DesignHub

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 and spatial-lag analyses has been added to the spatial regression method.

Based on customer feedback, we have designed feature enhancements in SpaceStat 4.0 that improve the appearance, functionality and performance of maps and graphs. You will also find that the extensive help documentation has been updated, revised and expanded.

Additionally, we’ve responded to your requests for a SpaceStat virtual class by adding a series of tutorials to our website. Each tutorial comes with a SpaceStat project designed to get you started working with a specific concept, and provides a landing page with a description, time estimate and associated project links.

New LeSage-Pace Estimator

A LeSage-Pace estimator for spatial-error and spatial-lag analyses has been added to the spatial regression method.

“I am involved in developing and applying multiple regression models for the mass valuation of residential real estate properties. Modelers such as me are always seeking to find improved model accuracy. The spatial regression models in SpaceStat are of particular interest. The addition of the LeSage-Pace output makes it easier to compare to other methods. Incidentally the Spatial Error Model has been the best performer among all models I have tested lately. It is featured in a book I have written on spatio-temporal methods in mass appraisal to be published June 2014. Also thanks to BioMedware for making this change to the product.”

Richard A. Borst, PhD
Tyler Technologies, Inc.

Recently Published Research using SpaceStat…

Spatial Relationship Quantification between Environmental, Socioeconomic and Health Data at Different Geographic Levels

Int. J. Environ. Res. Public Health 2014, 11(4), 3765-3786; doi:10.3390/ijerph110403765
Authors: Mahdi-Salim Saib, Julien Caudeville, Florence Carre, Olivier Ganry, Alain Trugeon  and Andre Cicolella

“We used Spacestat to evaluate relationships between spatial data collected at different geographic scales. Spacestat is easy-to-use and provides powerful tools that make possible spatial data processing, exploratory analysis, and the quantification of spatial relationships in environmental health research. Spacestat is extraordinarily useful for stakeholders seeking to prioritize prevention actions in the context of environmental inequalities reduction.”

Julien Caudeville
French National Institute for Industrial Environment and Risks (INERIS)
Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France

Space-time clusters of breast cancer using residential histories: A Danish case control study

BMC Cancer.2014, 14:255.  DOI: 10.1186/1471-2407-14-255
Authors: Nordsborg Baastrup Rikke, Meliker R Jaymie, Ersbøll Kjær Annette, Jacquez M Geoffrey, Poulsen Harbo Aslak, Raaschou-Nielsen  Ole

Background

A large proportion of breast cancer cases are thought related to environmental factors. Identification of specific geographical areas with high risk (clusters) may give clues to potential environmental risk factors. The aim of this study was to investigate whether clusters of breast cancer existed in space and time in Denmark, using 33 years of residential histories.

Methods

We conducted a population-based case–control study of 3138 female cases from the Danish Cancer Registry, diagnosed with breast cancer in 2003 and two independent control groups of 3138 women each, randomly selected from the Civil Registration System. Residential addresses of cases and controls from 1971 to 2003 were collected from the Civil Registration System and geo-coded. Q-statistics were used to identify space-time clusters of breast cancer. All analyses were carried out with both control groups, and for 66% of the study population we also conducted analyses adjusted for individual reproductive factors and area-level socioeconomic indicators.

Results

In the crude analyses a cluster in the northern suburbs of Copenhagen was consistently found throughout the study period (1971–2003) with both control groups. When analyses were adjusted for individual reproductive factors and area-level socioeconomic indicators, the cluster area became smaller and less evident.

Conclusions

The breast cancer cluster area that persisted after adjustment might be explained by factors that were not accounted for such as alcohol consumption and use of hormone replacement therapy. However, we cannot exclude environmental pollutants as a contributing cause, but no pollutants specific to this area seem obvious.

Download a Free 14-day evaluation of SpaceStat