GIS I Mini-Term Project--"Blanding’s Turtle Habitat in Eau Claire, Wisconsin"

Blanding’s Turtle Habitat in Eau Claire, Wisconsin

Goals and Background

            My “Mini-Term Project” will having me identifying suitable habitat for Blanding’s Turtles (Emydoidea blandingii) within Eau Claire County in the state of Wisconsin.  Eau Claire County is located in the West Central area of Wisconsin.  The city of Eau Claire has a population around 66,000 people and is located in the North West portion of the county.  Today in the City of Eau Claire, healthcare, manufacturing, retail and educational jobs are the primary employment of residents.  The remainder of the county is comprised of rural farming communities with populations less than 2000 people per village or city.  The specific objective for this project is to locate highly suitable areas where Blanding’s Turtles could thrive.  Blanding’s Turtles are on the list of Special Concern species in Wisconsin. (WI DNR)  The Blanding’s Turtle take 17 to 20 years to reach maturity. (WI DNR)  With this given information I feel that it is important to protect all the possible habitat which could contain Blanding’s turtles.  Protecting this habitat will allow the species the best chance to thrive.  My intended audience would be for the Wisconsin DNR, and Eau Claire County zoning committee.   With this information these groups of people could prevent destruction of the turtle’s habitat.  This project is important for the longevity of the turtle species, coupled with the numerous other species including other turtles which call these areas home.

Methods

            Answering my question required me to identify specific areas which the Blanding’s turtles thrive.  I used the habitat description from the Wisconsin DNR to narrow down critical habitat requirements.  Blanding’s turtles prefer to nest in sandy soil and may travel up to 900 feet from a wetland to find suitable soil.  My first step was to project all of my layers to NAD 1983 HARN WISCRS Eau Claire County Feet.  Using this projection will give me the most accurate results for the entire county.  The SSURGO data I needed was in table form.  Using the SSURGO base map file I joined the table to the base map.  I selected all the sandy soil with in the SSURGO data.  Using the selection, I created a layer with only the sandy soil.  My second requirement had me locating land area within 900 feet of water using the hydrology data from Eau Claire County.  Using the buffer tool, I created a 900 foot buffer around the water features.  I used the dissolve tool to create a simplified area with in the buffer to speed up future processing.  The buffer area extended beyond the county boundary.  To eliminate this I used the clip tool to contain the buffer within the county boundary.  The third variable I used was residential land.  I obtained land cover data from the USGS in raster form.  I used the conversion tool to covert the raster image to a vector image.  Using land cover data from the USGS I selected low, medium, and high developed land, as these areas would not be suitable locations for turtles.  Using this selection, I created a layer containing only the residential land.  I then intersected the sandy soil area and the water buffer area to locate suitable habitat.  Using the erase feature, I excluded the residential area from the intersected soil and water area.  Using the dissolve tool I eliminated all the internal boundaries.  For my final map I added a locator map with the state of Wisconsin broken down by county and highlighted Eau Claire County within that map.  I projected the state map to NAD 1983 Harn Wisconsin TM US Ft for a proper appearance.  I then added a neat line, background, title, north arrow, scale, and legend to the entire map layout using ArcMap.  For my final step I exported the map to Adobe Illustrator (AI).  In “AI” I added my sources and a picture of a Blanding’s Turtle. 

Data Flow Model

Results

Final Map Results

References

Eau Claire County GIS (From Server at UWEC)

ESRI Data and Maps [Download]. (2010) Redlands, CA: ESRI [October, 2012]

U.S. Department of Agriculture, Natural Resources Conservation Service. (2013). Soil Survey Geographic (SSURGO) database fro Eau Claire County, Wisconsin. In. (Ed.) Fort Worth, TX: Author.  Retrieved from http://websoilsurvey.nrcs.usda.gov

U.S. Geological Survey, 20140331, NLCD 2011 Land Cover (2011 Edition): U.S. Geological    Survey, Sioux Falls, SD.

GIS 1 Lab 5

Goals and Background

The purpose of this lab is to use my knowledge from our lectures and tutorials to choose the appropriate vector geoprocessing tools with in ArcMap to extract specific information.  The second portion of the lab, I will use basic python script to run geoprocessing tools to extract specific information.

