Lab 4: Final Project

Introduction:


 Growing up and living in Waukesha County Wisconsin I have found a variety of activities that I enjoy doing and do regularly.  Golf is one of these activities that I regularly participate in.  Some of the best and most reasonable priced courses in Waukesha County are state owned courses that do a very good job of maintaining their courses but keeping the price at a level where college kids and younger can play at without breaking the bank.  This project is to find the best location in Waukesha County for a new public golf course.  State employees could use this information to put this new recreation area into existence. 


Data Sources:


To answer this question of where to put a new public golf course, I needed data that had the information that would help me answer this question.  I obtained this information from ESRI's 2013 USA data bases that my school, University of Wisconsin-Eau Claire has given me access to.  One of my biggest concerns with using this data is that I do not know if all of the golf courses will be in their golf course feature class.  This feature class has golf courses from the whole United States and I don't believe that is will be completely accurate.  New golf courses are being created all the time and this data set could be missing some.  A county level dataset of all the golf courses, state land, highways, and rivers and streams in Waukesha county would have been more accurate. 


Methods:


To figure out where the best location for a new golf course in Waukesha County was I decided there were a few criteria that needed to be met.  Over the years I have golfed at a few courses that were close to the highway and the noise of the cars interrupted the serenity of playing a relaxing round of golf.  Do to this I have decided that the land for the new must be at least 2 miles away from any major road.  The next criteria is that the land must be at least 5 miles from the next nearest course.  A good golf course is properly watered and maintained to being close to a water source is important.  This new location is within 1 mile of a river or stream.  Over the years I have had good luck with courses that are owned by the county or state being very high quality and beautiful so I have decided to find a piece of state owned land that hits each one of these criteria. 


Below is a data flow model with the spatial layers (grey ovals) and tools used (blue square) to transform data sets into a map that can be used to answer this spatial question.  The red oval at the bottom is the final feature class of the possible locations for a new golf course in Waukesha County.

Results:


After using multiple tools to manipulate different data classes to create a map of possible locations of a new golf course in Waukesha County, there is one section of land in the southwest corner of the county that is large enough and fits all of the criteria for the new location.  This area is away from golf courses, roads, and near a river or stream and would be the ideal location for a new golf course.

Evaluation of Project:


This was a good project to get a feel of what doing something like this would be like in a real world work situation.  It required the user to understand how to use different tools and they actually had to know how to use them to manipulate the data.  If I had to do this again I would use data from the county to get more accurate information.  I also just used parks as the state land and in real life these are already parks and would not and should not be turned into golf courses.  This project had many challenges from decided what spatial question to ask to finding good data that could be used to answer the question.  This was a good project that had many valuable real world applications.   
       

Lab 3: Bear Habitat Model

Goal:


The goal of this lab is to use various geoprocessing tools for vector analysis in ArcGIS to determine suitable habitat for bears in the study area of Marquette County, Michigan.


Background:


The Michigan Department of Natural Resources is interested in creating new areas for black bear management.  They recorded the location of sixty eight bears in a study area in Marquette County Michigan.  Using data from the Michigan Geographic Data Library, it is my job to determine the best areas for these new bear management locations. 


Methods: 


To find the optimum places to have new bear management areas, I used a variety of different spatial tools and skills to narrow down the data.


The first step was to transfer the bear locations from a Excel table to data points that could be used in an ArcGIS format.  Once the bear locations were in ArcMap, I used a spatial join to determine the suitable land cover types that the majority of bears were found within. Next I wanted to determine if being near a stream was consistent with the locations of the bears.  I used the buffer tool to create a 500 meter bubble around the streams then I used an intersect tool to find out that over 70% of the bears were located within 500 meters of a stream.  To bring this information together, I used the intersect tool to find the areas that were suitable land cover types and were within 500 meters of a stream.  This gave me the best areas that management zones could be incorporated in.  After reporting this data to the DNR they decided that the management areas should be 5 Kilometers away from any built up or urban areas.  Using a series of tools I made another map that fit into these parameters.


I also used the python script in ArcMap to complete some of the tools in the lab manually.   Below is a screenshot of the script used. 

Attached to the bottom of the page is a data flow model of the different data classes and tools I used to complete this project.





Results:


After determining the best areas for bear habitat and where the bears are actually located, I created a map that shows Bear Locations, Bear Habitat Management Areas, Streams, Suitable Bear Habitat and the Area of Interest.  It is apparent that there are a multitude of areas that are within 500 meters of a river, in suitable bear habitat, and are 5 kilometers away from urban areas.   This map could be very useful to a person at the DNR trying to decide where they should put bear management areas.

Sources:
Michigan Geographic Data Library

State of Michigan Open GIS Data
http://gis.michigan.opendata.arcgis.com/



Data Flow Model

Lab 2: Downloading GIS Data

Goals 

The goal of lab 2 is to gain an understanding on how to download and use data from an outside source in a GIS environment. In this lab I learned how to download, decipher and use data from the U.S. Census Bureau in a GIS system and turn that data into a GIS web map.

