Research Project Posters

Figure 1. Poster demonstrates the research conducted to identify evidence of the remains of the Great Synagogue of Vilnius, Lithuania.



Figure 1. Poster demonstrates the research aim to better understand the subsurface stratigraphy of Wisconsin Point by using a noninvasive methodology, ground penetrating radar (GPR).

GIS 1: Lab Four - Spatial Question & Vector Analysis

Introduction:

The object of this lab was to propose a spatial question that was relevant and could be answered with a simple set of criteria, where we would use the geoprocessing skills (vector analysis) that we have learned previously. The spatial question I proposed was, what is the best place for a new outdoor camping and recreation center? The intended audience for my question are outdoor enthusiasts looking to explore new outdoor areas, families looking for family friendly and easily accessible recreation areas to bring their kids, as well as others who do not own recreational items such as kayaks, canoes, and tents or those individuals that do not want/cannot travel long distances with those items. The idea behind the question stems from my passion to explore the outdoors. Additionally, being a college student I do not have the ability to own a kayak, but I love kayaking, so having a place where I could explore and be able to easily rent recreational items is ideal.

Data Sources:

In order to find a suitable area for a new outdoor camping and recreation center, I needed to determine the data pertinent to answering my question. I navigated through many datasets from the ESRI2013 database including base data, hydrographic data, and transportation data. The features I decided that were most important to answer my spatial question were the proximities of major highways, airports, lakes, summits, cities, previously established park and recreational areas, as well as already established national parks or national forests. My favorite place to explore the outdoors is the western United States, so I picked an area found in either Montana or Wyoming. To narrow my search to just one state, I picked an area based on high traveler destination. One place I’ve always wanted to visit is Yellowstone National Park and it is a highly recommended traveler location, so I focused in on the counties in and around Yellowstone National Park. A few data concerns I have with my criteria and area of interest is the reliability of the roads/highways present and the scale to which travelers will use to determine their likelihood of visiting. Also, because some data was unavailable, I was not able to determine the land cover present at these areas.

Methods:

In order to begin solving my spatial question, I picked an area of interest, Yellowstone National Park, and found a county that was included in my area that contained all of the criteria I was looking to use. The county I used was Park County, WY. I searched through data to find a precise area that visitors could access based on airport and highway accessibility, where there is abundant resources for outdoor recreation including lakes and summits for water activities and hiking, as well as a nearby city. I found Yellowstone Lake off of Entrance Road/Fork Highway, inside of Yellowstone National Park in Park County, Wyoming that does not currently have any recreation centers nearby. Because water is an important feature for outdoor recreation centers and camping, I made that my focal point based on its proximity to an airport, and used the additional criteria based on the location of the lake.

I created a blank file geodatabase and then browsed data form the ESRI2013 database. Once I concluded that the area I wanted to use was Park County, WY, I exported my county of interest to the geodatabase. Then, I had to clip all of the feature classes I was planning to use to my county: major highways, lakes, summits, airports, cities, previous parks and rec areas, and national parks/forests nearby. After I clipped all of the feature classes I was going to use, I decided to project my data using the projected coordinate system: NAD_1983_2011_StatePlane_Wyoming_West_FIPS_4904. After this, I was then able to move on and begin running analysis to answer my spatial question.

First, I created a buffer around Lake Yellowstone to find the number of summits present within a 100km radius. Then, I used that selection of summits within 100km of the lake to create an additional buffer of highways found within 25km of those summits. Once I had my selection of highways and summits I created a new layer with those features by intersecting the buffers and getting rid of the internal boundaries. Because I realized that 100km is a far distance to travel for outdoor activities, I decided to create a new buffer of only 25km away from the lake that included the summits and the highways. After, I had to intersect my 25km buffer of highways and lakes to only be included in Yellowstone National Park boundaries and the Park County boundaries (Figure 1). Once I had finished, I had an ideal location for a new outdoor camping and recreation center in Yellowstone National Park.

Figure 1. Digital flow model representing the methods used to obtain an answer to my spatial question.

