# FILE: 'scenario file' for SLEUTH land cover transition model
# (UGM v3.0 beta)
# Comments start with #
#
# I. Path Name Variables
# II. Running Status (Echo)
# III. Output ASCII Files
# IV. Log File Preferences
# V. Working Grids
# VI. Random Number Seed
# VII. Monte Carlo Iteration
#VIII. Coefficients
# A. Coefficients
and Growth Types
# B. Modes and Coefficient
Settings
# IX. Prediction Date Range
# X. Input Images
# XI. Output Images
# XII. Colortable Settings
# A. Date_Color
# B. Non-Landuse
Colortable
# C. Land Cover Colortable
# D. Growth Type
Images
# E. Deltatron Images
#XIII. Self Modification Parameters
# I. PATH NAME VARIABLES
# INPUT_DIR: relative or absolute path where input image files
are
#
located.
# OUTPUT_DIR: relative or absolute path where all output files
will
#
be located.
# WHIRLGIF_BINARY: relative path to 'whirlgif' gif animation
program.
#
These must be compiled before execution.
INPUT_DIR=../Input/demo200/
OUTPUT_DIR=../Output/
WHIRLGIF_BINARY=../Whirlgif/whirlgif
# II. RUNNING STATUS (ECHO)
# Status of model run, monte carlo iteration, and year will be
# printed to the screen during model execution.
ECHO(YES/NO)=yes
# III. Output Files
# INDICATE TYPES OF ASCII DATA FILES TO BE WRITTEN TO OUTPUT_DIRECTORY.
#
# COEFF_FILE: contains coefficient values for every run, monte
carlo
#
iteration and year.
# AVG_FILE: contains measured values of simulated data averaged
over
#
monte carlo iterations for every run and control year.
# STD_DEV_FILE: contains standard deviation of averaged values
#
in the AVG_FILE.
# MEMORY_MAP: logs memory map to file 'memory.log'
# LOGGING: will create a 'LOG_#' file where # signifies the
processor
# number
that created the file if running code in parallel.
# Otherwise,
# will be 0. Contents of the LOG file are
# described
below.
WRITE_COEFF_FILE(YES/NO)=yes
WRITE_AVG_FILE(YES/NO)=yes
WRITE_STD_DEV_FILE(YES/NO)=yes
WRITE_MEMORY_MAP(YES/NO)=no
LOGGING(YES/NO)=YES
# IV. Log File Preferences
# INDICATE CONTENT OF LOG_# FILE (IF LOGGING == ON).
# LANDCLASS_SUMMARY: (if landuse is being modeled) summary
of input
#
from 'landuse.classes' file
# SLOPE_WEIGHTS(YES/NO): annual slope weight values as affected
#
by slope_coeff
# READS(YES/NO)= notes if a file is read in
# WRITES(YES/NO)= notes if a file is written
# COLORTABLES(YES/NO)= rgb lookup tables for all colortables
generated
# PROCESSING_STATUS(0:off/1:low verbosity/2:high verbosity)=
????
