Introduction
What is your research question? What are the
specific objectives of your project? What is your intended audience? Who would
use this information?
While at my
Uncles house for Christmas break, I was talking to my cousin who has a 3 year
old who has never been camping. Being that camping is our family tradition, we
decided that we both wanted to go camping this summer. But we both had
different criteria for the campground we wanted to go. Being that my Cousin had
a 3 year old she wanted to be between Marshfield and Eau Claire, in case
our experiment failed and her child did not end up enjoying
the experience. Understanding the need to be close we decided to meet
halfway between both our locations. I wanted to go to a park that has a lake as
I have a kayak, and would enjoy kayaking while we camped. We also decided that
the closer to seclusion the better as we are used to camping
in relatively remote areas and we enjoy being deep in the woods.
While a remote location does not exist in the area, we both decided that we
would try and find a campground that was as far away from bigger cities as
possible. And my special question was born. My research question is
this, “where can my cousin and myself, who lives in Marshfield, go camping
in state or county parks that have a lake in which I can
kayak, is in an area between our two locations and away from light
pollution”.
My intended
audience was for myself and any family members who would like to come camping
with us, but this analysis could really be used for anyone who had the
same criteria or relatively close criteria about camping in
the area. The analysis could also be completed again using the same
criteria with a different location if anyone wanted to find another area of
interest.
Data Sources
What data did you need to answer this question?
Where did you get this data from? Provide pertinent metadata and citation
information for all data. (Providing a web address for the metadata is
sufficient). Do you have any “data concerns?” (e.g. scale, completeness of the dataset,
reliability, age, etc.)
The data that
I needed to answer my question was data that was retrieved from ESRI
servers specifically from the 2013 US Census Data, and 2013 US data. Links to
both sources are listed below. The specific data layers that I required were
U.S. Water Bodies, U.S. Parks and from the U.S. Census data,
I retrieved the U.S. National Atlas Cities data. The U.S. Water
Bodies Layer had to be selected specifically for lakes in Wisconsin.
While the U.S. Parks data had to be narrowed to Wisconsin as well, but no
further selecting or narrowing down of the data was needed. Similarly the U.S.
National Atlas Cities file had to be narrowed down for just a selection of
Wisconsin cities.
I have several
data concerns, as always worrying about how up to date and accurate the data
that you are using should always come up. Having said that I am not that
worried about the accuracy of this data as established rarely change as
new county, state, and national parks are not built or created that often.
Additionally, Lakes should be a stable data source in that lakes also do
not change in a drastic way where their location would be different or
the information provided by this data would be
inaccurate. My greatest concern is that while the data for parks is listed
at the county, state and national level, the metadata does not
list weather camping is available at these parks, and as such further
analysis on my part will be necessary to determine if these parks, do in fact,
have campgrounds.
Methods
What methods did you use to answer your geospatial
question? This section should include your data flow model and a brief
description of the methods.
The first step
that I took to map this information was to create a 75 mile buffer around both
Eau Claire and Marshfield. Being that the distance between the two cities is
100 miles I wanted to include the halfway point between both locations (50
miles), and an area of interest of about 1/4 the total distance on each
side of the half way point in order to get a large enough area for multiple
parks. I then 'Intersected' those buffers to get a defined search area. Then to
find out which parks would have lakes, I used the 'Select layer by location
tool' to find lakes near parks at a distance of 1/4 mile. The reason that I
used this distance, is that when looking at the polygons which made up the
parks, I noticed that they did not always touch the lake they would normally be
touching in the real world, and that no parks in my area of interest had
another lake that was within 1 mile of their location. I then via the
'intersect' tool, I Then created a 30 mile buffer around cities that had
populations of more than 10,000 in the vicinity around my area of interest.
My thought was that cities that had a higher population of 10,000 people
produce more light pollution, increasing in intensity as the city grew larger,
such that the larger the city the more light pollution it produces.
I then 'erased' all parks that fell
within the 30 mile radius of the towns which, under my criteria, produced light
pollution.

Results
What was the result of your project? This section should include you map as the result and explain the result.
What was the result of your project? This section should include you map as the result and explain the result.
After completing the above data flow model, and processing all of my data, I was able to determine that there are # of parks that would fit all my criteria for finding a park to camp at this summer that were in my area of interest (see Picture below).
There are some parks that look more temping then others, particularly parks with larger lakes.
The parks that
meet my criteria are shown in the map below. While the results do say that Lake
Eau Claire County Park does match my criteria and therefore was not erased from
my light pollution buffer, I would discard this park from my final results as
the majority of the park is erased from the buffer. It is also important to
note that the largest park on the map is actually the Chegquamegon National
Forest, and is not a campground but an area that could have many campgrounds.
So I would call this an area of interest rather than one specific result, which
is what my other locations actually are. Also it is important to mention that
while these parks do meet the criteria, there was no data which was available
to determine if these parks do have camping as a function of the park.
Data Sources: ESRI 2013 U.S. Census Data, ESRI 2013 U.S. Data.
Criteria: To find a State or County Park that was also near a lake, and away from light pollution, which would be a good place to camp during the summer.
Evaluation
What was your overall impression of this project? If you were asked to repeat the project, what would you change and how would you change it? What challenges did you face?
What was your overall impression of this project? If you were asked to repeat the project, what would you change and how would you change it? What challenges did you face?
I really enjoyed the project and the
ability to come up with my own spatail question
and preform my own analysis. With increased knowledge and
ability I would have procceded with my analysis differently. Being limited to
the data that was provided on the ESRI servers I was not able to use a data set
that specifically looked for campgrounds. If I were able to either find a data
set or create a data set of my own, digitizing campgrounds, to create a point
feature class, and then enter in specific attributes for those campgrounds to
tailor a date set to more specific details, the ability to answer my
spatial question would be more precise and accurate.
I think that the largest challenge that I faced was that I had a very specific geospatial question, and I had to come up with a way to find an answer using the data that was available. Overall, I very much enjoyed the project.
