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High-Performance Computing and GIS (HPCGIS) Laboratory

The High-Performance Computing and GIS (HPCGIS) Laboratory integrates high-performance computing and geographic information systems to advance geographic information science. Specific research areas include cyberGIS, data-intensive space-time analytics and modeling, big spatial data, parallel and high-performance computing, and geo-enabled social media.

Learn about our projects


  • Cheng Zhang (CS, Undergraduate) Welcome to the HPCGIS lab!
  • Eric presented recent advances of PCML at the CyberGIS All-hands Meeting in Reston, VA.
  • Congratulations Paleoscape team! Best Paper (Accelerating Discovery Track) and Best Lightning Talk at XSEDE'15 (see XSEDE Award Winners).
  • Eric had a great time talking about the future of the field and PCML at the 2015 Vespucci Institute in Bar Harbor, ME.
  • Congratulations Gowtham Kukkadapu and team: for winning the class prize for Streaming Data over the Internet and Data Mining
  • NSF RAPID Award: "Capturing Behavioral Response and Perceived Risk to Ebola Using Social Media"
  • SESYNC Award: "The Socio-Environmental Data Explorer (SEDE): Integrating Social Media and Environmental Data in CyberGIS to Explore Environmental Hazard Risk Perception"
  • HPCGIS Lab Students attend Supercomputing in Plain English (SiPE), Spring 2015 (http://www.oscer.ou.edu/education) - A huge thanks to Henry Neeman for hosting these workshops via teleconferencing for free.



LiDAR map of Kent, OH made using PCML

The Parallel Cartographic Modeling Language is a multi-institutional collaborative project aiming to create a novel programming language for cyberGIScientists that is designed for (1) usability, (2) programmability, and (3) scalability. The language is freely available on Github: https://github.com/HPCGISLab/pcml.

Capturing Behavioral Response and Perceived Risk to Ebola Using Social Media

This NSF-funded RAPID project is combining over 35 million social media posts from the platform Twitter (known as tweets) with traditional survey data to improve our understanding of information spread through social media and how it translates to on-the-ground perceptions and behaviors to disease outbreak. In this project, we surveyed the Kent State University community and nearby residents that were faced with a potential Ebola exposure during September 2014. We use these data to compare and inform results from social media data analytics to better understand how social media data can be used to capture perceived risk and behavioral response.

Socio-Environmental Data Explorer (SEDE)

The SEDE is a new project funded by the NSF-supported National Socio-Environmental Synthesis Center (SESYNC) to develop a web-based textual and visual analytic system that will integrate social media and environmental data in cyberGIS to enable scientists, decision makers, and stakeholders to explore environmental impact and perceptions of risks of hazards through space and time. We will use SEDE to explore the case-study of extreme weather events, disturbances with immediate and longer term implications for the resilience of social-environmental systems (SES).

Paleoscape Model

This project, led by Dr. Curtis Marean, is using the NSF-supported Extreme Science and Engineering Discovery Environment (XSEDE) to improve our understanding of the origins of modern humans. Specifically, this interdisciplinary project is developing a paleoscape model that simulates the distribution of natural resources and climatic conditions in the southern Cape region of South Africa. This project is coupling climate, vegetation, and agent-based models to better understand our own origins.

Agent-based Models

Map of infection spread across most of US

Agent-based models simulate individuals within artificial societies to understand coupled natural and human systems such as how disease spreads throughout populations of tens of millions of individuals. This research area examines the fundamental challenges posed by large and complex spatially-explicit agent-based simulations executed on high-performance computers. The Parallel Agent-based Modeling software component was released as an open-source CyberGIS software component to provide an illustrative application demonstrating the use of parallel and high-performance computing for agent-based modeling.

More Projects Coming Soon

The HPCGIS Lab has several collaborative projects underway that will be shared shortly.


HPCGIS Lab members

Founding Director

Graduate Students

  • Jayakrishnan Ajayakumar (Geography - PhD)
  • Gordon Cromley (Geography - PhD)
  • Haley Wachholz (Geography - Masters)

Undergraduate Students

  • Cheng Zhang (Computer Science - Undergraduate)

Past Students

  • Zhengliang (Ryan) Feng (Computer Science - Undergraduate)
  • Suman Jindam (Computer and Information Science - Masters)
  • Gowtham Kukkadapu (Computer Science - Masters)
  • Sandeep Vutla (Computer and Information Science - Masters)
  • Barton Yadlowski (Applied Mathematics - Undergraduate)


The High-Performance Computing and GIS (HPCGIS) Laboratory is located in McGilvrey Hall at Kent State University in Kent, Ohio. It is a state-of-the-art facility that is equipped with cutting-edge technologies.

Image of the HPCGIS Lab in McGilvrey Hall

HPCGIS Lab Facilities and Equipment

  • Dedicated HPCGIS Lab cluster for small-scale testing
    • 8 nodes
    • Intel Xeon E5-2630 @ 2.40Ghz (16 cores each)
    • 32GB/64GB nodes
    • Connected to 50 terabyte storage system
  • 50 terabyte (50,000 gigabyte) storage system for storing big spatial data (Department-wide system for collaborative data sharing)
  • Multiple virtual machines to support code development and small-scale testing
  • Multiple multi-core desktop machines
  • 65″ 3D LED TV

Kent State University, College of Arts and Sciences Cluster

The College of Arts and Sciences at Kent State University is equipped with a state-of-the-art cluster consisting of 386 computing cores, almost 1.5 terabytes of memory, and more than 30 terabytes of total disk space. It is divided into two sections to adapt to various research needs: one section is a traditional high-performance computing cluster and the other section can be used as a cluster of virtual machines. Each node consists of 16 cores (2.60 Ghz E5-2670 Xeon Processors) and 64 GB of main memory and several nodes also have NVIDIA Tesla M2090 Graphic Processing Units (GPUs).

  • 368 Intel Xeon processor cores (2.60 GHz)
  • 1472 GB Total Memory
  • 32 TB Total Disk Space
  • 4 NVIDIA Tesla M2090 GPUs


The HPCGIS lab is always looking for motivated students and new collaborators.

The High-Performance Computing and GIS Laboratory, directed by Dr. Eric Shook, is looking for highly motivated students that are interested in the areas of cyberGIS, large-scale space-time analytics and modeling, big spatial data, or applying high-performance computing to geospatial problems. Prospective graduate, undergraduate, or high school students interested in high-performance computing and geographic information systems (GIS) should contact Eric Shook (eshook@kent.edu) to chat. Additional information about our department including how to apply to our graduate program is available at the following webpage: http://www.kent.edu/CAS/Geography/futurestudents.

The HPCGIS lab is interested in building multi-disciplinary collaborations to answer novel research questions by leveraging cutting-edge high-performance computing, cyberGIS, and data-intensive analytics or modeling approaches. If you are interested in collaborating with the HPCGIS lab please contact Eric Shook (eshook@kent.edu).

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