Advanced GIS Modeling.pptx
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1、Spatial Modeling Review Advanced Modeling Stephen L. Sperry CRP 834 The This presentation is based on 2011 Esri User Conference Workshops Fall 2012CRP 834 Lecture 11: Advanced Modeling 2 Outline Model types Descriptive versus process Static versus dynamic Deterministic versus stochastic Modeling tec
2、hniques for adding time Fire model dynamic modeling introduction Additional Modeling Tools Fall 2012CRP 834 Lecture 11: Advanced Modeling 3 The Problem Modeling phenomenon as static events or have been generalizing time in the modeling process The phenomenon is reactive to what is occurring around i
3、t each time step Try to model time more explicitly Fall 2012CRP 834 Lecture 11: Advanced Modeling 4 Context - Types of Models DescriptiveProcess Model Types Based on the attributes at a particular location you assign weights and preferences You describe or quantify what is there Example - A housing
4、suitability model Describes a physical process This is not to be confused with a ModelBuilder process Example A hydrologic model of current conditions Fall 2012CRP 834 Lecture 11: Advanced Modeling 5 What Do You Model? The actual physical event or phenomenon Predict how the fire will grow or urban d
5、evelopment will spread The error inherent in the input and in the processing In the input data Measurement Local variation Outdated information In the parameters In the processes of the modeling tools In the assumptions Addressing error: With a priori knowledge of the error distribution Error propag
6、ation or scenarios 1 2 Fall 2012CRP 834 Lecture 11: Advanced Modeling 6 What Do You Model? (cont) Perform sensitivity analysis Understand the interaction of the parameters no randomness Systematically change one parameter (or input) to see how output changes Small change causes big change in output,
7、 model sensitive to the parameter Robust if the results do not change much 3 Fall 2012CRP 834 Lecture 11: Advanced Modeling 7 Context - Types of Process Models StaticDynamic Process Based on the conditions for a specified time interval; a slice of time Event: Define a stream network Error: Change th
8、e channel roughness coefficient in a hydrologic model Sensitivity: Change the threshold for a stream network model Time is explicit The output from one time interval is feed as the input to the next time step Event: Wildfire growth model Error: Vary the DEM (affecting slope) for each model run for w
9、ildfire growth Sensitivity: Systematically change a wind speed parameter in wildfire model Fall 2012CRP 834 Lecture 11: Advanced Modeling 8 Context - Types of process models DeterministicStochastic Process The precise outcome can be predicted Because full knowledge of the process and its relationshi
10、ps are understood Event: Wildfire growth model Error: Alter income layer in housing suitability Sensitivity: Change weight for distance to road for suitability model Events appear random Random or you do not have complete information and understanding? Results are probabilities Event: Parameter Wild
11、fire with spotting Error: Randomly add error to DEM in a stream network model Fall 2012CRP 834 Lecture 11: Advanced Modeling 9 Putting it Together CRP 834 Lecture 11: Advanced Modeling Fall 2012 10 How to model time explicitly The length of a time step must be identified The characteristics of the p
12、henomenon What you know of the phenomenon The resolution of the data Need a “Model in a model” A set of general movement rules must be defined that can be applied to the phenomenon each time step The rules must be applied to the phenomenon each time step - iterators Different responses might occur d
13、epending on the status of the phenomenon branching and merging Fall 2012CRP 834 Lecture 11: Advanced Modeling 11 How to model time explicitly The status of the landscape may change each time step feedback looping Multiple output will be created inline variable substitution Since you cannot precisely
14、 model every move or decision each time step some randomness must be used random Fall 2012CRP 834 Lecture 11: Advanced Modeling 12 Random number generation Need to create random numbers - Calculate Value tool Example: CalculateValue(“arcgis.Rand(integer 1 1000)“) A number of distributions are suppor
15、ted UNIFORM minimum, maximum INTEGER minimum, maximum NORMAL mean, standard deviation EXPONENTIAL mean POISSON mean GAMMA alpha, beta BINOMIAL N, probability GEOMETRIC probability PASCAL N, probability Fall 2012CRP 834 Lecture 11: Advanced Modeling 13 More on random number generation Need to reprodu
16、ce the random results seed The seed value that is used by the random function comes from a Random Number Generator environment setting Three generator types are supported Standard C Rand() function ACM collected algorithm 599 Mersenne Twister mt19937 Settings may be specifically set for each process
17、 Supports global vs. local streams Fall 2012CRP 834 Lecture 11: Advanced Modeling 14 More on random number generation Need to create a raster with random values with a given cell size and within a given extent - Create Random Raster Provides the same distribution options supported by the ArcGIS rand
18、 function Fall 2012CRP 834 Lecture 11: Advanced Modeling 15 Random points and assigning random values Need to randomly place a specified number of points in a new feature class. Points may be placed within one or more constraining polygons - Create Random Points Starting points for a simulation Repl
19、icate the spread of a phenomenon (e.g. from air born) Need to be able to create random values for fields - Calculate Field CalculateField sample.shp value “arcgis.rand(Normal,0,10)” Fall 2012CRP 834 Lecture 11: Advanced Modeling 16 Iteration and Feedback Fire Growth Model Fire Growth Model ( (Concep
20、tual)Conceptual) 1717 Fire Growth Model The fire model in the modeling framework At A, the two random variables for wind speed and direction are input into a conditional branch B is where the random surface is created identifying the temperature of the fire for each location C is the surface identif
21、ying the location of existing fires C1 becomes the input to C once the iteration is complete D is the resistance surface D1 becomes the input to D Fall 2012CRP 834 Lecture 11: Advanced Modeling 18 Fire Growth Model (cont) A burning fire after 13 iterations Note the leading edge of the fire is bright
22、 red. With each time step, each burning cell reduces its intensity of red until burning out A continuation of the burning fire after 16 iterations CRP 834 Lecture 11: Advanced Modeling Fall 2012 19 Fire model characteristics(cont) Move all or nothing based on a series of criteria Environmental facto
23、rs Wind speed, wind direction, rain, and temperature Characteristics of the fire Temperature of the fire Characteristics of the landscape Fuel load, aspect, slope Output of one time step is the input to the next time step Many aspects are not fully understood Wind, temperature, spotting Fall 2012CRP
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