SLEUTH: Land Transformation Model


Identification Information      
Model Title SLEUTH    
Version of Model 3.0    
Responsible Party of Model Compound    
  Responsible Party Individual Name Keith Clarke  
  Organization Affiliated with Responsible Party University of California, Santa Barbara, Geography Department  
  Position Name of Responsible Party Professor  
  Responsible Party Contact Information Compound  
    Delivery Point University of California, Santa Barbara
      Ellison Hall 3611
    City Santa Barbara
    Administrative Area CA
    Postal Code 93106-4060
    Country Unites States of America
    Electronic Mail Address kclarke@geog.ucsb.edu
    Telephone Number (805) 893-7961
    Facsimile Number (805) 893-3146
Date of Creation February, 2000    
Model Citation Clarke, K., (2000) SLEUTH: Land Cover transition Model, version 3.0, http://www.ncgia.ucsb.edu/projects/gig/project_gig.htm    
       
Intended Use      
Application Purpose 002 - Education    
  004 - Public Administration    
  099 - Other    
Other Application Purpose Urban Sprawl    
Educational Level 006 - Graduate Studies    
       
Description      
Conceptual Model Description SLEUTH: land cover transition model is a cellular automata urban growth model which is intended to simulate urban growth ranging in spatial extent from individual metropolitan areas to national coverage in order to aid in understanding how expanding urban areas consume their surrounding land, and the environmental impact it has on the local environment. This model simulates the transition from non-urban to urban land-use using a grid of cells (cellular automata) and of whose land-use state is dependent upon local factors (e.g. roads, existing urban areas, topography), temporal factors, and random factors. The growth is based on four "growth rules" or types of growth which include Spontaneous Growth, Road Influence, New Spread Center, and Edge Growth.     
The first in the time series of input data files act as the seed data on which these growth rules project the urbanization of the area. The modeler has influence over these growth rules through five coefficients. These coefficients include dispersion, breed, spread, slope and road gravity. These coefficients influence the rate of urbanization in a variety of methods. The subsequent input files affect the dynamic properties of the coefficients.    
The Model Process includes three phases: Calibration, Prediction and Testing. The calibration mode is a process by which a set of best fit parameters are derived for a data set. There are three phases of calibration: coarse, fine and final. After parameter values have been selected these are used as input for urban and landcover prediction. The test mode has two functions. The first is testing and confirming the functions of the model. Test mode will take input named in the schedule files and perform a single run on the data. (e.g.; the excluded area is in fact not being urbanized.)     
If the images do not make sense there is probably an error in the input data format. The second function of the test mode is creating animated frames to simulate historical growth or create a single projection of the future. Calibrate file options: (0) "existing" will execute the model with the calibrate file that was created last (1) "new" will create a new calibrate file and overwrite previously entered preferences (2) "crashed" will restart the model from the last set of coefficient parameters in the case of a run interruption.     
Model Typology 011 - Cellular Automata    
Topic or Field of Study 1205 - Urban Development    
Related Model Compound    
  Related Model Citation Batty, M., (1995) "The Computable City," Keynote Address: Fourth International Conference on Computers in Urban Planning and Urban Management, Melbourne, Australia, July 11-14. http://www.geog.buffalo.edu/Geo666/batty/melbourne.html   
    Batty, M., Longley, P., (1994a) Fractal Cities. London: Academic Press.   
    Batty, M., Xie, Y., (1994b) "From Cells to Cities," Environment and Planning B vol. 21; S31-S48.   
    White, R., Engelen, G., (1992a) "Cellular dynamics and GIS: modelling spatial complexity," working paper no. 9263, Research Institute for Knowledge Systems (RIKS), Maastricht, The Netherlands.   
    White, R., Engelen, G., (1992b) "Cellular automata and fractal urban form: a cellular modelling approach to the evolution of urban land use patterns," working paper no. 9264, Research Institute for Knowledge Systems (RIKS), Maastricht, The Netherlands.  
Source of Additional Information Compound    
  Additional Information Text Additional information can be found at the project web site.  
  Additional Information URL Address http://www.ncgia.ucsb.edu/projects/gig/project_gig.htm  
       
