Laura Tateosian is a Research Assistant Professor in the Center for Geospatial Analytics. She is a computer scientist with a research focus on visualizing geospatial-temporal data. She uses controlled studies and eye tracking technology to investigate innovative ways to represent and interact with geospatial data. Her work includes open-source natural resources database management and Web mapping, coastal terrain time-series visualization, aesthetic climatology data visualization, and narrative processing of geospatial content. Laura has developed graduate courses in geospatial algorithms and programming, including Principles of Geospatial Information Systems, Programming for GIS (in Python), and an advanced special topics course on Geospatial Visualization.
- An open-source platform for geospatial participatory modeling in the cloud , ENVIRONMENTAL MODELLING & SOFTWARE (2023)
- Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling , INTERNATIONAL JOURNAL OF REMOTE SENSING (2023)
- Curvature Weighted Decimation: A Novel, Curvature-Based Approach to Improved Lidar Point Decimation of Terrain Surfaces , Geomatics (2023)
- Forecasting global spread of invasive pests and pathogens through international trade , ECOSPHERE (2023)
- Plant pest invasions, as seen through news and social media , COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2023)
- Exploring public values through Twitter data associated with urban parks pre- and post- COVID-19 , LANDSCAPE AND URBAN PLANNING (2022)
- The persistent threat of emerging plant disease pandemics to global food security , PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)
- A Review of Geospatial Content in IEEE Visualization Publications , 2020 IEEE VISUALIZATION CONFERENCE - SHORT PAPERS (VIS 2020) (2020)
- A Review of Geospatial Content in IEEE Visualization Publications , Proceedings IEEE Visualization 2020 (2020)
- GIS-Based Estimation of Seasonal Solar Energy Potential for Parking Lots and Roads , IEEE Greentech Conference Proceedings (2020)
Plant disease outbreaks are increasing and threaten food security for the vulnerable in many areas of the world and in the US. Climate change is exacerbating weather events that affect crop production and food access for vulnerable areas. Now a global human pandemic is threatening the health of millions on our planet. A stable, nutritious food supply will be needed to lift people out of poverty and improve health outcomes. Plant diseases, both endemic and recently emerging, are spreading and exacerbated by climate change, transmission with global food trade networks, pathogen spillover and evolution of new pathogen genetic lineages. Prediction of plant disease pandemics is unreliable due to the lack of real-time detection, surveillance and data analytics to inform decisions and prevent spread. In order to tackle these grand challenges, a new set of predictive tools are needed. In the PIPP Phase I project, our multidisciplinary team will develop a pandemic prediction system called ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œPlant Aid Database (PAdb)ÃƒÂ¢Ã¢â€šÂ¬Ã‚Â that links pathogen transmission biology, disease detection by in-situ and remote sensing, genomics of emerging pathogen strains and real-time spatial and temporal data analytics and predictive simulations to prevent pandemics. We plan to validate the PAdb using several model pathogens including novel and host resistance breaking strains of lineages of two Phytophthora species, Phytophthora infestans and P. ramorum and the cucurbit downy mildew pathogen Pseudoperonspora cubensis Adoption of new technologies and mitigation interventions to stop pandemics require acceptance by society. In our work, we will also characterize how human attitudes and social behavior impact disease transmission and adoption of surveillance and sensor technologies by engaging a broad group of stakeholders including growers, extension specialist, the USDA APHIS, Department of Homeland Security and the National Plant Diagnostic Network in a Biosecurity Preparedness workshop. This convergence science team will develop tools that help mitigate future plant disease pandemics using predictive intelligence. The tools and data can help stakeholders prevent spread from initial source populations before pandemics occur and are broadly applicable to animal and human pandemic research.
This project will establish a partnership between the Center for Geospatial Analytics at NC State University and the Wake County Innovation Lab to form a team to develop geospatial visualizations and analytics solutions for internal and external Wake County government stakeholders. This effort will support projects for the Wake County Innovation Center / GeoLab that require specialized computational skillsets. Overall, the objective of the project is to develop solutions in emerging geospatial technologies, to contributing high-level geospatial science and computational skills, to the advancement of Wake County Innovation Lab application development and delivery, and to increase the geospatial analytical capabilities that the Innovation Lab provides to departments and stakeholders. The research group will focus on geospatial analytics and the development of visualizations and applications to model spatial solutions for county operations and engagement with citizens. Pilot projects may include augmented reality, BIM (Building Information Modeling) and the incorporation of sensor data streams. This project is intended to enhance the potential for Wake County Innovation Lab staff to make tangible progress on enhancing the geovisualization and geoanalytics in application development and delivery.
DO7 Small Conflict Economies
Sensemaking lies in the intersection of planning and communicating, and is comprised of technology (planning) and tradecraft (storytelling). Sensemaking is defined as ÃƒÂ¢Ã¢â€šÂ¬Ã…â€œan approach that involves planning and replanning about how to make sense of an issue; foraging for and harvesting sources of information; seeking to understand what they reveal; and communicating that knowledge to others.ÃƒÂ¢Ã¢â€šÂ¬Ã‚Â For DO 5, our work in sensemaking will focus on three activities: representing sensemaking knowledge, generating and maintaining narrative spaces, supporting interaction with spaces of narratives.
DO3 Task Order 2.4 Narrative Processing
LiDAR surveys of coastal regions over the past 15 years have generated time series of elevation data at unprecedented resolutions. For the first time, this type of data is available as a regional, multi-year time series, providing an opportunity for transition from traditional, static representation of topography to terrain abstraction as a 3D dynamic layer.To analyze dynamics of terrain in a continuous space-time domain we propose to model it using voxel models representing elevation dynamics, space-time gradients, and fastest change trajectories. The space-time cube approach is new for representation and analysis of terrain evolution. Effective visualization for this type of data has yet to be developed. Our preliminary experiments indicate the potential but need more work to be fully effective. Advanced visualization has the potential to improve our understanding and communication of complex patterns of terrain evolution at the level that will not only guide future research but also provide critical information for decision-making and on coastal impacts of climate change.
Under the terms of this agreement, NC State University staff will carry out the following research and data development activities to meet Fire Program needs for the Northeast and National Capital Regions of the National Park Service: 1. Evaluate and validate wildland fire management information fire occurrence data for the Northeast and National Capital Regions 2. Develop FARSITE landscapes for selected parks (SHEN, BLUE) and others as data become available 3. Develop and maintain Fire Program Analysis related data as needs dictate 4. Create a pocket reference guide for handheld global positioning systems for use in training and field activities 5. Investigate workload of updating ?old? fuel model maps to ?new? fuel model maps for DEWA, NERI, and SHEN. 6. Collaborate data sharing and workload efforts with other federal and state government agencies 7. Preliminary work of obtaining fire GIS data (jurisdictional, access, hazards, etc.) for the Appalachian Trail 8. Develop imagery and data for selected parks for incorporation into Google Earth 9. Develop a Regional Fire geodatabase containing data such as: Fire Program Analysis data and other incident management related data