Lab 5: Pick your Project

Introduction: For this lab we were tasked to come up with our own project. This "Mini Final" Project had the following requirements. We needed to come up with our own spatial question that was pertinent and relevant. So I thought, what is more relevant then Deer Hunting?! The nine day gun deer season just ended and I got skunked! I wanted to get that big buck and it was no where to be found. So I thought I would let GIS answer that never ending question, Where did that Big Buck Go? My intended audience is my nine man deer camp because we are all asking the same question.

Data Sources: I got my data from the Wisconsin DNR and Eau Claire County. Some of the DNR data required metadata which can be found at http://dnr.wi.gov/maps/gis/documents/orig_vegetation_cover.pdf. My only data concerns is the accuracy of the vegetation cover layer, because when you compare it to aerial data it does not always entirely match up. Also I know that within the Eau Claire County Data that some of the roads provided are not roads but rather private drives.

Methods: The requirements that I set forth for this project were this stipulations:
Must be in the Forest Zone of Eau Claire.
Must be .3 miles away from a road.
Must be within some sort of Pine Habitat since we know from experience that they typically head to pines for cover when the area is cold or heavily pressured.
Must be within .3 of the Anderson Property lines.

Data Flow Model

This is the Data Flow Model of my project. In the end, I believe that I made it a lot more complex than it needed to be.

Results: What I found is that using the requirements that I set, limited the area of "Where the Big Buck could be". I was only left with two sections in the southern portion of my map. One made up of White and Red Pine and the other made of Jack Pine. This was a very interesting way to try and find the Big Buck but the data was not reliable enough to get a legit location. Most of the Jack Pine area in my map is actually made up of corn fields and Poppel Trees. I limited the vegetation type to Pine only because of the cover it gives deer. Now deer could really be anywhere, it doesn't have to be pines, they can just as easily be in White Oak territory as well but for this project I wanted to eliminate the oaks from my methods ( Because I hunted oaks and didn't see nothing!). Also by their being roads in the study that should not be their, that limited the final area as well because of the buffer I added from the roads. I will say however that they are portions of this map that lie within the vegetation type that I was looking for that I believe may be holding a Big Buck. The habitat in these locations does suit deer that are trying to hide from the hunting pressure, as well as hunker down on some nice warm pine needles to stray away from the blistering cold and these north winds. However, this habitat is also prevalent in the northern part of my property but was excluded from the study because of some of the unnecessary road buffers on the east and west end of the property lines.

 

Final Map



The " Big Buck" areas are displayed as the Pink and Purple overlays within the Property Buffer area.

Evaluation: What I would change about this project is some of the data. I would go in and delete some of the unnecessary roads so that they did not influence me study. I would also try and find another variable that I could swap for vegetation type. I would still show the vegetation, but as extra information not as a limiting factor. It was hard to compile all the requirements for this project because as a hunter I know that its never what it seems and they always are where you don't think they are. I guess that's just the way of the woods, as the real factor in finding that Big Buck is putting the time and effort into staying out for as long as you can. As a whole though, I believe this project was a success as I demonstrated the skills and tools I have learned in GIS and I now have ambition to do further work with wildlife management using GIS. I would like to present my recommendation the Wisconsin DNR as to why they should install another Bear Management Zone in the central forest region of Wisconsin. By doing this Lab 5 project, I now have an understanding of the tools and information needed to due a wildlife evaluation of an area.

Sources: Wisconsin DNR, Eau Claire County

Lab 4: Vector Analysis within ArcMap

Goal: To find the suitable habitat for Black Bears within our study area in Marquette County Michigan and to make our recommendation to their DNR as to where the Bear Management Zones should be. This will all be done using multiple geoprocessing tools in ArcMap.

Background: What we want to do here is find habitat that best fits Black Bears and where we should recommend that they install Bear Management areas. The requirements that we are given are:
1. Has to be within 500 meters of a stream.
2. In Suitable land cover types.
3. Within already existing Michigan DNR management areas.
4. Further than 5 kilometers from Urban or Built Up lands.

Methods: We first added bear location data to our geodatabase as a feature class. We had to add the X,Y data which was the coordinates that the bears were located at the time of the study. From here we took a look at what Habitat the bears were in when this data was recorded. To do this we did a series of spatial joins so that each bear now had a habitat type associated with it. After summarizing we found that the three Habitats that held the most bears were Mixed Forest Land, Forested Wetlands, and Evergreen Forest Land. Another tool we used was Select by Location and with that we measured 500 meters from a stream to see how many of the bears were near the stream at the time of the study. We learned that 72% of the bears were within 500 meters. The next tool was the buffer tool where we did something similar to the select by location tool but here we created a new layer from the 500 meter buffer. After buffering we dissolved the new buffered stream layer so that it was one contiguous layer and didn't have all the buffer boundaries overlapping on each other. Then we moved on to the DNR management areas and used the clip tool so that we only had the management areas inside our stud area. After dissolving that layer we intersected the management areas and the Suitable Habitat layer that we got from buffering and dissolving the streams. The last part of this study was to exclude all areas within 5 kilometers of Urban or Built Up lands. To do this we selected the Urban and Built Up lands from the land cover layer and then buffered that 5 kilometers. Once that was done we clipped that Urban Exclusion from our suitable habitat to get our final product. From here we made a series of cartographic decisions to display this information in the best way possible.

Results: What I gathered from this Lab is that I believe there is rather limited areas to install Bear Management Areas. There is only a couple areas that stand out and remarkably, the largest two contiguous areas for Bear Management according to Habitat don't have any recorded bears even close to them!


Figures:

The final map of my recommendation to the Michigan DNR.



