Day: July 21, 2014

OGC Calls for Public Comment on Candidate Standard for Encoding Coverages in JPEG2000

/ Matt Artz / 1 Comment

OGC_Logo_Border_Blue_3DThe Open Geospatial Consortium (OGC®) membership seeks public comment on the candidate OGC GML Application Schema – Coverages – JPEG2000 Coverage Encoding Extension (abbreviated as “GMLCOV for JPEG2000”). This candidate standard can be downloaded from http://www.opengeospatial.org/standards/requests/123

GIS coverages (including the special case of Earth images) are two- (and sometimes higher-) dimensional metaphors for phenomena found on or near a portion of the Earth’s surface. Coverage instances may be encoded using the OGC GML Application Schema – Coverages (GMLCOV) Encoding Standard, which is based on the Geography Markup Language (GML), an XML grammar written in XML Schema for the transport and storage of geographic information. GMLCOV for JPEG2000 specifies an encoding of GML coverages for the JPEG2000 data exchange formats for still imagery (i.e. JPC, JP2, JPX). This document is the basis for the GML in JPEG2000 encoding standard v2.0 and a future format extension for WCS.

Suggested additions, changes, and comments on this candidate standard are welcomed and encouraged. Such suggestions may be submitted at by 20 August 2014.

The OGC® is an international consortium of more than 475 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. Visit the OGC website at http://www.opengeospatial.org/.

[Source: OGC press release]

A Flexible Spatial Framework for Modeling Spread of Pathogens in Animals with Biosurveillance and Disease Control Applications

/ Matt Artz / 1 Comment

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 638-661

By Montiago LaBute, Benjamin McMahon, Mac Brown, Carrie Manore, and Jeanne Fair

“Biosurveillance activities focus on acquiring and analyzing epidemiological and biological data to interpret unfolding events and predict outcomes in infectious disease outbreaks. We describe a mathematical modeling framework based on geographically aligned data sources and with appropriate flexibility that partitions the modeling of disease spread into two distinct but coupled levels. A top-level stochastic simulation is defined on a network with nodes representing user-configurable geospatial “patches”. Intra-patch disease spread is treated with differential equations that assume uniform mixing within the patch. We use U.S. county-level aggregated data on animal populations and parameters from the literature to simulate epidemic spread of two strikingly different animal diseases agents: foot-and-mouth disease and highly pathogenic avian influenza.

Inter-county level spread of FMD. Green dots indicate where there are susceptible populations of cattle, hogs and/or sheep according to the 2007 USDA NASS agricultural census data. Blue dots indicate where there are 10 or greater asymptomatic animals, red dots indicate where there are one or more symptomatic animals. Black crosses indicate counties which either had no initial susceptible populations or that are depopulated of susceptibles by mitigative measures, i.e., quarantine, culling and/or vaccination.

Inter-county level spread of FMD. Green dots indicate where there are susceptible populations of cattle, hogs and/or sheep according to the 2007 USDA NASS agricultural census data. Blue dots indicate where there are 10 or greater asymptomatic animals, red dots indicate where there are one or more symptomatic animals. Black crosses indicate counties which either had no initial susceptible populations or that are depopulated of susceptibles by mitigative measures, i.e., quarantine, culling and/or vaccination.

“Results demonstrate the capability of this framework to leverage low-fidelity data while producing meaningful output to inform biosurveillance and disease control measures. For example, we show that the possible magnitude of an outbreak is sensitive to the starting location of the outbreak, highlighting the strong geographic dependence of livestock and poultry infectious disease epidemics and the usefulness of effective biosurveillance policy. The ability to compare different diseases and host populations across the geographic landscape is important for decision support applications and for assessing the impact of surveillance, detection, and mitigation protocols. ”