SPATIAL DATA STRUCTURES IN G. I. S.

A Geographical Information System is a collection of spatially referenced data (i.e. data having locations attached to them) GIS uses geographically- referenced objects called features, which may be points, lines, polygons, or images (e.g., satellite, radar, aerial photos). Spatial data in a GIS can be displayed just like a paper map with roads, rivers, vegetation and other features represented as lines on a map complete with legend, border and titles, or it can also be represented as a set of statistical tables, which can be converted to charts and graphs

The most important feature of GIS is that spatial data are stored in a structured format referred to as a spatial data base. The way spatial data are structured will determine the how easy it is for the user to store, retrieve and analyze the information. Geographic Information Systems which store map features in vector format store points, lines and polygons with high accuracy. Geographic Information Systems, which store map features in raster or grid format, generalize the location of features to a regular matrix of cells.

BASIC TYPES OF SPATIAL DATA Point Data Line Data Polygon or Area Data Continuous Surface

Point, Line & Polygon Feature

Vector Data Format

Vector Data Legend

SPATIAL DATA IN TABULAR FORM

SPATIAL DATA STRUCTURE Spatial data is information about the shape, location, and relationship of geographic features. A GIS requires that hard copy maps be scanned or digitized in order to transfer them into a digital form. Some examples of spatial data include satellite imagery, topographic maps, road networks, or any other data set that contains location information about features. Two basic types of spatial data models have evolved for storing geographic data digitally. These are referred to as : Vector and Raster data.

DATA MODELS FOR SPATIAL DATA RASTER DATA VECTOR DATA NON TOPOLOGICAL TOPOLOGICAL HIGHER LEVEL DATA SIMPLE DATA DYNAMIC SEGMENTATION REGIONS TIN

VECTOR DATA FORMATS The vector data structure represents geographical features by a set of coordinates. Vectors, as x,y coordinates, define points, lines and polygons. The basic premise of the vector-based structuring is to define a two dimensional space where features are represented by coordinates on the two axes. Vector lines are often referred to as arcs and consist of a string of vertices terminated by a node. A node is defined as a vertex that starts or ends an arc segment.

One coordinate pair, a vertex, defines point features. Polygonal features are defined by a set of closed coordinate pairs. In the string representation, polygons are discretized by a set of xy coordinates pairs but have same vector as the beginning and ending vector –thus representing a self closing line as polygon. This method of storage is not optimal as the line building a polygon in two adjacent polygons are structured twice – once in each of the polygon. Modern vector based structures over come this non optimal storage by building and defining a topology.

TOPOLOGY Topology as used in GIS, expresses clearly the spatial relationship between features, such as two lines meeting perfectly at a point and a directed line having a clear left and right side. Topology is useful for detecting digitizing errors in digital maps and is necessary for some map overlay operations and network analysis.

The idea of topology is based on a conceptual definition of the relationship between points and lines and lines and polygon. Point features are related on a one to one basis with vectors and hence one xy pair represents a point.

A line is characterised by a one to many relationship with a set of vectors i.e. many points making a line. A polygon is represented by a set of lines – thus forming a closed area- and thus has a one to many relationship with lines i.e. many lines forming a close loop to make a polygon. In ArcGIS coverage is the topological file whereas shapefile is the non topological file.

RASTER DATA FORMAT Raster data models incorporate the use of a grid-cell data structure where the geographic area is divided into cells identified by row and column. This data structure involves a division of spatial data into regularly spaced cells. Each cell is of the same shape and size.

The location of each cell or pixel is defined by its row and column numbers. The value assigned to the cell indicates the value of the attribute it represents. Each cell in a raster cell is assigned only one value. In the raster data model, each cell represents an area of the land surface.

RASTER DATA STRUCTURE

RASTER DATA VETOR DATA (X2,y2) (X1,y1)

An example of raster data is any digital image such as a satellite image, a scanned aerial photograph, or a scanned map. Anything scanned creates a raster image, even if the image is of a vector object.

RASTER DATA

Raster data does not store points, lines, and polygons. A line at the appropriate resolution would look like a line, but at a lower resolution the line would appear to have stair steps along the edges of it. If the resolution was decreased enough, the line would no longer appear to be a line, but rather a series of adjoined squares.