Access and Availability      
Access or Use Constraints 001 - None: Public Domain    
Other Constraints None     
Availability Contact Compound    
  Availability Contact Individual Name Keith Clarke  
  Organization Affiliated with Availability Contact University of California, Santa Barbara, Geography Department  
  Position Name of Availability Contact Professor  
  Availability Contact Information Compound  
    Delivery Point University of California, Santa Barbara
      Ellison Hall 3611
    City Santa Barbara
    Administrative Area CA
    Postal Code 93106-4060
    Country Unites States of America
    Electronic Mail Address kclarke@geog.ucsb.edu
    Telephone Number (805) 893-7961
    Facsimile Number (805) 893-3146
Ordering or Access Procedure The model can be downloaded directly from the following web site: http://www.ncgia.ucsb.edu/projects/gig/download.htm    
       
System Requirements      
Hardware Requirements A PC workstation or mainframe    
Software Requirements X- Windows, (for dynamic output amnimations) whirlgif, C compiler    
Operating System Unix or Linux    
Expertise Required Compound    
  Expertise to Obtain The model and associated files are available for download from a public HTML account. Knowledge required to download includes basic understanding of HTML navigation and zip file conversions.  
  Expertise to Run The minimum required knowledge required to run the model includes: C programming language, geographic data and information systems, cellular automaton modeling, and land cover modeling, as well as covering the information available at the documenting web site: http://www.ncgia.ucsb.edu/projects/gig/v2/Imp/implement.htm  
       
Input Data Requirements      
Input Data Extent and Resolution Compound    
  Spatial Resolution and Extent Explanation The spatial scale and extent are user-defined, and, therefore, are variable. Although it has been used on a city to county scale, The model extent is being increased towards national and global scale.  
  Temporal Resolution It is assumed that one monte carlo growth cycle represents a year of growth.  
  Temporal Extent For statistical calibration of the model at least four urban time periods must be used, although a result can be obtained with only two. Also, for purposes of calibration, the roads must be represented in two or more time periods. The model requires two landuse layers for deltatron landuse modeling. The model can operate more then 100 years into the past and up to 50 years into the future.  
Input Data File http://www.ncgia.ucsb.edu/projects/gig/v2/About/abData.htm; http://www.ncgia.ucsb.edu/projects/gig/v2/About/abGrowth.htm    
Input Modeling Construct Description Compound    
  Name of Construct Slope  
  Construct Classification 02 - adjustable static parameter  
  Construct Description As a dataset member, values in each cell of the dataset .gif file represents the percentage of slope at that pixel address.  
  Construct Input Source dataset member  
  Dataset slope_data  
  Construct Type integer in each cell of the dataset .gif file  
  Construct Units percentage  
  Minimum Value  0  
  Maximum Value 100  
  Construct Repeatability 1  
  Construct Comments The slope is commonly derived from a digital elevation model (DEM), but other elevation source data may be used. Cell values must be in percent slope, not degree, which is a common default in some GIS software.  
       
  Name of Construct Land Use  
  Construct Classification 02 - adjustable static parameter  
  Construct Description As a dataset member, values in each cell of the dataset .gif file represents the land use at that pixel address.  
  Construct Input Source dataset member  
  Dataset landuse_data  
  Construct Type integer in each cell of the dataset .gif file  
  Construct Units values vary depending on the land use of the cell location. Each cell value should represent a unique land class. These values can be based on such schaemas as the Anderson Classification System, which is used to classify land cover.  
  Minimum Value  0  
  Maximum Value 255  
  Construct Repeatability 1  
       
