GIS / Modeling Skills

GIS

I am an Esri Certified ArcGIS Desktop Associate 10.1 (Verification #: 7SC7RPWCKM11KRDQ), and have made the switch to ArcPRO. I also use QGIS, R, and Python to perform a number of other spatial data processing steps, with the majority of actual spatial data analysis being performed in Python or R. Also utilize the model builder tool when appropriate for simple tasks.

Some examples of GIS / Spatial Science include:

A variety of spatial interpolations (Krigging, Inverse Distance Weighted, Empirical Bayesian Krigging, etc.)
Measuring and accounting for spatial autocorrelation (also temporal autocorrelation)
Running spatial cluster analyses, using the Getis-Ord Gi* statistic (Hot Spot Analysis) or using local Moran's I values for statistical significance
Multi-Criteria Decision Analyses

Modeling

A passion of mine is predictive modeling / machine learning (A regression is machine learning, so it can be as fancy as you make it). I have used and worked with a variety of models, as general practice dictates multiple models should be used to test a hypothesis.

Model examples include:

Linear Models (GLMs, GLMMs, LM) are pretty straight forward (pun absolutely intended). - GLM used for Jossart et al 2017 Manuscript
Non-Linear models, such Artificial Neural Networks
Random Forest
Multi-Layer Preceptron - MLP used for Jossart et al 2018 Poster Presentation
Logistic and multi-Logistic Regressions
Support Vector Machines
Classification and Regression Trees
Multivariate statistics (Repeated Measure ANOVA, Hoteling's T², etc.)

Modeling Software Used:

Favorite Python Package: Sci-Kit Learn (https://scikit-learn.org/stable/)
Favorite R Package: Caret (http://topepo.github.io/caret/index.html)

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