# TRANSITION_MATRIX(YES/NO)= pixel count and annual probability
of
#
land class transitions
# URBANIZATION_ATTEMPTS(YES/NO)= number of times an attempt
to urbanize
#
a pixel occurred
# INITIAL_COEFFICIENTS(YES/NO)= initial coefficient values
for
#
each monte carlo
# BASE_STATISTICS(YES/NO)= measurements of urban control year
data
# DEBUG(YES/NO)= data dump of igrid object and grid pointers
# TIMINGS(0:off/1:low verbosity/2:high verbosity)= time spent
within
# each module. If running in parallel, LOG_0 will
contain timing for
# complete job.
LOG_LANDCLASS_SUMMARY(YES/NO)=yes
LOG_SLOPE_WEIGHTS(YES/NO)=no
LOG_READS(YES/NO)=no
LOG_WRITES(YES/NO)=no
LOG_COLORTABLES(YES/NO)=NO
LOG_PROCESSING_STATUS(0:off/1:low verbosity/2:high verbosity)=1
LOG_TRANSITION_MATRIX(YES/NO)=yes
LOG_URBANIZATION_ATTEMPTS(YES/NO)=no
LOG_INITIAL_COEFFICIENTS(YES/NO)=no
LOG_BASE_STATISTICS(YES/NO)=yes
LOG_DEBUG(YES/NO)= no
LOG_TIMINGS(0:off/1:low verbosity/2:high verbosity)=1
# V. WORKING GRIDS
# The number of working grids needed from memory during model execution
is
# designated up front. This number may change depending upon modes. If
# NUM_WORKING_GRIDS needs to be increased, the execution will be exited
and
# an error message will be written to the screen and to 'ERROR_LOG' in
the
# OUTPUT_DIRECTORY. If the number may be decreased an optimal number will
# be written to the end of the LOG_0 file.
NUM_WORKING_GRIDS=5
# VI. RANDOM NUMBER SEED
# This number initializes the random number generator. This seed will be
used
# to initialize each model run.
RANDOM_SEED=47392074
# VII. MONTE CARLO ITERATIONS
# Each model run may be completed in a monte carlo fashion.
# For CALIBRATION or TEST mode, measurements of simulated data will
be taken
# for years of known data, and averaged over the number of monte carlo
# iterations. These averages are written to the "avg.log"
file, and the
# associated standard deviation is written to the "std_dev.log"
file. The
# averaged values are compared to the known data, and a Pearson correlation
# coefficient measure is calculated and written to the "control.stats.log"
# file. The input per run may be associated across files using the
'index'
# number in the files' first column.
#
MONTE_CARLO_ITERATIONS=2
# VIII. COEFFICIENTS
# The coefficients affect how the growth rules are applied to the data.
# For additional information see our publications and PROJECT
# GIGALOPOLIS web site (www.ncgia.ucsb.edu/project/gig/About/gwCoef.htm)
# and (www.ncgia.ucsb.edu/project/gig/About/gwRules.htm).
# A. COEFFICIENTS AND GROWTH TYPES
# DIFFUSION: affects SPONTANEOUS GROWTH and search
distance along the
#
road network as part of ROAD INFLUENCED GROWTH.
# BREED: NEW SPREADING CENTER probability and affects
number of ROAD
# INFLUENCED
GROWTH attempts.
# SPREAD: the probability of ORGANIC GROWTH from
established urban pixels
#
occurring.
# SLOPE_RESISTANCE: affects the influence of slope
to urbanization. As
#
value increases, the ability to urbanize steepening
#
slopes decreases.
# ROAD_GRAVITY: affects the outward distance from
a selected pixel for
#
which a road pixel will be searched for as part of ROAD
#
INFLUENCED GROWTH.
#
# B. MODES AND COEFFICIENT SETTINGS
# TEST: TEST mode will perform a single run through
the historical
# data using
the CALIBRATION_*_START values to initialize
# growth, complete
the MONTE_CARLO_ITERATIONS, and then conclude
# execution.
GIF images of the simulated urban growth will be
# written to
the OUTPUT_DIRECTORY.
# CALIBRATE: CALIBRATE will perform monte carlo
runs through the
#
historical data using every combination of the coefficient
#
values indicated. The CALIBRATION_*_START coefficient
#
values will initialize the first run. A coefficient
#
will then be increased by its _STEP value, and
#
another run performed. This will be repeated for all possible
#
permutations of given ranges and increments until the
#
_STOP value is reached or exceeded.
# PREDICTION: PREDICTION will perform a single
run, a
#
NUMBER_OF_ITERATIONS times in monte carlo fashion, using
#
the PREDICTION_*_BEST_FIT values for initialization.
