Sentences with phrase «modeling spatial data»

Our group gathers multi-disciplinary expertise aiming at developing methods and algorithms for extracting, analysing, and modeling spatial data from biological images.

Not exact matches

Using published data from the circumpolar arctic, their own new field observations of Siberian permafrost and thermokarsts, radiocarbon dating, atmospheric modeling, and spatial analyses, the research team studied how thawing permafrost is affecting climate change and greenhouse gas emissions.

The data have helped them learn more about the snakes» spatial range and behavior and develop population models they hope will be useful for conserving the locally threatened population of pine snakes.

Although the data suggest that spatial models can effectively forecast tree community composition and structure of unstudied sites in Amazonia, incorporating environmental data may yield substantial improvements.

High throughput genome sequencing and quantitative image analysis provide evolution, metabolic, and interaction data to build community metabolome maps, taxa / gene networks, and spatial ecosystem models.

From these data, we quantify the population genetic parameters of the intra-patient environment to aid modeling efforts such as the spatial - monotherapy work.

Disease prevention versus data privacy: Using landcover maps to inform spatial epidemic models.

ASTER data is used to create detailed maps of land surface temperature, reflectance, and elevation.ASTER captures high spatial resolution data in 14 bands, from the visible to the thermal infrared wavelengths, and provides stereo viewing capability for digital elevation model creation.

We carried out validation of the model using a variogram - based procedure, which tested the compatibility of the adopted spatial structure with the data.

We concluded that the adopted covariance model was compatible with the data, as the empirical semi-variogram fell within the 95 % tolerance intervals computed via Monte Carlo simulation and a spatial correlation test of residuals.

The ARM Aerosol Measurement Science Group (AMSG) coordinates ARM Climate Research Facility observations of aerosols and atmospheric trace gases with user needs to ensure advanced, well - characterized observational measurements and data products — at the spatial and temporal scales necessary — for improving climate science and model forecasts.

In the 1970s and»80s, British psychologist Alan Baddeley and colleagues developed a model of working memory that brings together how the brain accepts sensory input, processes both visual - spatial and verbal data, and accesses long - term memory; and how all of that input is processed by a function they referred to as central executive.

Also, the ability to specify and draw design concepts with network diagrams, data ow diagrams, logical ow charts, and models requires 2D and even 3D spatial awareness, often calling upon signicant artistic communication talent.

Methodologically, González Canché employs econometric, quasi-experimental, spatial statistics, and visualization methods for big and geocoded data, including geographical information systems and network modeling.

In this case, there has been an identification of a host of small issues (and, in truth, there are always small issues in any complex field) that have involved the fidelity of the observations (the spatial coverage, the corrections for known biases), the fidelity of the models (issues with the forcings, examinations of the variability in ocean vertical transports etc.), and the coherence of the model - data comparisons.

To test that I varied the data sources, the time periods used, the importance of spatial auto - correlation on the effective numbers of degree of freedom, and most importantly, I looked at how these methodologies stacked up in numerical laboratories (GCM model runs) where I knew the answer already.

To get a sense of the mix of whaling - era data, tracking and modeling used to estimate past blue whale abundance, read this PloS ONE paper by an overlapping research team from last year: «Estimating Historical Eastern North Pacific Blue Whale Catches Using Spatial Calling Patterns.»

We provide a list of issues that need to be addressed to make inferences more defensible, including the consideration of (i) data limitations and the comparability of data sets; (ii) alternative mechanisms for change; (iii) appropriate response variables; (iv) a suitable model for the process under study; (v) temporal autocorrelation; (vi) spatial autocorrelation and patterns; and (vii) the reporting of rates of change.

If the models can not even accurately simulate current climate statistics when they are not constrained by real world data, the expense to run them to produce detailed spatial maps is not worthwhile.

Wang, 2011: Detecting the ITCZ in instantaneous satellite data using spatial - temporal statistical modeling: ITCZ climatology in the east Pacific.

Nesting a regional climate model (with higher spatial resolution) into an existing GCM is one way to downscale data.

This will increase the spatial degrees of freedom beyond that of the PAGES 2k Consortium [2013] synthesis and provide clear targets for observation and model comparisons while honoring limitations imposed by current data availability.

Analyses of tide gauge and altimetry data by Vinogradov and Ponte (2011), which indicated the presence of considerably small spatial scale variability in annual mean sea level over many coastal regions, are an important factor for understanding the uncertainties in regional sea - level simulations and projections at sub-decadal time scales in coarse - resolution climate models that are also discussed in Chapter 13.

The RCPs provide a unique set of data, particularly with respect to comprehensiveness and detail, as well as spatial scale of information for climate model projections.

To claim anyone has created an accurate spatial model of the history of temperature change from such data is flat out dreaming.

«Both of these are established methodologies for doing spatial prediction otherwise known as interpolation» — phew, good thing you're not using «computer models» to fill in the data.

All model and observed data have same spatial coverage as HadCRUT4.

Most models accurately reproduce the spatial distribution of explosive cyclones when compared to reanalysis data (R = 0.94), with high frequencies along the Kuroshio Current and the Gulf Stream.

We conclude that the most valid model of the spatial pattern of trends in land surface temperature records over 1979 — 2002 requires a combination of the processes represented in some GCMs and certain socioeconomic measures that capture data quality variations and changes to the land surface.

European Forest Data Centre; EFDAC; free software; Free Scientific Software; Free and Open Source Software; Europe; forest information system; European Forest Fire Information System; EFFIS; geospatial; geospatial tools; semantic array programming; morphological spatial pattern analysis; GUIDOS; reproducible research; environmental modelling

As Mike noted, we should stay focused on the suite of (very interesting and) important scientific questions raised by this post — especially those related to the idea of spatial / temporal patterns of climate data in relation to concepts and models of their likely physical causes.

DelSole et al. (28) also found 2.5 cycles by extracting the spatial pattern in the Intergovernmental Panel on Climate Change, Fourth Assessment Report (IPCC AR4)(29) model control runs that best characterizes internal variability and by projecting the observed global data onto this pattern.

Because of their large spatial coverage, satellite data have proven useful in evaluating dust sources, transport and deposition in global models.

Prior to 1988, the satellite data that Trenberth uses is not available, but it is known that long term records in radiosondes contain large inhomogeneities due to improving observing systems, increasing spatial resolution (but still very little ocean coverage), and the NCEP data in particular contains large model biases.

The long story short is that Numenta's software, called Grok, is able to recognize patterns (e.g., temporal and spatial) from streaming data and then automatically build models that allow it to predict what will happen next.