Introduction:
A tool like ArcMAP is invaluable for producing high-quality, effective maps. However, without adequate understanding of the process of transferring data and images from other sources into ArcMAP, quite the opposite may occur.
Lab 2 is an exercise meant to build familiarity with downloading data from the U.S. Census Bureau, transferring it to ArcMAP, and building maps with that data.
The U.S. Census Bureau
The U.S. Census Bureau is one of the main sources of concentrated data for the purpose of geographic comparisons (as well as for many other fields). Their mission "is to serve as the leading source of quality data about the nation's people and economy." More directly, U.S. Census data is used to determine Congressional seats in each state, "to make decisions about what community services to provide," and "to distribute more than $400 billion dollars in federal funds." Mastering the process of transferring data from this vast source of information is a key skill for map-makers to develop.
Before doing so, it is important to familiarize with the terminology used by the U.S. Census Bureau as well as any nuances to be aware of. To learn more about U.S. Census terms and processes please access the following links:
Exercise Objective
In this exercise, I will access and download from the U.S. Census Bureau. This data will be
transferred to ArcMAP in order to produce two maps reflecting this information.
Methods:
Downloading 2010 Census Data from U.S. Census Bureau
After navigating to the U.S. Census Bureau website at the following link
I completed the following steps to download data:
1. Choose Data Set & Geography
First, I selected Advanced Search (as seen in Figure 1).
Figure 1: This figure shows the U.S. Census Bureau American Fact Finder Home Page. By selecting Advanced Search (circled in red), you can choose both the data and geography specifications you desire.
This opens up a screen from which you can select your data set (topics) and geographies needed (as seen in figure 2).
Figure 2: This figure shows the selections from which data sets and geography specifications can be selected.
2. Choose Specific Survey Source
Once you have chosen you data set and geography specifications, you are provided with many different options to choose from as the specific source of information. The U.S. Census Bureau produces many different reports, so it is necessary to specify which report you would like to access.
3. Download Data Set
After selecting the appropriate source for this exercise, I simply had to select "Download" and specify where I wanted the file to be downloaded to.
4. Unzip Downloaded File
The file that is downloaded to your location of choice is delivered in the form of a zip file. After right clicking on the zip file in your folder, I chose to extract (or "unzip") all the data I had requested from the U.S. Census Bureau. After doing this there were several files containing the data I had selected.
5. Save CSV file as an MS Excel File
The files received from the U.S. Census Bureau come mostly in CSV files. After opening the CSV file containing my data, I chose to "save as" an xlsx file. This is an important step especially for combining data. In order to merge data, a program such as Microsoft Excel must be used. All of your data needs to be in the same format in order to be merged.
6. Download Shape Files from U.S. Census Bureau
Next, I navigated back to the U.S. Census Bureau geographies selection. The U.S. Census Bureau has shape files coincide with the data you select. For my purposes, I clicked on the map tab and made sure that all the regions of my data set were represented (all the counties of Wisconsin), clicked download, spatial data formats, and shapefile.zip. Just as was seen with the data set, I downloaded this zip file to my folder, extracted the files from the zip file, and saved them into my folder.
Joining Data Together in ArcMAP
Import shape file to ArcMAP
Next, I opened the ArcMAP program to a blank map, and imported the shape file of the counties of Wisconsin that I had downloaded from the U.S. Census Bureau as well as the Excel file I had saved containing my data set.
This shape file contained data pertaining to each county which could be seen by right clicking on the shape file and opening the attribute table. However, it was not connected to the data set information from the U.S. Census Bureau.
Table Join the Two Attribute Tables
By right clicking on my shapefile, I was able to open up Arrange Tables and link the attributes in my shape file to the attributes of my data set. The key was to identify the common attribute, GEO#id, the two data sets shared. Now, I had a combined data set to facilitate making maps.
Mapping Data
Map 1
Two maps needed to be produced for this exercise. The first one was based on population data imported from the U.S. Census Bureau. This entailed defining the symbology for a graduated color map.
By selecting the shape file in the Table of Contents, I was able to open up the attributes and choose the number of classes and color scheme to represent my data. I also adjusted the numbers and number breaks to make the map easier to read and interpret.
