Assignment 3 Cartographic Modeling –Raw Spatial Data  Map Product for Decision Making Steps –Define Goal –Define important social or biophysical factors –Select modeling method –Develop database –Process data to create new information if needed –Convert factor data to reflect relative Value to Goal –Combine environment factor valuations to create final map. –Visualization

Process Goal: Create a habitat map for B. pondesii Define Factors: –Vegetation Type – likes hardwoods –Water – lives near ponds –Human Use – avoid human use areas Select Modeling Method: Binary Combination Develop Database: –Human Use: Roads, Town –Water: Water –Vegetation Type: Trees

Process Process data if needed –Reclassify Water create Ponds (1, Nodata) –Find Euclidean distance from ponds –Reclassify Town and Roads (1, Nodata) –Find Euclidean distance from both Town and Roads (using reclasses) –Combine Euclidean distance grids for Town and Roads using MIN function to get the minimum distance from either (i.e. MinDist) Convert factors to relative values (Binary: 1,0) –Hydro = Con(Euc_pond == 0, 0, Con(Euc_pond <= 500, 1,0)) –Hardwood = Con(Trees == 1, 1,0) – Human = Con(MinDist <= 150, 0,1) Combine factors using different local operators –Boolean: AND, OR –Arithmetic: + –Combinations

Euclidean Distance Minimum distance from a source (Value <> NoData) Input grid must have at least one source cell with the rest of the grid being NoData. Global Function Point 1 10, 15 Point 2 15,20

Euclidean Distance Source Cells have values. All other cells have NoData. Output represents the minimum distance to any source cell. Distance is in map units.

Boolean Logic All queries use Boolean Logic. Boolean logic involves True/False sets (Yes/No, 1/0) on which Boolean logical operators (or connectors) such as AND, OR, NOT and XOR can be applied. Going back to basic set theory, a group of individuals are either in a specific set or not. With Grids 0 = “not in set”, all other values indicate a cell is in the specified set. I always create a 1/0 Conditional Grid. NoData in a Grid will return NoData.

Boolean Operators AND, OR, XOR, NOT AND Decreasive Controlled by Low Values AND = AB AB

Boolean Operators AND, OR, XOR, NOT OR Increasive Controlled by High Values OR = AB AB

Boolean Operators AND, OR, XOR, NOT XOR In A OR B, but not in A AND B XOR = AB AB

Boolean Operators AND, OR, XOR, NOT NOT In A, but NOT B NOT = AB AB

Reclassification Methods Lookup – based on VAT attributes Reclassify – new to 9.2 Con Function

Lookup Creates a new raster by looking up values found in another field in the table of the input raster. Inputs: Input Raster, Field, Output Raster. Field must be numeric; Integer or Floating Point. Input must be integer, Output can be either. ValueCountAttr Input ValueCount Output

Reclassify Reclassifies or changes the values in a raster. Inputs: Input Raster, Field, Reclassification Method, Output raster. The input can be any numerical type. The output raster will always be of integer type. Many reclassification methods available, including Remap Tables created in the Reclassify tool. You are putting all the input data into classes. The reclassification scheme must be inclusive, else the algorithm will make classes using the remaining values through truncation.

Reclassify Algorithm You define thresholds from reclassification, the algorithm then applies a local conditional If/Else statement to the information. Basic If/Else formulation (to create 3 classes): If (input value is): >= Minimum Value - <= Threshold 1 = Class 1 Else > Threshold 1 - <= Threshold 2 = Class 2 Else > Threshold 2 - <= Maximum Value = Class 3

Con Also Conditional approach. Can be based on more than one input raster. Local function. Input and Output can be any numerical type. –Con can be used to create a floating point output. Should also be inclusive, else undefined values will be converted to NoData.

CON Function CONditional Expression One of the most powerful local map algebra functions. Apply in Single Output Map Algebra Function or Raster Calculator Basic Format: –Output = CON (Condition, True_expression, False_expression) Examples –Output = con(input1 > 5, 10, 100) –Output = con(input1 > 5, 10) –Output = con(input1 < 5, 10, con(input1 < 15, 50, 100)) –Ouput = con((input1 + input2) < = 5, sin(input1), cos(input2))

Examples of Con Function Outgrid = con(Ingrid > 5, 10, 100) Ingrid Outgrid

Examples of Con Function If no value or expression is specified for the false expressions: –Outgrid = con(Ingrid > 5, 10) the results will be the same as the above output except the cells that have a value of 5 or less Ingrid will be assigned NoData in Outgrid.

Examples of Con Function Multiple conditional statements can be used within the Con function, but each must have a value or expression that can be used to assign values to the output cells if the result of the evaluation of the condition is true. –outgrid = con(Ingrid1 < 5, sin(Ingrid1), con(Ingrid1 < 20, cos(Ingrid1), con(Ingrid1 > 50, 100, 0))) The function is sequential, so the second condition will acknowledge the first condition. How would the outgrid cells be defined for Ingrid1 values between 20 and 50?