Advanced GIS Modeling.pptx

advanced modeling,stephen l. sperry crp 834,the this presentation is based on 2011 esri user conference workshops,fall 2012,crp 834 lecture 11: advanced modeling,outline,model types descriptive versus process static versus dynamic deterministic versus stochastic modeling techniques for adding time fire model dynamic modeling introduction additional modeling tools,fall 2012,crp 834 lecture 11: advanced modeling,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 it each time step try to model time more explicitly,fall 2012,crp 834 lecture 11: advanced modeling,context - types of models,descriptive,process,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 suitability model,describes a physical process this is not to be confused with a modelbuilder process example a hydrologic model of current conditions,fall 2012,crp 834 lecture 11: advanced modeling,what do you model?,the actual physical event or phenomenon predict how the fire will grow or urban development will spread the error inherent in the input and in the processing in the input data measurement local variation outdated information in the parameters in the processes of the modeling tools in the assumptions addressing error: with a priori knowledge of the error distribution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 propagation or scenarios,1,2,fall 2012,crp 834 lecture 11: advanced modeling,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, model sensitive to the parameter robust if the results do not change much,3,fall 2012,crp 834 lecture 11: advanced modeling,context - types of process models,static,dynamic,process,based on the conditions for a specified time interval; a slice of time event: define a stream network error: change the 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 wildfire growth sensitivity: systematically change a wind speed parameter in wildfire model,fall 2012,crp 834 lecture 11: advanced modeling,context - types of process models,deterministic,stochastic,process,the precise outcome can be predicted because full knowledge of the process and its relationships 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 wildfire with spotting error: randomly add error to dem in a stream network model,fall 2012,crp 834 lecture 11: advanced modeling,putting it together,crp 834 lecture 11: advanced modeling,fall 2012,how to model time explicitly,the length of a time step must be identified the characteristics of the phenomenon 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 depending on the status of the phenomenon branching and merging,fall 2012,crp 834 lecture 11: advanced modeling,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 model every move or decision each time step some randomness must be used random,fall 2012,crp 834 lecture 11: advanced modeling,random number generation,need to create random numbers - calculate value tool example: calculatevalue(“arcgis.rand(integer 1 1000)“) a number of distributions are supported 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 2012,crp 834 lecture 11: advanced modeling,more on random number generation,need to reproduce 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 supports global vs. local streams,fall 2012,crp 834 lecture 11: advanced modeling,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 function,fall 2012,crp 834 lecture 11: advanced modeling,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 replicate 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 2012,crp 834 lecture 11: advanced modeling,iteration and feedback,fire growth model (conceptual),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 identifying 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 2012,crp 834 lecture 11: advanced modeling,fire growth model (cont),a burning fire after 13 iterations note the leading edge of the fire is bright 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,fire model characteristics(cont),move all or nothing based on a series of criteria environmental factors 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 2012,crp 834 lecture 11: advanced modeling,fire model characteristics,introduction to dynamic model creation change the paradigm you can not change another cell value iterators feedback specification of time through the rule set displaying the results - animations,fall 2012,crp 834 lecture 11: advanced modeling,tips on planning a dynamic model,what is the application? what do you want to find? what parameters affect the model? what data do you need? does the model require iteration? iterating what? what do you know about the phenomena? what is the time step for the model? how do you want to display the results? batch process graphs animations,fall 2012,crp 834 lecture 11: advanced modeling,iteration and feedback,additional tools,how to become proficient at building models?,know how to use geoprocessing tools complex operations are accomplished by combining simple operations an introduction to the commonly used gis tools geoprocessing model and script tool gallery know modelbuilder techniques iteration in-line variables model only tools,fall 2012,crp 834 lecture 11: advanced modeling,arcgis online help resource center,geoprocessing book,geoprocessing with modelbuilder,professional library,fall 2012,crp 834 lecture 11: advanced modeling,model iteration,repeat the same set of operations for example: process all feature classes in a workspace,fall 2012,crp 834 lecture 11: advanced modeling,model iterators,fall 2012,crp 834 lecture 11: advanced modeling,iterate tables,model transposes land-cover tables of states with a land-cover type and summarizes the statistics the iteration has been restricted with a wildcard l* and a table type of dbase it only iterates over dbase tables starting with the letter l the tool has two outputs,fall 2012,crp 834 lecture 11: advanced modeling,types of iterators,for: a fixed number of times while: until a condition is reached feature selection: for each feature in a feature class row selection: for each row in a table field value: for each value in a table multi-value: enter values to iterate dataset: for each dataset in a workspace feature class: for each feature class in a workspace files: for each file in a folder rasters: for each raster in a workspace tables: for each table in a workspace workspaces: for each workspace in a folder,fall 2012,crp 834 lecture 11: advanced modeling,model iteration key points,the entire model runs for each iteration only include the things you want run every time in the model use models and sub-models to separate what runs once and what runs for each iteration a model may only have one iterator use sub-models to combine iterators use collect values to pass a list of values back to calling model.,fall 2012,crp 834 lecture 11: advanced modeling,model iteration key points,use the name and value variables as in-line variables in pathnames and equations. dataset iterators can be self-repeating be careful feature, row, and value iterators can be each row or grouped by rows with the same field value. use iterate multi-value to iterate over values selected by browsing for datasets or typing is a set of values.,fall 2012,crp 834 lecture 11: advanced modeling,in-line variable substitution,use to make parameter values more flexible,fall 2012,crp 834 lecture 11: advanced modeling,in-line variable substitution,any string or path parameter can include in-line variables use % to indicate in-line variable keywords: variable names environment setting names built in keywords %n% is the current iteration number,fall 2012,crp 834 lecture 11: advanced modeling,using the %i% system variable with in-line variable substitution,for models that run a process on a list of inputs, each time the process runs, the output will have the same name as the output from the previous run of the process, and the previous output will be overwritten. to avoid overwriting previous outputs in successive iterations, append the name of the output with %i%, will give each output a unique name that indicates its position in the list of inputs.,fall 2012,crp 834 lecture 11: advanced modeling,in-line variable substitution examples,select expression airport_id = %airport_id% make sure to include quotes in the expression if needed! calculate field expression !shape.area! * %conversion_factor% path to dataset %scratchworkspace%out.shp directoryout%n%,fall 2012,crp 834 lecture 11: advanced modeling,model only tools,useful utility functions in modelbuilder,fall 2012,crp 834 lecture 11: advanced modeling,model only tools: calculate value,any python expression can supply a code block for complex logic can set any output data type useful for transforming strings into any data type,fall 2012,crp 834 lecture 11: advanced modeling,model only tools: select data,use to expose sub-datasets from the results of tools like solve, import from cad,fall 2012,crp 834 lecture 11: advanced modeling,model only tools,collect values use to collect values in an iterative model use to combine 2 multi-values into one use to convert a list to a multi-value get field value use to get a single value from a table merge branch use to bring multiple processing branches back together,fall 2012,crp 834 lecture 11: advanced modeling,model only tools,parse path use to parse the path and file from full path names stop use to stop an iterative model. similar to while can be combined with an iterator example: run 10 times or until some condition is true.,fall 2012,crp 834 lecture 11: advanced modeling,summary,descriptive models rich set of tools process; event, error analysis, and sensitivity analysis models static or dynamic suppo