 

Results:

The result of my project was an area anywhere in the 25 km radius of Yellowstone Lake, inside the Yellowstone National Park boundaries, and inside of Park County, WY (Figure 2). The major highway that runs from the Yellowstone Regional Airport into Yellowstone National Park is called Fork Highway. There is a city named Cody, which is just west of the airport along Fork highway where travelers can obtain any necessary items. From there, you follow Entrance Road all the way to Yellowstone Lake. Once you are in Yellowstone National Park, there are many summits within the 25km radius of the lake and the highway/road for exploring. Ideally, the recreation center would be to the east of Yellowstone Lake not too far off of the road, with adequate camping areas around the lake and hiking trails to the summits and surrounding areas.

Figure 2. The area for my proposed outdoor recreation center is shown in the aqua color on the east side of Park County, WY. The data used for assessment for my spatial question includes airport proximity, city proximity, water proximity, summit location and highway accessibility. Ideally, the outdoor recreation center would be to the east of Yellowstone Lake, not too far off of Entrance Road.

Evaluation:

My overall impression of this project is that I really enjoyed it. I thought it was great that we got to create our own spatial question and use our own criteria to solve it. If I had to repeat the project, I would like there to be stricter guidelines as to what kinds of questions we should propose. The challenges I faced with my project were unavailable criteria for the assessment of my proposed question. Overall, I really enjoyed the project, it made me think outside of the box and use everything that we had learned in class and using the MAG book to do a project that was completely our own.

GIS 1: Lab Three - Vector Analysis with ArcGIS

Background:

The goal of this lab was to develop a suitable habitat for bears present in the research area of Marquette County, Michigan using geoprocessing tools aimed at vector analysis in ArcGIS to then create a digital data flow model and a cartographically pleasing map showcasing the new suitable habitat. The purpose of this lab was to use GPS points (X,Y coordinates) of bear location from an excel file and determine the area most suitable for the bear habitat based on certain criteria.

Methods:

The first objective was to map the GPS points of X and Y coordinates found in an excel file. These points represented black bear locations in central Marquette County, Michigan. In order to map X and Y coordinates that are in a non-spatial database (ex: excel) you need to add the coordinates as an “event theme”. An “event theme” is a temporary display of X, Y data in ArcMap, but they have certain limitations because an “objectID” field is not officially generated in an “event theme”. Without an “objectID” field you cannot select features in the map layer, edit layer attributes, perform any interactive edits such as selecting points and moving them, or define a relate. X, Y coordinates describes points on the earth’s surface, the fields must be numeric; once you add data to your map it becomes an X,Y event layer and acts like other point feature layers.

To add the X,Y event theme, I simply added X,Y data under add data from the file drop down bar, using a projected coordinate system called NAD_1983_HARN_Michigan_GeoRef (Meters), indicating my X-Field as my X-points collected, and my Y-Field as my Y-points collected. Then, I exported my points into my geodatabase as a feature class to use for further mapping.

Following this process, I needed to determine the habitat that the bears were inhabiting by creating a new feature class with the bear location and the land cover type where it was found. I joined the feature class of bear location points and the type of land cover, based on the ObjectID field. It was a simple spatial join. Then, to determine the top three habitat types that the bears inhabited, I summarized the bear location by the land cover type in that location. The top three habitats the bears were found in were mixed forest land, evergreen forest land, and forested wetlands.

After determining the top bear habitat lands, I needed to find out how many bears were found near, within 500 meters of a stream, when the GPS point was collected. In order to do this, I created a buffer of 500 meters around all of the streams, then under data management and generalization, I used the dissolve tool to blend the layers. After, I used the select by location to determine the percentage of bears found within 500 meters of a stream; it was 92.7%. Sixty-three out of sixty-eight bears were found within 500 meters of a stream. Biologists consider a percentage above thirty to be important criteria.

Now to find suitable areas for bear habitats, I used the two criteria assessed earlier. I needed to find all of the locations with the top three land covers and within 500 meters of a stream. To do this, I needed to separate the land cover types by using the select by attributes to select the top three land covers and create a new feature class with only those types. Then, I used the intersect tool to combine the top three land cover areas and the stream buffer of 500 meters to create a new layer that would be considered suitable habitat areas. Because I combined two layers, I needed to remove the internal boundaries to create one layer by using the dissolve tool.

Once I established all of the suitable areas for a bear habitat based on land cover and location to a stream, I needed to find the suitable areas under DNR land management. To do this, I performed an overlay analysis intersecting the DNR managed lands and the suitable locations, removing the internal boundaries to create one new layer containing areas of suitable habitat within the DNR management areas.

