Introduction:
For my research
question I asked where a good place to live in Dane County would be. The
specific objective of my project was to find a residential place that was
within ten kilometers of W. Washington Ave in Madison, three kilometers of a
hospital, two kilometers of a lake and one kilometer of a park. It also had to be 500 meters away from W
Washington Ave. My intended audience would be a person aged 21 – 30 that would
be trying to move to Madison, WI. This location is ideal for this age bracket
because it is in the middle of the city but also near parks and lakes. This
means there are plenty of activities to partake in and shopping venues to
explore within walking distance. This location is also near significant
nightlife but is far enough away to not hear the noise from the main strip. It
is also always ideal to be relatively close to a hospital no matter who you
are. This information would most likely be used by people trying to move to
Madison from the age of 21-30.
Data Sources:
I used the ESRI geodatabase for all of the data that I needed. I believe
that the ESRI geodatabase is a credible source to use. I used county data to
answer questions about residential zoning and county boundaries. I used
national hydrographic data to find the boundaries of lakes and rivers. I used
national forestry data to find the boundaries for parks and I used US data for
hospital locations. I used major US roads to get the location of W. Washington
Ave, and I used US states to find a Wisconsin locator map. My data
concerns have to deal with accuracy, completeness and age of my datasets.
Considering that my data was mostly national, it would have probably been
better to find a dataset that is scaled down and dedicated solely to Dane
county. That way the boundaries for the county, parks and lakes would be more
accurate. Also, the data from parks is from 1992 and 1997. Water data was collected
in 1995 and 2002. Hospital data was gathered in 2006, and roads were gathered
in 2007. This means that my data may be a little out of date. If this is the
case, some boundaries of parks, lakes, residential areas and roads may have
changed.
Methods:
Answering my spatial
question involved creating a flow model to track all the steps I took from
beginning to end (figure 1). I used spatial query to find residential areas,
select by attributes to find hospitals and W. Washington Ave, and an intersect
to find lakes in Dane County. I used buffers to find the area that was close to
parks, lakes, hospitals and W. Washington Ave. Then these that would be
suitable for living were intersected and dissolved. After that I used an erase
of a 500 meter buffer of W. Washington Ave and my suitable living area to find
the final living area. After that I put all the data on a template and put in a
locator map. I selected Wisconsin from state data and then selected Dane county
from county data. I put in my final area on top of the county boundary to show
the location on a broader scale.
Results:
As you can see in figure 2, the result of my
project was a polygon of my living area that consisted of 47139488m2.
Most of the area resides in an isthmus of Lake Mendota and Lake Monona.
Luckily, there are plenty of parks around this area and within the city. There
are five hospitals within five miles of W. Washington Ave and 6 hospitals within
10 W. Washington Ave. Most of the areas
to the northeast, northwest, southeast and southwest that don’t fall within the
living areas of W. Washington Ave are restricted because they are plotted too
far away from hospitals. The area directly south of Lake Mendota is restricted
because it is too far away from any parks. There were some suitable areas
farther south of Madison, but they were too far away from the city activities
and the nightlife.
Evaluation:
My overall impression of this project is that it
contains very useful information. When moving to a new area, it is really
important to figure out where you want to be geospatially based on your needs
and wants. If you fail to take these into account, your life could end up much
more difficult than it needs to be. If I were asked to repeat the project, I
would go a lot more into depth about the criteria I would include in my map. I
would include buffer zones for grocery stores, transit systems and my place of
employment. I would also create several maps showing population data, housing
price ranges, % owner/renter occupied areas, and age and race demographics. I
would probably accomplish this by making several graduated colors maps to show
different percentages and ranges. The challenges I faced doing this project
consisted of figuring out what the best tools to use are in different
situations. I also had trouble figuring what to do first and the order of
operations after that. I also faced some data collection problems because there
was some data that I wanted but couldn’t get my hands on. I was also a little
wary about the accuracy of the data that I did collect because it was a little
outdated and some of the boundaries weren’t as precise as I had hoped for.
