spatial-temporal data modeling
Temporal-spatial modeling for fMRI data
... As a least squares based method, the SVD used for data reduction, however, is known to be highly susceptible to outliers. Hence the analysis from the follow-up ICA could be contaminated by those spikes. In this ...
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Ecosystem Modeling with External Data Spatial Temporal in Iraqi Marshland
... Used spatial analysis by GIS system to combine multiple datasets to get standardized information have been integrated of quantitative computing and qualitative analysis has enhanced the easy and credibility of the ...
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Spatio-temporal areal unit modeling in R with conditional autoregressive priors using the CARBayesST package
... their data within a common software ...a spatial and temporal main effects and interaction model (similar to Knorr-Held 2000); (iii) a spatially autocorrelated autoregressive time series model ...
39
Considering groups in the statistical modeling of spatio-temporal data
... with data that do not come from in- dependent, identically distributed random ...organized data are a peculiar example of this situation. Different forms of spatial data are available, which ...
17
popRange: a highly flexible spatially and temporally explicit Wright-Fisher simulator
... for modeling highly probabilistic spatial and temporal popu- lation ...chromosome-scale data. This grid-based population structure model allows spatial and migration flexibility, such ...
5
Joint outcome modeling using shared frailties with application to temporal streamflow data
... in the same general spatial location. Generally, streamflow on the prairies is dominated by snowmelt and spring rains; there is likely some similarity in flow at the stations, and this depends on the soil and ...
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Application of Empirical Orthogonal Function Models to Analyze Shoreline Change at Bangkalan Madura
... the temporal and spatial data linkage that may be generated as a linear combination of the corresponding function of time and ...in temporal and spatial data sets from shoreline ...
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Spatial Temporal Survey and Occupancy Abundance Modeling To Predict Bacterial Community Dynamics in the Drinking Water Microbiome
... the data set 100 times and then averaged the and f across these 100 subsampling events, which resulted in a maximum of 15 nonzero data points per ...these data, we tested a number of IOAMs (28) ...
13
Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors
... residual spatial trends and changing covariate ef- fects, including potential nonlinearity in those effects, to compete with each candidate covariate, in contrast to approaches where covariate selection is based ...
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INTEGRATION OF SPATIAL HETEROGENEITIES IN HYDROTEL FOR MODELING THE HYDROLOGICAL FUNCTIONING OF AGNéBY SUB-WATERSHED AT AGBOVILLE (CôTE D’IVOIRE)
... to modeling the superficial flows of the Agneby sub-watershed at Agboville by integrating spatial heterogeneities into the distributed model of ...sensing data (DEM, ETM+ image of Lansat-7), soils ...
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Modeling and Analysis for CPS Physical Entities Based on Spatio-Temporal Petri Net
... In this paper, a Spatio-Temporal Petri net model is proposed for the space-time consistency problem during the process of position transition of physical entities. The CPS physical entities are modeled by ...
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Developing three dimensional topological model for 3D GIS
... 3D data, the possibilities of manipulating data including the creation, the edition and the query of stored information is significant as ...the spatial data types and populate the ...the ...
118
Spatio-temporal niche patterns and thermal environmental cues: Implications for the persisitence of Nicrophorine burying beetles
... by temporal and spatial segregation ...low spatial overlap, evidence for contrasting space-use patterns is most notable between ...This spatial divergence by ...a temporal niche ...
79
Schubert, Erich (2013): Generalized and efficient outlier detection for spatial, temporal, and high-dimensional data mining. Dissertation, LMU München: Fakultät für Mathematik, Informatik und Statistik
... Based on these approaches for assessing the similarity of axes, we compute a pairwise similarity matrix of all dimensions. Then Prim’s algorithm is used to compute a minimum spanning tree for this graph, and one of the ...
290
Spatial and temporal epidemiological analysis in the Big Data era
... Big data; internet of things; data science; visualization; exploratory analysis; modelling, 40.. spatial analysis 41.[r] ...
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A spatio-temporal feature extraction algorithm for crop mapping using satellite image time-series data
... high spatial resolution of these data provides valuable information about the spatial patterns of the ...both spatial and temporal (spatio-temporal) features form time-series ...
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Bayesian Spatial Cox Proportional Hazard Model For HIV Infected Tuberculosis Cases In Chennai
... time-to-event data occur ...the spatial survival model, the importance of the spatial inequalities was considered including, the impact of difference in area level characteristics and individual ...
5
Spatial temporal data modelling and processing for personalised decision support
... of data using trains of spikes [Kasabov ...input data streams as a sequence of static data vectors, ignoring the potential of SNN to simultaneously consider space and time dimensions in the input ...
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COLOR DATA CLUSTERING ALGORITHMS (CDCA) USING SPATIAL TEMPORAL DATA MINING.
... algorithm is more suitable for similarity measure that can be represent by a distance measure each clustering represented by one data object. CURE accept any similarity measures and the cluster can be represented ...
8
Enhancement in a Formal V&V Approach for Real Time Databases
... is data verification and data validation. To cater to the data collisions and synchronization problems, data verification and validation comes to play its ...oriented data models for ...
8