The first part of this lab will have me locating suitable habitats for black bears in the given study area with in Marquette, Michigan.  The second part of this lab will have me locating the best locations for lake resorts withing the state of Wisconsin.  The second part will also have me creating a map of air pollution along the interstates of Wisconsin.

Methods

Part 1:  

The first and second section of this lab required me to relate locations of black bears to the specific habitat/landcover (Minor Type field) they were found in.  I was given an XY table in the data that contained the coordinates of bear locations with in a specific study area in Marquette, Michigan.  I created a feature class from the XY table.  I selected the projection and coordinate system that matched the other shapefiles I was given in the dataset.  I then added all the feature classes I was given in the feature dataset.  Then I used Select By Location to find which specific habitat/landcover (Minor Type field) the bears were located in.  From this selection I created a new feature layer that contained the bear ID number and the specific habitat information.  I then used the Summarize tool within the attribute table to determine how many bears were located with in each habitat section.

The third part of this section required me to determine how many bears were found within 500 meters of a stream when their GPS location was collected.  My first step was to create a 500 meter buffer around the given stream data.  I used the Buffer tool and the built in Dissovle tool to create by buffer area.  I then used Intersect with the buffer area I just created and the feature class of the bear locations to determine how many bears were within 500 meters of the given streams.

The forth section of this lab required me to determine suitable areas of bear habitat based on the findings of the first three sections.  Based on the locations related to habitat/landcover I determined that of 91% of the bears were located in 3 specific types of habitat/landcover.  I used Select by Attribute to single out these specific categories and created a layer from this selection.  I also determined that 72% of the bears were within 500 meters of the stream.  I deemed this distance from the streams as an important habitat characteristic and decided to use it for determining suitable habitat/landcover.

The fifth section required me to use the information I had already collected and use it to make a recommendation to the Michigan DNR for a bear management plan.  I had to find suitable habitat that was located on their management land.  I was given a feature class of the Michigan DNR management land in the data I was provided.  The management area I was given was for the entire county of Marquette, Michigan.  The study area was only a portion of the county.  I used the Clip tool to remove the management land that did not fall within the study area.  I then used the Dissolve tool to remove the internal boundaries of the management area file to speed up the processing time of future tools, as I was not concerned with the specific units with in the management land.  I then used the Intersect tool to find the management areas that were within the suitable bear habitat that I had found from the fourth section.

The sixth section I had to eliminate the areas of the DNR management land that were within 5 kilometers of Urban or Built up land from my previous findings.  I used Select by Attribute to select the the Urban or Built up land from the landcover shapefile.  I then created a new layer from this selection.  Then I used the Buffer tool and the built in Dissolve tool to create a 5 kilometer buffer around the Urban areas.  In my final step I used the Erase tool to remove the management land that fell with in the 5 kilometer buffer of the Urban areas.

The seventh and final section of part 1 I had to create a map displaying my results.  i added a locator map to display where the study area fell with in Marquette, County.  I then added a legend, north arrow, neat line with a background, and a scale to the map.  Per the assignment instructions I had to create a flow model of the methods that I used to create the map and include it on the layout with the final map.  I used the Model Builder tool within ArcMap to create the model.

Part 1 Final Map w/model layout

Part 2:

The second part of this lab has me creating a map of areas with high potential for the establishment of suitable resorts.  The resort areas should be around a lake that has an area that is greater than 5 square miles and not more than 10 miles from the city.

Using the data I was given, I imported the cities, interstates, lakes and counties shapefiles.  Using the Python window I created a script using the Buffer tool that would create a 10 mile buffer around the given cities from the data.  I then created a script using the Select by Attribute tool to select the lakes that were greater than 5 square miles in area and create a new feature layer from this selection.  I then created a script to exclude any lake that did not meet the predefined criteria.  I used the Clip tool in the script to preform this operation.

Buffer Tool Script

Select by Attribute Script

Clip Script

I then had to create a map to display my results.  I added a neatline, background, north arrow, scale, and legend to the map.  I then exported the map file to an Adobe Illustrator (AI) file to allow me to better label the lake names on the map.  Using "AI" I labeled the lakes that met the defined criteria.