Lastly, I wanted to create suitable bear habitats away from urban or built-up areas, so I created a 5 kilometer buffer around urban and built-up areas and used it to eliminate all of the areas that were DNR managed lands and suitable habitats within that area.

During the lab, I recorded my steps to create a digital data flow model.

Digital data flow model of methods performed during this lab.



Python code to adjust streams buffer and the urban areas buffer.

Results:

During objective two, it was determined that 92.7% of black bears were found within 500 meters of stream. Sixty-three out of sixty-eight bears were found within 500 meters of a stream. Biologists consider a percentage above thirty to be important criteria. The pink colors on the map show areas that are suitable for bear habitats under two criteria: land cover type and accessibility to streams (within 500 meters).The dark pink areas of the map are the final products for suitable habitat area because they are more than five kilometers from urban or built-up areas, are DNR managed lands, and contain both criteria. The light pink areas represent suitable habitat that is within five kilometers of urban or built-up areas, are DNR managed lands, and contain the two criteria.  

Figures:

The map represents suitable habitat areas for black bears in Marquette County, MI. The pink colors show areas that are suitable for bear habitats under two criteria: land cover type and accessibility to streams. The dark pink areas are the final products of suitable habitat area because they are more than five kilometers from urban or built-up areas. The light pink areas represent suitable habitat that is within five kilometers of urban or built-up areas.

Digital data flow model of step taken to achieve a suitable habitat for black bears in Marquette County, MI.

 

Sources:

All data downloaded from:

http://gis.michigan.opendata.arcgis.com/

Land cover 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 1: Lab Two - Downloading GIS Data

Background
The goal of lab two for GIS 1, was to learn how to download and map data from the U.S. Census Bureau. The U.S. Census Bureau has a mission to serve at the leading source of quality data about the nation’s people and economy. I had to follow several objectives to obtain this goal, including downloading datasets from the U.S. Census Bureau, joining data tables, and creating a web map. 

Methods

To begin, we had to obtain census data by choosing a variable of interest from the U.S. Census Bureau (http://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t). I choose people and basic count/estimate as a topic, and all counties in Wisconsin as the geography. From there, I downloaded an SF1 data set, because it is the basic standard census data. The variable I downloaded was total population from the 2010 SF1 dataset. After the dataset was downloaded as a zip file, I needed to unzip the files and save the CSV files containing tabular data as an excel workbook file. Then, I downloaded the map of Wisconsin counties as a shapefile.zip and unzip those files as I previously did. After obtaining the appropriate data, I had to join the data together to create a map in ArcMap.

First, I added the shapefile containing the counties of Wisconsin, then because this file contains no census data, I added the excel file I created earlier containing the tabular data of population. In order to map the population, I had to join the excel file and the shapefile tables together using the same attribute field (GEO#ID). By joining the same attribute field I was then able to map the total population of Wisconsin by county. I wanted to create a graduated colors map with my population values, but because the values were imported as a string field type, the values could not be mapped quantitatively. To fix this, I added a new field in the attributes table that was a double field type, containing the original values from the population data field. Then, I was able to map using graduated colors.

Second, I needed to create a new map in the same file, but under a different data frame. This time my variable of choice was males aged 25-29 in all counties of Wisconsin. I went to the U.S. Census Bureau website and downloaded a 2010 SF1 100% data dataset. I followed the same workflow as before to download the data, unzip and create excel files, and map the data for my variable of choice. Additionally, I had to join the shapefile of the Wisconsin counties and the excel file containing the tabular data on male population ages 25-29. Again, I had the same problem trying to create a graduated colors map because the values were imported as a string field type, not a double field type. So I created a new, double type field in the table containing the values for male population ages 25-29. Then, I was able to map using graduated colors, but I had to normalize my data by the total population of each county. I mapped the population of males aged 25-29 and normalized it by the total population.

Following these steps, I created a cartographically pleasing layout containing both maps of Wisconsin. While doing this, I had to consider changing the projection of the data frame to better suit the state of Wisconsin, and add the appropriate map elements including an author, source, title, legend, scale, and north arrow. The projection I used was NAD 1983 (2011) Wisconsin TM (US Feet). To finish my project, I added a light grey, canvas basemap.

