Question being asked

How do rurality indicator variables shift as distance increases away from Texas counties with high colorectal cancer (CRC) mortality?

In exercise 1, I used principal component analysis (PCA) to create a PCA-weighted rural index of the state of Texas using 3 scaled variables: population density, land development percentage, and median income. In this exercise I applied these same variables to determine how they change as distance increases away from the 4 Texas counties with the highest CRC mortality rates. To do this, I created multi-ring buffers around Anderson, Gonzales, Howard, and Newton county and computed averages of each rural indicator variable for each successive buffer “donut.” I hypothesize that as distance increases away from high CRC county centroids, rurality indicator measures will have more “urban” values (i.e. higher population density, higher percent developed, higher median income) and CRC mortality rates will decrease.

Tools and Data Sources Used

For this exercise, I utilized the intersection, feature-to-point, and multi-ring buffer tools in ArcGIS along with the latticeExtra/gridExtra plotting packages in R. The rural indicator data used in this analysis are from the same sources I used in Exercise 1: Texas county median household income (2010 Census Data), Texas county population density (2010 Census data), and rasterized discrete data of land use for the state of Texas (2011 National Land Cover Database). These data were then scaled using the same procedure from Exercise 1, which can be found here.  Aggregated CRC mortality rates for Texas counties were obtained for the years 2011-2015 from the National Cancer Institute’s Surveillance, Epidemiology, and End Results Program U.S. Population Data – 1969-2015.

Methods

Attribute Table Wrangling: The Texas county indicator variables were linked to county polygons in my Exercise 1, but cancer mortality data was not. For polygon linkage in this exercise, I imported the mortality data excel sheet into Arc and used the join procedure to insert the data into the existing attribute table (with indicator variables) for county polygons.

Centroid & Multi-ring Buffer Creation: First, I utilized the point-to-feature tool in Arc to create a layer of county centroid points from the county polygon layer. Once the county polygons had been converted to centroids, I identified the 4 Texas counties with the highest CRC mortality rates. Then, using the select features tool and multi-ring buffer procedure, I selected each of the 4 counties separately and created multi-ring buffers at 50, 75, and 100 Km. These distances were chosen based on the size of the selected counties and the size of the full state of Texas.

Intersection & Donut Summary Statistics: Once the multi-ring buffer layers were created, I intersected each of the 4 buffer layers with the original county polygon layer containing all relevant variables. Then, mean via the summary statistics tool were computed in Arc for population density, percent developed land, and median income for each successive donut in the multi-ring buffers. The computed tables of buffer donut means for each variable and county were then exported to Excel files.

R Plotting: The Excel files were then imported into R and line plots of buffer means by distance were created using the xyplot function within the latticeExtra package. Plots were then combined into figures by county using the gridExtra package.

Results

This figure of scaled population density means for county multi-ring buffer donuts indicates varying trends for population density between the 4 high CRC counties. Two counties have increasing population density as distance increases away from centroids, and two have decreasing population density as distance increases away from centroid. Only the buffer map for population density was presented above for post conciseness and space limitations on this blog site. More specific neighborhood relationships between CRC death rates and all indicator variables can be seen in the line plots and explanations below.

Line Plots of County Indicator Variables Over Buffer Distances

The above plots display with more specificity than the buffer map that areas surrounding the 4 counties have differences in indicator variable trends as distance increases away from county centroids. Both Anderson and Newton counties largely follow the trend hypothesized: as distance from the county centroids increases, rural indicator variables have more “urban” values and CRC mortality rate decreases. For Newton county, this trend does not hold for median income, because as CRC mortality decreases away from the county centroid, median income also decreases. For the other other 2 counties, Gonzales and Howard, the hypothesized relationship does not hold, because as distance increases away from county centroids, the rural indicator variables become more “rural” as CRC mortality decreases. This indicates that the associations between CRC mortality and rural indicator variables are complex and that neighborhood analysis does not capture all relationships.

Critique

This sort of neighborhood analysis was effective at determining trends in rural indicator variables in the areas surrounding high CRC mortality counties in Texas. The buffer map produced great broad results, where more generalized trends can be determined. The line plots specifically were highly useful for visualizing more specific changes in indicator variables and CRC mortality rates over distance. These results should be considered in the light of some limitations. First, county level data was used for all variables and the buffer donuts may be too large or too small to capture the true neighborhood relationships in the analysis, as statistical procedures were not utilized to determine the distances. Further, more buffer donuts may have been useful to see more nuanced trends over distance. Secondly, as can be seen in the buffer map, there is a lack of CRC mortality data for many counties in west and southern Texas due to data suppression in order to preserve patient confidentiality. This presents significant bias in result interpretation, especially in Howard county, where many of the counties surrounding it have suppressed CRC mortality data.

My future analysis of this data will likely be a comparative confusion matrix of the PCA-weighted index I created in Exercise 1 and CRC mortality data used in this exercise.