To overcome this effect when needed, a technique known as anti-aliasing is employed. Anti-aliasing is the process of applying an intermediary colour next to an aliased line. This creates the optical effect of a smooth line. Since the attribute of each cell is stored as a unique value, the total number of values to be stored is the product of the total number of column times the number of rows.

The smaller the area of land that each cell represents, the higher the resolution of the data, and the larger the file needed to store the data. Since the file size is related to the area of coverage, it increases by the square of the increase in resolution. For these reasons, raster files tend to be relatively large.

It is common for a raster file to be comprised of several million cells. However, many of the cells may contain the same value as neighbouring cells. Where there is considerable amount of cells having same value, significant reductions in the size of the raster file can be achieved by using various methods of data compression, such as run- length encoding and quadtrees.

If the data are highly variable from cell to cell, as with digital terrain data or image, then the large number of cells serve to capture the high spatial variability. If the number of values were reduced, some of the spatial information would be lost.

In run-length encoding, adjacent cells along a row that have the same values are treated as a group termed as run. Instead of repeatedly storing the same value for each cell, the value is stored once, together with information about the size and location of the run. Run-length encoding can significantly reduce the storage needed for a raster file.

The quadtree structure addresses both the resolution as well as redundancy issue. The quadtree data model provides a more compact raster representation by using a variable size grid cell. Instead of dividing an area into cells of one size, finer sub-divisions are used in those areas with finer detail. In this way, a higher level of resolution is provided only where it is needed.

COMPARISION IN RATER & VECTOR DATA MODELS RASTER DATA VECTOR DATA ADVANTAGES It is a simple data structure. Overlay operations are easily and efficiently implemented. High spatial variability is efficiently represented in a raster format. The raster format is more or less required for efficient manipulation and enhancement of digital images It provides a more compact data structure than the raster model. It provides efficient encoding of topology, and, as a result, more efficient implementation of operations that require topological information, such as network analysis The vector model is best suited to supporting graphics that closely approximate hand-drawn maps

RASTER VECTOR DISADVANTAGES The raster data structure is less compact. Data compression techniques can often overcome this problem. Topological relationships are more difficult to represent. 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. It is a more complex data structure than a simple structure. Overlay operations are more difficult to implement. The representation of high spatial variability is inefficient. Manipulation and enhancement of digital images can not be effectively done in vector domain.

Vector data is precise and has no approximation errors for measured quantities like area, length or perimeter. Because of discretization, raster data suffers from such approximation errors. Generally, raster data has higher storage requirements, though compression techniques can be adopted. Vector storage is also relatively high with a large number of pointers and file linkages.

Raster data is not easily accepting to association of attribute data with spatial features such as points, lines or polygons. This is due to the fact that the basic entity in the raster approach is the grid cell, and entities such as point, lines and polygons are not recognized as objects in their own right. Raster is limited by the cell size, and sampling or disaggregation to a lower size is not possible. Vector structure does not impose this limitation, therefore analysis and plotting are more accurate in vector structure.

CONVERSION OF DATA MAODELS Data modelled in one system can be converted into the other. That is, raster data can be vectorized and vice versa. Many systems even allow data modelled in raster form to be overlaid on vector. The process of converting vector data into raster data is essentially easier than the reverse process, which is converting data from raster format to vector format.

Vector to Raster conversion (Rasterization) Vector data containing polygons with associated attributes A grid with the desired cell size imposed The values of the grid cells become the values of the attributes of the polygons which contain them

Raster to Vector conversion (Vectorization) Polygons are then generated for each neighboring area by storing the x,y coordinates of the points along these borders Gridded satellite classification with class numbers contained each grid cell numbers Borders between differing class numbers are located

SELECTION OF DATA MODEL Selection of a particular data structure can provide advantages during the analysis stage. The vector data model does not handle continuous data, e.g. elevation, very well while the raster data model is more ideally suited for this type of analysis.

Accordingly, the raster structure does not handle linear data analysis, e.g. shortest path, very well while vector systems do. The selection of a particular data model, vector or raster, is dependent on the source and type of data, as well as the intended use of the data. Certain analytical procedures require raster data while others are better suited to vector data.

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