A New Approach to GIS Modeling for Transportation Planning: Expert Systems in GIS

1. A New Approach to GIS Modeling for Transportation Planning: Expert Systems in GIS Carl Shields, Daniel Davis, Susan Neumeyer, James Hixon, Archaeologists Barry Nichols, Biologist Kentucky Transportation Cabinet and Ted Grossardt, Ph.D., University of Kentucky Keiron Bailey, Ph.D., University of Arizona John Ripy, University of Kentucky Phil Mink, U of Kentucky Archaeological Survey

2. The Problem • Archaeology • Time-Consuming • Costly • Uncertainty • Locations • Quantity • Significance • Time and money

3. The Solution • Develop a Spatial Decision Support System (SDSS) • GIS Layers • Prehistoric landscape models • Known sites

4. Project Goals • Develop GIS Tools for KYTC Archeologists and Other Interested Parties to Use to Better Understand Areal Likelihood of Encountering Prehistoric Archeological Resources • Capture and Model Basic Settlement Pattern Relationships to Landscape Variables, Using GIS Data and Tools • Express Output as Comparative Likelihood: Very Low, Low, Moderate, High, or Very High

5. Caveat Utilitor • Let the user beware • Developed as a planning tool not as an academic model or as a tool to replace archaeological survey • We’ll all still have jobs! • Presence or Absence models • Cost is based on whether or not it is there • Data is used “as is” and only reclassed

6. Inductive Statistical ModelsVSDeductive Expert System Models Inductive Models Deductive Models Can be Non-linear Multi-vocal Less dependence on existing state databases New methodology less familiarity Cost effective May require newly derived datasets • Linear • Single perspective • Heavily dependent on existing state databases • Established methodologies and literature • Costly for statewide applications

7. Scale • County • Initial test • Woodford • Physiographic Region • Inner Bluegrass • Hazard Hills • State • Mosaic separate physiographic models

8. Landscape Properties That Interact to Influence Prehistoric Settlement Decisions • Slope in Degrees of the surface. • Minutes walk to nearest walkable water (including springs) • Minutes walk to nearest walkable confluence on streams with a Strahler order of 3 or higher. • Elevation above water in feet. • Strahlerorder of the streams.

9. Data Availability / Considerations • Slope – Elevation data is ever changing • Minutes walk to nearest walkable water – Tobler’s Hiking Algorithm • Confluence – What are we looking for here? • Biodiversity • Access to transportation • Elevation above water in feet – Topographic Index? • Strahlerorder of the streams– Derived or NHDPlus?

10. Fuzzy Variables Matrix and Categorical Meaning 4 X 3 X 2 X 4 X 3 = 288

11. Additional Modeling Considerations • Proximity to Sinkholes. • Default Assumption of ‘Moderate Likelihood.’ • Impoundment issues and the elevation model

12. Unique Landscape Coding Combinations

13. Archeologists Provide Likelihood Estimates To Build F.S. Model for All 180 CombinationsThrough Focus Groups and Large Group Interactive Sampling 2 1 4 3

14. Dress This Man 3 Jackets x 3 pants x 3 shirts x 3 ties = 108 combinations

15. Fuzzy Logic and Archaeological Site Modeling • Fuzzy Logic a New Method for Developing Archaeological Site Models • Popular in systems engineering and biological systems modeling • Non-linear • Ability to handle non-linear relationships between variables where there are too many interactions to model effectively

16. Typical ‘slice’ through five-variable FST model of archeological likelihood. Here, low slope (SLO) and low minutes to water (MIN) correspond to very high likelihood of encountering prehistoric artifacts (“A”). As slope increases, while remaining close to water, the likelihood drops to moderate at “B”. Finally, as minutes to water becomes greater, the likelihood of artifacts drops off uniformly toward “C”, regardless of the slope value at the limit. The other values for walking time to confluence (DTC), feet above water (DWT), and the Strahler order (WAT) are fixed for this slice. Because Slope has four categorical values and Minutes to Water has three, this surface represents all twelve of likelihoods associated with the interactions of these two when the other three input factors are held constant. This slice is an illustrative one from an early model and not necessarily representative of later versions. Archeologists Provide Likelihood Estimates To Build F.S. Model for All 180 Combinations Through Focus Groups and Large Group Interactive Sampling Archeologists Provide Likelihood Estimates To Build F.S. Model for All 180 Combinations Through Focus Groups and Large Group Interactive Sampling Archeologists Provide Likelihood Estimates To Build F.S. Model for All 180 Combinations Through Focus Groups and Large Group Interactive Sampling

17. Prehistoric Landscape Models M8_arc4v62 M6_arc5vz4 25 Models Developed for the Inner Bluegrass

18. Testing and Verification • Recorded sites used to test models • The ‘Efficiency’ test statistic (Kvamme’s gain statistic) for such models varies from 1 = perfect efficiency to 0 = no different than random choice. • The measured values below equal or exceed the performance of standard statistical predictive models that use dozens of variables and hundreds of sample sites. • This model uses six variables derived fundamentally from the geography of slope and stream patterns. • MN Model • 85% Sites in 35% (High and Med) • Gain Statistic 0.6118 or better

19. IBG Results 12% of surface contains 31% of sites

20. Hazard Hills • Applied the 4 best IBG Models to Hazard Hills Physiographic Province

21. Hazard Hills Results 5% of surface contains 30% of sites

22. Conclusions • Archaeological site modeling using Fuzzy Set Theory and GIS can produce robust SDSS for use by highway planners and others • Currently applying 4 best performing models to the Outer Bluegrass and the rest of Eastern KY • Next Phase examine Western KY and Outer Bluegrass • Impact of OH and Miss Rivers on existing models

23. State Model

24. Next Steps • Variant resolution elevation models • Other factors • Soils • Floodplain • Mapping access to habitat diversity • Mapping landforms • Topographic indices • Feature Analyst

25. Blackside Dace Habitat Modeling

26. Factors Chosen for Modeling • Gradient • Canopy • Riparian vegetation type • Water conductivity • Riparian zone width • Bridges/culvert density • Link Magnitude/Stream order

27. Expert Systems Modeling Factors

28. Predictive Weighted Expert Systems Model

29. Statistical Predictive Model

30. Model Comparison

31. Model Comparison