Downloading GIS Data

Introduction:

The goal of this lab was to learn how to access, download, and map data from the U.S. Census Bureau. I was responsible for understanding the U.S. Census data and then picking a variable of my choice to download and map on ArcMap.

Methods:

I began this lab by familiarizing myself with the 2010 results published by the US Census Bureau. I also explored the different topics, geographies, and datasets that one could use within the websites search menu.

Objective One - I had to download the total population for all counties within the state of Wisconsin. Once I downloaded and unzipped that information then I had to save the csv files as an Excel Workbook. After that task was accomplished the files that I downloaded only contained tabular data, which means that the information is not associated to the geography or spatial representation for the Wisconsin counties.

Objective Two - At this step I ran into some terrible maneuvering through the US Census information. I had to switch the year in the search menu because the map tab under geography would not recognize the 2014 dataset. When I went to download the information the website was having trouble processing my request. After getting through that issue I finally was able to download the data. Then Internet Explore did not recognize the websites information so I had to change the computers settings so it would recognize the files that were being processed on the US Census Bureau’s website. Finally, I was able to download the files without any kinks.

Objective Three – I started a new blank map in ArcMap and uploaded the 050_00 shapefile and the P1 table onto the map. Next I examined the attribute tables for both the shapefile and the P1 table. In order to join the two tables I had to determine which attribute field the two tables had in common. I found the both the 050_00 shapefile and the P1 table had the GEO_ID field in common. You cannot join two tables together without determining a common attribute field to base the join off of. After that I conducted a table join between the 050_00 shapefile and the P1 table. I this point I had successfully uploaded an MS Excel file directly into ArcMap and performed a table join.

Objective Four – Once I completed joining the tables I was able to proceed and map the specific information that I was interested in. When I went into the symbology tab to map the total population for Wisconsin counties I ran into an error. The field type that I was interested in mapping could not be mapped quantitatively. In order to correct this problem I had to go ahead and add a field to the 050_00 shapefile attribute table. I renamed the new field as D001new. Then I had to use the field calculator tool to populate my newly created field. Once the D001new field was calculated then I could move forward and map Wisconsin’s total population by county quantitatively.   

Objective Five – For this objective I was responsible for selecting a variable of my choice from the U.S. Census Bureau and mapping it. Originally I wanted to map the characteristics of veterans who either have served or are currently serving in the United States Armed Forces. However, when I looked at the dataset none of the statistics were produced in the 2010 SF1 100% data, which forced me to pick another variable. I decided to download data by Sex and Age from the Census website. Specifically I chose the 21-year-old sample for males because my brother just turned 21, and I was curious what the male 21-year-old demographic looked like across Wisconsin. Throughout this objective I followed the same steps that I did in objectives one through four. I will admit that after already producing a map in this fashion it was easy to replicate another map.    

Results:

The results from the total population map shows that mostly counties that are highly urbanized have a higher concentration of inhabitants. For example, Dane County and Milwaukee County appear to have the highest population concentration when compared to the rest of Wisconsin. As for the map depicting 21-year-old males in Wisconsin, the results are similar to the total population map but a few differences are apparent. For example, Douglas County, Eau Claire County, and La Crosse County now are comparable to that of Dane County and Milwaukee County. This makes sense because these five counties are home to some of the biggest private and public educational institutions throughout Wisconsin. Which makes these five counties highly populated with 21-year-old male students.       

Figure 1:

Figure 1 Shows population statistics by counties in Wisconsin. The map on the left depicts Wisconsin’s total population by county. The map on the right depicts the male population that falls within the 21-year-old age bracket. 

Source: U.S. Census Bureau Website

http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t

Lab 1 - Eau Claire Confluence Project

Background

For this lab I had to assume the role as an intern for Clear Vision Eau Claire. While interning at Clear Vision Eau Claire they announced that they were establishing a public-private partnership between local developers, University of Wisconsin-Eau Claire, and the Eau Claire Regional Arts Center to construct a new development. This new development earned the title as the Eau Claire Confluence Project. In collaboration with the other partners, Clear Vision Eau Claire has asked the interns to apply their knowledge and skills with GIS to create base maps that feature several components relating to the confluence project. These components include maps with voting districts, land use, civil divisions, census boundaries, and more.    

