Final Project: Finding a Suitable Biology Field Retreat Site for UWEC

Introduction:

Pigeon Lake was a biology department field retreat for freshmen biology students as part of the first class in the biology core here at UWEC. It gave freshmen a chance to get to know each other, as well as their professors and got them out into the field surrounded by biology. After 2014 Pigeon Lake was closed, and since then the retreat has been lost to the biology core, and a suitable replacement has been sought in recent years to reintroduce into the biology department. I wanted to run a search of the Forests in Wisconsin that might possibly replace the Pigeon Lake experience. In order to do this I made up a list of criteria for a site to exhibit: it would need to be close to water, as well as reasonably close to campus, be within a forested area and be available for camping/visiting. I intend to analyze the forests around Eau Claire to see if any of them met all these criteria. This data could potentially be used by the Biology department of UWEC in order to assist in the decision making process as well as to potentially discover new sites that may have otherwise been missed. This project is important because the Pigeon lake experience is one that is appreciated by students who have previously gone, and is missed by professors in the biology program. I therefore set out to find the possible sites for a campground that could provide a similar experience to Pigeon Lake.

Methods:

Using the city layer provided with the textbook, I selected the city of Eau Claire, created a new layer which just contained the city of Eau Claire and then executed a multiple ring buffer with 2 sections each with 50 miles in radius (to represent the optimal distance from Eau Claire for the site). I then intersected my layer of county Forests and the Buffer ring in order to gain the county forests that were within the necessary distance. As a byproduct of the two ring buffer, I had now placed forests into two groups, those found within 50 miles of Eau Claire, and those between 50.0001 and 100 miles of Eau Claire.

I searched the Arc.GIS online catalogue to find a shapefile that would display the water and lake features within Wisconsin, and after finding one, input it into the map. I then executed a clip of the river/stream features, removing all the stretches of stream that did not lie directly inside the forest layer. I then ran a buffer on the river features to create a 2 mile buffer zone (the acceptable hiking distance for students to reach the river), I then ran another clip of the Forests layer for those that fell within the boundary of this new buffer zone.

 I then found a layer of where camping is prohibited (using the USGS protected areas database). I  then erased the river features that intersected the protected areas and then proceeded to buffer the new stream/river feature and then to ensure that the forests didn’t fall within the protected zone I did another erase of the buffer layer and the protected land layer. I then finally intersected the county forests and the buffer zone in order to find those that were within the proper distance of a stream and therefore would be a possible camp site.
My methodology is summarized below in Fig 1.

Fig 1: Data Flow Model

Results:

The results of this project can be seen below in Fig 2. In general a large proportion of the forests around Eau Claire are potential campsites to replace Pidgeon Lake. I am particularly happy with the large density of northern campsites all within a 2 hours’ drive of Eau Claire. The map also includes sites divided into those sites within one hour’s drive and those within two hours’ drive.

Fig 2: Final Map

Citation:

Eseri ArcGIS content team (10th edition, 2010-06-30), US Counties Generalized, Provided on disk drive, 5/10/2015

Eseri ArcGIS content team (10th edition, 2010-06-30), US Cities Generalized, Provided on disk drive, 5/10/2015

Klaus Courtney (2013), Forestry Business Services Bureau, Dissolved County Forest (cfrcdw9xx), Accessed from http://gapanalysis.usgs.gov/padus/data/download/ Downloaded 5/13/2015

UW-EauClaire_Geography (2015-02-03), Robinson_Wisconsin_Waters_Service, Accessed through Arc.GIS online, Accessed 5/12/2015

US Geological Survey, Gap Analysis Program (GAP). November 2012. Protected Areas Database of the United States (PADUS), version 1.3 Combined Feature Class. Accessed from http://gapanalysis.usgs.gov/padus/data/download/. Downloaded 5/12/2015.

Lab 5: Vector Geoprocessing

Goals and Background:

The purpose of this lab was to give me practice selecting and applying appropriate vector geo-processing tools as well as to allow me to try my hand at python scripting in the second section. It also was a chance to see how I could use arcmap tools in order to achieve project goals, so that I could start thinking about my final project and how I would utilize the arcmap toolset in order to answer a spatial question.

