Mini Term Project

Introduction

            The area I chose to study is Eau Claire County, Wisconsin. I chose this area of study because I have spent many hours in the County Parks and wanted to create a project that involved an area that I was quite familiar with. The purpose of my project was to create a map that displayed areas in the county that are the most prone to having fires. The two main areas that I wanted to study where the railroads and the County Parks. My intended audience is for people who plan to visit the local county forests and state parks. The reason that this research is relevant is that county parks receive large numbers of visitors every year and many of these visitors who come to these destinations create campfires. The park visitors should be aware of the potential risks of fire in the areas in which they plan to camp. Another audience that this research would be pertinent to would be fire response groups located in Eau Claire County as the final map displays regions within the county that are the most prone to experiencing fires. After importing the fire locations layer, it was evident that the railroad was an area of great importance as in the areas that surround the railroads have seen many fires over the years.

Data

            Data for this project was acquired through the University of Wisconsin-Eau Claire department server. I used data from the Wisconsin Department of Natural Resources to display the locations of previous fires as well as the locations of County Parks. I also used data from the Wisconsin Department of Natural Resources to show the locations of railroads, lakes, rivers and streams in Eau Claire County. Concerns about the data include questions on the spatial accuracy of the fire locations as well as the size and intensity of the fires themselves. The fire locations did have the necessary dates so I felt confident in the legitimacy of the temporal accuracy of the data.

Methods

            To begin my project, I imported a layer that contained the counties of the United States and selected Eau Claire County and then created a new a layer for the country. I then imported the Rivers, Lakes, Fire Locations, Railroad, and County Parks layers. Once all the layers were into ArcMap and clipped them all individually by Eau Claire County Shapefile creating new layers that only contained data within Eau Claire County. Once I had all of the necessary layers overlaid over the Eau Claire County, I merged the Rivers and Lakes layer to create a single layer that contained all the water present in the county. I then applied a 500 meter buffer to this layer because these areas are less susceptible to fire. Once the buffer was created, I used the dissolve tool to smooth the buffer feature and get rid of all the excess polygons contained in the buffer. I then erased the river and lake buffer from the Parks layer leaving me with areas in the parks that were at least 500 meters from water bodies. I then intersected the Fire Locations layer with the Parks layer. With this layer now containing the locations of fires, I then classified the parks by the number of fires in each park. I then placed a 200 meter buffer around the Railroad layer. After placing the buffer around the Railroad layer, I then and spatial joined the Fire Locations layer by all the fires that were within the 200 meter buffer that I placed around the Railroad Layer. After placing the Fire Locations in the correct places, I deleted the excess Fire Locations from the Map. What was left was all the Fire Locations contained in the 200 meter Railroad buffer and inside the County Parks Layer. 

Results

            The map below shows the County Parks that have had a 200 River and Lake buffer erased from their shapefile. The County Parks are also classified by the number of fires that have occurred in the park along with the locations of the fires. The map also displays the locations of fires within 200 meters of the major railroad tracks. Based on the locations of the fires across the county, it was very evident that the County Parks and railroads were a large contributor prevalence of fires in Eau Claire County. Lake Eau Claire State Park has the highest number of fires contained within the borders of the park 

Evaluation

            The Mini Term project was a great experience that truly helped grow my GIS skills. Through the use of different geoprocessing function such as Buffering, Merging, Intersecting and spatial joins, GIS has the ability to solve and visualize problems in the “real-world” through the means of modeling. This project solidified the belief that GIS are a very valuable tool solving many complex problems in the world today across a vast number of different disciplines and fields. 

Working through my own project and having to problem solve, helped solidify the skills that I learned throughout the semester in Geography 335. It helped me develope a spacial question to a hypothetical problem and work through the challenges of using multiple different geoprocessing tools and functions solving these problems proved to be invaluable to the implementation of concepts covered throughout the course. As well as providing valuable experience working with different databases. If I were to do this project over again, I would choose a larger area of study so that a greater number of spatial variables could be applied and create a more complex and well-rounded project.

Sources

Data for this project was acquired through the University of Wisconsin-Eau Claire department of Geography and Anthropology server as well as mgisdata that came with the Price book located in the GIS class’s lab folder (geogsql.uwec.edu)

Image from: http://borealforestfacts.com/wp-content/uploads/2015/09/ForestFires.jpg

Lab 5: Vector Processing

Background and Goal

The goal of this lab was to solve geospatial questions using a variety of different geoprocessing tools and Python Scripting.

