Labs 4 & 5: Mini-Final Project

Introduction:

The research question that I chose to answer is "Where is the best place to live in Rusk county, Wisconsin?" Seeing as this question is very dependent on an individual's own personal opinion of what "the best place to live" means, I will be using my own preference to set the parameters of the project. 

I grew up in the woods of northern Minnesota, so I would like to live within walking/biking distance or a rather short driving distance of a forest, preferably a national forest or a county forest. However, I found after some research that Rusk county does not have any national forests so it will have to be in proximity with a county forest.

I also grew up on a lake that we could easily get to on foot, so I would like to live near a body of water. Since there are not as many lakes in Wisconsin (or at least in Rusk county) as there are in Minnesota, I will make it within ten miles of a lake, that way it would be either a short drive in a car or a reasonable bike ride away.

The county seat is the city of Ladysmith, Wisconsin, which is located almost exactly in the center of Rusk county. I would like to be near to the town while not residing in it; close enough that a grocery store trip is not a long time to drive for (I am used to driving at least a half hour to get to a grocery store because of the remote place I am from - Max, MN). I also want to be near Ladysmith because the Flambeau mine is located there. I have been doing research on the Flambeau VMS deposit and originally chose Rusk county to become more familiar with the area. So, between ten and twenty miles of Ladysmith would be ideal.

In summary, for the purposes of my project, the best place to live in Rusk county, Wisconsin would be:

• within 6 miles of a county forest
• within 10 miles of a lake
• between ten and twenty miles of the city of Ladysmith, WI

Data Sources:

The cities and Wisconsin state boundary data came from the University of Wisconsin - Eau Claire Geography departmental server where a map service has been published for the   2013 ESRI U.S. Census data. The county, lakes, county forests, roads data came from the Wisconsin DNR 2014 database. 

I would have liked the lakes feature class to include all the lakes and the lakes in the county - with the basemap I can see that there are some lakes are not included in the data.  I would have also liked reservoirs, such as the Dairyland reservoir, which is along the Flambeau River and can also be seen in the basemap, to be included either in with the lakes feature class or as a separate shapefile. Including these would have changed the region called Best Area on the map.

For cartographic aesthetics I would like if the town of Conrath was not without any roads going to it; it looks so isolated. However, the shapefile only has major roads, so since Rusk county is small and rural it makes sense that many roads would not be considered major roads.

Methods:

Since I only am working with Rusk county, after putting the datasets into ArcMAP, I used the clip tool to make everything only as big as the Rusk county boundary. I then set to work creating the data flow model, which I had previously sketched out on paper, using the model builder feature (Figure 1). 

Figure 1

I used the Select tool to choose one town from the towns (clipped from cities) feature class: Ladysmith. Then I executed two buffers on the selection to get two different sized circular areas around the town of Ladysmith. I only wanted the area between ten and twenty miles of Ladysmith, not the part within ten miles, so I used the Erase tool to get rid of the ten mile radius around the town. I was then left with a donut-shaped region with Ladysmith at the center. The county forests feature class only needed a buffer, as did the lakes feature class, six miles and ten miles respectively. The Intersect tool was used to get the area that all three areas overlapped, and finding that a corner of the Good Criteria area was outside of the county boundary, I used the Clip tool to make everything only within the area of interest, Rusk county.

Results:

Figure 2

The final product from the previously stated process is an area that is between ten and twenty miles of Ladysmith, Wisconsin, within six miles of a county forest, and within ten miles of a lake, which is represented on the map in Figure 2 as the purple crosshatched regions. In my opinion, the best place to live in Rusk county, Wisconsin would be inside this area. 

Evaluation:

This project was enjoyable in the aspects where I was able to design my own investigation and understand the process of finding out the desired result. However, I did come across a bit of a problem when I was doing the analysis. While running the tools in the model builder, things were going as expected, but then all of a sudden, no tool would work anymore. I would get an error every time I tried to do something and these errors did not make sense. It was a long and confusing extent of work time that followed, as I changed things in the data workflow model, and the analysis process, and even in my project's parameters in order to get around the unknown problem. I created several different models and maps but they all ended in errors from tools not being able to be run. I would like to say thank you to Mattheus, and Scott, for kindly helping out and teaching me new ways to do things in ArcMAP, even though nothing worked and we were still confused. In the end, the problem turned out to be a simple one. When creating a new geodatabase for the project, not knowing the difference I had selected a personal geodatabase instead of a file geodatabase. The restrictions on the personal geodatabase would not allow any other tools to be run which is why I was getting an error every time. It was a simple fix after a lot of searching for a solution and I was back on my way. I now understand the importance of making sure I create the right kind of geodatabase, and I will not be likely to make the same mistake again. 

In addition to making the personal geodatabase a file geodatabase right away, I would make the change of including all of the lake-like bodies of water in the lakes feature class, as discussed in the Data Sources section.

This project was a great learning experience, and I now feel like I am able to navigate, use, and understand ArcMAP and ArcGIS.

GIS Lab 3: Vector Analysis with ArcGIS

Introduction:

The goal of this lab was to use geoprocessing tools in ArcGIS for vector analysis to find suitable bear habitat in the specified study area in central Marquette county in the Upper Peninsula of Michigan based on the physical environment and population data for the area. We were to create a data flow model and learn some python commands as well as create a map.

