polluted water is not as embedded within the community as it is in some of the coastal Boston or
Chelsea neighborhoods. I chose to summarize by number of criteria met by each of the
remaining EJ communities, so this doesn’t reveal any specific demographic information to better
clarify who is being most impacted by environmental justice.