GIS I: Lab 5 Mini-Final Project

                Where would be the most ideal location to build a new par 3 9-hole golf course in Outagamie County? Outagamie County already has ten golf courses, so it would be important to plan a course to be in a region of the county where there is not a course. Thus giving this new golf course its own new audience and not stealing business from other courses. In order to find a location for this new golf course I downloaded data off the GIS servers containing information about major roadways, city or town centers, previously built golf courses, and parks. For this lab I will only use park locations as suitable locations to build a golf course instead of available farm land or forestry land due to the fact of time and resources. For this project my intended audience would be the Park and Recreation departments for the cities of Outagamie County, or a private owner who is interested in building a new golf course. But because this golf course is going to be built on land already being used for parks it will have to be for the Park and Rec Departments.

                In order to answer my question on where to build a new 9-hole golf course in Outagamie County, I had to download multiple data sets.

The following paragraphs will work through the data flow model.

These data sets started with the United States and County data. From those two files I was able to export Outagamie County and begin to add additional material to the map for just Outagamie County rather than the whole country. Next I added Cities and Major Highways to the county data. Before adding any other data I created a buffer on both of these data sets. I created a five mile buffer around the cities and a one mile buffer around the major highways. I then intersected the two data sets to come up with the largest area possible for a new golf course to be built. I still have more data to add to the equation before finding the ideal location, but this first area gave me a rough idea on where I could put the course.

 The next step was to import the Recreation areas (the Wisconsin DNR has all golf courses listed under Recreation areas) and perform a query to select just the golf courses. After selecting just the golf courses I created a three mile buffer around them. This area was to inform whoever will be looking at the data that this is an area where not to build a golf course. My next step was to erase the data based on the county data. This then got me my second set of ideal locations (the second set did not take into account of the locations that the first set found, instead that will be incorporated into the third ideal location).

 The light green circles represent the three mile buffer around the golf courses, while the pink area represents any area not inside the three miles and therefore available land to use for the development of a new golf course. The next step I took was to intersect the first and second selected location areas and figure out where using both sets of data the next best location would be.

After intersect the two previous ideal locations, I have a much better understanding on where ideal land would be that is still within five miles of a city and one mile of a major highway, but is still three miles away from any other golf course. On the above image the light yellow/white color is all available area to build this new golf course, while the light blue is any area that does not fit all three categories. The next step was to insert the parks data. The park data will be intersected with the map above and any park that falls within the white area will be available to be selected.

 The available parks to be used in the project show up on the map above in a bright green color. The only down side of this data is that the data does not take into account with what is already in the park. The last step of my project was to create a query on the available parks to find out of the available locations which ones have an area greater than 0.2 square miles of available space to use. The results came up with two available parks that fit all the criteria. These two parks are Shiocton’s Lake Park and Appleton’s Memorial Park. They appear on the map below in a bright yellow color.

All data for this project was downloaded from ESRI servers or Wisconsin Department of Natural Resources servers.

                Working with this data I came across many issues with the data. The first issue I came across was when I imported the data set labeled golf courses only three came up. All three courses were along the US-41 Highway leaving everything but the Southeast corner available for a course. I overcame this issue by importing Recreation Areas and finding out they had ten golf courses in the county, which is a much better number to work with and they were all spread out. My next issue did not take much longer to come across and that was dealing with cities. Outagamie County only has a couple major cities, and those are the only ones which came up with the cities data set. To fix this problem I imported the places data set and performed a query to just get cities in Outagamie County, therefore giving me many more cities to work with.  My next issue I came across was not being able to plan for a course to be built on non-park land because the data for that was not available and would take a much longer time to manually find a location for a golf course. So I had to settle for just the use of the parks. The next issue I had to overcome was the parks not taking into account what was already on the park land. I know from my background knowledge that Appleton’s Memorial Park has an ice rink and many softball fields on it that many youth teams use, along with those they also use the hill in the park for the finish line of a 5k race and use the manmade lake in the park for firework shows. As for Shiocton’s Lake Park there is a manmade lake and beach there that contributes great amounts of revenue to the city as well as baseball fields where local teams play at. The last issue I came across was the not current data sets I worked with.

                My project came up with two ideal locations for a new 9-hole golf course

                Overall I was greatly impressed with this project because we were able to choose our own idea for the project and got to go anyway with it that we wanted. If I was able to change anything about this project I would probably change how much time we were given to do it. I think if we were given much more time we could go into much more detail and go out and get real data for the project. I know for my question I would be able go into land availability much deeper and maybe contact other people about land use, but that may be way too much to handle in GIS 1 and more for an actual job or research project. The only real challenges I came across was using the wrong tool and then having to restart that step.

