GIS I - Lab 5: Application of Skills

Figure 1: This map shows the best suitable hunting land, located on publicly managed DNR land,
in Sauk County, Wisconsin. The green polygons denote land that matches the specified criteria. 

Goal

Background: The goal of this assignment was to develop a personal spatial question and carry out the correct means to answer that question. The spatial question that this lab seeks to answer is: Where are the best places in Sauk County, Wisconsin to hunt on land open to the public?

Purpose: The purpose of this assignment was to demonstrate knowledge of the skills learned from this course and apply them through various methods.

Methods

Data Collection: To begin the process of determining the best possible public hunting land in Sauk County, data had to be brought in from for streams, bodies of water, Wisconsin Department of Natural Resources (DNR) managed lands, county borders, state border, roads, and cities. Through ArcMap, database connections were made to the Wisconsin DNR Database and the ESRI Database. Through the DNR database the following data was acquired: Streams, DNR Managed Lands, County Borders, State Border. Through the ESRI database the following data was acquired: Bodies of Water, Roads, and Urban Areas. This data was brought into a file geodatabase created for this assignment. Concerns with this data stem from having to use a variety of different feature classes to examine one overall feature, such as the classes for streams and waterbodies.

Data Preparation:
Sauk County: In order to start utilizing the data, Sauk County had to be located and made into its own feature class. This allowed the analyst to use Sauk County to clip the other features to get details within only the target area. To do this, an attribute query was performed to locate Sauk County. Once the county was located, the option to make it into a feature class was chosen. Then, Sauk was projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection to make the feature class more conducive to viewing solely the county.

Roads: To begin using the Roads data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. This data had many different classes within it, depending on the size of the road. To differentiate between the different classes of roads, they were each given a different symbol, as seen in Figure 1. This class difference led to the need to create buffers based on different criteria to create different sized buffers. [Note: This was not a skill learned in this class, however, I was able to teach myself how to do it and used it for this feature class and for the Streams feature class]. To do this, a new field labelled "Buffer_Field" was added to the attribute table in the form of a Text field. Different values were assigned to this field, for example "0.5 Kilometers". This was added to each different class in order to create different sized buffers based on the size of the road and the perceived distance that a hunter would like to be away from the road in order to reduce the chance of traffic interfering with their hunt. The roads were then buffered by Field, rather than buffered by Distance. A dissolve was also applied to get rid of overlapping lines created by buffering. The resulting feature class was then clipped using Sauk County to get rid of buffers falling outside of the target area. Labels were activated for this data and a mask was applied to the labels to make them more visible on the map.

Urban Areas: To begin using the Urban Areas data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. A dissolve was applied to remove boundaries between cities like Lake Delton and Wisconsin Dells, and Sauk City and Prairie du Sac, who share borders. Then a buffer was applied around the city to account for anthropogenic influence. The data was clipped again using the Sauk County polygon to remove buffers that extended outside of the county. Labels were activated for this data and a mask was applied to the labels to make them more visible on the map.

Bodies of Water: To begin using the Waterbodies data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. A buffer was applied to buffer the area around the various bodies of water. This buffer assumed that wildlife would travel a certain distance to drink from a lake, pond, or river. Once the buffer was created and a dissolve was applied, it was clipped with Sauk County again to remove buffers that extended outside of the county. The data was then compared to the buffered urban class and an erase was applied, then to the buffered roads class and an erase was applied. This left waterbodies that were located a certain distance away from anthropogenic influence.