CALIBRATION_DIFFUSION_START= 0
CALIBRATION_DIFFUSION_STEP= 25
CALIBRATION_DIFFUSION_STOP= 100
CALIBRATION_BREED_START= 0
CALIBRATION_BREED_STEP= 25
CALIBRATION_BREED_STOP= 100
CALIBRATION_SPREAD_START= 0
CALIBRATION_SPREAD_STEP= 25
CALIBRATION_SPREAD_STOP= 100
CALIBRATION_SLOPE_START= 0
CALIBRATION_SLOPE_STEP= 25
CALIBRATION_SLOPE_STOP= 100
CALIBRATION_ROAD_START= 0
CALIBRATION_ROAD_STEP= 25
CALIBRATION_ROAD_STOP= 100
PREDICTION_DIFFUSION_BEST_FIT= 20
PREDICTION_BREED_BEST_FIT= 20
PREDICTION_SPREAD_BEST_FIT= 20
PREDICTION_SLOPE_BEST_FIT= 2
PREDICTION_ROAD_BEST_FIT= 20
# IX. PREDICTION DATE RANGE
# The urban and road images used to initialize growth during
# prediction are those with dates equal to, or greater than,
# the PREDICTION_START_DATE. If the PREDICTION_START_DATE is greater
# than any of the urban dates, the last urban file on the list will be
# used. Similarly, if the PREDICTION_START_DATE is greater
# than any of the road dates, the last road file on the list will be
# used. The prediction run will terminate at PREDICTION_STOP_DATE.
#
PREDICTION_START_DATE=1990
PREDICTION_STOP_DATE=2010
# X. INPUT IMAGES
# The model expects grayscale, GIF image files with file name
# format as described below. For more information see our
# PROJECT GIGALOPOLIS web site:
# (www.ncgia.ucsb.edu/project/gig/About/dtInput.htm).
#
# IF LAND COVER IS NOT BEING MODELED: Remove or comment out
# the LANDUSE_DATA data input flags below.
#
# < > = user selected fields
# [< >] = optional fields
#
# Urban data GIFs
# format: <location>.urban.<date>.[<user info>].gif
#
#
URBAN_DATA= demo200.urban.1930.gif
URBAN_DATA= demo200.urban.1950.gif
URBAN_DATA= demo200.urban.1970.gif
URBAN_DATA= demo200.urban.1990.gif
#
# Road data GIFs
# format: <location>.roads.<date>.[<user info>].gif
#
ROAD_DATA= demo200.roads.1930.gif
ROAD_DATA= demo200.roads.1950.gif
ROAD_DATA= demo200.roads.1970.gif
ROAD_DATA= demo200.roads.1990.gif
#
# Landuse data GIFs
# format: <location>.landuse.<date>.[<user info>].gif
#
#LANDUSE_DATA= demo200.landuse.1930.gif
#LANDUSE_DATA= demo200.landuse.1990.gif
#
# Excluded data GIF
# format: <location>.excluded.[<user info>].gif
#
EXCLUDED_DATA= demo200.excluded.gif
#
# Slope data GIF
# format: <location>.slope.[<user info>].gif
#
SLOPE_DATA= demo200.slope.gif
#
# Background data GIF
# format: <location>.hillshade.[<user info>].gif
#
BACKGROUND_DATA= demo200.hillshade.gif
# XI. OUTPUT IMAGES
# WRITE_COLOR_KEY_IMAGES: Creates image maps of each colortable.
#
File name format: 'key_[type]_COLORMAP'
#
where [type] represents the colortable.
# ECHO_IMAGE_FILES: Creates GIF of each input file used in
that job.
# File
names format: 'echo_of_[input_filename]'
# where
[input_filename] represents the input name.
# ANIMATION: if whirlgif has been compiled, and the WHIRLGIF_BINARY
#
path has been defined, animated gifs beginning with the
#
file name 'animated' will be created in PREDICT and
#
CALIBRATE mode.