Methods:

Part 1:
The first part of the lab had me using a series of criteria to narrow down land that would be acceptable bear habitat for the DNR. I converted a GPS file into a point feature class. Then used that feature class to determine the habitat that bears were most likely to inhabit. Finding those habitats across the study area, I used the intersect, Buffer, Dissolve and Erase tools from the tool bar's geo-processing overlay and extract subsets in order to create new feature classes which showed me the areas that met the new criteria. I created layers to meet the following criteria using the above tools: land with proper cover (select by attribute and intersect), within 500 m of a stream (Buffer, Dissolve, Intersect), on DNR managed lands (Dissolve, Intersect), and 5 Km away from built up and developed land (Buffer, Erase). After I completed this, I was left with a map of land that met the criteria and was therefore a candidate for becoming bear habitat. This map can be found in Fig 1 in the results section.

Part 2:
The second part of the lab served as an introduction to python scripting. I was tasked with using Python scripting to complete two tasks. First to create a map of lakes with a potential to house a resort. Secondly to map out air pollution potential based on a map of interstates in Wisconsin. For the first project I used the Buffer_analysis, Clip_analysis, SelectLayerByAttribute_management and CopyFeatures_management commands in the arcmap python command line to buffer lakes an appropriate distance from cities,  and find lakes of an appropriate size then create a new feature class for them. Finally, I clipped the two features to create a map of the Lakes that could be used as a resort lake. That map can be found in the results section below, in fig 2.
Additionally, I utilized a simple python command for MultipleRingBuffer in order to create 6 different 1 mile wide rings around the Wisconsin interstate map I was given in order to display the potential for air pollution based on distance from the highway. That map can be seen in fig 3 of the results section.

Results:

Part 1:

Fig 1: Results of Suitable bear habitat search

Part 2:

Fig 2: Results of Search for Resort Lakes

Fig 3: Air Pollution Risk indication of Wisconsin Land

Citations:
Data: Michigan Department of Natural Resources, Accessed by Dr. Wilson

Environmental Systems Research Institute (Esri), Accessed by Dr. Wilson

Thanks to Dr. Wilson for providing data for the lab.

Lab 4: Multiple Criteria Query

Goals and Background: The Purpose of this lab was to acquaint me with determining and using complex spatial and attribute queries. This was done in four parts: 1st to acclimate me to attribute queries, with multiple criteria, using three separate queries. 2nd to acclimate me to mixed spatial and attribute queries, using one example query. 3rd, and finally, to use a multiple criteria spatial query.

Methods: To accomplish the tasks set out in this lab I utilized the select by attribute and select by spatial relationship functions in Arc Map. I first had to determine what the question I was given was looking for, and how to show it in a query format. After determining what the query wanted me to find, I used Boolean Operators to create a query that would result in the proper data being selected. After selecting the data I set about creating a cartographically pleasing map of each query results. The queries, and the resulting maps can be found in the results section below.

Results:

Fig 1: Multiple Criteria Attribute Query

Fig 2: Resulting Map of Features Selected by Query

 

Fig 3: Multiple Criteria Attribute Query

 

Fig 4: Resulting Map

Fig 5: Multiple Criteria Attribute Query

Fig 6: Resulting Map

Fig 7: Multiple Criteria Spatial and Attribute Query

Fig 8: Resulting Map

Fig 9: Multiple Criteria Spatial Query

Fig 10: Resulting map

Sources:
Data: Price, M, (1963), Mastering ArcGIS--Sixth Edition Database
Data Additionally Provided by Dr Cyrril Wilson.

Lab 3: Downloading and Mapping GIS Data

Goal and Background: This lab was an exercise to introduce me to working with the United States Census Bureau and the advanced search tool therein to find, observe and ultimately map data from an online source. This lab was also useful to troubleshoot and review how to join tables and formatting them accordingly, as well as to review other skills I had acquired in map layout, creation and formatting.

Methods: The first step of this exercise was to find data from the US Census Bureau American FactFinder website (found here), the lab had me find data by searching for datasets which contained data for all counties in Wisconsin. The lab walked me through the steps of  finding data on population from the Census, and I was required to find a data set on my own. I found one on Urban vs.. Rural Land types for my own mapping. I did this by using the advanced search feature, searching for a specific dataset and geography, then reviewing the table before downloading it as a zip file. I also found a map using the advanced search feature, and downloaded a map of the Wisconsin counties. After extracting the data, I reformatted the population data fields from text to numeric in order to allow me to join the Census table to the downloaded state table. I then displayed the downloaded county map, and placed the table into my map as well. I then joined the table, with a one to one cardinality join through the Geo_ID assigned to each county by the US Census Bureau, allowing me to then map the counties, based on the newly joined data, through the properties menu.