  Name of Construct Excluded  
  Construct Classification 02 - adjustable static parameter  
  Construct Description As a dataset member, values in each cell of the dataset .gif file represents the presents or absence of excluded, or undevelopable, land at that pixel address.  
  Construct Input Source dataset member  
  Dataset excluded_data  
  Construct Type integer in each cell of the dataset .gif file  
  Construct Units Two options are available for this construct. The values can be based on a binary value (zero representing locations able to be urbanized), or can range between 0 - 100 based on the degree (based on percentage value) of existing or desired exclusion of that pixel location. For example, wetlands would be represented with a very low (percentage) value, based on the idea that the location is a desired area to preserve or that the water is difficult to divert in order to urbanize a location.   
  Minimum Value  0  
  Maximum Value 100  
  Construct Repeatability 1  
  Construct Comments Each pixel can be assigned a value between 0 - 255, however, values over 100 are read as 100.  
       
  Name of Construct Urban  
  Construct Classification 02 - adjustable static parameter  
  Construct Description As a dataset member, values in each cell of the dataset .gif file represents a binary value or presence or absence of urban land use at that pixel address. The values can also be varied by the user to represent different urban land use practices at the pixel location.  
  Construct Input Source dataset member  
  Dataset urban_data  
  Construct Type integer in each cell of the dataset .gif file  
  Construct Units The definition of "urban extent" is at the discretion of the creator of the dataset. The model requires a simple binary classification of urban/non-urban.  
  Minimum Value  0  
  Maximum Value 255  
  Construct Repeatability 1  
  Construct Comments The urban extent for the start year, or seed, is used to initialize the model and is the basis for the CA driven urban growth. For calibration, the earliest urban year is used as the seed, and subsequent urban layers, or control years, are used to measure several statistical best fit values. For this reason, at least four urban layers are needed for calibration.  
       
  Name of Construct Transportation  
  Construct Classification 02 - adjustable static parameter  
  Construct Description As a dataset member, values in each cell of the dataset .gif file represents the presence or absence of a road at that pixel address. The representation can be binary (road/no road) or can have a weighted, relative value depending on the accessibility or use of the road.  
  Construct Input Source dataset member  
  Dataset road_data  
  Construct Type integer in each cell of the dataset .gif file  
  Construct Units The value selected for roads can represent a a binary (road present / road not present) or can rank the accessibility or usage of the road based on the modelers' discresion.  
  Minimum Value  0  
  Maximum Value 255  
  Construct Repeatability 1  
  Construct Comments The road influenced growth dynamic included in SLEUTH simulates the tendency of urban development to be attracted to locations on increased accessibility.   
       
  Name of Construct Hill Shade  
  Construct Classification 02 - adjustable static parameter  
  Construct Description As a dataset member, values in each cell of the dataset .gif file assumed hillshade image of the extent of the study area. The primary function of this dataset is assist in understanding the spatial context of the region.  
  Construct Input Source dataset member  
  Dataset background_data  
  Construct Type integer in each cell of the dataset .gif file  
  Construct Units The pixel values represent the hill shade of the image, based on the aspect and slope of the particular cell which is derived from the DEM or other digital elevation data.  
  Minimum Value  0  
  Maximum Value 255  
  Construct Repeatability 1  
       
  Name of Construct Dispersion Coefficient  
  Construct Classification 03 - adjustable dynamic parameter  
  Construct Description The dispersion coefficient (previously referred to as diffusion coefficient) controls the number of times a pixel will be randomly selected for possible urbanization.  
  Construct Input Source dataset member  
  Dataset scenario  
  Construct Type integer value  
  Construct Units Percentage value based on the likelihood of dispersion  
  Minimum Value  0  
  Maximum Value 100  
  Construct Repeatability 1  
  Construct Comments Effects Spontaneous Growth and the search distance along the road network as part of the Road Influence Growth.  
       
