Lab 4: Personal Final Project

Goal:
As the final lab of the semester and the GIS 1 class in whole, we were tasked with applying all the skills we learned over the semester to each create our own final project. The final project would involve answering a question each of us formulated by looking at several collections of data provided to us. Using data provided from the Wisconsin DNR, I was able to gather data on reported invasive plant species locations within state forests. However, this data was largely limited to only the state forests of Wisconsin, while the nearby county forests remained largely unsurveyed. From this, I determined my research question. What areas of county forests were vulnerable to infestation from nearby state forests? The goal of the question was to identify these vulnerable areas in relation to the nearby infested areas so they could be properly monitored. This data would ideally be available to anyone on the state or county level that was interested in the preservation of natural biodiversity in these forests. Hopefully, the local DNR could use it to effectively survey and mange the local county forests.

Data and Source:
All of the data used in this lab was provided by the Wisconsin DNR. The data used includes a state forest feature class, a county forest feature class, a roads feature class, the reported invasive plant species locations within state forests, and the surrounding county and state lines. The problem with much of this data is that its to focused to be viewed at a statewide level. In order for an accurate and visible map to be generated, the map needed to focus on one specific county. Additionally, this specific county needed to county both designated state and county forests, which counties within Wisconsin didn't have. For this reason, Jackson County of Wisconsin was selected to be the area of interests. Additionally, estimating area for the infected locations presented difficulties, as these points lacked true values for the area infested, opting for rough ranges and estimates of area. Because of this, a 100 meter buffer was performed on the reported invasive plant locations to estimate area.

Methods:
In order for the any of the data to be used, it first needed to be cut to the Jackson County borders. This was done for the county forests, state forests, roads, and invasive species locations (SF Invasive) in order to create a focus area and to speed up load times. Afterwards, a 100 meter buffer was performed on the SF Invasive feature class to estimate the area infected. This buffer was intersected with the state forests feature class. Afterwards, the dissolve tool was used to remove internal boundaries, creating a feature class showing the area with reported invasive plant species in state forests. Additionally, the total area infested 100 meter buffer was also intersected with the county forests, as several of the locations reported did in fact lie beyond the boundaries of state forests and within the boundaries of the county forests. From this intersect, a feature class was created showing the county forests that were already reported as infected.
From previous knowledge of invasive species, it was believed that roads could assist in unintentional transportation. In order to verify this, a spacial query was performed to see if a significant portion of reported locations lay within 100 meters of roads. Indeed, a significant portion did. Because of this, it was necessary to take roads into consideration. A two mile buffer was performed on the SF Invasive feature class in order to create a logical range outside of the state forests where more invasive plants may exist. The resulting feature class was run through the buffer tool to decrease processing time. Then, the two mile buffer was intersected with a 100 meter buffer of the road feature class, as it was previously determined that a majority of invasive species locations lay within 100 meters of a road. This was then intersected with the county forests feature class to create a logical search area within county forests. The county area already known to be infected was erased from this using the erase tool, creating the final resulting area within county forests that was suspect of infection and should be checked.
With this completed, a map of Jackson County was created in order to highlight these suspect areas, showing them in relation to the county forests, state forests, roads, and the areas already known to be infected. A smaller reference map was also created to show Jackson County in relation to the rest of the state of Wisconsin. Furthermore, a data flow model was created showing the full process, minus the original clipping to scale, used to create the feature classes present in the final product.

Results:The resulting map shows that much of the county forest areas should in fact be surveyed for invasive plant species. According to the map, all of the area around the roads leading into the eastern county forest from the west should be surveyed, as well as as the areas around the roads leading into the central county forest from the north, east, and west. Small patches of these areas already show infestation. This is relatively unsurprising, given the presence of invasive plant species throughout the state forests of Jackson county.

Post-Project Evaluation:
Overall, I believe that this project served as an excellent way to creatively put together all of the GIS information and map building skills learned over the course of the semester. It provides a fairly accurate visual representation of the suspected presence of invasive plant species within state and county forests. If asked to repeat this project, I would wish to perform it with all the counties from which information was readily made available, as the project was limited in scope in order to prevent it from becoming overwhelming for a single individual. Additionally I faced challenges in creating realistic representations of areas already infected and for creating a reasonable search window for the areas suspect of infection. This is due to the fact that the area infected was based on an estimate I largely estimated and the 2 mile search window was arbitrarily decided after several buffers were performed. If this project were to be performed in the future, I would prefer to gather accurate area measurements for areas infested with invasive species and not have to base it around a estimated buffer performed on a series of roughly 850 points.

