Dataset for: Cluster Detection of Spatial Regression Coefficients

Popular approaches to spatial cluster detection, such as the spatial scan statistic, are defined in terms of the responses. Here, we consider a varying-coefficient regression and spatial clusters in the regression coefficients. For varying-coefficient regression, such as the geographically weighted regression, different regression coefficients are obtained for different spatial units. It is often of interest to the practitioners to identify clusters of spatial units with distinct patterns in a regression coefficient, but there is no formal statistical methodology for that. Rather, cluster identification is often ad-hoc such as by eyeballing the map of fitted regression coefficients and discerning patterns. In this paper, we develop new methodology for spatial cluster detection in the regression setting based on hypotheses testing. We evaluate our methods in terms of power and coverages for true clusters via simulation studies. For illustration, our methodology is applied to a cancer mortality dataset

Joint Outcome Model Fit for Average Poor Mental Health Days and Average Poor Physical Health Days.

<p>^a Average poor physical health days are represented by the intercept (measure = 0).</p><p>^b Poor mental health days are represented by measure = 1. The national average across counties is estimated as the sum of the intercept and slope for measure.</p><p><u>Joint Outcome Model Fit for Average Poor Mental Health Days</u> and <u>Average Poor Physical Health Days.</u></p

Data Used in Longitudinal and Joint Outcome Models.

<p>* 2004–2006 data were used in joint outcome models of age-specific mortality.</p><p>Data Used in Longitudinal and Joint Outcome Models.</p

Univariate Model Fits for Ages 45–54, 55–64, and Ages 65–74 Mortality Rates.

<p>Univariate Model Fits for Ages 45–54, 55–64, and Ages 65–74 Mortality Rates.</p

Univariate Model Fits for Average Poor Mental Health Days and Average Poor Physical Health Days.

<p>Univariate Model Fits for Average Poor Mental Health Days and Average Poor Physical Health Days.</p

Choropleth Map of U.S. County Rank Performance in Fair or Poor Health Prevalence.

<p>Choropleth Map of U.S. County Rank Performance in Fair or Poor Health Prevalence.</p

Joint Outcome Model Fit for Age-specific Mortality Rates.

<p>Joint Outcome Model Fit for Age-specific Mortality Rates.</p

Joint Outcome Model Fit for Fair or Poor Health Prevalence and Percent of Births with Low Birth Weight.

<p>^a Fair or poor health prevalence is represented by the intercept (measure = 0).</p><p>^b Low birth weight is represented by measure = 1. Its national average across counties is estimated as the sum of the intercept and slope for measure.</p><p>Joint Outcome Model Fit for Fair or Poor Health Prevalence and Percent of Births with Low Birth Weight.</p

Choropleth Map of U.S. County Rank Performance in Low Birth Weight Births.

<p>Choropleth Map of U.S. County Rank Performance in Low Birth Weight Births.</p

Choropleth Maps of U.S. County Rank Performance in Premature Deaths.

<p>Results based on independent model (A), longitudinal model (B), and joint outcome model (C).</p