Streams: To begin using the Streams data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. The data was then compared to the Waterbodies feature class and an "Erase" was applied to the data in order to remove data that was overlayed with lakes or rivers, as they were not properly displayed with a line feature class. This data had many different classes within it, depending on the size of the stream. This led to the need to create buffers based on different criteria to create different sized buffers. To do this, a new field labelled "Buffer_Field" was added to the attribute table in the form of a Text field. Different values were assigned to this field, for example "0.4 Kilometers". This was added to each different class in order to create different sized buffers based on the size of the stream and the perceived distance that animals would travel in order to drink from it. Smaller streams received smaller buffers because they would not supply as much water to the surrounding wildlife, or potentially because they only carried water during precipitation events (intermittent streams). The streams were then buffered by Field, rather than buffered by Distance. Once the buffer was created and a dissolve was applied, it was clipped with Sauk County again to remove buffers that extended outside of the county. The data was then compared to the buffered urban class and an erase was applied, then to the buffered roads class and an erase was applied. This left streams that were located a certain distance away from anthropogenic influence. Finally, a union was applied between the Waterbodies feature class and the Streams feature class in order to derive a new feature class labelled "Sauk Water". This feature class contained all units of water in Sauk County.

DNR Managed Lands: To begin using the DNR Managed Lands data it was clipped using the Sauk County polygon. This data was then projected to the NAD 1983 Wisconsin CRS Sauk (meters) projection. An attribute query was conducted for lands that were fully owned by the Wisconsin DNR or lands that were acquired through easements that allowed for public hunting. The interpretation of this data was assisted by Ann Runyard, GIS Analyst for the Wisconsin DNR. The query left only land that was available for public use of hunting. The data was then compared to the buffered urban class and an erase was applied, then to the buffered roads class and an erase was applied.

Data Utilization:
After all the data was prepared and the necessary feature classes were discerned, the data was finally able to be narrowed down to show only suitable public hunting land that fell within the proximity of a waterbody. To do this, an intersect was applied to show only data that met both of the necessary criteria of falling within the Sauk Water buffer and DNR huntable land. This left the answer to the initial query of the most suitable places to hunt publicly in Sauk County.

Cartographic Preparation: A proper map was created with a map to show location of the county within Wisconsin. A data flow model was also created to show workflow for the project.

Figure 2: Data flow model for the assignment. This shows the work flow carried out in order to achieve the desired output data.

Discussion
While I felt that most of this data provided a solid view of the potential lands to hunt in Sauk County, I also feel that more data would have better suited this query. It should be noted that the main focus of this project had deer in mind when the analysis was conducted. I feel that data regarding deer density would have helped show the most suitable land with the most potential for an encounter. There was data regarding deer management zones, but as they are not bound by county boundaries the data regarding density would have been skewed. I also feel that I ran into some issues with the hydrology aspect of this project. There are many different feature classes that deal with streams, rivers, ponds, and lakes, but I could not find one that contained a majority of those, so I had to settle for two feature classes and perform an erase followed by a union. With the streams feature class, lines were shown where lakes were present between an input stream to the lake and a drainage stream. The line between the two is what was erased, leaving only the input stream until the boundary of the lake and the drainage stream from the boundary of the lake. If I had the ability to input real world data gathering measures into my data I would attempt to get an overall idea on how many deer are registered per deer registration station. This would give me a general idea about the number of deer in the area, instead of a large scale classification based on spatially large zones.

Results
This project showed my aptitude to solve a question using applied GIS. It was a comprehensive project showing all that I had learned and gathered from previous coursework. As this is a potential field that I would like to go in to, understanding how to apply various tools and what those tools do is a crucial component to furthering my career.

Sources
Ann Runyard - GIS Analyst, Wisconsin Department of Natural Resources

ESRI Geodatabase

Wisconsin DNR Geodatabase

GIS I - Lab 4: Vector Analysis with ArcGIS

The map to the left shows a comparison of suitable habitat for bear and the ideal suitable habitat for bear in Marquette County, Michigan. The ideal habitat falls within pre-existing Michigan Department of Natural Resources land management zones. A small locator map in above the map on the left indicates the location of the study area, within Marquette County, in the state of Michigan. The map on the right shows the land use for areas within the study area. 

Goal

Background: The goal of this assignment was to create a map based on suitable habitat for bear in Marquette County, Michigan.

Purpose: The purpose of the assignment was to gain a better understanding of how to utilize and analyze data in order to properly show how the data relates.