Sources:
Data Source: Wisconsin DNR

Hupy, C. (2016). Lab 4: Mini-Final Project. Eau Claire, Wisconsin.

Lab 3: Vector Analysis with ArcGIS

Goal:
The goals of this specific lab were to introduce various geoprocessing tools and to create a map based on a given scenario using the data we were given along with various geoprocessing tools such as overlay, buffer, erase, and dissolve. The use of python code was introduced as well, demonstrating another way for the tools to be operated.
Background:
The scenario given was as follows. The DNR had issued the task of determining suitable bear habitat from within a study area contained in Marquette County, Michigan. Not only did the habitat need to fit the criteria necessary for it to be considered suitable for bears, it also needed to be contained within the DNR's preexisting management areas and exist at least five kilometers beyond any urban or built up areas within the county. All of the data was downloaded from the State of Michigan Open GIS Database, including the landcover, DNR management areas, and streams data.
Methods:
In order for the bear locations file to be analyzed within ArcGIS, the locations needed to be added as a XY theme event, as bear locations originally existed as an Excel Table File. This was done by choosing "File, Add Data, Add XY Data" from the main menu, and selecting the bear locations excel table and matching the XY coordinate fields within the table with the XY coordinate selectors. The coordinate system was set to the NAD 1983 HARN Michigan GeoRef (Meters) in order to eliminate distortion within the final product. This was then exported as its own feature class to save these modifications. This, along with the streams, landcover, study area, and DNR management areas was added to an empty map document.
A special query was performed to check for the importance of streams within bear habitat. A special query was performed to check if reported bear locations were near streams. A spatial query showed that a significant and majority portion of bear locations were within 500 meters of streams. As of such, this area was considered important to our final product. To determine what landcover bears primarily were found within, an intersect was performed with the bear locations and landcover feature classes. The resulting feature class was summarized based on the minor type field to determine what three landcover types bear were most prevalent within. The Mixed Forest Land, Forested Wetlands, and Evergreen Forest Land types were determined to be the most bear prevalent of the minor types. With this information, a query was performed to select these specific areas from the landcover feature class. A layer was created from this selection.
Since the area within 500 meters of streams was shown to contain a significant number of bear locations, a buffer tool was performed on the streams feature class of the area within 500 meters of streams. The output of this operation, along with the selection from the landcover feature class, was input into the intersect tool to create the proper bear habitat feature class. A dissolve tool was run on the bear habitat in order to clean up the internal boundaries. In order to make sure the bear habitat was within the DNR Management Zones, an intersect tool was run with the bear habitat and the DNR Management feature classes, with the output feature class being run through the dissolve tool to remove the internal management zone boundaries.
The final criteria for the management areas involved them being five kilometers or further from Urban or built up areas. Another query was performed to select the necessary areas from the greater landcover feature class, with the selection being added as its own layer. The selection was then run through the buffer tool to create the area five kilometers around all urban areas. This buffer feature class and the bear habitat within the DNR management zones was then run through the erase tool, with the input feature being the current habitat management area and the erase feature being the urban area buffer.
A cartographically pleasing map was created to show the final Bear Habitat Management Area in relation to the overall bear habitat outside of the management zones, including what was within urban areas, all bear locations, and the entire area of study. A data flow model was also constructed to show the entire string of tools used to arrive at the final map results. In addition, some of the previous steps in the process were performed with python in order to familiarize us with python coding, the results of which can be seen below.
Results:

 The map shows that although there is much viable bear habitat, bears tend to cluster to the to the west of the central longitude of the study and to the north of the central latitude of the study. However, the only viable DNR management areas lie along the central longitude of the study area, both from west to east. Much of the management area to the east if far from any reported bear locations, and many of the bear locations within the center of the study area falling too close to urban areas. I would recommend the DNR expands its area of management to the far northwestern corner of the map, in which many bears exist far away from the urban areas to the southeast.

