Class #2. Data Models: maps as models of reality, geographical and attribute measurement & vector and raster (and other) data structures

Transcription

1 Class #2 Data Models: maps as models of reality, geographical and attribute measurement & vector and raster (and other) data structures

2 Role of a Data Model

3 Levels of Data Model Abstraction

4 GIS as Digital Map Layers" All of the layers are referenced to the same coordinate system a spatial referencing system Each layer represents a different geographic theme, phenomena, or feature

5 Geographic Data Modeling: Overview Definitions Data models / modeling GIS data models Topology Example Water facilities

6 Definitions Data model set of constructs for representing objects and processes in the digital environment Representation Focus on conceptual and scientific issues

7 Modeling Geographic Reality with Digital Data

8 Conceptualizing Geographic Reality We model reality using digital data...but first we must choose how to conceptualize reality As discrete phenomena... readily-distinguished entities on the Earth s surface with distinct boundaries this is an object-based view of the world As a continuous surface... entities on the Earth s surface with continuous variation and without distinct boundaries this is a surface or field-based view of the world Which is right? depends on the phenomenon being modeled... sometimes both are right sometime it is scale-dependent

9 Raster and Vector Models Raster implementation of field conceptual model Array of cells used to represent objects Useful as background maps and for spatial analysis Vector implementation of discrete object conceptual model Point, line and polygon representations Widely used in cartography, and network analysis

10 GIS Data Models & Applications CAD Graphical Image Raster/Grid Network Geo-relational TIN Object Engineering design Simple mapping Image processing and analysis Spatial analysis / modeling Network analysis Geoprocessing geometric features Surface /terrain analysis / modeling Features with behavior

11 Vector Data Model

12 Vector Data Model point: primary data object single x-y coordinate pair lines: formed by joining two or more points at least two x-y coordinate pairs nodes: points composing lines polygons: formed by joining together multiple lines at least three x-y coordinate pairs

13 Vector Data Model most straight-forward to use for representing discrete phenomena (relatively) well-defined boundaries e.g. political units, roads, rivers, school districts, bus routes can be used for phenomena which don't have clearly defined boundaries, but which we consider discrete impose boundaries (i.e. discretize the entity) e.g. soil types, retail market areas, forest areas, income zones

14 Vector Data Model Application

15 Topology Science and mathematics of geometric relationships Simple features + topological rules Connectivity Adjacency Shared nodes / edges Topology uses Data validation Spatial analysis (e.g. network tracing, polygon adjacency)

16 Topological Polygon Data Layer

17 Topology: Spatial Links & Relationships

18 Geo-Relational Polygon Dataset

19 Raster Data Model cell: primary data object also called pixel -- usually for image data represented by x-y coordinate and a cell size cells are regularly spaced to cover entire data area called a tessellation

20 Raster Satellite Imagery

21 Terrain Surface & Topographic Operations on terrain data Analyses terrain data frequently produced using spatial interpolation and/or stereoscopic interpretation of aerial photography Terrain data models usually represented using a DEM (digital elevation model) also sometimes as a TIN (triangulated irregular network) Basic terrain surface properties, e.g.: slope angle (gradient) slope aspect (direction/orientation) curvature (convexity or concavity of surface) Combine basic properties to achieve more complex analyses or create models

22 Advantages of Raster GIS Representation of continuous surfaces Fast computer processing Fast display of surface data Ability to handle very large databases Well adapted for overlay analyses Problems feature-based representation, linear boundaries, discrete features

23 Vector to Raster (Rasterization) Simple (compared to vectorization) Affected by: output raster spatial resolution method used for determining cell values

24 Vector to Raster Raster spatial resolution finer resolution = better representation of the converted vector data coarser resolution = more information loss! Method used to determine cell values How do we know what is in each cell? We choose: cell center (centroid) majority weighting weighted values based on priority/importance

25 Raster to Vector (Vectorization) Points & polys - relatively simple points: if cell=value, then a vector point is created at cell centroid with attribute=value polygons: polygon with attribute=value is created for all adjoining cells=value; poly boundary follows exterior of cells Lines - more complex must somehow determine: start/end/intersection points (nodes) for lines shape points along lines (vertices) topological relationships

26 Raster to Vector a sample conversion a starting point:

27 TIN Surface of Death Valley, California

28 TIN Surface of Death Valley, California

29 TIN Structure: Node-Facet Topology

30 Nominal Ordinal Interval Ratio Levels of Measurement distinguish among data values based on qualitative differences distinguish among data on the basis of order, but without measurable differences between data values distinguish among ordered data values with measurable differences between them, but with an arbitrary origin distinguish among ordered data values with measurable differences between them, and a non-arbitrary origin

31 Types of DBMS Model Hierarchical Network Relational - RDBMS Object-oriented - OODBMS Object-relational - ORDBMS

32 Relational DBMS Data stored as tuples (tup-el), conceptualized as tables Table data about a class of objects Two-dimensional list (array) Rows = objects Columns = object states (properties, attributes) Most popular type of DBMS Over 95% of data in DBMS is in RDBMS

33 Relational Database Structure

34 Relation Rules Only one value in each cell (intersection of row and column) All values in a column are about the same subject Each row is unique No significance in column sequence No significance in row sequence

35 Relational Joins

36 A Relationally-Joined Table

37 Table (Object Class) Column = property Row = object

38 Point Quadtree

39 Summary Database an integrated set of data on a particular subject Databases offer many advantages over files Relational databases dominate Some limitations for GIS