GIS 335: Final Project

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 This means that my data may be a little out of date.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.