  Name of Construct Breed Coefficient  
  Construct Classification 03 - adjustable dynamic parameter  
  Construct Description The breed coefficient determines the probability of a spontaneous growth pixel becoming a new spreading center. The breed coefficient also determines the number of times a road trip will be taken.  
  Construct Input Source dataset member  
  Dataset scenario  
  Construct Type integer value  
  Construct Units A percentage value which determines the likelihood that cell neighboring a previously urbanized cell will also become urbanized.  
  Minimum Value  0  
  Maximum Value 100  
  Construct Repeatability 1  
  Construct Comments Influences the New Spread Center probability and affects the number of Road Influence Growth attempts.  
       
  Name of Construct Spread Coefficient  
  Construct Classification 03 - Adjustable Dynamic Parameter  
  Construct Description The spread coefficient determines the probability that any pixel that is part of a spreading center (a cluster of urban pixels > 2) will generate an additional urban pixel in its neighborhood.  
  Construct Input Source dataset member  
  Dataset scenario  
  Construct Type integer value  
  Construct Units A percentage value which influences the likelihood of urban spread.   
  Minimum Value  0  
  Maximum Value 100  
  Construct Repeatability 1  
  Construct Comments The spread coefficient determines the probability of Organic Growth from established urban pixels occurring.   
       
  Name of Construct Slope Coefficient  
  Construct Classification 03 - Adjustable Dynamic Parameter  
  Construct Description The slope_coefficient affects all growth rules in the same way: When a location is being tested for suitability for urbanization, the topography, or slope, at that location is considered. Instead of enforcing a simple linear relationship between slope and urban development, the slope_coefficient acts as a multiplier. If the slope coefficient is high, increasingly steeper slopes are less likely to urbanize. As the slope coefficient gets closer to zero, an increase in local slope has less affect on the likelihood of urbanization.  
  Construct Input Source dataset member  
  Dataset scenario  
  Construct Type integer value  
  Construct Units Percentage value influencing the development of urban areas on slopes.  
  Minimum Value  0  
  Maximum Value 100  
  Construct Repeatability 1  
  Construct Comments As value increases, the ability to urbanize steepening slopes decreases.  
       
  Name of Construct Road Gravity Coefficient  
  Construct Classification 03 - Adjustable Dynamic Parameter  
  Construct Description The maximum search distance for a road from a pixel selected for a road trip is determined as some proportion of the image dimensions.  
  Construct Input Source dataset member  
  Dataset scenario  
  Construct Type integer value  
  Construct Units The value assigned to the road gravity coefficient influences the affect that roads have on urban development. The exact method is described below under "construct comments"  
  Minimum Value  0  
  Maximum Value 100  
  Construct Repeatability 1  
  Construct Comments Applied value is derived from coefficient by: rg_value = (rg_coeff/MAX_ROAD_VALUE) * (row + col) where MAX_ROAD_COEFF_VALUE is defined as 100, and (row, col) are the row and column pixel counts, so that rg_value at its maximum (rg_coeff == 100) will be 1/16 of the image dimensions. If the rg_coeff value is less than 100, then the rg_value will be some proportion less than 1/16 of the image dimensions. rg_value is applied to spontaneous growth by: max_search_index = 4 * (rg_value * (1 + rg_value)) where rg_value defines maximum number of neighborhoods from selected newly urban pixel to search for a road. The first neighborhood (rg_value == 1) is made up of the selected urban pixel's adjacent 8 cells. The second neighborhood (rg_value == 2) would be the 16 pixels outwardly adjacent to the first neighborhood, etc. In this way the outward search for a road will continue until (a) a road is found, or (b) the search distance is greater than max_search_index.  
       
