Your Very Own Spatial Question!

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.

http://www.census.gov/geo/maps-data/data/tiger-geodatabases.html

http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

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.

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?

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. 

Being limited to the data provided, if I was to do this analysis again, I would go further to address some more specific details that I had not considered at the start of the project such as, not all parks have the same size lake and that may limit my enjoyment, future analysis would include a lake size. Or I may not focus on lake size but rather also search parks with rivers or parks with the greatest number of lakes within a short driving distance of any park if the lakes were smaller than a desired size (see above). This way even if the campground did not have a lake or a small lake, a short drive would still allow me to kayak while camping. 

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.  

Bear Habitat Research in Marquette County Michigan

Goal

The goal of this lab was to use the geographic inquiry process, along with data and geoprocessing tools for vector analysis, in ArcGIS, to determine specific areas that would be suitable for bear habitat study areas in Marquette County, Michigan.

Background

In combination with data of the State, County, and areas of DNR management Lands, we were given the task of determining specific areas in Marquette County, Michigan, that would be suitable for bear habitat study areas. The potential study area had been predetermined, with data of bear locations inside of the study area marked by X,Y coordinates. Inside the study area we also had data on streams and land cover. The land cover feature class was divided into major and minor types. The major land type had data on the demographics of the area, more specifically what land was by type; forest, barren land, agricultural, or urban, while the minor land type data determined the specific use or vegetation types of these areas. 

Methods

The first step in creating a map, with the specific areas of interest that could be used in the bear habitat study, was to use spatial vector tools to combine the bear location coordinates with the land cover feature class, in an attempt to determine the type  of land cover that the bears preferred or were found most in.  

Once this area was determined, information from Biologists indicated that the bears of interest might be found near streams. More spatial vector tools (buffer, dissolve and intersect) were used to find the percentage of bears that were within areas of 500 meter of streams when their location was collected. 49 of the 68 total bears that had their locations marked were found to be within a 500 meter distance of the streams that were confined in the study area.

The next task was to make a recommendation to the Michigan DNR for a bear management plan, based on suitable bear habitat located on DNR management lands. Analysis was preformed by overlaying the stream proximity buffer and the 3 most common land cover types which the bears were located in, but only within the study area in Marquette County. Once that area was determined, another intersect was preformed combining the most likely bear locations (near streams and by land cover) with areas of land that fall within the DNR management zones.

The final task was to take all of the information and data that we built up and exclude specific areas from our study. In particular the areas that the DNR did not want to be part of the bear habitat study areas, were areas that were near built up or urban areas. This task was done two ways, first using spatial vector tools, and secondly, using Python. With spatial vector tools, selecting land cover by built up or urban allowed us to create a new feature class based on those attributes. Creating a 3 kilometer buffer around built up or urban areas was the minimum distance which the DNR desired. Then simply erasing the potential bear habitat that fell within that buffer allowed us to exclude that area from our final analysis. In Python, code was used to accomplish the same task (see below).

Data Flow Model and Analysis:

*OB indicates what specific objectives were met at that stage of the model.

Python code:

Results:

Here is final map with analysis of the data, and a proposed bear habitat study area for the Michigan DNR. In the map on the left, we can see bear locations specifically near streams, and in most common land cover areas bears were found in, inside of the study area.

The map on the right is the final recommendation for the Michigan DNR, which includes most likely bear locations, in addition to being overlaid with specific criteria from the Michigan DNR, which included that the most likely locations for bear habitat study areas be on Michigan DNR land under their management, and being at least 3 kilometers away from urban areas.

  

Sources:

Michigan Center for Geographic Information

http://www.michigan.gov/cgi

USGS NLCD

http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

DNR Management Units

http://www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt_units.htm

Stream Locations

http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

U.S. Census Data

Introduction

The Goal of this Lab was to learn how to download data, maps, and information from the U.S. Census Bureau and after analysis, display the information that was obtained in both ArcGIS online and in our blog.

Methods

After downloading a map of Wisconsin, we also downloaded information that pertained to the state of Wisconsin from the U.S. Census Bureau, specifically the states total population by county, and a choice of one other data source, in this case I choose Vacancy information by county. After downloading the map in the form of a shape file, and converting the information that was chosen from the U.S. Census Bureau into excel files that could be used as attribute data, I then joined the data with a common field such that we could then map the data. Two maps were created, one with the information of Wisconsin Population by county, and one with the variable of our choice. Once both maps were created, I published the map with the variable of our choice, in my case vacancy rates by county, to ArcGIS online, making sure that the maps that were created were capable of being published without any errors. Once the maps were uploaded to ArcGIS online I made sure that the map information worked in the popup form to make the map interactive. Additionally, a map summary was entered, and the map was tagged and shared with the Geography and Anthropology community of the University of Wisconsin Eau Claire. Upon completion of the first objective, I then published both maps blow to this blog post.