Methods

To begin the lab I was given the task of finding suitable bear habitat in Marquette County, Michigan. To begin I brought in the land use, bear locations, and streams. The bear locations did not display the locations of the bears, to do display their location on the map I assigned the layer's table X and Y values that gave the location of the bears. After the location of the bears had been established, I intersected the bear locations layer and the land use layers. Once the two were intersected I summarized the table by land use type and the number of bears located within each. Once I found the top three land use areas for the number of bears found within, I selected those top three land use types and created a new layer. Another requirement for bear habitat was to be located within 500 meters of a stream, so I created a 500 meter buffer around the streams layer and intersected it with the newly created land use layer that contained only the top 3 bear habitat areas, giving me a new layer. Another requirement for the bear habitat was that it needed to be with DNR controlled areas. I brought in the DNR lands layer and intersected with the layer I had just created giving a new layer that contained lands that were within 500 meters of a stream, were the top 3 land use areas for the location of bears, and the qualifying DNR lands. The last requirement for the bear habitat was that it needed to be 5 kilometers from urban areas. To do this, I opened the attribute table of the original land use layer and selected urban areas and created a new urban land use layer. I then buffered this layer and erased it from the layer that contained the previous parameters giving me a final layer that showed the DNR managed lands that were in the top 3 bear habitats, at least 5 kilometers from urban development and located within 500 meters of streams. I then used that layer to create the map below.

Data Flow Model for the map created below

Bear Habitat Map created from the steps discussed above

The next section of the lab involved writing python script to complete geoprocessing functions. The first task was to find lakes that would be suitable for the establishment of lakeside resorts. I wrote scripts that would place a 10 mile buffer around the major cities in Wisconsin. I than ran another script that gave me all of the lakes found within this 10 mile buffer.

Python Script used to create a 10 mile buffer around major cities in Wisconsin

Map showing lakes that qualify as potential locations for lakeside resorts

After creating a map that displayed air pollution hazard zones along interstates. I wrote script that created a buffer around the interstates at 1,2,3,4,5,6 mile intervals. Once the buffer was created I categorized the buffer zones from high to low risk by proximity to the interstate.

 Python Script used to create the buffer around the Interstates

Air Pollution Hazard Map for the State of Wisconsin

Sources

Michigan Department of Natural Resources (DNR) and Esri.

Price, Maribeth. 2016.  Mastering ArcGIS. 7th Edition data. McGraw Hill

Wilson, Cyril 2012, A comprehensive Lake features for Wisconsin, Unpublished data.

Lab 4

Goals and Background:

This lab was designed for students to show their ability to use the query function in ArcMap. Students had to use Boolean Operators to create multiple criteria queries to extract data from file databases. Students also had to create spatial queries from the databases as well as mapping the results of their queries.

Methods:

To begin the lab, I imported the appropriate shapefiles from the class folder. I was given the task of developing an attribute query that would display counties in the United States that had a population between 4,000 and 3,000, and a population density of at 1,000 people per square mile. To do so I created the following query using the Select by Attributes tool in the attribute table (POP2010 >3000 AND POP2010 <4000) OR POP10_SQMI >1000. Once I selected the data, I then created a new data layer that included the newly selected data and created the map below (Figure A).

The next query I was assigned was to show counites that had a male population higher than female population and a senior citizen population of more than 6,500 in Wisconsin, Texas, New York, Minnesota, and California. The query I developed was (("STATE_NAME" IN ('California', 'Minnesota', 'New York', 'Texas', 'Wisconsin’)) AND AGE_65_UP >6500 AND MALES > FEMALES). I again created new data layer and created a map displaying the information extracted from the query (Figure B).

The next section of the lab involved both multiple criteria attribute queries as well as spatial queries. This section of the lab also focused on the state of Wisconsin instead of the entire United States. To begin, I imported the correct Wisconsin shapefile that contained all the relevant data to this section of the lab. I had to develop a query that would contain cities that had a population between 20,000 and 15,000, a land area of at least 5 square miles, a higher female population than male population, and be within 2 miles of a lake. I first created an attribute query that would give me all the information I needed with the exception of distance from a lake. The query I created was ("POP2007" >15000 AND "POP2007" <20000) AND ("AREALAND" >5) AND ("FEMALES" > "MALES"). I then created a new data layer that contained the information retrieved from the query. From that data, I then created a spatial query that would select cities within two miles of a lake and created a map containing the new layer (Figure C).

The final section of the lab was for me to create a map that showed the Chippewa, Eau Claire, Embarrass, Fisher, Hunting, Kinnickinnic, Mauneasha, Milwaukee, Moose, Namekagon, Pelican, Platte and Potato rivers and calculate their distances. I created the query ("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'). Because the river data layer had no information in regards to the length of each river I created a new field in the attribute table and calculated the rivers lengths giving me a total length of 759 miles. I displayed the rivers in the map as shown below (Figure D).

Results

The maps below were created in ArcMap using the queries discussed above.

Figure A

Figure B

Figure C

Figure D

Credits

USA geodatabase data is from the mgisdata that comes with the Price book. Wisconsin cities, interstates, rivers, roads, and counties shapefiles are from ESRI, 2011. Wisconsin Lakes were created by Dr. Wilson in 2012.

Lab 3

Background and Goals
The objective of this assignment was to display the ability of downloading online data and creating maps from the corresponding standalone tables. This assignment is also designed to give experience in obtaining data from the United States Census Bureau's website and applying that data in both static and dynamic maps.

Methods
To begin this lab, I went to the United States Census Bureau’s website to download data about Wisconsin's counties for year 2010. Once on the American Fact Finder page, I navigated to the Advanced Search menu. From there I searched 2010, total populations, Wisconsin Counties, and the Wisconsin shapefile repectedly. Once searched and selected, I downloaded the 2010  SF1 100% population data. I then repeated the previous steps but instead of searching for population, I searched for vacant housing under the occupancy tab in the topics section.