Input Dataset Description Compound    
  Name slope_data  
  Input Dataset File http://www.ncgia.ucsb.edu/projects/gig/v2/About/dtInput.htm  
  Conceptual Data Structure SLEUTH requires an input of grayscale .gif files. The model requires all input data layers to have a consistent number of rows and columns and be in the same projection.  
  Computational Representation grayscale .gif image file  
  Dataset Repeatability 1  
       
  Name landuse_data  
  Input Dataset File http://www.ncgia.ucsb.edu/projects/gig/v2/About/dtInput.htm  
  Conceptual Data Structure SLEUTH requires an input of grayscale .gif files. The model requires all input data layers to have a consistent number of rows and columns and be in the same projection.  
  Computational Representation grayscale .gif image file  
  Dataset Repeatability 2+n  
       
  Name excluded_data  
  Input Dataset File http://www.ncgia.ucsb.edu/projects/gig/v2/About/dtInput.htm  
  Conceptual Data Structure SLEUTH requires an input of grayscale .gif files. The model requires all input data layers to have a consistent number of rows and columns and be in the same projection.  
  Computational Representation grayscale .gif image file  
  Dataset Repeatability 1+n  
       
  Name urban_data  
  Input Dataset File http://www.ncgia.ucsb.edu/projects/gig/v2/About/dtInput.htm  
  Conceptual Data Structure SLEUTH requires an input of grayscale .gif files. The model requires all input data layers to have a consistent number of rows and columns and be in the same projection.  
  Computational Representation grayscale .gif image file  
  Dataset Repeatability 4+n  
       
  Name road_data  
  Input Dataset File http://www.ncgia.ucsb.edu/projects/gig/v2/About/dtInput.htm  
  Conceptual Data Structure SLEUTH requires an input of grayscale .gif files. The model requires all input data layers to have a consistent number of rows and columns and be in the same projection.  
  Computational Representation grayscale .gif image file  
  Dataset Repeatability 1+n  
       
  Name hillshade_data  
  Input Dataset File http://www.ncgia.ucsb.edu/projects/gig/v2/About/dtInput.htm  
  Conceptual Data Structure SLEUTH requires an input of grayscale .gif files. The model requires all input data layers to have a consistent number of rows and columns and be in the same projection.  
  Computational Representation grayscale .gif image file  
  Dataset Repeatability 1  
       
  Name scenario  
  Input Dataset File http://www.ncgia.ucsb.edu/projects/gig/v2/About/data_files/scenario_file.htm  
  Conceptual Data Structure The scenario file consists of, in excess of 50 constructs, divided into 13 sections. These sections are: I: Path Name Variables; II: Running Status; III: Output ASCII Files; IV: Log File Preferences; V: Working Grids; VI: Random Number Seed; VII: Monte Carlo Iteration; VIII: Coefficients; IX: Prediction Date Range; X: Input Images; XI: Output Images; XII: Colortable Settings; XIII: Self Modified Parameters. The description of these constructs can be found in the input data file provided above. Only the 5 coefficients (sec. VIII) have been described in detail under "Input Modeling Construct Description."   
  Computational Representation The scenario file is in an ASCII file format consisting of the formatted construct name followed by the desired value.   
  Dataset Repeatability 1  
       
Data Processing      
Programming Language C program running under UNIX that uses the standard C compiler (cc). It can be formatted for any other standard C compiler.    
Algorithmic Representation Each coefficient has a different influence over the growth rules. Details about the influence of these coefficients can be found at the following file: http://www.ncgia.ucsb.edu/projects/gig/v2/About/gwRules.htm    
Iterative Cycles The model runs on a Monte Carlo iteration cycle. The number of cycles can be set by the modeler before the modeling process begins. The output files and data are in a different format from the input data files.    
       
       
Model Output      
Output Representation Compound    
  Output Name animated_urban.gif  
  Output Description A GIF animation of the <location>_urban_{date}.gif files compiled by whirlgif  
  Output Type visualization  
  Output Symbolic Representation not Numeric  
  Output Computational Representation A series of .gif images constructed in whirlgif to be an animation.  
  Output Visualization dynamic  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally Optional Output  
       
  Output Name animated_land_n_urban.gif  
  Output Description A GIF animation of the <location>land_n_urban_{date}.gif files compiled by whirlgif  
  Output Type visualization  
  Output Symbolic Representation not Numeric  
  Output Computational Representation A series of .gif images constructed in whirlgif to be an animation.  
  Output Visualization dynamic  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally Optional Output  
       