Methods

Step One: In order to begin gathering the necessary data for this lab, a file geodatabase was created for storing feature class data. A Microsoft Excel table, containing XY coordinates for bears being monitored through radio tracking, was converted to be used in ArcGIS. This was done by adding the coordinates as an "event theme" in ArcMap, selecting "File" -> "Add Data" -> "Add XY Data" inputting the correct data, and exporting the final product to the file geodatabase. These points were then able to be saved as a feature class that provided the XY coordinates of all bears at the time of data collection.

Step Two: The next task was to prepare the data for analysis by running various tools to narrow down the search area for ideal bear habitat. The XY coordinates were spatially joined with a feature class showing landcover, in order to create a new feature class showing what landcover types bears were most likely to be found in. The results were then summarized to get a better, quantitative understanding of where bears were found.

Information was provided that stated that most bears seemed to be located near streams in order to have easy access to food and water. The stream feature class was selected and the boundaries surrounding the various stream segments were dissolved in order to create a single stream line. A buffer was created to encompass an area of 500 meters around the stream. Out of the sixty-eight bear locations recorded, forty-nine of the recorded locations were within the buffer created by the stream.

Both the landcover-bear spatial join and the stream buffer were intersected, showing the bear locations and landcover types within the boundaries of the stream buffer.

Step Three: With the newly acquired stream buffer showing bear locations and landcover, the next task was to use the acquired parameters to assess the best suitable habitat for bear management zones. This was achieved by first performing an attribute query to select the three most prominent landcover types that were inhabited by bears, determined by referencing the summarized table from the prior step. The newly clipped feature class was then dissolved so there were now unnecessary boundaries within the feature class. This selection was made into a feature class and the data was clipped from the stream buffer, to show the top three suitable habitats within 500 meters of a stream, where bears were present.

Step Four: After acquiring the data for the best suitable bear habitat within 500 meters of a stream, the next task was to compare this to current Michigan Department of Natural Resources management land, to determine the easiest place to expand upon a bear management program. The suitable habitat-stream buffer was intersected with the Michigan DNR lands in order to show all DNR land that fell within suitable lands within 500 meters of a stream. The resulting feature class was dissolved so there were no unnecessary boundaries.

Step Five: The final task was buffer urban areas so that potential bear management areas would not be found too close to urbanized areas. An attribute query was performed to isolate areas noted as "Urban" or "Built Up Land" and this was made into a feature class. Following this, the feature class was dissolved and a 5 kilometer buffer was applied to the feature to exclude areas within 5 kilometers of any urban areas. The buffer was then compared to the suitable lands within DNR lands within 500 meters of a stream. An erase tool was used in order to erase any data that fell within the urban buffer. This excluded all unwanted information and all that was left were the ideal areas for bear habitats, as noted in the left map, above.

Step Six: A proper map was created and a data flow model was also created to show workflow for the project.

Results

This assignment provided an opportunity to apply GIS to real world situations and queries. This was a particularly interesting assignment, as biogeography is a field that I am very interested in. Querying suitable bear habitats is a very applicable issue to work with. Many of the methods learned from this lab will be used to discern suitable hunting areas in the final project for this course.

Sources:
Michigan Geographic Data Library

GIS I - Lab 2: Downloading GIS Data

The image on the left shows the total population as population density per county with a darkening color ramp to illustrate higher values. The image on the right shows the number of 20-24 year olds per county described by percentage of total population.

Goal

Background: The goal of this assignment was to create a map based on data downloaded from the United States Census Bureau.

Purpose: The purpose of this assignment was to gain a better understanding of how to acquire data from various sources and use it for the creation of maps.

Methods

Step One: In order to collect the data needed to construct the maps, the United States Census Bureau website was used to initially acquire the data. Using the website, the data was found by narrowing down certain parameters. For this exercise, the "Basic Count/Estimate" for population was selected, followed by Wisconsin Counties, followed by the "Total Population". This data was downloaded as a zip file and the was unzipped to the lab folder. Two comma-separated values (CSV) files were  located in this file. One of these folders contained metadata concerning the data in the other CSV file. The other file contained all of the data, laid out in a table format. Both of these files were opened using Microsoft Excel and converted to an Excel Workbook by saving them as such. This allows the user to use the data in programs such as those in ArcGIS. 

