1. Predictive Models Lab RNR/Geog 420/520 Spring

2. Predictive Models zImportant to understand what we are attempting to predict yThese models predict location yThis prediction is based on reasoned or measured relationships zNo predictive model is perfect zSome are more efficient than others

3. 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 class

4. Components of the Model zVariables --- DEM, relief, slope, shelter, ridge zStudy Group --- 75 archaeological sites zControl Group --- 250 random point locations zSuitable Statistical model --- logistic regression

5. Model Assumptions zThe Processes Ancient Humans Used to Select Site Locations Were Not Random zPart of the Site Selection Process Involved Selection for Favored Environmental Zones zConsequently, It Should be Possible to Identify Specific Environmental Signatures for Specific Groups of Archaeological Sites

6. Creating the Regression Models in GRID zSample values of variables at site and non- site locations zSubject SAMPLE results to regression in GRID (with some cautions) zResults of the regression include coefficients and a constant, or y-intercept zModel made by multiplying variables by coefficients --- sum of these variables is the model zModel then scaled between 0 and 1 to create a probability surface

7. Sample Dephdemhridge… 1840149 0852 155 1854 151 0805134 1853062 …

8. Logistic Regression in GRID Grid: |> 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 = 0.393 Chi-Square = 51.608

9. 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. To Soil 1 Elevation Ridge Index Aspect Distance to Wadi Environmental Variables Regression Coefficients Weighted Variables Probability Model Sum of Weighted Variables Corrected Y-Intercept

10. Probability Models Group 1 Group 2

11. Model Strength