  Output Name animated_z_growth.gif  
  Output Description A GIF animation of the z_growth_types_{run}_{monte carlo}_{date}.gif files compiled by whirlgif  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation A series of .gif images constructed in whirlgif to be an animation.  
  Output Visualization dynamic  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
  Output Name animated_deltron.gif  
  Output Description A GIF animation of the deltatron_{run}_{monte carlo}_{date}.gif files compiled by whirlgif  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation A series of .gif images constructed in whirlgif to be an animation.  
  Output Visualization dynamic  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
  Output Name cumcolor_landuse.gif  
  Output Description A GIF image map of modal or "winning" land classes across monte carlo iterations for the stop date.  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally standard output  
       
  Output Name cumulate_urban.gif  
  Output Description A non-classified GIF image map of urban probabilities for the stop date  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally standard output  
       
  Output Name deltatron_{run}_{monte carlo}_{date}.gif  
  Output Description GIF image maps of deltatrons color classified by age. Images are produced for run, monte carlo, and date range specified by the DELTATRON_TYPE_PRINT_WINDOW flag in the scenario file.  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
  Output Name echo_of_<location>.{attribute}.gif  
  Output Description duplication image of input data  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
  Output Name key_{colormap_type}.gif  
  Output Description A GIF image map of all color table settings except GRAYSCALE (which is automatically set to 0-255). The (row, col) image dimensions are (256, number_of_image_columns).  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
  Output Name <location>_cumcolor_urban_<stop_date>.gif  
  Output Description A GIF image map of color classified urban probabilities for the stop date If land use is not being modeled (urban only) this image is the same as the annual. The hillshade image is used in the background.  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally standard output  
       
  Output Name <location>_land_n_urban_{date}.gif  
  Output Description Annual GIF image maps of urban and land use change for the final monte carlo iteration  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally standard output  
       
  Output Name <location>_urban_{date}.gif  
  Output Description In predict mode: Annual GIF image maps of color classified urban probabilities across all monte carlo iterations. The initializing urban layer is placed upon the urban probabilities and is classified with the SEED_COLOR. The hillshade image is used in the background. In test mode: Annual GIF image maps of urban change. Urban extent is color classified with the SEED_COLOR. The hillshade image is used in the background.  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally standard output  
       
  Output Name z_growth_types_{run}_{monte carlo}_{date}.gif  
  Output Description GIF image maps of urban change color classified by growth type. Images are produced for run, monte carlo, and date range specified by the GROWTH_TYPE_PRINT_WINDOW flag in the scenario file.  
  Output Type visualization  
  Output Symbolic Representation not numeric  
  Output Computational Representation a .gif image  
  Output Visualization static  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
  Output Name avg.log and std_dev.log  
  Output Description Measurements of simulated data, such as number of urban edges, clusters and urban pixels, are saved in the avg.log file. If in test or calibrate mode, for every year that real data exists (a control year) SLEUTH writes out a grid of urban extent (and land cover). Values are calculated from these grids and at the end of a run (a run begins with a single set of coefficient values and is executed n MONTE CARLO ITERATIONS) these values for each year are then averaged over the number of monte carlo iterations, and the result is written to the corresponding run number in control_stats.log. The standard deviations of the averaged values are written to the std_dev.log file. If in test mode, this process is performed for every year, not just the control years.  
  Output Type dataset  
  Output Symbolic Representation numeric  
  Output Computational Representation array of numeric values  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
  Output Name control_stats.log  
  Output Description Control_stats.log is generated in test or calibrate mode. It contains the r2 values of the simulated data (found in the avg.log file) compared to input urban (and land cover) data. The values for the input urban statistics can be written to the LOG# file by setting the LOG_BASE_STATISTICS flag in the scenario file to "YES". This is the primary file used to narrow coefficient ranges during calibration.  
  Output Type dataset  
  Output Symbolic Representation numeric  
  Output Computational Representation array of numeric values  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally standard output  
       
