GIS for Air Quality (Exercise 4)

Exercise 4

Another useful function of GIS is its ability to “join” external data in a spreadsheet or database to data in an existing map. To do this you must find a field that is common to both sets of data. In this exercise we will join lung and bronchus cancer death rates obtained from http://www.statecancerprofiles.cancer.gov/ with the county census data in an existing feature class. Both data sets have fields with county names. We will join those data using those county name fields. Then we will create color gradients to illustrate lung and bronchus cancer death rates in the state of Oklahoma and label the counties with the death rates. Since different states often have counties with the same name, we will create a new OK_Counties layer consisting of only Oklahoma counties from the national “counties” feature class. If you want to have more than one state you may need to manipulate the data to get FIPS codes in both files. A county’s combined state and county FIPS code is unique to each county in the US.

It should be noted that we are using the lower 95 percent confidence interval (CI) for the cancer data in this exercise. Health data have uncertainty associated with them due to the possibility of random chance impacting data. Larger populations have less uncertainty and small populations have more uncertainty due to random chance. So, the whole US has less uncertainty than the state; the state has less uncertainty than a county; counties with greater populations have less uncertainty than counties with smaller populations. The Lower 95% confidence interval applies statistics to each county and based on population size accounting for differences in random chance, and provides a number where we are 95% certain it is this number or higher. In essence, by using this number we are comparing each county cancer death rates in a way that statistically minimizes uncertainty due to population size. This could also be done by normalizing data within ArcMap.

Open ArcMap – Follow the procedures you used in the first exercises to open a new Blank map. Then, as done in the first exercise, set it to relative path and set the coordinate system for the data frame (right click Layers in the Table of Contents) to the same coordinate system you used in Exercise 1 (GCS NAD1983). Then save the map document as Exercise4 in the Course folder.

Now let’s add the “counties” feature class to the new map.

From the counties layer we will select counties in Oklahoma and use the export data function to save a new shapfile for counties in Oklahoma.

Save the map . You will need it later

This was just a brief introduction to joining health data to an existing map. There are several other ways to add data. Presenting data in a map form provides a picture summary that drives the point home in a way everyone can grasp quickly. Congratulations on completing Exercise 4! Exercise 5 is 3-d mapping. Please feel free to explore other ways of presenting this health data before moving on.

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