Current practices in cancer spatial data analysis: a call for guidance

There has long been a recognition that place matters in health, from recognition of clusters of yellow fever and cholera in the 1800s to modern day analyses of regional and neighborhood effects on cancer patterns. Here we provide a summary of discussions about current practices in the spatial analysis of georeferenced cancer data by a panel of experts recently convened at the National Cancer Institute

The crossroads of GIS and health information: a workshop on developing a research agenda to improve cancer control

Cancer control researchers seek to reduce the burden of cancer by studying interventions, their impact in defined populations, and the means by which they can be better used. The first step in cancer control is identifying where the cancer burden is elevated, which suggests locations where interventions are needed. Geographic information systems (GIS) and other spatial analytic methods provide such a solution and thus can play a major role in cancer control. This report presents findings from a workshop held June 16–17, 2005, to bring together experts and stakeholders to address current issues in GIScience and cancer control. A broad range of areas of expertise and interest was represented, including epidemiology, geography, statistics, environmental health, social science, cancer control, cancer registry operations, and cancer advocacy. The goals of this workshop were to build consensus on important policy and research questions, identify roadblocks to future progress in this field, and provide recommendations to overcome these roadblocks

Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public

BACKGROUND: To communicate population-based cancer statistics, cancer researchers have a long tradition of presenting data in a spatial representation, or map. Historically, health data were presented in printed atlases in which the map producer selected the content and format. The availability of geographic information systems (GIS) with comprehensive mapping and spatial analysis capability for desktop and Internet mapping has greatly expanded the number of producers and consumers of health maps, including policymakers and the public. Because health maps, particularly ones that show elevated cancer rates, historically have raised public concerns, it is essential that these maps be designed to be accurate, clear, and interpretable for the broad range of users who may view them. This article focuses on designing maps to communicate effectively. It is based on years of research into the use of health maps for communicating among public health researchers. RESULTS: The basics for designing maps that communicate effectively are similar to the basics for any mode of communication. Tasks include deciding on the purpose, knowing the audience and its characteristics, choosing a media suitable for both the purpose and the audience, and finally testing the map design to ensure that it suits the purpose with the intended audience, and communicates accurately and effectively. Special considerations for health maps include ensuring confidentiality and reflecting the uncertainty of small area statistics. Statistical maps need to be based on sound practices and principles developed by the statistical and cartographic communities. CONCLUSION: The biggest challenge is to ensure that maps of health statistics inform without misinforming. Advances in the sciences of cartography, statistics, and visualization of spatial data are constantly expanding the toolkit available to mapmakers to meet this challenge. Asking potential users to answer questions or to talk about what they see is still the best way to evaluate the effectiveness of a specific map design

Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design

Continuous 'pseudocolor' maps visualize how a quantitative attribute varies smoothly over space. These maps are widely used by experts and lay citizens alike for communicating scientific and geographical data. A critical challenge for designers of these maps is selecting a color scheme that is both effective and aesthetically pleasing. Although there exist empirically grounded guidelines for color choice in segmented maps (e.g., choropleths), continuous maps are significantly understudied, and their color-coding guidelines are largely based on expert opinion and design heuristics--many of these guidelines have yet to be verified experimentally. We conducted a series of crowdsourced experiments to investigate how the perception of continuous maps is affected by colormap characteristics and spatial frequency (a measure of data complexity). We find that spatial frequency significantly impacts the effectiveness of color encodes, but the precise effect is task-dependent. While rainbow schemes afforded the highest accuracy in quantity estimation irrespective of spatial complexity, divergent colormaps significantly outperformed other schemes in tasks requiring the perception of high-frequency patterns. We interpret these results in relation to current practices and devise new and more granular guidelines for color mapping in continuous maps

micromapST: Exploring and Communicating Geospatial Patterns in US State Data

The linked micromap graphical design uses color to link each geographic units name with its statistical graphic elements and map location across columns in a single row. Perceptual grouping of these rows into smaller chunks of data facilitates local focus and visual queries. Sorting the geographic units (the rows) in different ways can reveal patterns in the statistics, in the maps, and in the association between them. This design supports both exploration and communication in a multivariate geospatial context. This paper describes micromapST, an R package that implements the linked micromap graphical design specifically formatted for US state data, a common geographic unit used to display geographic patterns of health and other factors within the US. This package creates a graphic for the 51 geographic units (50 states plus DC) that fits on a single page, with states comprising the rows and state names, graphs and maps the columns. The graphical element for each state/column combination may represent a single statistical value, e.g., by a dot or horizontal bar, with or without an uncertainty measure. The distribution of values within each state, e.g., for counties, may be displayed by a boxplot. Two values per state may be represented by an arrow indicating the change in values, e.g., between two time points, or a scatter plot of the paired data. Categorical counts may be displayed as horizontal stacked bars, with optional standardization to percents or centering of the bars. Layout options include specification of the sort order for the rows, the graph/map linking colors, a vertical reference line and others. Output may be directed to the screen but is best displayed on a printer (or as a print image saved to any file format supported by R). The availability of a pre-defined linked micromap layout specifically for the 51 US states with graphical displays of single values, data distributions, change between two values, scatter plots of paired values, time series data and categorical data, facilitates quick exploration and communication of US state data for most common data types

micromapST

The linked micromap graphical design uses color to link each geographic units name with its statistical graphic elements and map location across columns in a single row. Perceptual grouping of these rows into smaller chunks of data facilitates local focus and visual queries. Sorting the geographic units (the rows) in different ways can reveal patterns in the statistics, in the maps, and in the association between them. This design supports both exploration and communication in a multivariate geospatial context. This paper describes micromapST, an R package that implements the linked micromap graphical design specifically formatted for US state data, a common geographic unit used to display geographic patterns of health and other factors within the US. This package creates a graphic for the 51 geographic units (50 states plus DC) that fits on a single page, with states comprising the rows and state names, graphs and maps the columns. The graphical element for each state/column combination may represent a single statistical value, e.g., by a dot or horizontal bar, with or without an uncertainty measure. The distribution of values within each state, e.g., for counties, may be displayed by a boxplot. Two values per state may be represented by an arrow indicating the change in values, e.g., between two time points, or a scatter plot of the paired data. Categorical counts may be displayed as horizontal stacked bars, with optional standardization to percents or centering of the bars. Layout options include specification of the sort order for the rows, the graph/map linking colors, a vertical reference line and others. Output may be directed to the screen but is best displayed on a printer (or as a print image saved to any file format supported by R). The availability of a pre-defined linked micromap layout specifically for the 51 US states with graphical displays of single values, data distributions, change between two values, scatter plots of paired values, time series data and categorical data, facilitates quick exploration and communication of US state data for most common data types