Lab 4: Vector Analysis with ArcGIS

     The goal in lab 4 was to conduct a vector analysis with ArcGIS using multiple different geoprocessing tools to determine the most ideal place for bears to make into a new habitat for bears in the study area of Marquette County, Michigan.
     The purpose of this lab was to gain a better understanding of using various geoprocessing tools and how they are to be used in the proper way to figure out the problem at hand. We were given multiple sets of data, bear habitat, streams, land type, and the county data, and using our knowledge learned we had to apply multiple geoprocessing tools to figure out where the best place for the bears of Marquette County to be moved to.
     In lab 4 we followed a vague set of guidelines to help us conduct the project. The vagueness was to assure that we as students had a full grasp on the information at hand and would be able to use it in a real world scenario. After importing the county data, streams, and bear data we had to change the landcover feature so it could easily be seen which was what category. After that we conducted a summarize on the data to see which landcover areas had the most bears. These three areas were Evergreen Forests, Residential areas, and Lake regions. The next step was to create a 500 meter buffer, and dissolve, around the streams in Marquette County. About 72% of the bears live within the 500 meters of the stream, making it a very important component in finding a new habitat for the bears. The next step I did was intersect the data around the the streams with the top three bear landcover types and only have those areas selected with the areas around the streams. Next was to incorporate the data with DNR management locations and intersect them. Using the newly added DNR management data I intersected it with the likely areas of the bear habitats. The next step was to use the newly added DNR and landcover bear habitat data and eliminate any areas within 5 kilometers of a Urban or Built up land. Any area within the 5 kilometers of an urban or built up area was erased and not used in finding a new bear habitat. Lastly, after all that I had my ideal location. I then made it  a visually pleasing map with a scale, little picture of Michigan showing the highlighted county, a legend showing all that is on the map and finally the sources used.
    The results found in this lab were all ideal areas for bears is around water(due to the buffer) and away from any built up areas. Several bears live around the new ideal locations, but no so many live in the ideal regions.

Sources
Michigan Geographic Data Library
 Landcover is from 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
Streams from
 http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

GIS I Lab 2: Downloading GIS Data

Introduction

The goal of this lab was to learn how to download data from the United States Census Bureau and map the data in ArcMap. In this lab we learned how to download Census data, Wisconsin total population and Wisconsin median age. We then learned how to download a shapefile from the Census boundaries. The next goal was to learn how to map the data and join the data in the shapefiles. The final step was to create a layout that would be presentable to someone who would publish a map.

Methods

In order to complete this lab, we first had to understand a few topics that would come up often. Some basic topics we needed to understand were, what was the US Census Bureau, What exactly are Census Boundaries as well as Statistical Boundaries, and lastly, 2010 SF1 100% Data, this data is based off of data collected every 10 years by the United States Constitution. The first step was to go to the United States Census’s website and select the desired category you wished to find. For this project we selected the total population based on county and then selected Wisconsin. Next, as previously mentioned about the SF1 statistics, we had to select the “Total Population” from the list of downloadable contents. After downloading the file, we had to unzip it and save it into our folder. After checking to make sure they were in our correct folder we changed the format so it would be able to be read by ArcMap. Step two, after downloading the data we then had to get the Wisconsin state and counties data map from the census website. Found under the geographies selection tab, find the Wisconsin counties map and save it to the same folder as the previous data. Step three, in a new ArcMap document add the newly downloaded shapefiles downloaded and open the tables containing the data. After opening the two tables we then joined them together by the GEO#id. After joining the tables we then had to assign a quantitative color scheme to it to further show the denseness of population in Wisconsin. This was done under the properties menu, under symbology. The key is to select a good color scheme that will show the difference in data sections. Following the same steps as listed earlier, I had to download another set of data. The data I chose to use was the average age by county in Wisconsin. Again this data had to be downloaded, unzipped, formatted for ArcMap, and joined to a table which could show the data. Lastly, it was given the same color scheme as the previous map to help show relationships between the two sets of data. The last step was to create a presentable layout, which would make it easy to understand and see. This included, putting the two maps together on the same page, adding a legend, title, north arrow, scale, date, source, and author, and lastly adding a background map provided by ESRI to the map for more detail.

