GIS I Lab 2: Downloading GIS Data

Introduction: The purpose of Lab 2 is learn how to access and use data from online sources, particularly U.S. Census Data. Data is then used to create two aesthetically pleasing maps displaying various demographic data for Wisconsin, and create a web map accessible to members of the University of Wisconsin Eau Claire Geography and Anthropology Department.

Methods:

• Obtain Census Data - http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t
• I narrowed the search query to the topic People>Basic Count/Estimate>Population total, and the geography of Counties>Wisconsin.
• Two data sets were downloaded: P1 Total Population 2010 SF1 100% Data and P13 Median Age By Sex 2010 SF1 100% Data.
• Also downloaded, was a shapefile of the state of Wisconsin with the county boarders drawn in. This was found by selecting geographies, specifying County - 050>Wisconsin>All counties in Wisconsin, and clicking on the Map tab.
• To access the files, I unzipped them to the proper file location. 
• Format Data - Once the data was accessible, I formatted it so that it was compatible with ArcMap.
• The data sets came with excel files with the meta data and the tabular data. All the tables were fairly simple and compatible with ArcMap so I did not alter them at all. However, if I did this lab again, I would change some of the column names in the tabular sets so that the information it held was more obvious by the title. 
• The data sets were stored as comma separated files, so I saved them as Excel Workbooks.
• The reformatted data sets and the Wisconsin Counties shapefile were added to a blank ArcMap.
• Join Tables - The two data sets lack actual geography and spatial representation of Wisconsin Counties, and should be joined with the Wisconsin counties shapefile.
• Each data set was joined in separate data frames using the GEO#id.
• The tables have been joined, but the information from the data sets had been imported as string field types. I added a new field, type Double, and used the field calculator to move the data over.
• Map the Data
• Two data frames were used to generate two maps on one layout, each displaying one of the data sets.
• The first map displays the population of each county (quantities, graduated colors, Jenks Natural Breaks).
• The second map shows the median age of each county (quantities, graduated colors, Jenks Natural Breaks).
• To view the maps created, check out Figure 1!
• Creating a Web Map
• I copied the maps I had just made into a new file and deleted the Total Population Data Frame so that I could create a web map with just the Median Age information.
• After logging onto ArcGIS Online inside of Arc Map, I published my map as a feature service to the UWEC Geography and Anthropology page on Esri Enterprise.
• The service is titled Wisconsin_Demographic_Information_Kubishak
• To enable popups on my map, I set the capabilities to Feature Access instead of Tiled Mapping.
• Then I published the service.
• Once the feature service was in the cloud, I could create a web map.
• The popup windows were configured to only display the county name and median age.
• This was then saved as a web map and shared with the UWEC Geography and Anthropology Department.
• If you are part of the UWEC Geography and Anthropology Department, click here to view my web map!

Results: Personally, I found the results of the maps generated in Lab 1 quite interesting.

• Total Population Map - As expected, Milwaukee County has the highest population in the state, as it is also the largest metropolitan area. Close behind are Waukesha and Dane counties, an extension of the Milwaukee metropolitan area, and the state's capitol, respectively. Overall, counties with larger populations tend to be in the the south-east corner of the state. Counties highlighted in other parts of the state correspond with larger cities; Brown county and Green Bay, Marathon county and Wausau, Portage county and Stevens Point, Wood county and Wisconsin Rapids, La Crosse county and La Crosse, St. Croix county and Hudson, Eau Claire County and Eau Claire, and finally Chippewa County and Chippewa Falls. Based on this trend I was surprised however, that Douglas county did not light up because of Superior.

• Median Age Map - I found the results of this map very interesting. Overall the range of median age is 31-51 years old, with the majority of counties in the oldest median age bracket (48-51) located more north. Many of the counties highlighted in the first map with greater total populations had the youngest median ages (31-35), notably Milwaukee, Dane, La Crosse, Eau Claire, and Saint Croix counties. Dunn county and Menominee county also fall in the lowest median age bracket. UW Stout is located in Dunn county and may play a role in bringing the median age down. Menominee county has a rather large Native American reservation and could be populated by more families, lowering the median age. However, more research should be done investigating the role colleges and reservations play on median age. All of the counties in the oldest median age bracket are in the the lowest population total bracket. As a native Wisconsinite who has traveled Wisconsin extensively, my experiences have stereotyped many of these counties as "up north" vacation destinations and retirement communities, notably Buffalo, Pepin, Bayfield, and Door Counties, so their bracketing made sense to me. I do not have hold that stereotype for Juneau and Adams counties in the central sands area however, so they surprised me by being in the oldest age bracket. 

Figure 1

Sources: U.S. Census 2010

GIS I Lab 1: Base Data

Goal and Background: The goal of Lab 1 was to use and display the GIS skills I have learned in class thus far on a project relevant to my student status at the University of Wisconsin Eau Claire (UWEC).
The Confluence Project is an arts performance and display space and student housing complex geographically positioned at the confluence of the Eau Claire and Chippewa Rivers in Eau Claire, Wisconsin, and at the social confluence of UWEC and the greater Eau Claire community. The project would be funded by both public and private sources, and thus was a rather political issue, voted on in November 2014.

Many materials needed to be generated to conceptualize the project, and anticipate potential political hurtles in City Council and in the voting population. Lab 1 put me to work recreating some of those materials. 

Methods: Six maps were generated; EC County Civil Divisions, Census Boundaries, PLSS Features, EC City Parcel Data, Zoning, and Voting Districts. For each of the six maps, an Imagery base map was used. A legend, compass, and scale was included in each map. The proposed site is highlighted and labeled in yellow in each map. For the maps scaled out beyond 1:140,000, a callout box was created labeling and pointing to the proposed site. EC County Civil Divisions map was overlain with the county civil divisions and symbolized categorically showing municipality type. Census Boundaries is overlain with the US Census blocks and symbolized by quantities, 2007 population normalized to square miles. PLSS Features is overlain with the quarter-quarter PLSS of the area, and highlighted with neon-green outlines for visibility. EC Parcel Data is overlain with the parcel lots in the City of Eau Claire, symbolized as solid polygons, and centerlines in blue for visual reference. Zoning was overlain with the land zoning for the city of Eau Claire, and categorically symbolized by zoning class. Classes were simplified into six broader categories. Centerlines were also drawn on for visual reference. Voting Districts is overlain with the voting districts for the City of Eau Claire, and labeled by ward number. Wards were also symbolized categorically using the ward number for the unique values. This allows a for better visual representation of the outlines of each ward.

Results: The resulting map, Figure 1 (displayed below) shows all 6 maps in separate data frames on a single layout. Eau Claire County Civil Divisions shows the geographic location of the proposed site; Eau Claire County, Wisconsin in the city of Eau Claire. Census Boundaries shows the population density of the area according to the 2010 US census. The Confluence is in an area with 3600-5000  people living per square mile. PLSS Features shows that site is located in NE 1/4 NW 1/4 of Eau Claire Section 20. EC City Parcel Data map shows which legal parcels will be combined to create the Confluence. Zoning describes the present zoning of the site and the sites around it. The parcels are presently zoned as Central Business District among other Central Business District parcels. Voting Districts labels the voting districts in the city of Eau Claire, to aid in campaigning for Confluence approval.

Figure 1

Sources: City of Eau Claire and Eau Claire County 2013