Choropleth Mapping with Estimate Reliability Information

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Sample Data Sets

Manual (v.3)

Download SEER*CMapper (v.3)

(Java installation required)

Manual (v. 4.2)

Download SEER*CMapper

(v. 4.2 latest,

August, 2021)



A choropleth map is a popular map type to display health statistics. Areal units are assigned to different classes according to the statistical estimates of the units and each class is assigned a color (or a pattern or symbol) on the map. Many methods have been introduced to determine class break values to form classes, and some (e.g., natural breaks, quantile, and equal interval) are popular in GIS, but none considers the errors associated with the estimates in determining class break values.


The Class Separability concept introduced in 2015 (Sun et al. 2015) can be used to assess the likelihood that estimates on two sides of a class break value are statistically different i.e., the level of separability. Using this concept, the class separability classification method was introduced to help determine class break values with high levels of separability. The SEER*CMapper is a Java-based stand-alone tool that can be downloaded to and used in local computers. SEER*CMapper can:


1) be used to create state and county level choropleth maps using data from SEER*Stat;

2) create choropleth maps using the class separability method and other popular classification methods (natural breaks, equal interval and quantile); and

3) evaluate the separability levels of any map classification results.


SEER*CMapper v.3 requires Java installed or enabled (JRE or JVM). Version 4 is the latest release in Fall, 2020, and it does not require users to install Java.



Two variables are needed: estimates and the associated standard error (SE) or the margin of error (MOE). The MOE can be at 90% or 95%. These variables are included in the SEER*Stat data exported in text format. Boundary data of areal units are not required when using the SEER*Stat data as SEER*CMapper includes the state and county boundary data of the U.S. SEER*CMapper can also handle shapefile data, as long as the attribute table includes the estimate and the associated SE or MOE variables as attributes.


Developers and Funding Sources

SEER*CMapper was developed by David W. S. Wong and Yunfeng Jiang of Spatiotemporal Information Systems, LLC. The concept of class separability was developed in a project funded by the National Institutes of Health (NIH) under Award Number R01HD076020 through George Mason University. The development of SEER*CMapper was partially funded by the NCI/NIH contract # NNSN261201700718P. The content in this website is solely the responsibility of the author and does not necessarily represent the official views of the NCI/NIH.