Step Two: After converting the CSV files to Excel files, they were able to be utilized in ArcMap. First however, shapefiles were downloaded through the Census website. They were downloaded as a zip file like the CSV files, unzipped, and the shapefile was extracted and placed into ArcCatalog. From ArcCatalog the saved Excel files are able to be copied and placed into the ArcMap file with the newly downloaded shapefile. From here, a table join is conducted in order to link the Geo_ID field from the shapefile with the Geo#ID field from the Excel data. This can happen because the two attribute tables share this field, the field type, and the character with (the necessary requirements for a table join). 

Step Three: Now that a table join has been carried out, the ability to map population data is available. In ArcMap, the shapefile feature class is right-clicked, Properties is selected, Symbology is selected, and the Graduated Color option is used to distinguish between different classes of population. The data was classified into seven classifications based on natural breaks in the data collection, known as "Jenks" classification. Following this, the proper pieces were added to make the map understandable and useable by others. This included a North Arrow, Legend, Scale, Title, Author, and Source.

Step Four: The same work flow was carried out again for another set of data. The data was gathered from the Census Bureau, this time using the Age Group and Sex: 2010 dataset. It was downloaded as a zip file and extracted, added to a new layer within the same ArcMap file as the first, joined by the Geo_ID field and was then able to be manipulated. In order to properly show this data through map visualization the data needed to be normalized. The data for Age Group 20-24, Both Sexes, was normalized with total population to properly display the actual percent. The map was assigned a graduated color scheme and proper map items as described before.

Results
This exercise was able to show that there are many different places to find data that can be used for maps. It showed how to convert tables and implement them for use in ArcMap.  It also taught how to join tables for use in mapping. Finally, it familiarized me with the Census Bureau website, which is key to utilizing and analyzing demographic data for different purposes.

Sources:
Zach Hilgendorf
United States Census Bureau

GIS I - Lab 3: Utilizing GPS

This map shows various points, lines, and polygons gathered using
different methods, on the University of Wisconsin-Eau Claire campus.

Goal

Background: The goal of this assignment was to create a map based on Global Positioning data gathered through the use of a Trimble Juno SB handheld unit.

Purpose: The purpose of the assignment was to gain a better understanding of how to acquire data from the field and how to analyze that data for use in maps and potentially for other products as well.

Methods

Step One: In order to initiate the process of data acquisition through the use of a Trimble Juno SB handheld unit, a geodatabase was created through ArcCatalog. Feature classes were created in the form of points, lines, and polygons and a field named "Type" was assigned to each created feature class in order to assign attributes to the feature classes in the field. The geodatabase was given the coordinate system "NAD_1983_HARN_Wisconsin_TM meters". A blank map was opened in ArcMap and the features were added to the map, along with a "CampusImage" raster file. Different symbologies were assigned in order to make the features more aesthetically pleasing. 

Step Two: The map containing the feature classes that was just created then was uploaded to the Trimble Juno SB unit for deployment into the field. Using ArcMap, the extension to allow data management through ArcPad was activated. This menu allows for the uploading and downloading of data acquired from the Trimble Juno units that use the ArcPad program. The correct files were selected and they were uploaded onto the Juno units.

Step Three: In the field, various methods were used in order to demonstrate the various ways to collect data. In order to commence data acquisition, ArcPad was launched through the Trimble Juno, the correct map was opened, and the correct feature was selected for acquisition. Six polygons were recorded in this exercise, three of which were recorded using point averaging, and three of which were recorded using point streaming and each polygon's "Type" was specified in the field. Point averaging is a process where an initial point was recorded averaging three point in the surrounding area and mathematically placing one point, depending on the average of those three other points. This resulted in a series of connected, straight lines. Point streaming is a process of continuous data acquisition through recording data points every few seconds, resulting in a non-linear line. Issues can arise with both methods if the Position Dilution of Precision (PDOP) value is too high, increasing the likelihood that that the location of the data will be off. In the field on the day of acquisition, the PDOP value was significantly low, usually reading 1.4. A lower PDOP, especially anything below a 4.0 will provide significantly accurate data acquisition. 

