Recently my colleague Huang Yi, PhD, Nantong University, and I published a paper that used SpaceStat to find Blue Zones in China. The data came from the Chinese and local governments. Â After loading and validating the data we used spatial time series methods to...
SpaceStat Posts
Workshops! GIS for Community Impact: From Technology to Translation – Oakland, CA
by Susan Hinton | Mar 13, 2015 | GIS Technology
Save the dates April 13 and 14: Oakland, California April 13: Pre-Workshop Short-course: Space Time Analysis for Health and the Environment This 1-day class will provide instruction on the space-time analysis of data relating health events to potential...
Announcing the Release of SpaceStat 4: software for the visualization, analysis, modeling and interactive exploration of spatiotemporal data
by Geoffrey Jacquez, Ph.D. | May 3, 2014 | BioMedware News
Download a Free 14-day evaluation of SpaceStat SpaceStat 4.0 represents a major reworking of the underlying architecture of the application. Multithreading has been introduced improving the performance of many methods. A LePace-Sage estimator for spatial-error...
Announcing Our Membership in Esri’s Business Partner Network and the Release of SpaceStat 3 with Geodatabase File Format Integration
by Geoffrey Jacquez, Ph.D. | Sep 5, 2011 | News
I am very excited by our release of SpaceStat 3, which links BioMedware to Esri technology using the geodatabase file format and is an important step towards addressing unmet needs in data access and geohealth analysis techniques. This is very timely, as...
BioMedware Joins Esri Partner Network
by Susan Hinton | Sep 1, 2011 | News
Partnership Provides an Easy Way for Esri Users to Add SpaceStat’s Advanced Space-Time Analysis Into Their Workflows Ann Arbor, MI – Sept. 6, 2011 – BioMedware, a leader in geohealth software development and research solutions, today announced its membership in the...
The small numbers problem–Part 2
by Geoffrey Jacquez, Ph.D. | Mar 24, 2011 | Learn with BioMedware
Using persistence in spatial time series as a diagnostic for extreme rates in small areas. In my last blog on the small numbers problem, we found that rates calculated with small denominators (e.g. small at-risk populations) have high variance and we thus have little...
The small numbers problem Part 1: What you see is not necessarily what you get
by Geoffrey Jacquez, Ph.D. | Nov 4, 2010 | Learn with BioMedware
The ability to quickly create maps of health outcomes such as cancer incidence and mortality in counties, census areas and even Zip codes is now available through websites and data portals. (See for example Atlasplus, State Cancer Profiles, and Cardiovascular Disease,...