WRITE_COLOR_KEY_IMAGES(YES/NO)=yes
ECHO_IMAGE_FILES(YES/NO)=yes
ANIMATION(YES/NO)= yes
# XII. COLORTABLE SETTINGS
# A. DATE COLOR SETTING
# The date will automatically be placed in the
lower left corner
# of output images. DATE_COLOR may be designated
in with red, green,
# and blue values (format: <red_value, green_value,
blue_value> )
# or with hexadecimal beginning with '0X' (format:
<0X######> ).
#default DATE_COLOR= 0XFFFFFF white
DATE_COLOR= 0XFFFFFF #white
# B. URBAN (NON-LANDUSE) COLORTABLE SETTINGS
# 1. URBAN MODE OUTPUTS
# TEST mode: Annual images
of simulated urban growth will be
#
created using SEED_COLOR to indicate urbanized areas.
# CALIBRATE mode: Images
will not be created.
# PREDICT mode: Annual
probability images of simulated urban
#
growth will be created using the PROBABILITY
#
_COLORTABLE. The initializing urban data will be
#
indicated by SEED_COLOR.
#
# 2. COLORTABLE SETTINGS
# SEED_COLOR: initializing
and extrapolated historic urban extent
# WATER_COLOR: BACKGROUND_DATA
is used as a backdrop for simulated
#
urban growth. If pixels in this file contain the
#
value zero (0), they will be filled with the color
#
value in WATER_COLOR. In this way, major water bodies
#
in a study area may be included in output images.
#
#SEED_COLOR= 0XFFFF00 #yellow
SEED_COLOR= 255, 255, 0 #yellow
WATER_COLOR= 0X0000FF # blue
# 3. PROBABILITY COLORTABLE FOR URBAN GROWTH
# For PREDICTION, annual probability
images of urban growth
# will be created using the monte
carlo iterations. In these
# images, the higher the value,
the more likely urbanizaion is.
# In order to interpret these
'continuous' values more easily
# they may be color classified
by range.
#
# If 'hex' is not present then
the range is transparent.
# The transparent range must
be the first on the list.
# The max number of entries is
100.
# PROBABILITY_COLOR:
a color value in hexadecimal that indicates a
#
probability range.
# low/upper:
indicate the boundaries of the range.
#
#
low, upper, hex, (Optional Name)
PROBABILITY_COLOR= 0, 50,
, #transparent
PROBABILITY_COLOR= 50, 60, 0X005A00, #0, 90,0
PROBABILITY_COLOR= 60, 70, 0X008200, #0,130,0
PROBABILITY_COLOR= 70, 80, 0X00AA00, #0,170,0
PROBABILITY_COLOR= 80, 90, 0X00D200, #0,210,0
PROBABILITY_COLOR= 90, 95, 0X00FF00, #0,255,0
PROBABILITY_COLOR= 95, 100, 0X8B0000, #dark red
# C. LAND COVER COLORTABLE
# Land cover input images should be in grayscale GIF image format.
# The 'pix' value indicates a land class grayscale pixel value in
# the image. If desired, the model will create color classified
# land cover output. The output colortable is designated by the
# 'hex/rgb' values.
# pix: input land class pixel value
# name: text string indicating land class
# flag: special case land classes
# URB - urban class
(area is included in urban input data
#
and will not be transitioned by deltatron)
# UNC - unclass (no
data areas in image)
# EXC - excluded
(land class will be ignored by deltatron)
