Lab 4 - Final Project

Introduction

For my final project, I wanted to figure out where the best place to put a new pool in Eau Claire was. To figure this out, I had to ask my self a few questions. What are some things that a new pool should be located close to? What are some things that a new pool should be located a certain distance away from? After brainstorming for awhile, I finally decided on some answers to those questions. My overall spatial research question was: Where is the best place to build a new pool in Eau Claire that is not within 1 mile of other pools, in a census tract with a population density of 900 or higher, not within .5 miles of a body of water (due to the popularity of recreational use of the rivers and lakes), and finally within .25 miles of a major road in Eau Claire. I wanted to do this for my project because I am on the swim team for UWEC and our pool is out of date. This information could be used by the University or the City to build either a public pool or a competition pool. 

Data Sources

For this project, I used more data than I thought I would. The main data sources that I used were the water bodies, major roads, census tracts, and county data. I also used data for surrounding counties, and smaller roads purely for cartographic purposes. The One data source that I had to create was the pools. This was just a matter of digitizing in some points where pools were located. To do this I searched all pools in the area and went in and placed points where they should be located. This was necessary because a data set for pools was not provided to us. All of the data used came from the UWEC database. The data that I took from the database was collected by:

- Esri USA census data

-Wisconsin DNR

The biggest data concerns that I have came from the Wisconsin DNR data. I was most concerned about the bodies of water and the accuracy of their location. I know that it is generally in the correct area but there were some areas where it looked like the small roads data came way to close to the rivers. This shouldn't greatly effect my final map because the potential errors wouldn't make enough of a difference to change the data drastically. 

Methods

There were a lot more steps needed to come to my conclusion than I would have expected. A lot of the methods that I used were to make my final map cartographically pleasing, but many of them were also to show the correct data. The four required tools that I used in my project were buffer, dissolve, union, and erase. 

The first thing that I needed to complete in my data flow model was to find the non ideal areas for the new pool. This required work with the pools and water bodies data. The first thing that I did with this data was to create a buffer around them. The pool buffer was 1 mile and the water bodies buffer aw .5 miles. Once the buffers were created, I then used the dissolve tool to create a less distracting shape. Once both of the buffers were created and dissolved, I used the union tool to bring them together and create on feature class. I then put that feature class aside until the end. 

The next process was to get the ideal areas for the new pool. These areas were located inside of census tracts with population density greater than 900 people so that it could be located in a popular area. It also needed to be located within .25 miles of major roads. This is so it will be easy to find for people traveling to the pool. This process started with putting a buffer on all of the major roads. The next requirement was to select the tracts by attributes. The attribute used was POP12_SQMI. This is just data that calculated the population density for me. Otherwise I would have had to calculate the density. Once I got my selection of tracts, I created a new layer and deleted the old one. This gave us our general area that all of our other rules would take place in. 

The next step was to contain everything inside of the desired areas. This step was basically making the map nice to look at and not a jumbled mess. In doing this, I used the erase tool a lot. I first used the erase tool to create a shape that was the county of Eau Claire but did not include the area inside of the ideal tract are. I then used this shape to go through and erase the data for the major road buffer, water body buffer, and pool buffer from outside the tract area. I now had all of my important data located inside the tract area. 

Map shows all criteria contained in ideal tract areas.

The next step was also to create a cartographically pleasing map. I did the same cookie cutter method as in the last step, but this time I used the counties surrounding Eau Claire county. I used the surrounding counties to erase the streets, major roads, bodies of water, and tracts that extended over the border of Eau Claire county. 

The final step was bringing the first two steps together. This was a simple task. All I needed to do was erase the ideal area that was inside of the non ideal area. The final product was a large area that was not ideal to build a new pool and some small areas that would work well for a new pool based on the criteria that I used. The following image is the data flow model for this project.

Data Flow Model

Results

The results that I came up with at the end of this project were great. My overall goal was to narrow down the ideal area to a much smaller area based on the criteria I used. As you can see on the map below, The blue represents the Ideal area within the tract boundary to build a new pool based on the criteria used. I decided to keep the non ideal area inside the census tract because I liked how it showed that there was a much greater area where the pool shouldn't be built. The following Image is the final product of this project.

