1. Spatial Data Models Geography is concerned with many aspects of our environment. From a GIS perspective, we can identify two aspects which are of particular relevance: what is present ? where it is ? GIS imposes specific conceptual and technological models on how we implement those aspects.

2. Spatial data model cont’d geographic data can be classified into two basic types: spatial data (corresponding to where it is) attribute data (corresponding to what is present)

3. Spatial Data Models cont’d In terms of specific computer models. There are two fundamental approaches to the representation of the spatial component of geographic information: Vector data model Discrete features, such as customer locations and data summarized by area, are usually represented using the vector model. In the vector model, objects or conditions in the real world are represented by points and lines that define their boundaries Raster data model Continuous numeric values, such as elevation, and continuous categories, such as vegetation types, are represented using the raster model.

4. Spatial Data Models cont’d Each spatial data model imposes certain constraints on how we can model the world, and the types of processes we can (easily) reproduce in a computer environment. One spatial data model is not better than the other, but one may be better than the other in certain data handling / modeling situations.

5. Real World

6. Example of real world In this example, our landscape consists of rolling hills grassland lake rivers forest stands marsh It can be represented as a series of raster layers and/or vector layers.

7. Raster Data Model The raster data model represents features as a matrix of cells in continuous space. Each layer represents one attribute (although other attributes can be attached to a cell). And most analysis occurs by combining the layers to create new layers with new cell values.

8. Raster Data Model cont’d Cell size you use for a raster layer will affect the results of the analysis and how the map looks. The cell size should be based on the original map scale and the minimum mapping unit. Using too large a cell size will cause some information to be lost. Using a cell size that is too small requires a lot of storage space, and takes longer to process.

9. Raster Data Model

10. Raster model

11. Raster model cont’d Grid cells Cells are interconnected to create planar surfaces representing all the space of a single area of study. The matrix of cells, organized into rows and columns is called a grid. In raster data model the focus is more in location. A raster data model is more like a photograph rather than a map. Geographic features are represented in grid cells or pixels (short of picture element) filled with values.

12. Raster representation

13. Raster Accuracy and Representation When a picture is stored in a computer the raster data model is used. The function of each grid cell is determined by the information it contains, and the characteristics of each grid cell can be defined by: Classification: Of each cell as belonging to a group of similarly classified pixels that become an object, like a street ; Measurement: Of the value of its color or shade of gray; Comparing the relative pixel: To a known reference point, such as mean sea level (for elevation), or the point of an oil spill. In the raster data model, the accuracy of the map depends on the scale of the map, the resolution and, hence, accuracy depends on the real world area represented by each grid cell.

14. Raster Resolution The larger the area represented the lower the resolution of the data. The smaller the area covered the greater the resolution and the more accurately features are represented.

15. Raster Topology Raster data structures do not provide the precise locational information because geographic space is now divided into discrete grids, like as we divide a ground surface into uniform squares. Spatial relationships are implicit; thus their explicit storing is not required as in vector data model. Raster data is geo-referenced by specifying the coordinate system to which a grid is registered, the real-world location of the reference point, and the cell size in real-world distances. The coordinates of the grid cell give the location of a grid cell

16. Point, line and area in raster structure In the raster data model, points are represented as single grid cells. The absolute location of the point is somewhere inside the grid cell. Lines are represented as a series of connected grid cells. Each point of the line must occur somewhere within one of the displayed grid cells. For areas, all points inside the area that is bounded by a close set of lines must occur within one of the grid cells to be represented as part of the same area.

17. Vector Data Model Vector is a data structure, used to store spatial data as Points, Lines and Polygons.

18. Vector data Model

19. Vector data Model cont’d Monument location point feature Roads collection of points, forming a line Land Ownership collection of points forming a closed polygon

20. Vector representation

21. Vector representation of data In the vector based model (fig.2), geospatial data is represented in the form of coordinates. In vector data, the basic units of spatial information are points, lines (arcs) and polygons. Each of these units is composed simply as a series of one or more co-ordinate points, for example, a line is a collection of related points, and a polygon is a collection of related lines.

22. Vector representation of data cont’d Locations, such as the address of a customer, or the spot a crime was committed, are represented as points having a pair of geographic coordinates. Lines, such as streams or roads, are represented as a series of coordinate pairs. Areas are defined by borders, and are represented by closed polygons. They can be legally defined, such as a parcel of land; administrative, such as counties; or naturally occurring boundaries, such as watersheds.

24. Raster Vector Conversion Rasterisation: vector to raster conversion The process of converting vector data, which is a series of points, lines and polygons, into raster data, which is a series of cells each with a discrete value. This process is essentially easier than the reverse process, which is converting data from raster format to vector format. Vectorization: raster to vector conversion The process of converting an image made up of raster cells into one described by vector data. This may or may not involve the encoding of topology.

25. Raster representation of discrete features

26. Raster Vector Conversion cont’d Modern GIS software provides for conversion between raster and vector data structures. 1. Digitize data in vector format then convert to raster for ease of modeling 2. Convert vector to raster for printing, plotting 3. Convert raster to vector to utilize topologic data structures 4. Convert to be compatible with other data types in system

27. Characteristics of Vector Data Characteristics of vector data are: Feature driven (point, line, polygon objects), Vector (directional Lines) Represented by non-continuous surfaces (identifies specific features) Associated easily to a database (easy to query and analyze) Vector Data- sequential points or vertices Arc- string of vertices terminated by a node Node- vertex that starts or ends an arc Polygon- set of closed coordinate pairs

28. Characteristics of Raster Data Characteristics of raster data are: Simple 'grid' structure of rows and columns. Based on cells or picture elements (pixels). Linear feature (e.g. a road) is a contiguous set of cells. Resolution based on size of grid (cell) -> the smaller the cell, the higher the resolution. Features are considered homogenous within a pixel. Storage increases with the square of the resolution.

29. Raster Data Model Advantages: 1. It is a simple data structure 2. Overlay operations are easily and efficiently implemented 3. High spatial variability is efficiently represented in a raster format. 4. The raster format is more or less required for efficient manipulation and enhancement of digital images.

30. Disadvantages: 1. The raster data structures is less compact. Data compression techniques can often overcome this problem. 2. Topological relationships are more difficult to represent. 3. The output of graphics is less aesthetically pleasing because boundaries tend to have a blocky appearance rather than the smooth lines of hand-drawn maps.this can be overcome by using large number of cells, but may result in unacceptably large files. Raster Data Model

31. Vector Model Advantages 1. It provides a more compact data structure than the raster model. 2. It provides efficient encoding of topology, and, as a result, more efficient implementation of operations that require topological information, such as network analysis. 3. The vector model is better suited to supporting graphics that closely approximate hand-drawn maps.

32. Disadvantages 1. It is more complex data structure than a simple raster. 2. Overlay operations are difficult to implement. 3. The representation of high spatial variability is inefficient. 4. Manipulation and enhancement of digital images cannot be effectively done in the vector domain.

33. Questions: What are some advantages & disadvantages of raster data? What are some advantages & disadvantages of vector data? Why are vector data structures more complex than raster ?