Methods

In this lab I learned the skills of downloading data from an outside source, choosing what data could be best represented in a map, how to properly format and transfer a Microsoft Excel table into ArcMap, and how to transfer a map from ArcMap onto ArcGISonline.

The first step was going to the U.S. Census Bureau's website and narrowing down my search for data by choosing my geography and topic.  With the search narrowed down to Wisconsin county population demographics I downloaded the proper population data required to make this map.  Next I formatted the downloaded data into a Microsoft Excel file that could be properly transferred into ArcMap.  After the data was in ArcMap I downloaded the Shapefile that was associated with the data from the Bureau's website.  The next step was joining the counties and population tables together to be easily represented in ArcMap.  Once all the data was in ArcMap I created a map that represented the Population by counties in Wisconsin.  To do this I changed the symbology to a graduated colors map that represents changes in population.  I also did this process with data for the average age of Wisconsin population and made side by side maps to compare average age distributions in Wisconsin.

To create the online map of Wisconsin's population by county I logged into ArcGISonline in ArcMap and created a feature service to connect my map online.  Once this was done I went onto ArcGISonline and created an interactive map of Wisconsin by editing capabilities, description, tags, and sharing services.  I formatted the pop-up to display the county name and population information.  Once this was complete I published my map to be shared with the Geography and Anthropology department at my university.  

Results

When the maps of population numbers and average age of population are side by side, there is a pattern that in areas with higher populations there is a lower average age.  In areas that have low populations, the average age is much higher.  This can be seen in the northern part of the state as well as areas in the south towards bigger metropolitan areas.

Sources

American Fact Finder. (2010). Retrieved from United States Census Bureau:
http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refrest=t

Hupy, C. (2016). Lab 2: Downloading GIS Data. Eau Claire, Wisconsin

GIS I Lab 1: Base Data

Goals and Background


Clear Vision Eau Claire announced a public-private partnership between local developers, UW-Eau Claire and the Eau Claire Regional Arts Center that intends to construct a new development at the confluence of the Chippewa and Eau Claire Rivers in downtown Eau Claire.  This Development, the "Confluence Project", plans to break ground on a new community arts center/university student housing and commercial retail complex in downtown Eau Claire beginning in 2014.  The goal of this project is to get experience working with local data and using it to represent a real world application, the Confluence Project.


Methods


First I examined the classes in the City of Eau Claire's and Eau Claire County's geodatabases and gained an understanding in the data they represented.  The second step I took was digitizing the area in which the confluence project would take place.   This digitized image was placed over satellite imagery to help the viewer understand exactly where the project is taking place.  I saved this digitized image as a feature class to use in future maps. The last step was to create six different maps that represented different data that is important to the confluence project, Civil Divisions, PLSS Features, Census Boundaries, Voting Districts, City of Eau Claire Parcel Data, and Zoning Classes.


The first map contains civil divisions in the area that surrounds the confluence project.  The City, Town, and Village municipalities are clearly visible with a legend that leaves no confusion to which one is which.   To create this map I added a base map of the satellite imagery then added the civil divisions.  To make the map more clear, I assigned colors to the municipality types and made them transparent so the satellite image could be seen beneath it.  I then added the proposed site with a callout then a scale and a legend.


The second map shows the PLSS Features and where the confluence project falls within it.  Again I added the base map with the proposed site for the confluence project highlighted.  Next, I added the PLSS Quarter Quarter zones with distinct lines.  To finish this map off I added a legend and scale with uniform backgrounds that made it easy to read.


The third map shows Census Boundaries. This shows the population per square mile in downtown Eau Claire.  With the same base map I added the block group feature class symbolized the population with a normalized value of square miles.  I put a border around each of the block groups and added a clear legend and scale.


The fourth map shows voting districts around the proposed site.  Over the same base map I added the voting districts with clear boundaries and labels.  A callout was added to make the proposed site stand out more.


The fifth map shows City of Eau Claire Parcel Data.  Over the same base map I added feature classes for water, centerlines, and parcel area.  I used a color that stands out more for the parcel area's because I thought it was more important in this map than centerlines and the river.  The centerlines and river are still clearly visible but the parcel area stands out more.  A legend and scale are also provided.


The last map is a map of the different Zoning classes in downtown Eau Claire.  Over the same imagery I added the zoning class feature class and symbolized unique values and assigned a color scheme that was not confusing to someone looking at the map.  I had to combine all of the zoning classes into 6 classes.   A scale and legend were attached to this to clarify which color is what type of zone.




Results


This is the finished image with the six maps as described above.  It is easy to conclude that Clear Vision Eau Claire took all of these things into consideration when selecting a place to put the confluence project.

Sources

Clear Vision Eau Claire. (2014). About Us. Retrieved from Clear Vision Eau Claire: http://clearvisioneauclaire.org/

Hupy, C. (2016). Lab 1: Base Data. Eau Claire, Wisconsin.