Dr. Goovaerts has developed executables for several projects:

- An automatic variogram modeling function was developed and included in SADA (Spatial Analysis and Decision Assistance) and SpaceStat software.
- For the ASARCO study, a computer program was written to allow employees at Ecology to compute for each block-group within the modeled area:
- the mean arsenic concentration,
- the average (expected) number of parcels that exceeds a given Threshold
**T’**, - the probability that a fraction
**X’**of the parcels exceeds a given Threshold**T’**, and - the fraction
**X**of the parcels for which the threshold T is exceeded with a given probability**P**. - For the New York City DOHMH project, an approach was developed and implemented into an executable called
*outbreak-simulation.exe*to: - compute outbreak magnitudes based on a parameterization of the Serfling base model for five different syndromes, the four seasons and three spatial distributions (Single zip code, zip code cluster, and citywide),
- generate outbreaks for three different epidemic curves (single-day spike, point-source exposure, and propagated transmission) and three durations (3, 5, and 15 days),
- add the simulated syndrome counts to the actual daily counts recorded for the period 1/1/2010-12/31/2011 accounting for the frequency of visits from ZIP codes to hospitals.
- Public-domain code was developed and published to perform Poison kriging of rate data. Goovaerts, P. 2005. Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging.
*International Journal of Health Geographics*,**4**:31. - Public-domain code was developed and published to perform multiple indicator kriging. Goovaerts, P. 2009. AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences.
*Computers and Geosciences*,**35**, 1255-1270. - Code was developed for a NASA project (PI, Dan Brown) to simulate the spatial distribution of land-cover types under various constraints using simulated annealing. Brown, D.G., Goovaerts, P., Burnicki, A., and M.Y. Li. 202. Stochastic simulation of land-cover change using geostatistics and generalized additive models. Photogrammetric Engineering and Remote Sensing, 28(10): 1051-1061.

Both parameters **X’** and **T’** are specified by the user who can choose multiple fractions and thresholds per run. The parameters T and P can also be specified by the user but only one value is allowed by run.