After creating those maps, I had to publish a web map on ArcGIS Online, using my second variable. I made a copy of my second map, deleting all other items including all other data frames, basemaps, original joined shapefiles, and tables. I exported all of the features of my second variable into a new shapefile and imported my symbology properties from my previous map. In ArcMap, I signed into my University of Wisconsin – Eau Claire account on ArcGIS Online, where I then created a feature service from this new ArcMap document. I had to include a service name, “Wisconsin_Demographic_Information_Pingel,” an item description, and tags before I was able finish creating my feature service. After the feature service was created, I was then able to analyze and publish my web map. Using ArcGIS Online (http://www.arcgis.com/home/), I logged onto the UWEC Geography and Anthropology ArcGIS account to share my map. Before sharing my map, I needed to update some of the contents such as the map name and display attributes.

Results

Patterns on the map indicate that the highest male population between the ages of 25-29, are in central and southern Wisconsin. Additionally, the most of the counties with the highest male population (Eau Claire, Brown, Dane, La Crosse) are counties within the University of Wisconsin college community system.

(http://uwec.maps.arcgis.com/home/webmap/viewer.html?webmap=f46e642bf6e04872abdb34b82982e842) 

Sources

U.S. Census Bureau and ArcGIS Online:

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

http://www.arcgis.com/home/

GIS I Lab 1: Base Data

Background:

  In the spring of 2012, the University of Wisconsin-Eau Claire, Clear Vision Eau Claire, Haymarket LLC and local developers created a project that would better the future of the local community and college community. The project is called the “Confluence Project." It is located at what is referred to as the “Haymarket Site” at the confluence of the Chippewa and Eau Claire Rivers along Eau Claire Street and Graham Avenue. The project plans to have a community arts center with performing and fine arts including studios, costume shops, dressing rooms, galleries, classrooms, and offices for the arts organization. Additionally, student housing and commercial retail are part of the cultural endeavors in the downtown Eau Claire project. This would bring up to 375 residents to the area allowing the music, theater and art culture to expand. The objective for this lab was to construct a map of the proposed site for the Confluence Project and the relevant base data using ArcMap. The map created included: civil divisions, census boundaries, public land survey system features, Eau Claire city parcel data, zoning, and voting districts.

Methodology:

  There are two geodatabases that I would be using so to begin, I had to become familiar with the data; I noted the different feature classes and their respective geodatabases. One of the geodatabases was for the city of Eau Claire, while the other was for the county of Eau Claire. After this, I became acquainted with public land survey systems. I looked at how many townships are present in Eau Claire and created a brief legal description of the two parcels in the proposed site using the City of Eau Claire Web GIS. Upon completing the legal descriptions, I was able to start digitizing the map for the proposed site of the Confluence Project.

  First, I created my own geodatabase containing a feature class of the proposed site with a world imagery basemap. Once I did this, I was able to start constructing the map with the relevant base data. I built a data frame of civil divisions as a locator map that included the county boundary and changed the symbology so I could see the different divisions and the aerial behind it. Civil divisions are important because it entails the collection of boundaries on land ownership for the local and state government management. Second, I created a new data frame of the census boundaries that allowed me to see the blockgroups and tracts group’s boundaries. This is important for the US Census Bureau for the ten-year population count. Next, I included the public land survey systems feature map showing the quarter-quarter sections survey around the proposed site. 

  After this, I generated a new data frame including the parcel data for the city of Eau Claire. The parcel data shows survey information associated with individual lots such as lot lines, and parcel corners. Next, I created a zoning data frame that showed the different areas based on zoning class. This included areas such as: residential, commercial, industrial, public property and transportation.  Lastly, I created a voting districts data frame, displaying the voting district classes for the city.  

 

Results:

  The results from lab one (Figure 1) show the different features processed from the base data relevant to the Confluence Project.   



Figure 1. Six maps displaying the relevant base data inlcluding (left to right, top to bottom): Civil Divisions, Census Boundaries, Public Land Survey System features, Eau Claire City Parcel Data, Zoning, and Voting Districts, for the Confluence Project. Data collected from the City of Eau Claire and Eau Claire County 2013.

Sources: City of Eau Claire and Eau Claire County 2013
 

Mapping Services. (n.d.). Retrieved October 2, 2015, from    http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services