1. Environmental Data Content and Form Stuff

2. 4 D Geo-Environmental Data Cube (X, Y, Z, T) Environmental data represent measurements in the physical world which has space (X, Y, Z) and time (T) as its dimensions. The specific inherent dimensions for geo- environmental data are: Longitude X, Latitude Y, Elevation Z and DateTime T. The needs for finding, sharing and integration of geo-environmental data requires that data are coded in this multidimensional data space

3. 4+ Dimensional Geo-Environmental Data Space Environmental data represent measurements in the physical world which has space (X, Y, Z) and time (T) as its dimensions. The specific inherent dimensions for geo-environmental data are: Longitude X, Latitude Y, Elevation Z and DateTime T. Additional dimensions may include parameters, pollutant source, etc. The needs for finding, sharing and integration of geo-environmental data requires that data are coded in this multidimensional data space

4. Semi-Static Views (slices) through 4D Data Space Possible Cross-sections through the 4 D Data space - data point..Temperature (x i, y i, z i, t i ) - image Temperature (x range, y range, z range, t range ) XY MAP: Z,T fixed Vertical Profile:XYT fixed Time Chart: X,Y,Z fixed Vertical Cross sect: YT fixedVertical Cross sect: XT fixed Vertical Profile Trend: X,Y fixed

5. Hierarchy of Data Objects: DataGranule, Data Series, DataCube Measure A measure (in OLAP terminology) represent numerical values for a specific entity to be analyzed (e.g. temperature, wind speed, pollutant).OLAP A collection of measures form a special dimension ‘ Measures’ (??Can Measures be Dimensions??)special dimension Data Granules A data granules– discrete, atomic data entities that cannot be further broken down. A data series is an ordered collection of data granules DataSeries is a collection of DataGranules having common attributes All data points in a measure represent the same measured parameter e.g. temperature. Hence, they share the same units and dimensionality. The data points of a measure are enclosed in a conceptual multidimensional data cube; each data point occupies a volume (slice or point) in the data cube. Data points in a measure share the same dimensions; Conversely, each data point has to have the dimensional coordinates in the data cube of the measure that it belongs to. Dimension Y DataSeries Dimension Z Dimension X DataCube DataGranule

6. Environmental Data: Multi-Dimensional Data can be distributed over 1,2, …n dimensions 1 Dimensional e.g. Time dimension i j k j i Data Granule i 1 Dimensional e.g. Location & Time 1 Dimensional e.g. Location, Time & Parameter View 1 Data Space View 2 Views are orthogonal slices through multidimensional data cubes Spatial and temporal slices through the data are most common