Results

In the total population map of Wisconsin, we see that the majority of the population lives in the South Eastern part of the state, with the greatest amount of the population living in the region from Brown County to Milwaukee and Kenosha County and stretching to Dane County. With similar high population statistics in Marathon, Eau Claire the surrounding areas. This makes sense as these counties have the larger and largest cities and their metro areas are in these counties, with Green Bay, Milwaukee, Kenosha, Madison, Wausau, and Eau Claire respectively. This, of course is not new information to anyone with knowledge of Wisconsin’s population distribution. Interestingly when this information is compared to the Vacancy rates per county in the state of Wisconsin we see that these counties are in the lower distribution of Vacancy rates, sitting between approximately 4-22% vacancy rates. The vacancy rates appear to be the highest in the Northern most portion of the state, with the highest rates of vacancy in the counties directly near the Upper Peninsula of Michigan, continuing to the counties near Lake Superior and stretching over to the northern most counties near the Minnesota boarder. Just given general population trends that the population of the United States is shifting to mostly City and Urban areas, it would appear after brief analysis that this trend is also following in the state of Wisconsin, at least based on the 2010 Census data.

Final Version of both maps in ArcMap

Sources:
     • U.S. Census Bureau
     • Esri
     • HERE
     • National Geographic (base map)

Eau Claire Base Data of the Confluence Project

Background

UW-Eau Claire and the Eau Claire Regional Arts Center have come together with Clear Vision
Eau Claire, Market & Johnson and commonwealth development as Haymarket, LLC. Haymarket,
LLC, plans to construct a new development at the confluence of the Chippewa and Eau Claire
Rivers in downtown Eau Claire. Dubbed the "Confluence Project", this site would be home to the
new Arts Center which will contain performance spaces, galleries, offices, classrooms, studios,
student housing, and a commercial retail complex. The construction of the Confluence Project is
set to begin in 2014.

Goal

Clear Vision Eau Claire is a county wide initiative to develop a collaborative vision for Eau
Claire. During an internship for Clear Vision Eau Claire, our job is to create base maps and a
basic report of all relevant information for the Confluence Project, including public land
management, administration and land use base maps.

Methods

Maps were created using both ArcMap and ArcCatalog. In ArcCatalog, a new feature class was
created, digitizing the site for the proposed Confluence Project. In ArcMap, a new map was
created with this feature class imposed on a World Imagery Basemap. This new map would serve
as the basic layout for all maps that follow. The Civil Divisions map was created by
adding municipality  boundaries to the basic map layout, and highlighting the different civil
divisions by color. The Census Boundaries map, adds a population density layer and census
boundaries to the basic layout, and shows population density in relation to the proposed site as
well as specific location lines for Census Boundary tracts as they run through the City of Eau
Claire. The PLSS Feature map, overlays the Public Land Survey System quarter quarter squares
over the basic layout and shows were the proposed site would lie in reference to the PLSS, the
data of the PLSS is included in the legal report to follow. The EC Parcel Data map overlays the
land parcel data for Eau Claire County on the basic layout map, showing the specific location of
the proposed site's land parcels, as well as the parcels of the surrounding area. The roads of Eau
Claire and the Eau Claire and Chippewa River are also overlaid adding the water layers and
center line layers to the basic map layout.  The Zoning map added the zoning classifications to
the basic map layout and shows the proposed site in relation to zones of the City of Eau Claire. 
the Voting Districts map adds, the ward information and division lines to the basic layout map,
and shows where the proposed site will lie in relation to the voting wards of the City of Eau
Claire.  

Results

The Civil Divisions map was created to reference the Confluence Project in the context of the
location in relationship to City of Eau Claire and the Town of Eau Claire, as well as the  Eau Claire
County line. The Census Boundaries map tells us that the site will be in an area that has a high
population density as well as being connected to areas with a high population density. In
combination with the Zoning map we can see that the proposed site lies in a commercial area
but is not far from residential neighborhoods, which are densely populated, making it easily
accessible to many in the downtown area.

Sources

Data from the City and County of Eau Claire, 2013


Clumpner, Dan; Market, Dan; Richgruber, Ben; Community for the confluence organizers (2015). 
Questions from the community. Retrieved from http://communityfortheconfluence.org/common-questions/