Raster Analysis
Perimeter = # of cells of the perimeter * resolution
Perimeter of cells is more estimated than calculated
Units are the same as the units in the resolution
For vector analysis, we calculate the perimeter based on the coordinates, so it is more precise than in raster analysis
Problem: the more complex the polygon, the more grid cells will be at the diagonal to their neighbour, resulting in less accurate results
Pythagoras theorem to find the diagonal distance of the cell (L2 = d12 + d22 = 2*d2)
Diagonal distance > lateral distance
Area = # of cells * area of a cell
- Area of a cell is calculated by the resolution
There are 2 ways of measuring distance in raster data:
Euclidean distance
For each cell, the distance is calculated to the source cells(s) by calculating the hypotenuse of the square triangle and multiplying the distance by the resolution
Cell distance
Calculate the distance for the origin through the center of neighboring cell
Useful for calculating the path from different points of the grid (i.e. shortest path)
Useful for cost distance
Calculates the least accumulative cost distance for each cell from or to the least-cost source over a cost surface.Method of calculating cost distance, is by calculating the cost of the links between the cells or the importance of each cell:
*Lateral link = 1 * ((Ci + Cj)/2)
Diagonal link: 1.414 * ((Ci + Cj)/2)
Where:
Ci – Value of cell i (seen in Cost_Ras)
Cj – value of cell j (seen in Cost_Ras)
From the cost distance, derive the accumulative cost path and choose the least accumulative cost path
Sum of all the accumulative cost between a destination and the source
Keep the path with the lowest value
Requires: a source raster, a cost raster, derived distance cost, a destination, and an algorithm for calculating the least cost path
Transform a raster file into a vector file (features)
All neighboring cells are connected to each other to determine line or polygon limits
Points become the center
Converting vector to raster
The value of each cell is based on the value of ONE selected field
Different methods to determine the value of a cell sharing multiple polygons
Errors are common in the converting process
The functions associated with raster cartographic modeling can be divided into five types:
Local functions
Works on a cell in a single location
Focal functions
Work on cell locations within a neighbourhood
Zonal functions
Work on cell locations within zones
Global functions
Work on all cells within the raster
Applications
Those that preform a specific applications (i.e. hydrologic analysis function)
Overlaying data means to put the data on top of each other, allowing us to combine several grids and produce a new raster layer.
Combine 2 or more raster files
You may need to reclassify one (or more) of the files to create a useful overlay
Raster calculator - use arithmetic, logical, and Boolean operators to select areas of interest
You can add one grid to another one:
The 0-class typically refers to a component that we are not interested in investigating
Masking allows selection of pixels from one grid based on pixels from another grid when we are overlaying raster data:
Mask grid must be binary (0 or 1)
0 = I don’t want
1 = I want
The mask can be used to ‘clip’ other layers
If one grid has a higher resolution compared to another, you should divide the bigger resolution one to match the smaller resolution grid to not lose information (Resampling)
Perimeter = # of cells of the perimeter * resolution
Perimeter of cells is more estimated than calculated
Units are the same as the units in the resolution
For vector analysis, we calculate the perimeter based on the coordinates, so it is more precise than in raster analysis
Problem: the more complex the polygon, the more grid cells will be at the diagonal to their neighbour, resulting in less accurate results
Pythagoras theorem to find the diagonal distance of the cell (L2 = d12 + d22 = 2*d2)
Diagonal distance > lateral distance
Area = # of cells * area of a cell
- Area of a cell is calculated by the resolution
There are 2 ways of measuring distance in raster data:
Euclidean distance
For each cell, the distance is calculated to the source cells(s) by calculating the hypotenuse of the square triangle and multiplying the distance by the resolution
Cell distance
Calculate the distance for the origin through the center of neighboring cell
Useful for calculating the path from different points of the grid (i.e. shortest path)
Useful for cost distance
Calculates the least accumulative cost distance for each cell from or to the least-cost source over a cost surface.Method of calculating cost distance, is by calculating the cost of the links between the cells or the importance of each cell:
*Lateral link = 1 * ((Ci + Cj)/2)
Diagonal link: 1.414 * ((Ci + Cj)/2)
Where:
Ci – Value of cell i (seen in Cost_Ras)
Cj – value of cell j (seen in Cost_Ras)
From the cost distance, derive the accumulative cost path and choose the least accumulative cost path
Sum of all the accumulative cost between a destination and the source
Keep the path with the lowest value
Requires: a source raster, a cost raster, derived distance cost, a destination, and an algorithm for calculating the least cost path
Transform a raster file into a vector file (features)
All neighboring cells are connected to each other to determine line or polygon limits
Points become the center
Converting vector to raster
The value of each cell is based on the value of ONE selected field
Different methods to determine the value of a cell sharing multiple polygons
Errors are common in the converting process
The functions associated with raster cartographic modeling can be divided into five types:
Local functions
Works on a cell in a single location
Focal functions
Work on cell locations within a neighbourhood
Zonal functions
Work on cell locations within zones
Global functions
Work on all cells within the raster
Applications
Those that preform a specific applications (i.e. hydrologic analysis function)
Overlaying data means to put the data on top of each other, allowing us to combine several grids and produce a new raster layer.
Combine 2 or more raster files
You may need to reclassify one (or more) of the files to create a useful overlay
Raster calculator - use arithmetic, logical, and Boolean operators to select areas of interest
You can add one grid to another one:
The 0-class typically refers to a component that we are not interested in investigating
Masking allows selection of pixels from one grid based on pixels from another grid when we are overlaying raster data:
Mask grid must be binary (0 or 1)
0 = I don’t want
1 = I want
The mask can be used to ‘clip’ other layers
If one grid has a higher resolution compared to another, you should divide the bigger resolution one to match the smaller resolution grid to not lose information (Resampling)