National Science Foundation Grant to Help Model Building Construction Choices, Climate, and Indoor Pollutants

vatech1…from Building Design and Construction

A National Science Foundation grant is funding Virginia Tech engineer Deborah Young-Corbett in efforts to improve public school infrastructure.  Young-Corbett is an assistant professor who directs the Occupational and Construction Hazard Reduction Engineering Laboratory, part of Virginia Tech’s Myers-Lawson School of Construction and the Virginia Department of Civil and Environmental Engineering at Virginia Tech.

“Young-Corbett is among a breed of engineering faculty called industrial hygienists. She emphasizes the control of health hazards that come from the built environment or that are inherent to the construction process. She received her doctorate in industrial and systems engineering from Virginia Tech, and holds two master’s degrees. One is in ecology from N.C. State University and the second one is in industrial and systems engineering, also from Virginia Tech. Her bachelor’s degree is in biology.

“Using her engineering skills, Young-Corbett is working with structural equation modeling and geographic information systems (GIS) to create models to describe the impacts of building construction choices, climate, and indoor pollutants on human health. Her models employ more than 6,000 data points related to building construction, HVAC design, environmental contaminants and inhabitant symptoms collected from some 100 randomly selected public and commercial buildings in 37 cities in 25 states.”

NASA’s Hurricane Data Portal: Investigating of the Science of Hurricanes

trmm_quikscat_example2NASA’s Hurricane Data Portal is designed for viewing and studying hurricanes in the Atlantic region by utilizing various measurements by the NASA remote-sensing instruments. The portal consists of four main components:

  • Current Conditions (in pre-selected regions and updated daily): the latest maps and profiles from NASA satellites, such as, TRMM, AIRS, etc.
  • Event based: the latest maps and profiles for an active tropical storm or hurricane.
  • Science focus: Examples/stories describing the data usage in hurricane monitoring and research.
  • Archives: maps and profiles from past tropical storms and hurricanes.

The GES DISC Hurricane Data Portal goal is to assist the science community in future research and investigations of the science of hurricanes. In building collaborations and becoming part of the community we can help serve in the efforts to understanding scientific aspects of hurricanes by providing data access and visualization tools and services.

GIS and Climate Change Resources at the 2009 ESRI User Conference

uc20091ESRI continues to bring focus to climate change concerns and responses. On opening day, renowned biologist Willie Smits will present to users his organization’s efforts for rebuilding forest habitats. In addition, ESRI is sponsoring a Climate Change GIS Showcase along with a three day, global climate change track that offers sessions about policy, monitoring carbon, conservation efforts, and alternative energy sources and technologies. Scientific papers include Carbon Profiles and Alternative Energy and a panel discussion GIS and Renewable Energy. Panel discussions offered by Planet Action will emphasize the value of SPOT imagery data to monitor land use change. People working at the local and national levels will present papers about their community programs in tracks called GIS and Policy Making and  Local Climate Action Plans.

Distinguished speakers joining the climate change conversation include Dr. D. James Baker, Director of the Global Carbon Measurement Program; Dr. Gary Richards from the Australian Department of Climate Change; and Jim Geringer, former governor of Wyoming, who now serves on the National Academy of Sciences Committee of America’s Climate Choices.

National Digital Forecast Database: Gridded Weather Forecast Information Now Available in a GIS Format

atmo1…from the Spring 2009 issue of Atmospheric Front

“The National Oceanic and Atmospheric Administration’s (NOAA) National Weather Service (NWS) Los Angeles/Oxnard, California, weather forecast office (WFO) is providing gridded weather forecast information to customers in a geographic information system (GIS)-friendly format. Weather forecast information is available as GIS shapefiles and can be brought into a desktop GIS by adding an ArcIMS image service in ArcGIS or ArcGIS Explorer. This service is part of an effort to meet the growing customer and partner demand for weather forecast products in a GIS-compatible format.”

Jim Tobias Building a Web-based Mapping System for a Tuberculosis Genotyping Information Management System (5QaG&S):

jimtobiasWelcome to the first installment of’s Five Questions about GIS and Science (5QaG&S). Who are you and what do you do?