Results: From this lab, I created two maps found below.

Fig 1: Population of Wisconsin Counties (top) and Rural Prevalence in Wisconsin (bottom).

 

The Top map displays the population of Wisconsin counties and illustrates how people are concentrated towards the south eastern part of the state, near the big cities like Madison or Milwaukee. The bottom map shows a related phenomena to population, the percent of rural land in a county. This map shows an inversed relationship, where smaller population counties almost all have a larger percent rural land cover.

Data Source:
U.S. Census Bureau; generated by Philip Schadegg; Using American FactFinder; <http://factfinder2.census.gov>; (12, March, 2015)

U.S. Census Bureau; 2010 Census; TOTAL POPULATION, P1, summary file 1; generated by Philip Schadegg; Using American FactFinder; <http://factfinder2.census.gov>; (12, March, 2015)

U.S. Census Bureau; 2010 Census; URBAN AND RURAL, H2, summary file 1; generated by Philip Schadegg; Using American FactFinder; <http://factfinder2.census.gov>; (12, March, 2015)

Information on Coordinate System: Wisconsin DNR, DNR Coordinate Reference System, Retrieved from: <http://dnr.wi.gov/maps/gis/wtm8391.html>

Lab 1:

Goal and Background: Lab one was a test in my ability to discern and use appropriate geographic and projected coordinate systems in different types of data. As well as testing my ability to solve problems in data related to their co-ordinate systems. It also served as an introduction to the cartographical elements of arc.map software.

Methods: This lab was divided into four parts, each with their own task to accomplish.

Part 1: The entirety of part one was using different projection co-ordinate systems on a world map. For this section, I made 5 different data frames and I had to define the layer coordinate system to a set projection 4 times on a world map, and then  make one projection of my choice on a world map. This was accomplished by using defining the co-ordinate system using the co-ordinate system tab on the data frame properties. The 4 assigned co-ordinate systems were Sinusoidal (world), Equidistant Conic (world), WGS 1984 and Mercator (world). I used the PolyConic (world) projection co-ordinate system as my additional system of my choice.

Part 2: This section built off the same skill set as part one, but instead focused on a country and state level. I used a state feature class of the US, then created a layer from one selected state feature from the class, making a separate map of only the State of Wisconsin. Additionally, I matched two different features that were incorrectly matched with on-the-fly projecting, to use the same co-ordinate system using the Projection and Transformations feature-project, by importing the desired co-ordinate system from one feature to the other. Creating a map of the US with a separate map of Michigan roads incorporated into it.

Part 3: Part three was an introduction to the cartographical tools in arc.map software, I took the previous 7 maps, and using the layout view, and other tools from arc.map's insert menu to insert a neat line and titles to each map, as well as using the align center, left right and resize options.

Part 4: This was by far the hardest of the sections for me, because it required me to observe both metadata and projections to solve errors between datasets, due to lack of defined co-ordinate systems and incorrect co-ordinate systems. This was done by using the projection tool and the define projection tool, as well as using the data properties and the view item description button. The reason this was so difficult for me was that I needed to realize that defining projection and projection were different things. Once I had realized that I could not simply project a dataset that had no projection assigned to it, but instead needed to define its projection, the lab went much smoother.

Results:

Fig1: My Map results from Part 1-3.

This map showcases the appearance of the world from different projection co-ordinate systems, as created in part 1, as well as an adjusted map of the United States to accommodate new data, and a map of a single feature isolated from a separate feature class, as created in part 2

Fig 2: My Map of Central Wisconsin Counties and Rivers

This map showcases the results of my projection corrections after

Sources:

Textbook:
Price, M, (1963), Mastering ArcGIS--Sixth Edition

Part 1: Anonymous (nd), Country, Provided in textbook

Part 2: Anonymous (nd), STATES, Provided in textbook
Anonymous (nd), stroads_miv5a, Provided in textbook

Part 3: no data used

Part 4:
Anonymous (nd), Lower_Chip_strms, Given by instructor
Eseri ArcGIS content team (10th edition, 2010-06-30), US Counties Generalized, Given by instructor


Thanks to Dr. Cyril Wilson for his assistance in Part 4 of the lab.