Erich Seamon

Hi – my name is Erich Seamon.   I am a climate and data scientist at the University of Idaho, where I am currently working on my Ph.D. in agricultural relationships to climate processes under Dr. Paul Gessler.  I have extensive experience in data science, GIS, and climate science impacts, and am a certified Project Management Professional (PMP)  as well as a Geographic Information Systems Professional (GISP).

Climate Science Background and Experience

Below is a synopsis of several different climate science projects I have worked on over the past 10 years @ the University of Idaho.  I have particular experience…

GIS Background and Experience

I have a background in geographic information systems (GIS), using ArcGIS desktop, ArcGIS server, python, R, as well as managing spatial data within several flavors of databases (SQL Server, Oracle, DB2, MongoDB). I regularly deploy GIS-based web services, manage geodatabases, and use modeling efforts to understand spatial patterns and relationships.

Participating Research Grants

The following are research teams that I have previously or am actively engaged in as a climate scientist and PhD student.


My research interests are described, including work with GIS, agricultural insurance crop loss, as well as a line of work in GIS and child psychology.

Secure Research Status

A secure page that provides a status of my research to related colleagues and mentors.

Data Science Example: Agricultural Commodity Loss

In the attached R Markdown notebook, I explore agricultural insurance crop loss data in comparison to climate data, looking at basic exploratory analyses.

Data Science Example: Climate Data Extraction

In the attached Python Jupyter notebook, data from two distinct sources are used: 1) stream temperature observational locations, taken from the USFS NorWEST regional stream temperature database, and 2) gridded climate data from the University of Idaho’s GridMET team (led by University of Idaho’s Dr. John Abatzoglou).

Data Science Example: Landslide Analysis in Oregon and Washington

In the Python Jupyter notebook below, landslide data from oregon is extracted, analyzed, and run thru a series of analysis techniques including decision trees/random forest analysis.

Data Science Example: Daily Climate Change Wordclouds

This is a Twitter wordcloud, constructed daily, using the phrase “climate change”. The most recent 500 tweets from the time of the run (2pm) that contain the phrase “climate change” are organized in a corpus and then a term document sparse matrix.

GitHub Repositories

Github repositories of all my R, python, javascript, and shell scripting work. Each repository has a readme that describes the overall project and code file descriptions.