Sources:

Hupy, C. (2016). Lab 3: Vector Analysis with ArcGIS. Eau Claire, Wisconsin.

In State of Michigan Open GIS Data. Retrieved May 6, 2016, from http://gis.Michigan.opendata.ArcGIS.com/

DNR Management Units sub-link: http:www.dnr.state.mi.us/spatialdatalibrary/metadata/wildlife_mgmt._units.htm

Landcover sub-link: http://www.mcgi.state.mi.us/mgdl/nlcd/metadata/nlcdshp.html

Streams sub-link: http://www.mcgi.state.mi.us/mgdl/framework/metadata/Marquette.html

Lab 2: Downloading and Displaying GIS Data

Goals:

The purpose of this lab assignment was to teach us students how to access, download, use, and manipulate mat data frames which include demographics data taken from an online source. In this particular assignment, 2010 SF1 100% demographics data was obtained from the American Factfinder website of the U.S. Census Bureau. The data we were tasked with obtaining and working with was Wisconsin county demographics, in this particular case population, and a variable of individual choosing. Anything was a viable choice, so long as it was available in the 2010 SF1 100% data format, although we were advised to steer away from race demographics, do to the complexity of race data, as individuals can list themselves of on of up to eight races. Afterwards, we were tasked with publishing the population demographics data as a pop-up style map and sharing it with the ArcGIS UWEC Geography and Anthropology organization.

Methods:

I began with visiting the American Factfinder website of the U.S. Census Bureau. Using the search made available by the website, I located and downloaded a 2010 SF1 data set under the title TOTAL POPULATION. I extracted the files into the work folder I specifically designated for this lab. I opened and viewed both the tabular data and metadata CSV files in Microsoft Excel, and after determining that this rather simple data set required no major changes to make it compatible with ArcMap, saved the tabular data as an Excel file. I opened the file into ArcMap and checked the attribute table for errors that may have been created in the transfer process, finding none. I returned to the American Factfinder website and downloaded a Wisconsin counties shapefile.zip file and extracted its contents into the lab folder holding the data retrieved earlier. I created a blank map document in ArcMap and added both the Excel and shapefile to the map. I opened the attribute tables of both the shapefile and the tabular excel data and proceeded to construct a table join between the two, using the GEO_ID and GEO#id data columns as the basis of the join. After verifying the join, I created a new field in the shapefile attribute table with the field type of Double. Using the field calculator tool, I set the new field to equal that of the D001 field, which held the county population data. I did this because the original D001 filed was of the string field type, which could not be mapped as a graduated colors map, like the new D001 double field. I proceeded to map the new D001 field as such with seven population classes.

I then returned once again to the American Factfinder website and obtained data for a variable of my choosing for mapping and comparison. In my particular case, I chose to search for data containing population demographics of different individual age groups. I wished to know the population percent of individuals between the ages of 20 and 24 in various counties, the same age category I fall within. After I found the data I was looking for in the 2010 SF1 format, I downloaded and unzipped it into its own sub-folder within the larger lab folder I set up specifically for this lab assignment. I opened and viewed both the tabular data and metadata CSV files of the age demographics data. Within the tabular data CSV file, I discovered and removed an extra field descriptor row that would have prevented proper viewing in ArcGIS before converting it to and Excel file. Within ArcGIS, I created a second data frame for the viewing of the age demographics data and added both the age demographics Excel table and the shapefile from before into the new data frame. Using a similar join which also utilized the GEO ID fields, I joined the attribute tables of the age demographics data and the county shapefile. I converted the field total population of the age group I sought to analyze (ages 20 to 24) to a double field using the same method as before, by creating a new field of the double type and using the field calculator to set the data of the new field. Once this was completed I created a graduated colors map of the percent population of individuals between the ages of 20 and 24 by using the newly created field and normalizing with the total population field already present in the shapefile attribute table. Once both of these maps were constructed, I created a special layout displaying both data frames along with corresponding titles, legends, north and scale markers, the proper state level coordinate systems, and citations with each data frame displayed against a topography basemap for reference. I organized all of this using proper map construction rules that I have learned previously.

Afterwards, I created a feature service of the population demographics map shared it as a service to the ArcGIS online account I was given and had properly signed up for earlier by the University of Wisconsin, Eau Claire Geography department. From my ArcGIS online account, I constructed a pop-up map that would display both the county name and population as each county was clicked on. As per instructed, I shared this map privately with the UW-Eau Claire – Geography and Anthropology organization.

