As a geospatial data analyst, I create the fulcrum that allows organizations to leverage the data they collect. Often the original intent, extent, or complexity of a data set impedes their use. The following portfolio contains examples of problems I have resolved, either in my studies or work experience, where I made data work.
  • Mapping Dams onto the NHDPlus Hydrography Dataset
  • Visualizing the Climate Change Tree Atlas (CCTA)
  • Recreating Sacramento Soil Moisture Accounting Model (SAC-SMA) Parameters


The NHDPlus is a medium-resolution hydrography data set, supported by the EPA and USGS, that contains flow characteristics of rivers and streams across the US and is used widely in modeling. However, this data set does not incorporate structures like hydropower dams, which the USGS mapped onto the high-resolution NHD dataset. I created a Python script that analyzed the spatial differences between the two representations, and mapped the dams to locations along the NHDPlus stream network. The combined data is useful in ecological assessment, hydropower prospecting, and water supply modeling.


I created a Flex widget that allows users to visualize the Climate Change Tree Atlas, created by the USFS Northern Research Station. The CCTA contains rasters of the potential range of 134 different tree species under 10 different climate scenarios. Originally presented as Google Earth overlays, this data can be difficult to synthesize. Using the widget, the user can select an area of interest and the tool will buffer the area to the minimum size of analysis, query the tree atlas geodatabase, calculate the area weighted importance value for each species, and display a summary plot.


The objective of this project was to derive the Sacramento Soil Moisture Accounting (SAC-SMA) parameters from base soil data. These parameters are used in the USFS Eastern Forest Environmental Threat Assessment Center's Water Supply Stress Index (WaSSI) model. The SAC-SMA parameters were developed by the National Weather Service for use in flood forecasting, but only extend over the conterminous US. I built an ArcGIS toolbox to generate these parameters using soil datasets from different regions, but still based on the methods in the source literature.