215-SpatialDB.ppt case about space for study

Editor's Notes

• #15: GIS systems are used to collect, model, store and analyze information describing physical properties describing the geographic world. It’s possible to divide GIS into these three main categorize. In geographic object applications, objects of interest are identified from a physical domain. For example power plants, electroral districts, product distribution districts and city landmarks. In cartographic and terrain modeling applications, variations in spatial attributes ar captured. For example, soil charactertis, crop density and air quality. Both cartographic and terrain based applications require a field-based representation whereas geographic applications require object based.
• #16: GIS data can be represented in two formats: vector and raster. Vector data represents geometric objects such as points, lines and polygons. For example if you were modeling a lake, you could use represent it as a polygon or a river by a series of line segments. Raster data is characterized as an array of points, where each point represents the value of an attribute for a real-world location. Raster images are n-dimensional arrays where each entry is a unit of the image and represents attributes. Two dimensional units are called pizels, while three dimensional units are called voxels. Raster data is typically used with maps of land cover classes such as temperature, rainfall, pasture, urban areas, and standing water. GIS data undergoes various types of analysis. For example an application such as soil erosion studies, environmental impact studies or hydrological runoff simulations, data undergoes various types of geomorphic analysis – which is the measurement of slope values, gradients, aspect (a complex description of the gradient), convexity (the change of the gradient) When used for decision support applications it may undergo aggregation and expansion operations using data warehousing as well as querying. GIS data must integrate both vector and raster data from a variety of sources. Sometimes edges and regions are inferred from a raster image to form a vector model or conversely, raster images are used to update vector models.
• #17: GIS data models are usually grouped into broad categories: object and field. So imagine a forest consisting of clusters of pine, fir and oak trees. What would be the best way to model the forest and capture the aggregate information? Consider a function that maps the underlying geographic space of the forest onto a set consisting of three values (fir, oak and pine). This function would be a field whose varying spatial distribution captures the diversity of the forest. The function itself would be constant over the areas where the tree types were alike and sharply jump into different values when the tree species change. For an object on the other hand, this function will be composed of a series of polygon that correspond to the different areas with trees.
• #18: As we know from this class, ER diagrams are typically used to initial model databases. However for spatial databases ER diagrams are unable at least intuitively to capture some important semantics inherent in spatial modeling. As a result a field model cannot be naturally mapped using the ER model. For example ther is no notion of key attributes and unique OIDS’s in a field model. Although in traditional ER modeling, the relationship between entitites are derived from the application under consideration, in spatial modeling there are always inherent relationships between spatial objects. So if ER are the best solution for best conceptual spatial data modeling, what is? The answer is using pictograms.
• #19: Pictograms are a seriees of shapes that can be used to capture concepts related to spatial geometry. Basic Shapes: In a vector model the basic elements are point, line and polygon. Multi-Shapes: To deal with objects which cannot be represented by the basic shapes, this set of aggregate shapes were defined. Derived Shapes: If the shape of an object is derived form the shapes of other objects it’s pictogram is italicized. User Defined Shapes: Apart from the basic shapes of point, line and polygon, user-defined shapes are possible. Any possible Shape: A combination of shapes is represented by a wild card * symbol inside a box, implying that any geometry is possible. Alternative Shapes: Alternative shapes can be used for the same object depending on certain conditions, for example objects of size less than x units are represented as points while those greater than x units are represented as polygons. They are represented as the concatenation of possible pictograms. Similarly, multiple shapes are needed to represent objects at different scales: for example at higher scales lakes may be presented as points, and at lower scales as polygons.
• #22: There are two wetlands – Darr and Stubble on the shores of Lake Erie. Using data collected from April lto June in 1995 and 1996 we want to predict the spatial distribution of a marsh-breed bird, the red-winged blackbird. A uniform grid was imposed on the 2 wetlands with different types of measurements recorded at pixel. In total, values of 7 attributes were recorded at each pixel. Understanding how the birds interacted with their environment was key to creating the parameters of this studyinig. Forr example vegetation durability was chosen over vegetation species because specialized knowledge about the bird-nest habits suggested that the choice of next location is more dependent on plant structure, plant resistance to wind and wave action than on the plant species. So three attributes selected to use in this study: vegetation durability, distance to opn water and water depth. In this study determining the spatial accuracy – how far the prediction are from the actual nests was critical because of dramatic change in the m eaning of the information if was A or B in this slide.
• #23: Landsat MSSR – remote sensing satelittles MSSR – Multispectrum Scanner Raster SPOT – System Pour le’Observation de la Terre