predictive models lab rnr/geog 420/520 spring. predictive models zimportant to understand what we...
DESCRIPTION
Broad Model Types zDeductive models are based on reasoning in which the conclusion follows necessarily the presented premises zInductive models base validity on observations about part of a class as evidence for a proposition about the whole classTRANSCRIPT
Predictive Models Lab
RNR/Geog 420/520Spring
Predictive ModelsImportant to understand what we are
attempting to predict These models predict location This prediction is based on reasoned or
measured relationshipsNo predictive model is perfectSome are more efficient than others
Broad Model TypesDeductive models are based on
reasoning in which the conclusion follows necessarily the presented premises
Inductive models base validity on observations about part of a class as evidence for a proposition about the whole class
Components of the Model
Variables --- DEM, relief, slope, shelter, ridge
Study Group --- 75 archaeological sites
Control Group --- 250 random point locations
Suitable Statistical model --- logistic regression
Model Assumptions
The Processes Ancient Humans Used to Select Site Locations Were Not Random
Part of the Site Selection Process Involved Selection for Favored Environmental Zones
Consequently, It Should be Possible to Identify Specific Environmental Signatures for Specific Groups of Archaeological Sites
Creating the Regression Models in GRID
Sample values of variables at site and non-site locations
Subject SAMPLE results to regression in GRID (with some cautions)
Results of the regression include coefficients and a constant, or y-intercept
Model made by multiplying variables by coefficients --- sum of these variables is the model
Model then scaled between 0 and 1 to create a probability surface
SampleDep hdem hridge …1 840 149 0 852 1551 854 151 0 805 1341 853 062…
Logistic Regression in GRIDGrid: |> regression hsam.txt logistic brief <|coef # coef------ ---------------- 0 -3.797 1 -0.001 2 0.014 3 0.006 4 0.000 5 0.055------ ----------------RMS Error = 0.393Chi-Square = 51.608
Making Probability Models
X 0.097414 =
X 0.064497 =
X 0.001453 =
X -0.011092 =
X 0.007072 =
X 0.004799 =
X 0.014648 =
Relief
Shelter
Dist. ToSoil 1
Elevation
RidgeIndex
Aspect
Distanceto Wadi
EnvironmentalVariables
RegressionCoefficients
WeightedVariables
ProbabilityModel
Sum ofWeightedVariables
Corrected Y-Intercept
Probability Models
Group 1 Group 2
Model Strength
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
5
10
15
20
25
Perc
ent o
f Sam
ple
Probability Score
Umayyad Group 1
Non-Sites
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
10
20
30
40
50
Perc
ent o
f Sam
ple
Probability Score
Umayyad Group 2
Non-Sites
83.0100171
gain24.0
58441
gain
modelwithinphenomenatotalofpercentagemodel withinarea total ofpercentagegain 1