Add the feature layer called minn county cancer profiles

This tutorial describes one type of analysis of areas of influence that can be performed using ArcGIS Online and Google Sheets. This example examines potential differences in incidence rates for all cancers in counties surrounding natural-gas-fired power plants in the USA versus counties outside those areas.

Literature Review

Risk Factors
It is usually not possible to know the exact causes for any specific incidence of cancer. However, there are a variety of risk factors that increase the probability that a person can develop cancer, including:

and death rates to behavioral differences (like smoking), as well as differences in availability of medical care (early screening), and demographic differences.

Natural gas emits 50 to 60 percent less CO2 compared to coal-fired plants and produces very limited amounts of the sulfur, mercury, and particulate pollution commonly associated with coal-fired plants. Accordingly, natural gas is considered to be more environmentally-sensitive than coal, and switching from coal to natural gas is associated with public health benefits (UCS 2018).

While there appears to be little literature on cancer risks associated with the burning of natural gas, the increased production of natural gas to fuel the move away from coal is associated with increased air pollution, and those pollutants are linked to cancer (UCS 2018; Vogel 2017).

National Cancer Institute. 2018. "Cancer Statistics." Accessed 12 October 2018. httpwwcancer/understanding/statistics.

National Cancer Institute. 2018. "Risk Factors for Cancer." Accessed 12 October 2018. httpwwcancer/causes-prevention/risk.

Question and Hypothesis

Our research question is, "Do counties surrounding natural-gas-fired power plants have higher rates of cancer than areas outside those areas of influence?"

Our cancer data source is the "State Cancer Profiles, Incidence Rate Report for United States by County, 2010-2014," from the Centers for Disease Control and Prevention and The National Cancer Institute. The data may be accessed

Map the Counties

demonstrates the creation of a choropleth using a cancer rate feature layer already created in this ArcGIS Online organization. Note: When searching, be sure to select ‘My Organization’ and use ‘cancer’ in the Search box. Add the feature layer called ‘Minn 2010-2014 County Cancer Profiles’. Change Style to symbolize by the field called ‘All Cancers Incidence Per 100K’.

Filter Points

Task 3 – In the same map, adjust your new Power Plants layer to only show natural gas power plants and adjust symbology to a 12 point black dot.

Task 4 – Using the ‘Perform Analysis’ button, find existing locations of counties that are within 25 miles of a power plant.

To isolate the counties around the power plants that we hypothesize may have higher cancer rates, under your ‘County Cancer Profiles’ layer, click the Perform Analysis button, then, Find Locations, Find Existing Locations analysis tool. gives the specific steps.

5.Show credits to make sure you haven't made a mistake that will use up yourcredit quota. Credits required over 10 generally indicate some problem.

6.We symbolize them by cancer rate with a red color ramp so the will stand out in the map. Click the Change Style button and using the Counts and Amounts option, select the ‘Cancer Incidence per 100K’ field and use a reddish color ramp. Ensure that your color ramp shows the higher incidence of cancer rates as darker red (invert if necessary).

Task 5 – Save your map, give it a meaningful title, tags, summary. Then share it with

Please also include your URL address bar and the date and time stamp in the lower right hand

corner of your screen.

1)Under each layer, click the table button. Navigate to the fields mentioned above and left click on the field name.

2)Choose Statistics. An information box appears.

Because our confidence intervals overlap and we cannot be certain that the cancer rate in counties over 25 miles from a natural-gas-fired power plant is actually higher than the rate inside 25 miles, we have corroborated our hypothesis that counties within 25 miles of natural- gas-fired power plants will not have significant differences in overall cancer rates than the rest of the USA.

1.Create a new Google Sheets spreadsheet from Google Drive. Give it an appropriate name above the menu bar (something like ‘Natural Gas Power Plant Counties’ – your name).

2.In the first row, type in the field names for the Counties, Cancer Rates, and MOE (or Margins of Error). Along with entries for Gas and Non-Gas Counties as new records in Column A. Enter the numbers as shown below:

This adjusts the bars so they look very similar. Note: Your chart may ALREADY be drawing this way.

14.Move the chart to its own sheet and publish the chart to get a shared link or an iframe – With the chart selected (shown with blue handles), click the three small dots in the corner and click Publish Chart. Select Chart 1 and Click Publish and OK. Copy the URL and save if you’d like, but its not necessary for the lab.

Data Quality Problems

Survey data subject to a number of issues that affect its accuracy. For example, areas with sparse populations limited medical facilities might underreport cancer diagnoses to researchers. People in some areas might also be suspicious of or unresposive to researchers, resulting in less-accurate data.

Human mobility is also a problem. Because people usually change locations during the day (home, work, school) and occasionally relocate residences, the effects of carcinogen exposure may not become visible in the area where the actual exposure took place.

This is a variation on the where changing the areas used for analysis can change the results of the analysis, even if the underlying phenomena is the same.

Temporal Lag

For example, power plant emissions might be expected to have a greater impact oncertain organs, such as lungs or breasts. Since the analysis in this example used a cancer rate that included all different types of cancer, increased rates for specific types of cancer might get diluted in the aggregated value.

The Small Numbers Problem

Assuming that correlation indicates causation is the post hoc fallacy, from the Latin phrase post hoc ergo propter hoc(after this, therefore because of this). A logical s an often plausible argument using false or invalid inference.

As described in the literature review, cancer has numerous risk factors. In this example, we only looked at one of those variables (proximity to power plants). A more sophisticated analysis would consider covariates in an attempt to determine how important each one of those variables are in explaining different county cancer rates. Common analytical techniques used to do this include , and

For example, a regression model might consider not only whether a county is near a natural- gas-fired power plant, but also incorporate covariates that quantify lifestyle risk factors like obesity rates, rates of regular consumption of fruits and vegetable, and rates of smoking. The resulting model would contain coefficients that indicate the relative percentage of influence on cancer rates that can be attributed to each of the variables.

The ecological fallacy is the use of aggregated statistics to explain individual situations.

© 2000-2019 Michael Minn or respective copyright holders.