Once the data was downloaded, I edited the data in Microsoft Excel.  To do so I formatted the populations and vacancy columns to numbers so that it could be brought into ArcMap. In the case if vacant house I created a separate column in which I divided the total number of houses by the number of vacant houses to find the percentage of vacant houses per county. The reason I used the percentage of vacant houses instead of the total number of vacant houses was because I wanted to show the percentage of vacant houses. If I were to use the total number of vacant houses the data would have been skewed by counties with larger populations.

Once the Excel spreadsheets were formatted correctly I converted the spreadsheets to excel worksheet files (xlsx) where they could then be brought into ArcMap. In ArcMap I brought in the Wisconsin shapefile and the newly formatted spreadsheets as standalone tables. I then joined the standalone tables to the shape file by their common attributes in a one to one relationship. Once the tables were joined, I created a new geodatabase to store the new feature classes I was going to create using the Wisconsin shape and its joined tables. I created a new feature class for both the 2010 populations and vacant housing files. I then opened a new map document and imported the new feature classes from the newly created geodatabase. I then classified the new feature classes by quantities in the symbology tab in feature properties and selected an appropriate monochromatic color scheme for each. Once I edited the ranges of the classifications I created maps for the two feature classes in the layout view.

Once I had my static maps completed my next task was to take my vacant housing feature class and create dynamic map on ArcGIS online. I signed into ArcGIS by using my UWEC enterprise account in ArcMap. Once signed in I used the Share As menu where I published my map as a service connected to the (UW-Eau Claire – Geography and Anthropology) hosted service.

Once the map information was uploaded to ArcGIS online, I opened a new web browser and signed into my enterprise account in ArcGIS online and went to the My Content tab. Once in My Content I added my feature layer that I imported from ArcMap into an online dynamic map.

Results
The first two maps below were created using the layout view in ArcMap using the data collected from the U.S. Census Bureau. The third map was created via ArcGIS online using the imported feature class from ArcMap.

Sources
Data Access and Dissemination Systems (DADS). (2010, October 05). American FactFinder
      Search. Retrieved March 08, 2017from                                                                                         https://factfinder.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=
       

Lab 1

Background and Goals:

   The design of this lab was to demonstrate knowledge of geographic and projected coordinate systems. In addition, troubleshooting potential problems when uploading files from geodatabases/project folders that do not have a geographic coordinate system or do not have the same coordinate system as other layers in the folder.

Methods:

    To begin this lab I created six separate data frames. Each data frame was to display a different projected coordinate system. I labeled each data frame to the specific projected coordinate system that I wished to display.

    The first coordinate system that I was to display was the geographic coordinate system without adding a projection coordinate system. To do so, I imported the country shapefile from the World folder. Along with the country shapefile, I imported the geogrid shapefile that would serve as a reference when comparing the different projection systems. Once imported, I checked the geographic coordinate system for both files by looking in the files properties in the Table of Contents. After confirming the two files where both in the GCS_WGS_1984 geographic coordinate system, I moved on to the next section of the project which entailed projecting the previous shape files into different projected coordinate systems.

    The next task I was assigned was to project the country and geogrid shapefiles into  projected coordinate systems. I selected individual data frames and imported the two shapes and confirmed they had the same geographic coordinate systems. Once I confirmed the two files had the same geographic coordinate systems, I could then proceed in changing the individual data frames into different projected coordinate systems. To do so, I opened the Data Frame Properties tab for each individual data from and changed the projection to the appropriate projection system under the coordinate system tab. For the North American Lambert Conformal Conic data frame I was asked to import the states.shp and stroads_miv5a.shp shapefiles and then project the data frame into the North_America_Lambert_Conformal_Conic projection coordinate system. Once I confirmed that the two shapefiles had the same geographic coordinate systems  GCS_North_American_1983, I could then project the data frame into the correct projection system being the North_America_Lambert_Conformal_Conic projection system by selecting it it under the coordinate system tab in the Data Frame Properties menu.

    The next section of the project was to show the ability to add a projected coordinate system to a shapefile that does not have a projected coordinate system. To do this I imported the Central_WI_Cts and the Lower_Chip_strms shape files from the Central Wisconsin folder. Both of these file shared the same geographic coordinate system GCS_North_American_1983. Because the two files had the same geographic coordinate system, I could then project the two the data frame into the NAD_1983_StatePlane_Wisconsin_Central_FIPS_4802_Feet projected coordinate system by using the define projection tool located in the Projection and Transformations tab of the data management tools. I chose this projected coordinate system because the counties contained in the  Central_WI_Cts shapefile are located within the Central Wisconsin Zone in the State Plane Coordinate System.

Results:

    The following maps were created by using the previous data in the layout view of ArcMap. The different data frames were arranged and labeled according to the projection used in each data frame. Once the layouts were finished the files were exported as jpegs.  

Sources:

University of Wisconsin Eau Claire [downloadable file]. URL: https://uwec.courses.wisconsin.edu/d2l/le/content/3572845/viewContent/22681789/View