Final Map

Evaluation

This was a very interesting project to complete. I really enjoyed working with all of this data and using ArcMap to find a conclusion to my original question. I was surprised how easily all of the steps came to me. I can really tell that through the course of the semester I have gained a lot of knowledge in the usage of ArcGIS. If I were to go through this project again, I might try to use the city boundaries instead of tracts. This might just make it easier to understand the final area. Most people don't know where tract boundaries are in a county. The biggest challenge that I faced during this project was digitizing the pools in and buffering the area around them. For some reason the first couple times that I would attempt to buffer, it wouldn't create the correct size buffer. Eventually it just worked. Overall, I felt like this was a very good project to end on. It gave us the freedom to study what we wanted and work with the data that we wanted. It also made sure that we were familiar with the different tools and how to use them. It really tested our overall knowledge of ArcGIS and how toe effectively answer a spatial question that we came up with 

Other Sources

locations of pools

http://www.ci.eau-claire.wi.us/departments/recreation-services/recreation/aquatics

Lab 3

Goal

The goal of lab 3 was 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

For our third lab, we are to learn and become more familiar with tools that are used in ArcGIS. We have been given all of the data about Marquette County, and all of the data for bear locations. This lab gives us general rules to follow but does not tell us exactly what to do. In the methods section, I will describe how I went about problem solving and using the tools available to me to analyze the given data and produce a final solution to the overall goal. 

Methods

Lab 3 was divided into eight different sections in order to work towards our goal step by step. For the first section, we started off with the obvious steps of organizing all of the data that we needed for our lab in our personal class folder. We were given an excel table with data on the locations of bears in the study area. We brought the table data into ArcMap and plotted the X,Y points on the map with a specific coordinate system. We then exported the points as a feature class that we could work with in ArcMap. 

In the second section of the lab, we brought all of the data in the bear_management_area feature class. We made sure that it was all in order and then proceeded to preform a spatial join of the new bear_locations feature class and the land cover feature class. This gave some extra data to describe the places where the bears were. We then preformed a summarize operation on the new table to see which land cover areas contained the most bears. Areas 380, 387, and 237 contained the most bears. 

For task three, we needed to use the buffer tool on the streams. This would gives a visual aid of the areas that were within 500 meters of all of the streams. Our goal was to find out if streams were an important part of a bears habitat. I found that 49 out of 68 bear locations were located within the 500 meter buffer. This means that about 72% of the bears were located close to streams, proving that streams were an important characteristic to a bears habitat. 

For the fourth task, we needed to find suitable areas of bear habitat for the study area. So what I did was combine areas 380, 387, 237 and the buffer area created earlier. I used the union tool to combine both of these areas. I then used the dissolve tool to eliminate the overlapping of lines to make the area look nicer and less confusing. 

Task 5 has us make recommendations to the Michigan DNR for a bear management plan. For this section, we add the DNR management data for Michigan. Because we are looking at a specific area in Michigan, we need to select by location the DNR management zones withing the study area. Then we use the intersect tool to combine the suitable habitat that we came up with in section four, with the DNR management zones. At the end, I used the dissolve tool to get rid of the small zone boundaries in the intersected area.

Task 6 has us go one further step. We presented our data to the DNR and the liked it but decided that these suitable habitats can not be located close to urban areas. I created a five kilometer buffer around all of the urban or built up areas of the study area. I then used the erase tool to remove any data from the suitable habitats in the DNR zones that fell within five kilometers of an urban or built up area. This provided our final suitable habitat for bears in our study area. This all includes areas that are close to streams, in a suitible land cover type, in a DNR management zone, and away from any urban areas. 

In our last section, we worked with python. We wrote code to execute different tools in ArcMap. The following screen shot shows the code that we executed in the last section.

 

Results

After going through all of the sections of this lab, I feel like I have become much more comfortable with some of the different tools we have learned about. I also feel like building data flow models has become easier and more helpful for me. The final map that we created had some very interesting results. As shown above, we went through many different steps to come up with a specific area that would be ideal for bear habitats. The interesting thing that I found was that none of the bear locations that we worked with in the very beginning, were located within the final ideal locations that we came up with. Of course that doesn't necessarily mean that things were  not executed correctly, it just means that things don't necessarily come down to the data that we use. 

