Special Topics in GIS Student Spotlights

GIS4930 Special Topics in GIS, Mrs. Amber Bloechle

Statistical Analysis Prepare Week

In Statistical Analyst Prepare week we prepared census data for geostatistical analysis, created and calculated attribute fields with Python scripts, applied the Spatial Join function to join shapefiles, wrote the introduction and background sections of the written report (following specific guidelines), and created a basemap of the study area. 

Student Learning Outcomes:

• Prepare US census data and other relevant data for geostatistical analysis in ArcGIS. 
• Automate creation of new fields and calculations within attribute fields using Python scripts.
• Self-assess comprehension of key concepts with regards to spatial analysis techniques presented and methods utilized in this module.

STUDENT SPOTLIGHT AWARD

The following student was chosen for their exception work on the Statistical Analysis Prepare Week assignment:

James Tennyson 

About James:  James is originally from Mississippi and has also lived in Pensacola very near the UWF campus. Currently, he has lived in the Pacific Northwest since 2000 and doesn't want to be anywhere else. He took his first GIS class in 2004 as part of a training course, and has been hooked on GIS ever since. James has used GIS in his past three jobs. The past five years were spent working on a research project as a fish biologist and GIS analyst for Oregon State Univ. in Corvallis, OR. Before that as a Water Resources Environmental Specialist for the Washington Dept. of Ecology, both in Spokane and Yakima, WA, in two very different capacities. In his spare time (when he can get some), he loves to mountain bike and road bike, any biking! Being in the Pacific Northwest affords so many outdoor activities but his favorite is poking around the woods in the Fall when the Chanterelles start fruiting.

What we like: James did an excellent job with his map. He included an inset map for geographic reference, he identified Charleston (the city referenced in his title), and he implemented other basemap layer such as roads and counties to give his map more meaning. We would also like to commend him on his color and symbology choices a swell as his overall organization. He also did a great job of finding outside documents to support his paper. Way to go, James!

Statistical Analysis Analyze Week

This week we reviewed regression analysis basics, ordinary least squares and geographic weighted regression. We also defined dependent and independent variables for regression analysis, ran the ordinary least squares model, and completed the 6 checks for OLS results to determine which variables were significant and non-significant. Deliverables this week included a map showing StdResidual results from the final OLS model and the Methods section the final report including the OLS Results table and the map.

Student Learning Outcomes:

• Apply regression analysis basics, ordinary least squares and geographic weighted regression analyses to locational data in ArcGIS. 
• Define dependent and independent socio-economic variables (derived from US Census and research) for use in regression analysis.
• Run an Ordinary Least Squares (OLS) model in ArcGIS as basis of analysis.
• Complete 6 checks as provided by ESRI Help for OLS results to determine which variables are significant or non-significant.
• Self-assess comprehension of key concepts with regards to spatial analysis techniques presented and methods utilized in this module.

STUDENT SPOTLIGHT AWARD

The following student was chosen for their exception work on the Statistical Analysis Analyze Week assignment:

Kala Knapp 

About Kala: Kala is an Environmental Ecologist living in Deerfield Beach, FL. She studied Ecology and Geology as an undergraduate at Florida Atlantic University. She initially started this program with the hopes of more job opportunities and has since gotten her current job as an Arborist with FPL on the Vegetation Management team and she utilizes GIS daily. Kala has found a passion for GIS and the many applications. She still between graduate degrees but GIS is in the top 3 choices!

What we like: Kala provided a clear understanding and description of the project. She detailed her process and also provided detailed explanations regarding her OLS table and map. We also liked that her map includes an inset map and basemap layers such as counties for geographic reference as well as her color choice and organization. This was a very tough project for everyone but her presentation simplified its complexity. Great job, Kala!

Check back next week for next week's spotlight on an Internship student!