Six point values were also acquired. In this exercise, three lamps and three trees were recorded and their "Type" was specified in order to gain a better understanding of the process. 

A line was also recorded in order to gain a better understanding of how to acquire line data through ArcPad. Instead of only taking a point average line in this portion of the exercise, I also retraced my steps using a point streaming method. The various methods can be seen, labeled, on the map above. Not much difference was noticed, other than swaying in my line that I walked. As noted before, the PDOP value was significantly low on this day, so the point streaming values seemed to be very accurate.

Step Four: After recording the data in the field, it was transferred back onto the computers used before to construct a map in ArcMap. This process involved reconnecting the Trimble Juno units to a computer, navigating to the correct folder, and copying the data into our class folders. Following this, the ArcPad Data Manager was used to check in the data, using the map that was created during the first part of the exercise. It should be noted that I encountered some initial errors here. When attempting to transfer the data back, an error message was received that told me that I was unable to check in the data because the map was not editable. After some troubleshooting, it was found out that at some point I had attempted to organize my lab folder and the original file was moved to within another file. This severed the connection between the initial geodatabase and the current data. The geodatabase was moved back to where it had been and the data transferred in the way it was supposed to.

Step Five: A map was constructed with all the recorded data and the necessary components for a distinguishable, cartographically and aesthetically pleasing map.

Results

Through this exercise, I was able to gain a much better understanding of multiple software components. I gained a better understanding of the construction of geodatabases and building feature classes through ArcCatalog. I learned how to transfer data to and from Trimble Juno SB units. I learned how to properly record data while in the field for multiple feature classes, using multiple methods. Finally, I learned some various methods to troubleshoot on the fly. For example, at the beginning of my data collection I was unable to get a satellite fix. A simple reboot of the system was able to rectify this issue. As stated previously, I had issues transferring my data back from the Juno units. I was able to rectify this by remapping my geodatabase back to its original location.

Sources:
Data Collection by Zach Hilgendorf on October 16, 2013
NAIP 201X

GIS I - Lab 1: Base Data

In this blog these will be referred to as:
 Top left to top right: Fig. A, Fig B, Fig C
Bottom left to bottom right: Fig. D, Fig. E, Fig. F 

Goal

Background: The goal of this project was to assess the features surrounding the future site of the University of Wisconsin-Eau Claire Confluence Project, or the Haymarket Site, a public-private collaboration focused on enhancing the cultural center of the city and university. The projected site is owned by the group Haymarket Concepts LLC, a group partnership between "Commonweal Development Corp., Market & Johnson Inc. and Blugold Real Estate LLC, a subsidiary of the UW-Eau Claire Foundation." 

Purpose: The purpose of this assignment was gain a better understanding of the use of base data (Civil divisions, zoning, PLSS, etc.) and make use of it to highlight key data points.  

Methods:

Figure A: In order to create the Civil Divisions Map, data was acquired from the Eau Claire County Geodatabase and the Basemaps in the ESRI Basemap Bank. This data was in the form of a polygon feature class that encompassed the entirety of the county and an satellite image of the world, zoomed into Eau Claire. Another geodatabase was created to house another feature class labeled pro_site (alias Proposed Site). This class was given an extra field labeled Parcel No in order to record the specific city data for the parcel numbers of the proposed site. This feature class, pro_site, was used in each Data Frame created, hereafter. Once the feature classes were created or added the colors were changed so that the Proposed Site stood out, the Civil Divisions were given distinct colors and made transparent, and a callout box was assigned to the location of the site in order to highlight where it was on a large scale view of the area. Finally, a legend was created to show the various data features and a scale was added to show distance in miles. Both the legend and scale were put onto a solid colored background so they were easier to view.