Goals

The goal of this lab was to become familiar with various spatial data sets for a variety of uses. Some of these uses included land use, public land management, and administrative use. Another goal of this lab was to prepare base maps for the Eau Claire Confluence Project.

Methods

I began by focusing my attention on studying the 2009_07_13_EauClaire Geodatabase and the City of Eau Claire Geodatabase. Within these geodatabases I became familiar with the feature datasets, in order to understand the topology and location for which I would be directing my attention too.

Next, I established a new geodatabase for the proposed site of the confluence project. Once the EC_Confluence geadatabase was created I used that information, as well as parcel areas from both the City of Eau Claire and Eau Claire County. From the parcel areas I was able to locate the two buildings using the Identification tool. Then I went ahead and digitized the two buildings that are located at the proposed site of the confluence project. This task was made easy due to the functionality of the Snapping tool and the formation of the polygon features that represent the two buildings.

Objective number three called for understanding the Public Land Survey System (PLSS), as well as representing this information in a data frame at the proposed site of the confluence project. In this objective I used PLSS feature from the geodatabases and then using the Identification tool I was able to read about the PLSS features, which contained numerous information including the legal descriptions. This information was valuable, but to further understand the legal descriptions I read a section from the link of the Wisconsin State Cartographer's Office.

In the next objective I created a brief legal description for the two parcels of the proposed site of the confluence project. To gather the parcel ID and the parcel number I had to use the Identification tool. Once I gathered the necessary values I went the City of Eau Claire's Property and Assessment Search Website. At this website I entered in the parcel ID for each property and read through the legal descriptions.

In the final objective I was asked to create six separate data frames, or base maps, that contained relevant information concerning issues related to the Eau Claire Confluence Project. During this final objective I built a date frame of the Civil Divisions that make up the demographic of Eau Claire County. Since this data frame is zoomed out I used the Drawing tool to create a callout label for the proposed site, in order to give the audience an appreciation for the civil divisions.  The callout label indicates the location of the proposed site and makes it easier for the audience to see the spatial data represented. In a new data frame I mapped the Census Boundaries. In this data frame I used the POP2007 as my value field and then I normalized the data by SQMI. Next I created the PLSS map by utilizing the PLSS features from the geaodatabases. This data frame was fairly easy to construct because I had become familiar with the datasets in an earlier objective. Somewhere during this last objective I got a sidetracked and ended up needing to rely on Dr. Hupy’s YouTube video display. I was glad I could apply the lessons learned in this informative video to all my data frames. The City of Eau Claire parcel data was the next frame I created. In this frame you can see that I relied a certain features, like water and centerlines, to emphasize the spatial information being portrayed. Next, I made a Zoning map to illustrate the different types of land use in and around the area of the proposed site for the confluence project. Also, in the Zoning map I placed a centerline feature for a reference and to emphasize the spatial patterns. Last, I created a data frame that contains information of the voting districts of the City of Eau Claire. After all the base maps were created I conducted edits to each frame to make it cartographically pleasing, in order to be easily understand by the intended audience.   

Results

After much work and research about the proposed site for the Eau Claire Confluence Project you can examine the results by studying Figure 1 below.

Citations

Hemstead, Brenda. "PLSS - Legal Descriptions | PLSS." PLSS - Legal Descriptions | PLSS. Wisconsin State Cartographer's Office, 20 Mar. 2014. Web. 19 Feb. 2015.

http://www.sco.wisc.edu/plss/legal-descriptions.html

"Eau Claire, WI - Online Property Assessment Database - Search." Eau Claire, WI - Online Property Assessment Database - Search. Eau Claire Wisconsin, n.d. Web. 19 Feb. 2015. 2014.

http://www.bis-net.net/cityofeauclaire/search.cfm

Hupy, Christina. "Lab 1 Example." YouTube. UWEC, 17 June 2013. Web. 19 Feb. 2015.

http://youtu.be/p5UZYebNqJU

Source: City of Eau Claire and Eau Claire County 2013