Lake Map

The second section of part 2 I had to create a map modeling air pollution around Wisconsin Interstates.  I was instructed to produce a map that showed the area within 6 miles of the interstates and break them in to 6 impact zones based on their distance from the interstate.

Using the Python window I created a script using the Multiple Ring Buffer tool to create a 6 ring buffer that had a distance of 1 mile between the rings based on the location of the interstates.  I then had to create a map to display my results.  Like my previous maps I added a legend, neatline, background, north arrow, and a scale to the finished map.

Multiple Ring Buffer Script

Pollution Map

Sources:  

Part 1-- Michigan Department of Natural Resources (DNR) and Environmental Systems Research Institute (ESRI)

Part 2--  Price, Maribeth. 2014. Mastering ArcGIS. 6th Edition data CD. McGraw Hill.  Lake features are from Wilson, Cyril 2012, A comprehensive Lake features for Wisconsin, unpublished data.

GIS 1 Lab 4

Goals and Background

The purpose of this lab is to build our understanding and increase our skill for using the query function within ArcMap.  In this lab we will be preforming multiple criteria attribute queries, along with spatial queries.  I will be using the information from the in class lectures and Tutorial 6 of our textbook Mastering ArcGIS to complete this lab assignment.

Methods

   Part 1. 

      Question 1.  For this part of the lab we were instructed to use data that was downloaded from our textbook cd.  We were to import the counties feature from the USA geodatabase.  I then had to preform a query that would return the counties with a population between 3000 and 4000 people in 2010 and also all counties in 2010 that had a population density of at least 1000 persons per square mile.  The multiple criteria query that I came up with looked like the following, POP2010>= 3000 AND POP2010<=4000 OR POP10_SQMI>=1000.  I was then instructed to create a map showing the counties that fulfilled the criteria of the query.  I added a neatline w/background, title, legend, north arrow, scale, and my name to the finished product.

Question 1 Map

      Question 2.  For this question I was instructed to use the same data as question 1.  I was instructed to create a query that would return county records for Wisconsin, Texas, New York, Minnesota, and California where the male population was greater than the female population and also for these states, the counties where the population of seniors (age 65 and above) is over 6,500. After clearing the search from the previous question I created a multiple criteria query which appeared like the following,STATE_NAME IN ( 'Wisconsin', 'Texas', 'New York', 'Minnesota', 'California') AND MALES > FEMALES AND AGE_65_UP >6500.  I was then instructed to create a map showing the counties that fulfilled the criteria of the query.  I added the same features as question 1 to the finished map.

Question 2 Map

      Question 3.  Question 3 had me add additional parameters to the query in question 2.  I was instructed to add the counties from Washington, Maryland, Illinois, Nebraska, District of Columbia, and Michigan that contained a population of seniors greater than 6500 that reside in counties with more than 30,000 housing units.  The query I created looked like the following, STATE_NAME IN ( 'Wisconsin', 'Texas', 'New York', 'Minnesota', 'California') AND MALES > FEMALES AND AGE_65_UP >6500 OR STATE_NAME IN ( 'Washington', 'Maryland', 'Illinois', 'Nebraska', 'District of Columbia', 'Michigan') AND AGE_65_UP>0 AND HSE_UNITS>30000.  I was then instructed to create a map showing the counties that fulfilled the criteria of the query.  Like the previous maps, I had the same additional feature to complete the map.

Question 3 Map

   Part 2. 