# hex/rgb: hexadecimal or rgb (red, green, blue) output
colors
#
#
pix, name, flag, hex/rgb, #comment
LANDUSE_CLASS= 0, Unclass , UNC , 0X000000
LANDUSE_CLASS= 1, Urban , URB , 0X8b2323
LANDUSE_CLASS= 2, Agric ,
, 0Xffec8b #pale yellow
LANDUSE_CLASS= 3, Range ,
, 0Xee9a49 #tan
LANDUSE_CLASS= 4, Forest ,
, 0X006400
LANDUSE_CLASS= 5, Water , EXC , 0X104e8b
LANDUSE_CLASS= 6, Wetland ,
, 0X483d8b
LANDUSE_CLASS= 7, Barren ,
, 0Xeec591
LANDUSE_CLASS= 8, Tundra ,
, 0X323232
LANDUSE_CLASS= 9, Ice&Sno ,
, 0XFFFFFF
# D. GROWTH TYPE IMAGE OUTPUT CONTROL AND COLORTABLE
#
# From here you can control the output of the Z grid
# (urban growth) just after it is returned from the spr_spread()
# function. In this way it is possible to see the different types
# of growth that have occurred.
#
# VIEW_GROWTH_TYPES(YES/NO) provides an on/off
# toggle to control whether the images are generated.
#
# GROWTH_TYPE_PRINT_WINDOW provides a print window
# to control the amount of images created.
# format: <start_run>,<end_run>,<start_monte_carlo>,
# <end_monte_carlo>,<start_year>,<end_year>
# for example:
# GROWTH_TYPE_PRINT_WINDOW=run1,run2,mc1,mc2,year1,year2
# so images are only created when
# run1<= current run <=run2 AND
# mc1 <= current monte carlo <= mc2 AND
# year1 <= current year <= year2
#
# 0 == first
VIEW_GROWTH_TYPES(YES/NO)=NO
GROWTH_TYPE_PRINT_WINDOW=0,0,0,0,1995,2020
PHASE0G_GROWTH_COLOR= 0xff0000 # seed urban area
PHASE1G_GROWTH_COLOR= 0X00ff00 # diffusion growth
PHASE2G_GROWTH_COLOR= 0X0000ff # NOT USED
PHASE3G_GROWTH_COLOR= 0Xffff00 # breed growth
PHASE4G_GROWTH_COLOR= 0Xffffff # spread growth
PHASE5G_GROWTH_COLOR= 0X00ffff # road influenced growth
#************************************************************
#
# E. DELTATRON AGING SECTION
#
# From here you can control the output of the deltatron grid
# just before they are aged
#
# VIEW_DELTATRON_AGING(YES/NO) provides an on/off
# toggle to control whether the images are generated.
#
# DELTATRON_PRINT_WINDOW provides a print window
# to control the amount of images created.
# format: <start_run>,<end_run>,<start_monte_carlo>,
# <end_monte_carlo>,<start_year>,<end_year>
# for example:
# DELTATRON_PRINT_WINDOW=run1,run2,mc1,mc2,year1,year2
# so images are only created when
# run1<= current run <=run2 AND
# mc1 <= current monte carlo <= mc2 AND
# year1 <= current year <= year2
#
# 0 == first
VIEW_DELTATRON_AGING(YES/NO)=NO
DELTATRON_PRINT_WINDOW=0,0,0,0,1930,2020
DELTATRON_COLOR= 0x000000 # index 0 No or dead deltatron
DELTATRON_COLOR= 0X00FF00 # index 1 age = 1 year
DELTATRON_COLOR= 0X00D200 # index 2 age = 2 year
DELTATRON_COLOR= 0X00AA00 # index 3 age = 3 year
DELTATRON_COLOR= 0X008200 # index 4 age = 4 year
DELTATRON_COLOR= 0X005A00 # index 5 age = 5 year
# XI.SELF-MODIFICATION PARAMETERS
# SLEUTH is a self-modifying cellular automata. For more
information
# see our PROJECT GIGALOPOLIS web site:
# (www.ncgia.ucsb.edu/project/gig/About/gwSelfMod.htm)
# and publications (and/or grep 'self modification' in
code).
ROAD_GRAV_SENSITIVITY=0.01
SLOPE_SENSITIVITY=0.1
CRITICAL_LOW=0.97
CRITICAL_HIGH=1.03
#CRITICAL_LOW=0.0
#CRITICAL_HIGH=10000000000000.0
CRITICAL_SLOPE=15.0
BOOM=1.1
BUST=0.9