Results

The results of the two maps show the total population as well as the median age in the State of Wisconsin. An interesting pattern that stuck out in this map was that the youngest median age county also had the largest population, Milwaukee. Generally, the higher population, the younger the average age was. And for some parts of the map, the further north you went the less populated the counties were, as well as, the higher the median age was. It is clear which counties have colleges, Eau Claire, Green Bay, Madison, La Crosse, and Milwaukee. These counties have a higher population, as well as, a lower average age by county.

Source

The geographic census data was provided by the United States Census Bureau
2010 Census Data used
Background map provided by ESRI

GIS I Lab 3: Introduction to GPS

Goals and Objectives
The goal of this lab was to create a geodatabase, as well as prepare the geodatabase for the data we would go out and gather using Trimble Juno GPS units for the data collecting. We will also become more familiar with the Trimble Juno GPS units as well as the ArcPad app in the units. We will collect points, lines, and polygons using ArcPad on the Trimble GPS units and import this collected data into ArcGIS to create an image of grass areas, light poles, trees, footbridge, and campus buildings.

Methods
In order to gather our data we first had to upload an image that contained the University of Wisconsin- Eau Claire campus as well as all the campus buildings already digitized. Next we had to create three different feature classes to be added to the image, these feature classes were, points for light poles and trees, polygons for grass areas, and lastly polylines for the footbridge. The next step after adding the three feature classes to the map was to upload the map to the Trimble Juno GPS unit and save the map and features under a specific name. In order to complete this next step we will need a USB cable that can connect the GPS unit to the computer. Once connected find the folder with the unique name and copy it into the storage space of the Trimble Juno GPS unit. After uploading the map open up ArcPad and choose your already uploaded map and wait for the GPS to get a fix on your location. Once the GPS has your location fixed you may begin obtaining your data in the campus mall. There are two different ways to obtain the data, one is through point averaging where you have to tap the add a GPS vertex button every time you want to add a point. Once all data points are added press the proceed to attribute button and enter the name of the feature created. Repeat this process for the next features. For our lab we needed to use the point averaging technique for three grassy areas, three light poles, and three trees in the campus mall. The other way to obtain data is using point steaming, point streaming is a continuous data collecting technique. Once you have finished collecting the data for the feature you again press the proceed to attribute button and again title you feature. Using this technique we will map three grass areas and the walking bridge. Once all the data is collected you may deactivate the Satellite tracking and proceed to a computer with your Trimble Juno GPS unit. Reconnect the unit to the computer and open the uniquely named folder in the storage cell. On the computer open up the ArcPad data manager toolbar and press the green plus to navigate to your folder containing your obtained data. Select the features you wish to add and select OK. The data should appear in the map and then we use our previously learned cartography skills to create a presentable image to save as a PDF.

Results
The results from this lab compared to the image used  will appear to not match, but this is only because the image is from before construction of the new Davies Student Building was beginning construction. The only patterns shown on the map were the cluster of grass areas in the campus mall as well as the footbridge matching up to the base layer map.

Sources
GPS data collected by Drake Bortolameolli
GPS unit used; Trimble Juno 3B GPS number 13
Aerial photo provided by: National Air Photography Program

GIS I Lab 1: Base Data

Goal
The goal of lab one is to become familiar with the many different types of spatial data used in Eau Claire County. These data sets range from public land management, to administration, and land use. We will use these valuable data sets to produce six different maps showing the location of the Confluence Project as well as the surrounding area.

Background
As of spring 2012, Clear Vision Eau Claire has announced a new public-private partnership to begin construction on a new project called the Confluence Project. This project will be built where the Chippewa and Eau Claire Rivers meet in Downtown Eau Claire, across from Phoenix Park. The Confluence Project will be a new community arts center as well as a new University Student Housing and commercial retail building. Ground breaking is scheduled to begin in 2014.

Purpose
The purpose of this lab is to gain more knowledge in working with base maps and being able to understand basic city data.

Maps/Methods
There were six different maps made for this lab, each map shows a different set of data. Starting with the top left corner and working our way right, and then going to the second row left corner and working right again (maps pictured below "Map 6").

Foreknowledge: Before the creation of any of the upcoming maps several articles were read and research on the project was done using internet sources listed at the bottom of the page. The area where the Project Proposal Site is was digitized by use of the parcels feature. All maps use the same coordinate system, WGS 1984 Web Mercator. In order to get the six maps to fit on a page, the page size was changed to 17x11 inches.