      Question 4.  For the second section of this lab we were instructed to download data that was provided to us from our instructor.  Question 4 asked me to create a query that would return cities in Wisconsin with a 2007 population between 15,000 and 20,000 people, area of the city that is at least 5 square miles in land area, and also where the female population is greater than the male population and lastly the cities must be within 2 miles of a lake.  I imported the Wisconsin, cities, and lakes shapefiles from the data I had downloaded.  I create a query to extract all of the counties that met the criteria with the exception of being withing 2 miles of a lake.  That query looked like the following, ("POP2007" >= 15000 AND "POP2007" <= 20000) AND "AREALAND" >=5 AND "FEMALES">"MALES".  Using the cities that were selected from the query I then used a spatial query to return those cities that were within 2 miles of a lake.  I was then instructed to create a map displaying the cities that were returned from the query.  I was instructed to add the Roads shapefile for display purposes to my final map.  The original files were in NAD 1983 UTM ZONE 15N projections and the map was tilted off center.  To create a more visually appealing map I decided to change the projection to NAD 1983 StatePlane Wisconsin Central FIPS 4802.  I first changed the data frame to StatePlane projection and then I projected all the layers to the same projection for the best accuracy.  I also added the same additional feature to the final map as the previous questions.

Question 4 Map

      Question 5.  For this question I was instructed to calculate the total length of a list of rivers that was given to me.  I created the following query to select the named rivers, "PNAME" IN ( 'CHIPPEWA R', 'EAU CLAIRE R', 'EMBARRASS R', 'FISHER R', 'HUNTING R', 'KINNICKINNIC R', 'MAUNESHA R', 'MILWAUKEE R', 'MOOSE R', 'NAMEKAGON R', 'PELICAN R', 'PLATTE R', 'POTATO R').  After selecting the rivers through the query I created a new feature that contained only the selected rivers.  In the attribute table I created a new field to calculate the length of the rivers.  Using the field calculator function I calculated the length of the rivers to the nearest mile per the instructions.  Using the summarize feature in ArcMap I was then able to calculate the total length of the rivers that were selected.  I was then instructed to create a map that included the major roads and lakes as a backdrop for the map.  This map was in the same projection as the question 4.  I projected in the same as I did the map for question 4 to make it more visually appealing.  I again added the same additional feature to the final map.

Question 5 Map

Sources

Price, M. (2014). Mastering ArcGIS (Sixth ed., pp. 9-326). New York, NY: McGraw-Hill.
Data used was given to me in a zip file and downloaded to my computer.  Available upon request with the permission of my instructor.

GIS 1 Lab 3

Goals and Background:  The purpose of this lab was to become familiar with downloading data and converting the data to a usable format that has the ability to be mapped in ESRI Arc Map.  The other goal of this lab is to become familiar with joining standalone tables to other tables to allow the data from the standalone table to be mapped.

Methods:  

   Part 1:  I was instructed to consult the U.S. Census Bureau website to obtain the data for this lab exercise.  Through the advanced search feature on the Census website, I set the parameters for the search.  I was instructed to use the 2010 SFI 100% Data under the topic menu of the options. Then I was instructed to select All Counties Within Wisconsin under the Geographies tab.  This gave me access to the data from the 2010 Census that applied to all the counties with in the state of Wisconsin. Then I was instructed to select and download Total Population from the list of the data we had.  Once I had the data downloaded, I opened the data set in Arc Catalog which opens in Excel.  I had to change format the entire column with the population numbers in it from a text format to a numeric.  This allows the numbers to be read correctly by Arc Map for calculation and mapping purposes.

   Part 2:  For this section I was instructed to download the map file for the state of Wisconsin.  This is easily done through the U.S. Census website by clicking on the map tab and then clicking download.  This extracts the map shapefile from the website for use in Arc Map.

   Part 3:  This step required me to bring in all the data that I had downloaded from the U.S. Census website into Arc Map.  I imported the shapefile of Wisconsin that showed the state broken down by county.  Then I imported the data set of the population that I downloaded and formatted from the U.S. Census website.  I then joined the table from the population data set to the data set in the shapefile using the GEO#id, as these fields were the same in both data sets.  In order to properly map the population I had to create another field to nominalize the data by.  I used the field calculator in arc map to to calculate the area of each county.

   Part 4:  Step 4 required me to map the data that I had downloaded and imported in to Arc Map.  In the symbology tab I chose Quantities and then Graduated Colors, to make a Choropleth map of the population.  I had to pick my color representation of choice and my class structure.  For this map I chose yellow to brownish/red color scheme and the number of people per square mile for my class structure.