A data flow model of the steps and tools taken to make the map.

Sources:


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

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

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

Lab 3: Downloading GIS Data

 

 

In this lab we were put to the task of downloading U.S. 2010 Census Data from the Census Bureau and making two maps from that data of Wisconsin. We first had to go the Census Bureau Website and find the data that we wanted to map. We first had to download a map of Wisconsin showing us all the different Counties. The first data set that then took was the 2010 SF1 100% Total Population Data. This is compiled based the on Decennial Census that is mandated every 10 years in order to reapportion seats in the House of Representatives. We took the Total Population data and downloaded the shapefile to our Student Coursework folder. In downloaded as a Zip File so we had to extract that file to our lab folder. In that data contained two Microsoft Excel files containing the data and the metadata which tells us what some of the short speak pertains to within the data file.

 

Selecting the Total Population data from the 2010 SF1 100% Dataset

 

From here we opened up ArcMap and expanded the map of Wisconsin containing the different counties. Then we dragged the Population Data onto our map. The skill we learned now was how to join two tables in Arc in order to then take the data from one table and map it on the other. To do this we joined the two tables based on a common field. This was GEO_ID which represented the different counties. Then due to some issues we had to add another field into the table and go to the field calculator of which then we were able to link the new field with an existing field so that we can map the data.

The two tables we joined, notice how there is two seperate GEO#id fields.

 

Now that we figured out how to get the data to be mapped, we selected the population as the variable we wanted to map and made some cartographic decision as to how many classes we wanted to have and what color scheme worked out the best. I selected to have seven classes so that Northern Wisconsin wasn't one big unsightly blob of low population counties. Since the three major cities in Wisconsin (Milwaukee, Madison, Green Bay) are so large, they really stand out in the map, which is good but before I made that cartographic decision Northern Wisconsin was irrelevant. Now if you look at the map at the very bottom of the blog post you can see the major cities pop out but you can also see that there are three or four different population classes towards the North.

The map on the left show five different classes while the finished product on the right shows seven.

This concluded the brunt of the lab. From here we selected another variable we wanted to map from the Census Bureau and went through all the steps to download, join tables, and how to make cartographically pleasing decisions on classification and color schemes. After we completed both maps we put them into a layout where we added the essentials of a map such as a title, scale, legend, north arrow, and the author name. I also added a basemap to my layout to make it more pleasing to the eye and to add a better sense of place. I decided to use to the light gray canvas basemap instead of the typical world imagery basemap. I thought that they light gray made more sense because the world imagery would distract your eyes and since it was not a physical variable map, I thought that the canvas was more appropriate for a human variable map.

 

I can definitely say that after spending the time to learn what this lab was teaching, I am very comfortable with downloading data from the census and joining the tables in order to make these maps.



ESRI Virtual Training vs MAG Training

 

In class we have now done both the ESRI Virtual Training and the MAG Training as well. I believe that both the platforms are very similar in their effectiveness  of teaching us GIS. However, I would have to give the upper hand to the ESRI virtual training.

While using ESRI for learning Geodatabases, I felt that is was a more straightforward and to the point form of training. Some problems that I have had in the past with MAG are that there is too much busy work. While using MAG we would have to constantly digitize over and over again. This took up too much time and frustrated many of us. ESRI got straight to the point and gave us the A to Z on how to use it.

I liked ESRI's format of teaching in that they give you information about the subject, then they work you through how to use it, then they do a quick review, then you take your exam and get your certificate. The certificate in my mind is a overlooked accomplishment. Many people I think view these certificates as refrigerator material where I think they should be valued as much more. Accreditation is everything nowadays and you should be proud of that acknowledgement that you just received. I think it is great that ESRI allows you to receive these certificates because you never know when they may come in handy later on in life.

ESRI's format for me was just overall more efficient. The periodic questions were well placed and kept you on track so that you didn't get lost in the abundance of information given. I am definitely looking forward to the next ESRI virtual training lab so that I can further improve upon my GIS skills in the best manner possible.

GIS 1 Lab 1: Base Data

The city of Eau Claire in conjunction with the University and surrounding partners recently approved an Arts Center Project in downtown Eau Claire. This project when completed is expected to be a landmark when coming to Eau Claire. Many people will experience the arts like never before and be able to in a location with such rich history as the confluence of the Chippewa and Eau Claire rivers.

We took a look at the confluence project in the lens of these six maps. It is important to remember when planning a project such as this to observe all things spatial as you want the location to be flawless. 

The Confluence is located in the middle of the City of Eau Claire as you see in the Civil Divisions Map, and it also has the largest population of the surrounding Census Tracts in the "downtown" area of Eau Claire. The Public Land Survey System is a way of breaking down places into geographic grids and the breaking those grids into even smaller grids such as the Quarter Quarter grids depicted in the PLSS Features Map.

 The last three maps give us some more background information about the location of the Confluence and surrounding areas. With the project being located on the river there are not that many parcels adajcent to the proposed arts center and it is also located at the corner of two streets making it not too heavily traveled by commuting traffic.  The project is located within the Central Business District zoning area of Eau Claire, which only further adds to the aura of downtown. To finish, the project is located entirely within the 31st voting ward of Eau Claire.

All these maps were created using ARC Map and credit is given to the city of Eau Claire and Eau Claire County for the data. Techniques to make these maps included a variety of color and symbol changes. Changing the opacity of these colors allows us to see the aerial images behind giving us a better sense of place. Bright colors were used so that our eyes are drawn to the objects that I felt were important for viewers to see. Maps and their data are only as good as the Cartographic choices that are associated with them. Maps are made so that we can explain data through a spatial manner which allows readers to better understand what the author(cartographer) intends to teach them.