Figure B: In order to create the Census Boundaries map, data was acquired from the Eau Claire County Geodatabase and Basemaps in the ESRI Basemap Bank. ArcCatalog was used in order to acquire metadata to learn what Tracts and Block Groups were. To show relevant data pertaining to the location, data was added to show the population of people from the 18-21 year old age group normalized in comparison to square mileage. This shows how dense the population of people of the 18-21 year old age is per square mile, zoomed into the city of Eau Claire. The data was assigned a graduated color scheme where low density was designated by blue and high density was designated by red. Following this, transparency was applied to visual data in order to show the aerial image underneath to reference location. Finally, a legend was created to show the various data features and a scale was added to show distance in miles. Both the legend and scale were put onto a solid colored background so they were easier to view.

Figure C: In order to create the Public Land Survey System (PLSS) Features Map, data was acquired from the Eau Claire County Geodatabase, the City of Eau Claire Geodatabase, and the Basemaps in the ESRI Basemap Bank. This was a relatively simple map to create. After adding the county and city PLSS data, the color of the PLSS Quarter-Quarter section was altered in order to show the smallest visible increment provided and describe the location of Parcel 1 and Parcel 2. Finally, a legend was created to show the various data features and a scale was added to show distance in miles. Both the legend and scale were put onto a solid colored background so they were easier to view.

Figure D: In order to create the Eau Claire City Parcel Data Map, data was acquired from the City of Eau Claire Geodatabase and the Basemaps in the ESRI Basemap Bank. This was another simple map to create, as little data interpretation was required. However, proper aesthetic compositional skills were required. Properly coloring this map so all data sets were apparent was the only concern in this map. After setting all the colors to a noticeable color selection this map was complete. Finally, a legend was created to show the various data features and a scale was added to show distance in miles. Both the legend and scale were put onto a solid colored background so they were easier to view.

Figure E: In order to create the Eau Claire City Zoning Classes Map, data was acquired from the City of Eau Claire Geodatabase and the Basemaps in the ESRI Basemap Bank. This map required the use of ArcCatalog, as the use of metadata was required to properly assign values to the specific zones of the city of Eau Claire. After applying the Zoning Class feature class, each individual class was grouped together in the Symbology Tab of the feature properties menu. The grouped zones were given an alias (Commercial, Transportation, Residential, etc.) and then were assigned unique and different colors in order to easily discern one from another. Road lines were also used in this map and were assigned a color and line weight in the Symbol menu. Finally, a legend was created to show the various data features and a scale was added to show distance in miles. Both the legend and scale were put onto a solid colored background so they were easier to view.

Figure F: In order to create the Eau Claire Voting Districts Map, data was acquired from the City of Eau Claire Geodatabase and the Basemaps in the ESRI Basemap Bank. This was an easy map to create, as it required very few feature classes and labelling experience. After utilizing the city Voting Wards feature class, labels were turned on and a halo was placed around the Ward Numbers in order to make them stand out from the blue background. A scale was added to this map and a color background was put in place so it was easier to view. A callout box was assigned to the location of the site in order to highlight where it was on a large scale view of the area.

Results
Patterns: The only patterns noticed in this lab were found in Fig. 2 and Fig. 4. In Fig. 2 it can be seen that the main density of 18-21 year olds is located within the University owned grounds and those surrounding it (the Third Ward, Upper Campus, Water Street). It should also be noted that this trend extends to the area surrounding the proposed building site of the Confluence Project. The other pattern is noticed in Fig. 4, where different zones are apparent. The main zone in this map is residential, covering most of the map. The other prominent zones are Central Business, located mainly in the Downtown area, and Public Properties, located where there are parks or state natural areas.

Sources:

City of Eau Claire

Eau Claire County 2013

City of Eau Claire. (2013, 09 23). http://www.bis-net.net/cityofeauclaire/search.cfm. Retrieved from http://www.bis-net.net/cityofeauclaire/search.cfm 
Eau Claire Regional Arts Center. (n.d.). Confluence. Retrieved from http://www.eauclairearts.com/confluence/   
University of Wisconsin-Eau Claire. (n.d.). News @ uw-eau claire. Retrieved from http://www.uwec.edu/News/more/confluenceprojectFAQs.htm