  Output Name coeff.log  
  Output Description Due to SLEUTH's self-modification qualities, these values may be modified slightly after each growth cycle. The coeff.log file stores coefficient values for every run, monte carlo iteration and year. This data is can also be written to the avg.log file. This file is used in the final step of calibration to derive forecasting coefficients.  
  Output Type dataset  
  Output Symbolic Representation numeric  
  Output Computational Representation array of numeric values  
  Output Modeling Construct Description Compound  
    Output Construct Name same as output name
    Output Construct Description same as output description
    Output Construct Dataset same as output name
    Output Construct Type see output representation
    Output Construct Units coded values dependent on specified output
    Output Construct Repeatability 1
    Output Construct Comments the specified dataset only contains one construct
  Output Optionally optional output  
       
Data Post-Processing Requirements No post processing is required. The images can be introduced back into a GIS environment and used as data layers for further analysis in their spatial context. For ArcInfo (for example) -Convert images into Arc acceptable format (e.g.: TIFF) -Convert images into grids with Arc: IMAGEGRID -Georeference grids with Grid: CONTROLPOINTS    
Output Documentation http://www.ncgia.ucsb.edu/projects/gig/v2/About/abData.htm    
Output Comment The model provides versatile output data that can provide useful information for a variety of applications. This includes both numeric and graphic outputs.     
       
Calibration Efforts and Validation      
Confirmation Dataset The downloadable model software is supplied with a test scenario titled: scenario.demo200_test file.    
Calibration Efforts The model is calibrated on the input data sets. The processes are found in the additional information.     
Model Experiments and/or Case Studies Compound    
  Model Experiment Description Documentation of case studies are available through the model experiment URL address.  
  Model Experiment URL Address http://www.ncgia.ucsb.edu/projects/gig/v2/Pubs/pbDynamics.htm  
Expert or Peer Review Leonard Gaydos, Chief (tested model), Research, Technology and Applications, USGS Western Mapping Center, 345 Middlefield Rd, MS 531, Phone: (650)329-4330, Fax: (650)329-4710, E-Mail: lgaydos@usgs.gov    
Current Use or Application As of July, 2001, national (48 contiguous states) and local (Santa Barbara County) data sets are being calibrated by the model. There have been two applications in Portugal. The model scale is being increased towards national and global scale.    
Known Error All known bugs are discussed and resolved at the "Discussion" web site and associated discussion board, both found at the following URL address: http://www.ncgia.ucsb.edu/projects/gig/v2/Discuss/discussion.htm    
       
Metadata Source      
Metadata Creation Date 24-May-01    
Metadata Responsible Party Compound    
  Metadata Responsible Party Individual Name Scott J. Crosier  
  Organization Affiliated with Metadata Responsible Party University of California, Santa Barbara  
  Position Name of Metadata Responsible Party Graduate Student, Geography Dept.  
  Role of Responsible Party Creator  
  Metadata Responsible Party Contact Information Compound  
    Delivery Point University of California, Santa Barbara
      3510 Phelps Hall
    City Santa Barbara
    Administrative Area CA
    Postal Code 93106-4060
    Country Unites States of America
    Electronic Mail Address scott@geog.ucsb.edu
    Telephone Number (805) 893-2714
    Facsimile Number (805) 893-8617
Metadata Source of Information Clarke, K., Project Gigalopolis, http://www.ncgia.ucsb.edu/projects/gig/project_gig.htm    
Metadata Standard Name and Version Alexandria Digital Earth Prototype, Metadata for Models Working Group, Content Standard for Computational Models, Version 1.0, May 21, 2001 Draft    


Last Updated on 8/17/01
By Scott Crosier and Noah Goldstein
Thank you to Jeannette Candau for her input
Email: scott@geog.ucsb.edu