Methods:

In the process of attaining the suitable bear habitat, I used the tools Join, Buffer, Intersect, Dissolve, Clip, Select (Query), and Erase. Figure 1 is the data flow model used in the analysis. 

Figure 1

First, I found the area that was best for the bears based on where bears had been recorded via GPS and the proximity to streams. Then I used the DNR management areas to find where the best areas for bears that would also be managed by the DNR. Finally, I used the Urban or Built Up areas to make sure the Suitable Habitat would be away from areas of high human population. 

Figure 2

I learned some basic python coding for a similar process demonstrated in Figure 1. Figure 2 is the coding in python to find the area within 1 km of streams and outside 5 km of urban areas.

Results:

Figure 3

The map in Figure 3 was generated using the model workflow in Figure 1. The light purple areas represent All Suitable Habitat - the areas 500 meters from a stream and in the most favorable types land cover areas for bears. The dark purple areas inside All Suitable Habitat represent DNR Managed Suitable Habitat, where All Suitable Habitat overlaps the DNR Management areas for Marquette County. These areas are not near any orange areas because the orange represents any Urban or Built-Up Lands - there is a higher density of people in those locations, which is not ideal for bears.

Sources:

All data was downloaded from the State of Michigan Open GIS Data http://gis.michigan.opendata.arcgis.com/

• Landcover from USGS NLCD

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

• DNR management units from

 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 1 Lab 2: Downloading GIS Data

Introduction:

The goal of this lab is to learn how to download and map 2010 population data from the U.S. Census Bureau, and publish a web map using the data.

Methods:

The U.S. Census Bureau's American FactFinder website was used to download Wisconsin county demographic data. The total population of each county and population data about sex by age for WI counties were downloaded in the form of tables and a shapefile. After viewing the table data in Excel, the data was opened in ArcMAP and joined to the shapefile table, the data was able to be mapped (Figure 1). In order to map the population data about sex by age, the fields containing the male and female numbers for people age 85 and older were combined to show the total population age 85 and older in each county. Then this data was normalized by the total population to show the percentage of the population in each county that is age 85 and older.

Through ArcGIS Online in ArcMAP, the map was arranged to be and shared as a web map by creating a web map service, and published in ArcGIS Online as a web map.

Results:

Figure 1

The image above (Figure 1) is the side-by-side map that was generated in this Lab. The frame on the left displays the total population in 2010 for each WI county and the frame on the right displays the percentage of the population in each WI county that is age 85 and older.  In comparing the two maps' information, it can be observed that the counties with less total population have a higher percentage of people age 85 and older. Not all counties follow this trend, however; Menominee county is one that has a lower total population and a low percentage of people age 85 and older. It can also be recognized from this map that in counties where there is a larger city, such as Madison, there is a much less percentage of people age 85 and older. 

Webmap:

To view my published web map, follow this link:

http://uwec.maps.arcgis.com/home/webmap/viewer.html?webmap=f1c455e7b36645c0a6222d98ca36173f

Sources:

 U.S. Census Bureau 2010, American FactFinder

GIS 1 Lab 1: Base Data

Background:

This Lab was about Eau Claire, Wisconsin's Confluence Project, which is a public and private collaborated plan to build multi-purpose spaces at what is known as the "Haymarket Site" in downtown Eau Claire, at the confluence of the Chippewa and Eau Claire Rivers. These buildings will include areas for University student housing, arts display and performance, offices, classrooms, studios, retail, parking, and a public plaza.

The goals of this lab were to produce base maps for the Confluence Project while becoming familiar with the land use, public land management, and administration spatial data sets for Eau Claire, WI. It is also an opportunity to use and present what I have learned hitherto in the class.

Methods:

I looked at the City of Eau Claire and Eau Claire County datasets, digitized the proposed Confluence Project site, learned about the Public Land Survey System (PLSS), devised a brief legal description for the proposed site, and put a six-framed map together using ArcMap 10.3 that displays the Confluence Project's Civil Divisions, Census Boundaries, PLSS Features, Eau Claire (EC) City Parcel Data, Zoning, and Voting Districts. Each frame was created using features from the 2009-07-13_EauClaire and City of Eau Claire databases. Legends, scales, a north arrow, and callout boxes were included, and for each frame, a World Imagery basemap was used.

Results:

Eau Claire Confluence Project Proposal

Figure 1

The image above (Figure 1) is the six-framed map that was created. It displays a range of aspects concerning the Confluence Project in an easily comparable format. The Civil Divisions frame shows that the Confluence Project site is within the city limits of Eau Claire. The Cencus Boundaries frame shows that the site is in an area with 3600-5000 people per square mile. The PLSS Features frame show what quarter quarter section the site falls inside. The EC Parcel Data frame shows the parcel area of the site and has the centerlines included for spatial clarification. The Zoning frame shows that the site is in the Central Business District, and very near to Public Properties and Residential zones. The Voting Districts frame shows that the site is in voting district number 31. From this map, the viewer is able to discern information about voting patterns, legal parameters, and citizen benefit.

Sources:

City of Eau Claire and Eau Claire County 2013 geodatabases.