Eliza Davenport Whiteman
UEP 232 – Assignment 5
November 1, 2012
General Inquiry: For my final assignment for the class I am going to be doing an analysis of beef
slaughter facilities in the state of Maryland. For this assignment, I decided to start looking into slaughter
facilities and their geography in relation to cattle production in the state. The end goal for the project is
to assess where in the state there is a need for additional slaughter facilities.
Selection by Attribute
To start I selected all of the
slaughter facilities in the state (red
triangles) that were defined by the
subtype of “slaughter” or
“slaughter, processing”. This
selection excludes any facilities that
are on-farm or are mobile slaughter
units, which will provide a better
assessment of geographic areas of
the state that are underserved.
Selection by Location
To determine geographic proximity
to slaughter facilities, I selected from
the cattle farms layer for all farms
within 60 miles of my selected
slaughter facilities from above (blue
dots). (I selected 60 miles based on
the distance used by Columbia Urban
Design Lab for a similar assessment of
slaughter facilities in New York.) I
found that almost all of the cattle farms were within 60 miles of a slaughter facility, with the exception
of all the farms in Charles and St. Mary’s counties (the southern two counties on the eastern side of the
Chesapeake).
View Statistics by Selected Feature:
Neither of the two data layers used up to this point (MD slaughter facilities and MD cattle farms) have
any numeric fields, so in order to view statistics by selected feature, I added in a data layer of population
density in the state of Maryland. This metric will help me to assess how large the population is in Charles
and St. Mary’s counties. If these counties are very densely populated, then the meat raised in that
region may be more likely to be distributed locally. However, if there is a low population density with
large cattle production, then it may make more sense to distribute the product more broadly, which
could be an argument for locating slaughtering facilities at a greater distance.
The total population for the two counties (which I calculated by selecting the two counties and then
viewing statistics from within that selection) is 251,702, which is only 4.36% of the state population of
5.77 million - suggesting that the meat may need to be distributed more broadly.
Summarize by Attribute Field
To see how many of the slaughter facilities in the state
provide processing services, I did a summary by attribute
field using Processing Service as the category and then
looked at the count that was tabulated. This summary
table showed that there are 16 different types of
processing services offered among the slaughter units in
the state. Most only had one location per service although
a couple had more than one. The categories seem to be
rather arbitrarily defined and if I wanted to do any
analysis of the data around types of processing services, I
would need to define the categories more effectively. The
count also shows that there are 33 points in the data that have no information in the processing services
field (meaning the field was left blank or they do not offer processing services.
Field Calculator/Geometry Calculator
Finally, in order to determine the area (in square miles) of the two counties that have farms without
close access to slaughter facilities, I added a field to the county data layer to calculate geometry. I then
used the statistics table to be able to assess to total square miles in the two counties.
One spot where I am concerned there may be error in my data came when selecting by location the
cattle farms that were within 60 miles of my selected slaughter facilities. The image below shows my
selected farms (in blue) and the slaughter facilities (red triangles), as well as all the cattle farms in the
state (orange circles). It appears that some of the orange circles are just as far from slaughter facilities as
the selected cattle farms, which I think is probably due to the data layers being in different coordinate
systems or using different units.
All data sourced from Johns Hopkins Center for a
Livable Future, Maryland Food Systems Mapping
Project.