Results:

From this lab assignment, I learned the fundamentals of downloading and utilizing demographic data from credited sources. I learned how to properly set up and convert the data I had retrieved into a usable format that could be opened within ArcGIS programs. I also learned how to join the fields from two attribute tables together and what form the fields need to be in so the data could be manipulated into maps that revealed important information and patterns. This data could then be exported to external locations for viewing by others, like my population demographics map.

As to the data's results, my population data frame map showed a population concentration around city centers and to the east and south-east portions of the state. In addition, the age demographics map revealed surprising results. At first, it seemed to favor city centers but with less focus than the population map. However, certain counties showing a high percentage of of individuals between 20 and 24 years of age when compared to the counties immediately neighboring them. This seemingly random pattern stumped me at first, until the pattern suddenly hit me as I was viewing my current home county. This pattern was displaying counties which held college or university campuses within their borders.

Sources:

Hupy, C. (2016). Lab 2: Downloading GIS Data. Eau Claire, Wisconsin.

In American Fact Finder. Retrieved April 8, 2016, from United States Census Bureau.

Lab 1: Working with Base Data of the Confluence Project

Goals:

The goal of this first introductory lab was to familiarize ourselves with working with various data sets in Arc GIS and learn the basics of digitizing and manipulating these data sets. This was done by acting the part of a Clear Vision Eau Claire Intern, digitizing the lot sites for the Eau Claire Confluence Project, a new development between local developers, UW-Eau Claire and the Eau Claire Regional Arts Center. I was tasked to prepare base maps containing all relevant data surrounding the Eau Claire Confluence and organize these maps in a clear and concise manner.

Methods:

The first thing I did was create a blank geodatabase, renamed for the Eau Claire Confluence, and a new featured class labeled Proposed Site into this geodatabase. From there, I imported the Eau Claire County Coordinate System from a census feature, giving the new feature class the appropriate coordinate system. From there, I opened the up the proposed site feature class along with the base imagery and parcel area datasets. Using a provided aerial image of the Confluence Project and by using the parcel area lot lines as an outline, I digitized a polygon feature for the Proposed Site Feature Class. Using a PLSS Quarter-Quarter dataset from the city, I learned about the township and range system for determining the location of a plot specifically for the Confluence Project.  The legal outline used to assist in the digitizing and the location identification was taken from the City of Eau Claire,Wisconsin’s mapping website.

With the newly digitized proposed site area, I constructed six data frames to show the relation of the Confluence Project to various geographic features. A Civil Divisions data frame, showing the Confluence Project in relationship to the municipality type of the surrounding area. A Census Boundaries data frame, showing the relationship to the surrounding population density of Eau Claire. A PLSS Features data frame, showing the relative PLSS Quarter Quarter divisions. An Eau Claire Parcel data frame, showing the surrounding lot parcels. A Zoning data frame, showcasing the zoning class and distribution of the surrounding area. And one final data frame, Voting Districts, to show the surrounding voting districts. These data frames were all constructed to be cartographically pleasing, with scale markers in all data frames, and constructed into a singular map with legends for all the necessary data.

Results:

This lab project resulted in the creation of the map seen below. From creating this map, I learned of the process of digitizing data and creating as aesthetically pleasing and concise map to show relevant data related to a desired feature class. I also learned of the legal descriptions involved in creating such a parcel taken from a city parcel system.

 

Sources:

City of Eau Claire and Eau Claire County. Retrieved March 11, 2016, from

http://www.eauclairewi.gov/departments/public-works/engineering/mapping-services

Hemsted, B. (2015, March 18). PLSS - Legal Descriptions . In Wsconsin State Cartographer's Office. Retrieved March 11, 2016, from http://www.sco.wisc.edu/plss/legal-descriptions.html

Hupy, C. (2013). Lab 1 Example [Online video]. Retrieved March 11, 2016, from

http://youtu.be/p5UZYebNqJU

Hupy, C. (2016). Lab 1: Base Data. Eau Claire, Wisconsin.

In Community for the Confluence. Retrieved March 11, 2016, from

http://communityfortheconfluence.org