Map 2
For the second map, I had to go back to the U.S. Census Bureau Website and select data for a variable of my choosing and import it to a new data frame in the arcMAP table of contents. Following the same steps employed previously, I imported data that would allow me to portray the percentage of males age 25-29 in each county compared to the total population.
In order to do this, when I set the symbology, I had to normalize the number of 25-29 year old men in each county by the total population for each county.
I also had to adjust the format of the numbers associated with this data. Due to normalization, the numbers were in long decimal form. This was not easy to read or interpret. Therefore, I switched the number format to percentage. This made it a little better, but I still needed to "even out" the numbers for easy interpretation by adjusting the ranges and labels of my data.
Both Maps
Because both of these maps were of the entire state of Wisconsin I projected each data frame using NAD_1983_Wisconsin_TM. This Transverse Mercator Projection is specifically suited to the entire state of Wisconsin.
Also, I added a base map to each data from to provide a little area context and enhance the appearance of the map. These basemaps came with a separate file that included references connected to the areas being shown. As a result, these references were visible through my shape file map. I had to turn off the reference layer to avoid this problem.
Lastly, titles, north arrows, reference information, a scale bar, and a legend were added to each map. The legend shows numbers that were altered to make the map easier to interpret.
Results:
The following maps were produced as a result of this exercise.
Map 1
The first (as seen in Figure 3) shows the total population for each county in Wisconsin.
Figure 3: This is a map showing total population for each county in Wisconsin categorized into 6 categories. The major population concentrations are around Madison and Milwaukee especially in Dane, Waukesha, and Milwaukee counties.
I know that 6 classes is a little bit of stretch because colors can be hard to differentiate. Still, using Jenks Natural Breaks, counties with between 4,000 and 60,000 people were being categorized together. This seemed a bit of a stretch. By adding the extra class, I was able to cut that number in half which seemed far more reasonable. I carefully chose my color gradient, and judged whether or not I could distinguish the six different colors. Because of the way the data is dispersed, I found it to be quite easy, thankfully.
Highly populated counties are right where they would be expected - in the Madison and Milwaukee Areas, around Green Bay, Wausau, Lacrosse, and along the corridor between Eau Claire and the Twin Cities. Equally expected is the tendency for less populated counties to be found in the northern-most counties This map is more important as a reference point for the second map
Map 2
The second map (as seen in Figure 4) represents the percentage of males age 25-29 in all of the Wisconsin counties.
Figure 4: This map shows the percentage of 25-29 year old men in each Wisconsin county as a proportion of the total population. Similar patterns to what can be seen in Figure 3 exist with the most notable exception being Waukesha county.
Because this data needed to be normalized by the total population in each county, percentages are seen in the legend instead of raw numbers. I considered normalizing using the total male population, rather than total population, but I really was trying to identify where there were large concentrations of, potentially, young professional men. This would be an indicator of the areas where employment is being found, etc. The point was also to look at the "brain drain" concept where young people move to large cities with better paying jobs and a more vibrant atmosphere.
I realize I could have, and probably should have, just looked at all people age 25-29 rather than just men. I simply got pigeon-holed into looking at certain data and how to think about it. This map is not really a fair representation of what I wanted to look at for that reason.
That being said, I feel the idea of brain drain is fairly well-represented by this map, though it is definitely incomplete. Most of the counties with larger population centers have larger populations of 25-29 year old men as a proportion of the total population in the county. I would assume that adding 25-29 year old women would only serve to enhance this pattern. Still, it would be interesting to compare the two sexes to identify if there are areas young men or young women are going in greater proportion than the other and to identify the reasons.
Also, there is a noticeable pattern of fewer 25-29 year old men as a percentage of total population in many of the northern-most counties.
One thing that really started to develop more strongly to me by looking at this map was the tendency for larger percentages of 25-29 year old men along the major highway corridors from Milwaukee over to Madison and up through Appleton and Green Bay as well as from Madison up to Eau Claire and then over to the Twin Cities. Even in counties where there are smaller population totals in Figure 3, there are still some of the higher concentrations of 25-29 year old men. Mobility could be a key factor for this demographic even if they choose not to live in counties with large population centers.
Sources:
Factfinder2.census.gov. (2014). American factfinder - search. [online] Retrieved from: http://factfinder2.census.gov/faces/nav/jsf/pages/searchresults.xhtml?refresh=t [Accessed: 6 Mar 2014].
No comments:
Post a Comment