A brief visual primer for the mapping of mortality trend data

Abstract

Maps are increasingly used to visualize and analyze data, yet the spatial ramifications of data structure are rarely considered. Data are subject to transformations made throughout the research process and then used to map, visualize and conduct spatial analysis. We used mortality data to answer three research questions: Are there spatial patterns to mortality, are these patterns statistically significant, and are they persistent across time? This paper provides differential spatial patterns by implementing six data transformations: standardization, cut-points, class size, color scheme, spatial significance and temporal mapping. We use numerous maps and graphics to illustrate the iterative nature of mortality mapping, and exploit the visual nature of the International Journal of Health Geographics journal on the World Wide Web to present researchers with a series of maps.

Figure 1

Age-Adjusted Mortality 1993–1997 Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 2

Unadjusted Mortality Rates 1993–1997 Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 3

Age Adjusted Mortality Rates 1993–1997 Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 4

Age/Sex Adjusted Mortality Rates 1993–1997 Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 5

Age/Sex/Race Adjusted Mortality Rates 1993–1997 Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 6

Age/Sex Adjusted vs. Unadjusted Low Mortality Rates 1993–1997 Blue = Age/Sex Adjusted White = Normal and Low Mortality Orange Outline = Unadjusted Rates

Figure 7

Age/Sex Adjusted vs. Unadjusted High Mortality Rates 1993–1997 Red = Age/Sex Adjusted White = Normal and Low Mortality Purple Outline = Unadjusted Rates

Figure 15

Change in Mean Across Standardization Model

Figure 8

United States Mortality: 3 Class Quantiles Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 9

United States Mortality: 3 Class Natural Breaks Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 10

United States Mortality: 3 Class Standard Deviations Red = High Mortality White = Normal Mortality Blue = Low Mortality

Figure 11

United States Mortality: 5 Class Natural Breaks Red = Highest Mortality Light Red = High Mortality White = Normal Mortality Light Blue = Low Mortality Blue = Lowest Mortality

Figure 12

United States Mortality: 7 Class Natural Breaks Red = Highest Mortality Medium Red = Mid/High Mortality Lightest Red = High Mortality White = Normal Mortality Lightest Blue = Low Mortality Medium Blue = Mid/Low Mortality Blue = Lowest Mortality

Figure 16

Local Moran's I

Figure 13

Local Moran's I Scatterplot of Persistent Mortality 1993–1997 Red = High Cnty-High Cnty Light Red = High Cnty-Low Cnty White = No Significance Light Blue = Low Cnty-High Cnty Blue = Low Cnty-Low Cnty

Figure 14

Clusters of Persistent Mortality 1968–1997 Dark Brown = High Mortality (5-6/6) Medium Brown = High Mortality (4/6) Light Brown = High Mortality (3/6) White = Normal Mortality Light Green = Low Mortality (3/6) Medium Green = Low Mortality (4/6) Dark Green = Low Mortality (5-6/6) Red Outline = Unhealthy Clusters Blue Outline = Healthy Clusters Yellow Box with H inside = High Mort Clusters Yellow Box with L inside = Low Mort Clusters