Raster data is represented by a grid of cells, where each cell contains numeric or qualitative values. Raster data comes from sources like images, maps, and satellite imagery. Common analyses of raster data include buffering, reclassification, hillshades, interpolation, and surface calculation. Buffering assigns "in" and "out" values to cells based on their distance from a feature. Reclassification reassigns cell values. Hillshades create shaded relief maps from elevation data. Interpolation estimates values between known data points. Surface calculation performs cell-by-cell mathematical functions on rasters.

1. Raster Data Analysis
PRESENTED BY: ABDUL RAZIQ
AMEER MUAVIA
KHIZAR NAWAZ

2. Raster Data:
 Definition:
 A graphic representation of features and attributes.
 Made of grid which contain information.
 Cell size:
 Cells may be of any size.
 What does cell size tell us?
 coarser or finer the pattern or features are that appear in raster.

3. Raster Grid:
 It was usually square but may be of different shape.
 Each group of cells could either call as a layer of a cells.
 Various layers are overlying each other
 Entities represented by grid:
 Grid made of cell.
 Values applied to cells.

4. Representation Of Data:
 In Vector Data Model:
 Features themes use coordinate and lines to represent geographic features.
 Representation of Raster data:
► Raster grid use cell to represent geographic spatial features.

5. Raster Data Includes:
 Images.
 Scanned maps.
 Air photo.
 Satellite Images.
 Grids DEM.

6. Raster Data Analysis
 Buffering
 Reclassification
 Hill shades
 Interpolation
 Surface calculation

7. Buffering:
 Buffering is the result of classifying cells according to whether the cells lie
inside or outside the buffer
 Output cells are either assigned an in value or an out value.
 In value cells are whenever the cell-to-cell distance is less than the
specified buffer distance,
 While out value cells are further than the buffer distance .
 There are many occasions when buffering is required.
 One example of using a buffer is showing greenway areas around lakes.
 Another example of using a buffer is showing areas along highways where
the traffic noise is above a certain level .

8. Reclassification:
 It is a process of reassigning a values or list of values.
 Reason:
 set specific values to exclude no data from analysis.
► Change values in response to new information or classification schemes.
► To replace one set of values with an associated set
► Example:
► To replace values representing soil types with pH values.
 Assign values of preference, priority, sensitivity, or similar criteria to a
raster.

9. Hill shade:
► By placing an elevation raster on top of a hill shade raster and adjusting
the transparency of the elevation raster.
► To create a visually appealing relief map of a landscape.
Shadows with high sun angle Shadows with low sun angle

10. Interpolation:
 Interpolation is the process of estimating unknown values between
known values.
 It can be used to predict unknown values for any geographic point data,
 such as elevation, rainfall, chemical concentrations, and noise levels
 Why inter
 that spatially distributed objects are spatially correlated; in other words,
things that are close together tend to have similar characteristics.

11. Input elevation point data Interpolated elevation surface

12. Surface calculation:
 Cell by cell evaluation of Mathematical functions

13. Tools For Raster Analysis:
 Map algebra
 Hill shades
o Slopes
o Aspects
► Raster modeling
► Raster and vector integration
o Raster to polygon conversion
o Contour generation
o Surface interpolation from point data

14. Raster Data Analysis Operations:
 Local (cell-by-cell) operations
 Neighborhood operations
 Zonal operations
 Raster distance measure operations - similar to buffering

15. Local Operations
 Cell-by-cell based,
 Creates a new raster from either a single or multiple input raster.
 Includes Reclassification and Map Algebra.

16. Neighborhood operations:
 Neighborhood operations are a method of analyzing data in a GIS environment.
 They are especially important when a situation requires the analysis of relationships
 between locations, rather than interpret the characteristics at individual locations.

17. Zonal Operations:
 Uses groups of cells that have the same value or like features
 Can be contiguous or non contiguous
 For single raster zonal
 Operations measure the geometry of each zone (area, perimeter,
thickness, centroid)
 For two raster (an input raster and a zonal raster) a summary of values
for the input values in
 each zone of the zonal raster is generated in an output raster (summary
statistics and measures)

19. How does it work?
 An imaginary grid is placed over an area,
 Each cell in the grid is given a numeric code.
 Number was given to descried the dominant attributes in the data
 Each cell may be
 Qualitative
 Numeric
 A feature identifier