Jim Tobias: Jim Tobias, MS, GISP. I am currently building a web-based mapping system for a Tuberculosis (TB) Genotyping Information Management System for the US Centers for Disease Control and Prevention. How did you get started with geospatial technology?

Jim Tobias: I started with geospatial technology in 1992 with NOAA and was supporting marine mammal research in the Gulf of Mexico and Caribbean. How does geospatial technology help you do your job / scientific work?

Jim Tobias: Geospatial technology is critical to disease control and prevention and I am working to re-ignite interest in mapping in the spirit of Dr. John Snow and his investigations of cholera in London during the 1854 outbreak. How important is a formal process/methodology (for example, the scientific method; the geographic approach) when using geospatial technology in your scientific work?

Jim Tobias: Perhaps the greatest part of the ArcGIS software is the ModelBuilder and the ability to build workflows with standard inputs, processes, and outputs that can all reside within a single geodatabase. This speaks to the Scientific Method and allows researchers to run analyses and then zip and ship those inputs, models, and outputs to other researchers where experiments can be replicated, vetted, and examined with transparency of process. What features or capabilities would make geospatial technology even more valuable for scientific work?

Jim Tobias: The Model Builder should be expanded and should begin to offer a full visual programming environment such as Orange Data Mining tools.  The Orange Data Mining tools allow visual programming of drag-and-drop Python widgets that encapsulate analytic methods in the spirit of Dr. John Tukey and his Exploratory Data Analysis. The Python widgets can be strung together visually and used to create programs that I would challenge a hard-coding programmer to build in several weeks. It is possible to build very robust visual EDA and ESDA models and workflows and applications within a matter of 15 minutes using the Orange Canvas and visual programming techniques. These models and applications can be rapidly shared and are all Python and so one begins to build a Swiss watch with transparent gears that any scientist can examine, modify, and a framework to build upon for future analytics. A similar environment is the KNIME and the TAVERNA project. These folks have all recognized that the pace of science can be accelerated and built within an open, transparent, and replicable environment that caters to the Scientific Method.

If you are a scientist working with geospatial technologies and would like to participate is the 5QaG&S interview series, please email me at martz(at)esri(dot)com.

Spatial Analysis of Trends in Extreme Precipitation Events in High-Resolution Climate Model Results and Observations for Germany

jgrBy L. Tomassini and D. Jacob, Regional Climate Modeling, Max Planck Institute for Meteorology, Hamburg, Germany.

From J. Geophys. Res., 114.

A statistical extreme value analysis is applied to very high-resolution climate model results and observations encompassing the area of Germany. Two control runs representing the current climate, as well as three scenario simulations of the regional climate model REMO, are investigated. The control runs were compared against high-resolution observations. The analysis is divided into two main parts: first trends in extreme quantiles of daily precipitation totals are estimated in a station-by-station analysis. In the second part, the spatial characteristics of the estimated trends in heavy rainfall are investigated over the area of Germany by fitting a parametric geostatistical model to these trends. The rule of thumb of estimating trends in extreme quantiles of heavy precipitation based on the Clausius-Clapeyron relation, about 6.5% per 1°C temperature increase, has been roughly confirmed for Germany by our study with respect to the observations, but the climate model computes weaker trends. In the control simulations, the climate model tends to underestimate trends in heavy rainfall compared to observations. In the scenario simulations, positive trends prevail (as in the observations). They are, however, relatively small when set in relation to the uncertainties. The trends become significantly positive to a larger spatial extent only in the A2 scenario simulation. The estimated shape of the extreme value distributions does not change significantly in the scenario simulations compared to the climate model control runs. The parameter estimates for the geostatistical model for the trends in extreme quantiles of daily precipitation sums are rather uncertain. The most striking feature of the analysis is a reduction of the spatial variance of the trends over the considered area of Germany in the scenario simulations compared to observations and, in particular, the climate model control runs.

Spatial Analysis of Plague in California: Niche Modeling Predictions of the Current Distribution and Potential Response to Climate Change

plagueBy Ashley Holt, Daniel Salkeld, Curtis Fritz, James Tucker, and Peng Gong.

From the International Journal of Health Geographics, 2009, 8:38.

Plague, caused by the bacterium Yersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance.

Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras.

Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions.