Project FA16c: Geospatial Big Data Visualization with Technology II

Project FA16c: Geospatial Big Data Visualization with Technology II
Researcher: Eun-Kyeong Kim
Position type: literature review, writing
Application deadline: 5:00 p.m. on Thursday, August 25, 2016
Application URL:

Project and position description

I am seeking one undergraduate student to assist with writing a survey paper and one chapter of online textbook on “Geospatial Big Data Visualization.” The selected student will be a part of our team for a NSF research project on “Building a Big Data Analytics Workforce in iSchools” that I am involved in as a graduate project manager. In this NSF project, we develop different types of learning modules (digital storytelling about big data, security analysis in the cloud, and big data mining) and teaching methods/materials. You can check more detailed information on our project website: The aim of this UROC project is to investigate the current trends in geospatial (big) data visualization using various cutting edge technologies including high performance computing and/or immersive virtual environment and their domain applications. Potential tasks of undergraduate assistants in this UROC project are to: 1) Review an extensive literature on geospatial (big) data visualization with technology; 2) Discuss with me about challenges and opportunities in geospatial data visualization in a big data era; 3) Submit a paper to a GIS journal; 4) Present our study at one of upcoming conferences/workshops. For undergraduate assistants who are interested in geospatial big data analytics and data visualization, this project will be a great opportunity to learn basic concepts of big data analytics and data visualization methods. It will also be a great addition to your resume to write and submit a journal paper as well as present a research paper in the professional conference/workshop.

Desired qualifications

Candidates should have the ability to critically review and discuss existing literature on research topics of interest and construct concise and logical arguments. Candidates who are willing to learn geospatial big data analytics and geographic education are preferred; candidates who have background knowledge in GIS and data analysis are preferred. Candidates must be willing to contribute 135 total hours of quality work toward the project over the semester, resulting in 3 credit hours applied to the transcript. There is no monetary stipend attached to this assistantship, although the experiences gained in this work will be immediately valuable when applying for graduate school or full-time employment.

To apply

Prospective candidates should complete the application form at by 5:00 p.m. on the indicated date. Follow all instructions.