Figures

Data Flow Model

Final Map

Sources

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

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 Lab 2

GIS Lab 2

Jeff Schweitzer

Goal

The goal of this lab was to learn how to download data from the US Census Bureau and use that data to create a map to share on ArcGIS Online. 

Methods

For this lab, we started off by looking over US Census Bureau data and practicing downloading the data. We used a shape file that was provided by the US Census Bureau for the base of our map. Once the base map was set, I downloaded data for the total population of Wisconsin and the female population for Wisconsin. I made a graduated colors map to show the difference in female population percentage in each county in Wisconsin. I thought this was an interesting data set to work with because I wanted to see if certain counties were strongly effected. Once we finished our map, we made a service in ArcGIS Online of our map. We were then able to put the shape file on top of a map in ArcGIS. When the map was in place, we then shared our map with the UWEC Geography and Anthropology department. 

Results

Our results were a map that has been shared in ArcGIS Online and a standard map mad in ArcMap using data that we downloaded from the US Cesus Bureau. 

Sources

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

http://uwec.maps.arcgis.com/home/index.html

GIS I Lab 1

GIS Lab 1

Jeff Schweitzer

Goals

The main goals of this lab were to get me familiar with the type of data that will be used when proceeding with the Confluence project. The different types of maps used show the same area but with different data attached to help visualize the area better. The objectives for this lab were to become familiar with some of the given data about Eau Claire County. We wanted to digitize some of the data to emphasize certain pieces of data pertaining to Eau Claire. After looking at some of the data, we were to create a legal description of the two parcels where the Confluence project will be taking place. Finally, we produced a layout that showed a number of maps that we created emphasizing different data.

Methods

I started out this lab by examining the data that was given to me. I used ArcCatalog to view all of the data. I went into the properties of some of the feature data classes to see all of the details within the data. Once I felt like I had a good knowledge of the data being used, I opened ArcMap so that I could start to digitize some of the data. I began by putting a base map into ArcMap. I found where the confluence project is taking place. I then added a layer that showed block groups. This highlighted the parcels that would be used. I proceeded to digitize the areas for the Confluence project. What I created was kind of the base for the six different maps I created. I then proceeded to create a legal description of the two parcels. I used http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services which allowed me to get all the necessary information on the two parcels. I was able to enter the parcel ID and the site knew what lot was connected the the ID. 

After the legal description was created, I was able to start creating the maps. The first map is the voting districts. This map separates the different voting districts. I did this by adding the voting districts class and making it so that each district is labeled by its district number. At the base of the map are the two parcels marking the Confluence project. The next map is the Civil Divisions map. This map was made by adding the the civil divisions and the county data. I then changed the symbology so that the city, town, and village were displayed in different colors. The next map the was created was the EC City Parcel data map. This map displays centerlines, parcel area, water, and of course the proposed Confluence area. This map was created adding the centerlines, parcel area, and water feature classes to the map. I then customized the color of each element so the data was separated. The next map is the PLSS features map. This map was created by adding the proposed area for the confluence project as well as all of the PLSS data. The next map shown is the Census Boundaries map. This map shows population per square mile. The map was created by adding the blockgroups and the tracts feature classes. I then changed the symbology to show population density in the blockgroups in Eau Claire. I also included the data showing where the proposed Confluence project will take place. The final map is the Zoning map. This map divides Eau Claire into different zones. The different zones are commercial, residential, Industrial, transportation, central business district, and public properties. This map was created by adding the zoning areas feature class. I then changed the symbology so that the different types of commercial, residential, industrial, transportation, central business district, and public properties areas were grouped together. I then assigned each zone with a different color so that you can differentiate between them. I also added the centerline as well as the proposed Confluence project site. Each one of these maps had an Imagery basemap so that you could relate the data to an area in Eau Claire. 

Results

The results of this lab turned out very nicely in my opinion. Each map shows different and important data that will be looked over as the Confluence project moves along. The main things that I think should be seen in the results are the Census Boundaries and Zoning information that surrounds the two parcels for the Confluence project. 

            Figure 1. shows the six different maps created to show different pieces of data surrounding the Confluence Project.

Sources

"Mapping Services." City of Eau Claire, Wisconsin :. N.p., n.d. Web. 02 Oct. 2015.http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services

"Help." Help. N.p., n.d. Web. 02 Oct. 2015.

http://resources.arcgis.com/en/help/