James D. Carroll


More info:

Refer to the links below to learn more about the use of different ArcMAP tools and resources.


- ArcGIS Resources



This portfolio covers a variety of GIS analysis projects prepared for the NC State University GIS 520 course in the study of Advance Geospatial Analytics using ArcGIS software. The course objective is to build on the student's skills learned in GIS 510, and to then expand those skills and understanding of the uses of geospatial analysis technology in a wide variety of fields and industry. Part of this learning process includes the ability to learn how to clearly communicate the resulting data for presentation, management, and decision-making purposes.

The list of course skills to be learned were:

  1. Integrate and analyze data in various formats
  2. Search for, retrieve, evaluate the suitability of, and intergrate datasets for specific types of analysis applications
  3. Describe the analysis capabilities of various kinds of geospatial technologies
  4. Indentify data limitations for particular analytical applications
  5. Select and perform apporpriate advanced geoprocessing functions for specific objectives
  6. Apply appropriate analysis techniques for different types of decision-making objectives
  7. Build simple customizations for the ArcMAP user interface

I found this course to be an excellent and challenging learning experience which greatly improved my skills and knowledge of important GIS analysis methods, processes, and technologies used in many areas of research, professional fields, business, and industry. As you view each project exercise listed in the menu to the left, I believe you will appreciate the capabilities and the value which advanced GIS analyst skills can provide your organization or business. Please contact me to discuss opportunities with your organizaton, or if you are interested in GIS analysis consultation, assistance, or related services for your projects.

Thank you,
James (Dan) Carroll

Words of Wisdom for Living with GIS Uncertainty:

  1. Understand what you don't know about your data. Read the metadata. Don't use data that have no provenance and cannot be researched.
  2. Investigate alternative outcomes using what you know about the error in your data. Try to get a sense of how wrong your analysis might be.
  3. Rely on multiple data sources if you can. Datasets produced in different ways by different vendors can act as checks and balances on error.
  4. Document your own uncertainty in the notes you publish with your analysis.


The Beginning