Map 1: This first map was created with ArcMap 10.2. I used the World Imagery map as the base layer. The base layer was provided by ESRI. After fitting the map to show the entire Eau Claire County I inserted the previously digitized area of the Proposed Site (Confluence Project) as well as the Civil Divisions of Eau Claire County. After inserting the Civil Divisions of Eau Claire County I changed the color scheme to hollow and made the outlines a very bright and distinctive color pattern. The reasoning behind making the Civil Divisions hollow was so the land features as well as the Proposed Site could be visible. After adding the two previously mentioned features I created a legend as well as a scale to add to the map and titled it "Civil Division." The last step I did for this map was to add a call out box pointing to where the Confluence Project will be located.

Map 2: After finishing the Civil Divisions map I made another data frame. Still using ArcMap 10.2 this new data frame is going to show where the Census Boundaries around the Confluence Project will be. I used the same World Imagery map as my base layer. For this map instead of showing the entire county, I zoomed into a very small portion of the City of Eau Claire. This map shows the Tracts features of Eau Claire as well as the Block Groups. The Tracts were placed onto of the Block Groups to show where they overlapped each other. As was done in the first map the Confluence Project was labeled with a call out box and the parcels were digitized in a soft pink color to stick out on top of the baby blue background representing the Block Groups. As well as the first map this one also contained a legend as well as a scale.

Map 3: After the creation of the Census Boundaries map, I created another data frame in ArcMap 10.2. This one is titled "PLSS" or Public Land Survey System. This map also uses the World Imagery base layer, and was zoomed into the City of Eau Claire so the Confluence Project site would be easily visible. This map only needed one feature to be added, PLSS_qq. This public land survey system showed the parcels of the area and surrounding area of the Confluence Project. The PLSS_qq features were also made hollow, in order to show the land and building features/images. The Public Land Survey System lines were changed to a bright blue so it could be seen over the map but with little disturbance of the base layer. Just like the previous maps, this one had a legend and scale as well as a call out box labeling the Confluence Project.

Map 4: The fourth map needed a new data frame in ArcMap 10.2. This map shows many new features which the other three maps have not yet shown. This map also uses the World Imagery map provide by ESRI. The map shows the center lines of all the roads in the surrounding area. Another feature shown in this map is the parcel area, which outlines the many parcels of the City of Eau Claire. The next newest feature shown is the water bodies in the City of Eau Claire. This was changed from a random default color to a blue color to better represent water. Lastly, similar to the previous maps the Confluence Project is digitized into its Proposed Site. This map was given the title, "City of Eau Claire Parcel Data" and it too has a legend in its upper right corner and a scale in the bottom left corner. Using a call out box again, the Confluence Project is labeled pointing to the Proposed Site.

Map 5: This map titled, "City of Eau Claire Zoning Classes" is another newly created data frame. This map uses the World Imagery base map as the previous maps have also. This map is a little different than the previous because it involved grouping categories together to make the map much simpler to see. These feature groups meant for the creation of a new legend with customized labels. Commercial, Residential, Industrial, Central Business District, Public Properties and Transportation zones are all visible on this map. Instead of being zoomed out to see the entire city I zoomed in so the Confluence Project is visible and the map will not look too cluttered with the different color classes. Lastly, this map was given a call out box pointing to the digitized soft pink Propose Site for the Confluence Project. It was also given a scale as well as a Legend with customized labels.

Map 6: The last map of this lab uses the World Imagery base layer map and is titled, "City of Eau Claire Voting Districts." This map is a much simpler map and only has one feature added to it, besides the Proposal Site for the Confluence Project. In the map the voting districts for the City of Eau Claire were added, given a hollow symbology and a bright yellow line so the base layer was still visible. Each voting district is labeled in a vibrant white to stick out and be easily legible. The map contains a call out box showing where the Confluence Project Proposal Site is and has a scale in the bottom left corner. Unlike the other five maps, this one does not contain a legend.

The top left map is Map 1 and works its way right. Bottom left map is map 4 and also works its way right.

Results
In map 2, Census Boundaries, and in map 6, City of Eau Claire Voting Districts, I noticed that both the voting lines and Tracts boundaries share similar boundary lines. The reasoning behind the shared boundary lines would be because of the Chippewa River. Rivers make excellent boundaries since they are very easily visible and great physical dividers.

Sources
City of Eau Claire, and County of Eau Claire, 2013
http://www.eauclairearts.com/confluence/
http://www.uwec.edu/News/more/confluenceprojectFAQs.htm
http://volumeone.org/news/1/posts/2012/05/15/3134_arts_center
http://www.bis-net.net/cityofeauclaire/search.cfm