Chapter 30 Activity 14: Area Data VI

NOTE: The source files for this book are available with companion package {isdas}. The source files are in Rmarkdown format and packed as templates. These files allow you execute code within the notebook, so that you can work interactively with the notes.

30.1 Practice questions

1. Describe and discuss the possible sources of autocorrelation in the residuals of a model.
2. List possible corrective/remedial actions when residual autocorrelation is detected.
3. Under which situations is a Spatial Error Model an adequate modeling strategy?

30.2 Learning objectives

In this activity, you will:

1. Explore a dataset with area data using visualization as appropriate.
2. Discuss a process that might explain any pattern observed from the data.
3. Conduct a modeling exercise using appropriate techniques. Justify your modeling decisions.

O’Sullivan D and Unwin D (2010) Geographic Information Analysis, 2nd Edition, Chapter 5. John Wiley & Sons: New Jersey.

30.4 Preliminaries

It is good practice to clear the workspace to make sure that you do not have extraneous items there when you begin your work. The command in R to clear the workspace is rm (for “remove”), followed by a list of items to be removed. To clear the workspace from all objects, do the following:

rm(list = ls())

Note that ls() lists all objects currently on the workspace.

Load the libraries you will use in this activity (load other packages as appropriate).

library(isdas)
library(sf)
library(spatstat)
library(spdep)
library(tidyverse)

Choose a data set with area data that interests you. These are two possibilities:

30.4.1 New York leukemia data

data("nyleukemia")

A SpatialPolygonsDataFrame that contains the following variables:

• AREANAME name of census tract
• AREAKEY unique FIPS code for each tract
• POP8 population size (1980 U.S. Census)
• TRACTCAS number of cases of leukemia (1978-1982)
• PROPCAS proportion of cases per tract
• PCTOWNHOME percentage of people in each tract owning their own home
• PCTAGE65P percentage of people in each tract aged 65 or more
• Z transformed proportions
• AVGIDIST average distance between centroid and TCE sites
• PEXPOSURE “exposure potential”: inverse distance between each census tract centroid and the nearest TCE site, IDIST, transformed via log(100*IDIST)

This can be converted to a simple features object as follows:

nyleukemia.sf <- st_as_sf(nyleukemia)

30.4.2 Pennsylvania lung cancer

data("pennlc")

A SpatialPolygonsDataFrame that contains the following variables:

• county: Name of the county
• cases: Number of cases of lung cancer
• population: Population by county
• rate: Lung cancer rate by county
• smoking: Smoking rate by county
• cancer_ rate: Lung cancer rate by county (%)
• smoking_rate: Smoking rate by county (%)

This can be converted to a simple features object as follows:

pennlc.sf <- st_as_sf(pennlc)

30.5 Activity

Capstone Activity

This is a capstone activity where you can work free-style on a data set of your choice, and put in practice what you have learned with respect to the analysis of area data.

1. Partner with a fellow student to analyze the chosen dataset.

2. Visualize and explore the dataset using appropriate tools.

3. Analyze your dataset by means of regression modeling. Which should be the dependent variable in your dataset? Why?

4. Discuss the results of your analysis, including possible limitations, and possible ways to improve it (e.g., what additional variables would you like to use?)