   Part 5:  Using all of the same techniques as the previous steps I was instructed to choose my own variable to download and map for the state of Wisconsin on the county level.  I created a new data frame layer in Arc Map to build this map.  I chose to map the number of vacant houses per county.  I chose to norminalize this data by the total number of houses and displayed it as a percent for each county.  I chose to use an opposing color scheme for this map as it was going to be displayed side by side with my first map.

   Part 6:  After creating these two maps I had to create a layout that put both maps on one page for display purposes.  I was also to project the map in a suitable projection for the area.  I chose to project the map in the NAD 1983 Central Wisconsin State Plane projection to minimize the distortion and increase the accuracy of the map.  Then in the layout view of Arc Map, I added a scale, north arrow, title, legend, neatline, background, and my name to the final design of the two maps.

   Below is the final product of all the above sections.

GIS 1 Lab 1

Goals and Background: The goal of this first lab was to take the skills that I had practiced in the earlier weeks through Tutorial 1 & Tutorial 11 from the book, Mastering ArcGIS and apply them to different data.  The purpose of these tutorials and the lab was to gain a good understanding of geographic and projected coordinate systems along with their differences.  Another goal of this lab was to use my knowledge of coordinate systems to fix data sets that were improperly created and did not have a coordinate system assigned to them.  The final idea of the lab was to be able to see and identify projection errors and fix the errors correctly to produce a high quality and accurate map.

Methods:  I started with data that was supplied to me by my instructor.  This lab was broken into 4 parts.

   Part 1:  The goal of this section was to become familiar with world projections and dataframes.  I imported the country and geogrid data given to me by my instructor.  Then I changed the color and line thickness to make the map more visually appealing and readable.  For the assignment I had to create a total of five maps setting each map to five different projections.  Every one of the maps had its own data frame and used the country and geogrid data mentioned before.  I was required to use the following projections:  Geographic Coordiante System (WGS 1984), World Mercator, World Sinusoidal, Equidistant-Conic, and one of our own choice, to which I chose Azimuthal Equidistant.

   Part 2:  The goal of this section was to become familiar with state level projections and dataframes. I created a new data frame for this section and imported the states shapefile.  Then using the selection tool I created a new layer of the state of Wisconsin only.  I then deleted the states layers leaving only the state of Wisconsin on this dataframe.  I then changed the projection to UTM NAD 1983 Zone 16 using the project tool.

   The next part of this section required me to import the states shapefile again in a new data frame. Then I had to import stroads_miv5a, which was the state roads of Michigan.  Though they appeared to overlay correctly they were not in the exact same projection.  So I had to project the road data into the same projection as the states shapefile.  Then for the final step I changed the projection of the data frame to North American Lambert Conformal Conic per the instructions.

   Part 3:  This section required me to take the previous seven maps and arrange them on one page in the layout view of ArcMap.  I arranged them to fit nicely onto one page and then labeled them accordingly,  I added north arrows of different varieties to each one of the maps and a title to the top of the page.  I also added a background color to help make the map more visually appealing.

   Part 4:  The main goal of this section was to identify and fix projection problems with data that was given to you without the proper labeling or identification.  I imported the Central_WI_Cts shapefile given to me by my instructor.  This file turned out to not have any Geographic or Projected Coordinate system assigned to it.  I was then given the Metadata information and used the Define Projection tool and applied the correct Geographic & Projected Coordinate system to the file.  Then I had to choose the best projection for the data.  I choose the NAD 1983 State Plane Wisconsin Central FIPS 4802, as this would give the best map with the least amount of distortion.  I then had to import Central_WI_rvs shapefile into the same data frame layer.  This data had the correct Geographic Coordinate system assigned to it but it didn't have the any projection assigned to it.  I again used the Define Projection tool to assign the correct projection to the file.  Then, again using the project tool I projected the file into NAD 1983 State Plane Wisconsin Central FIPS 4802 as I did the previous file.  Finally, I changed the data frame layer projection to North American Equidistant Conic so that you could see the entire map on a flat surface.

Reference
Price, M. (2014). Mastering ArcGIS (Sixth ed., pp. 9-326). New York, NY: McGraw-Hill.
Data used was given to me in a zip file and downloaded to my computer.  Available upon request with the permission of my instructor.