a new approach to gis modeling for transportation planning: expert systems in gis carl shields,...

A New Approach to GIS Modeling for Transportation Planning:

Expert Systems in GIS

Carl Shields, Daniel Davis, Susan Neumeyer, James Hixon, ArchaeologistsBarry Nichols, Biologist

Kentucky Transportation Cabinet and

Ted Grossardt, Ph.D., University of KentuckyKeiron Bailey, Ph.D., University of Arizona

John Ripy, University of KentuckyPhil Mink, U of Kentucky Archaeological Survey

The Problem

• Archaeology– Time-Consuming– Costly

• Uncertainty– Locations– Quantity– Significance– Time and money

The Solution

• Develop a Spatial Decision Support System (SDSS)

• GIS Layers– Prehistoric landscape

models– Known sites

Project Goals

1. Develop GIS Tools for KYTC Archeologists and Other Interested Parties to Use to Better Understand Areal Likelihood of Encountering Prehistoric Archeological Resources

2. Capture and Model Basic Settlement Pattern Relationships to Landscape Variables, Using GIS Data and Tools

3. Express Output as Comparative Likelihood: Very Low, Low, Moderate, High, or Very High

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

Inductive Statistical ModelsVS

Deductive Expert System ModelsInductive Models• Linear• Single perspective• Heavily dependent on

existing state databases• Established methodologies

and literature• Costly for statewide

applications

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

Scale

• County– Initial test– Woodford

• Physiographic Region– Inner Bluegrass– Hazard Hills

• State– Mosaic separate

physiographic models

Landscape Properties That Interact to Influence Prehistoric Settlement Decisions

1. Slope in Degrees of the surface.

2. Minutes walk to nearest walkable water (including springs)

3. Minutes walk to nearest walkable confluence on streams with a Strahler order of 3 or higher.

4. Elevation above water in feet.

5. Strahler order of the streams.

Data Availability / Considerations

1. Slope – Elevation data is ever changing

2. Minutes walk to nearest walkable water – Tobler’s Hiking Algorithm

3. Confluence – What are we looking for here?

– Biodiversity– Access to transportation

4. Elevation above water in feet – Topographic Index?

5. Strahler order of the streams – Derived or NHDPlus?

Fuzzy Variables Matrix and Categorical Meaning

Degrees Slope Minutes to Water

Minutes to Confluence

Elevation Above Water

Strahler Order

1: <= 5 1: <= 2 1: <= 10 1: <=10 1: <= 1

2: 5-10 2: 2-4 2 >10 2: 10-25 2: 2 & 3

3: 11-20 3: >4 3: 26-60 3: >=4

4: > 20 4: >60

4 X 3 X 2 X 4 X 3 = 288

Additional Modeling Considerations

1. Proximity to Sinkholes.

2. Default Assumption of ‘Moderate Likelihood.’

3. Impoundment issues and the elevation model

Unique Landscape Coding Combinations

Archeologists Provide Likelihood Estimates To Build F.S. Model for All 180 Combinations

Through Focus Groups and Large Group Interactive Sampling

1

2

3

4

Dress This Man

3 Jackets x 3 pants x 3 shirts x 3 ties = 108 combinations

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

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.

Prehistoric Landscape Models

M8_arc4v62

M6_arc5vz4

25 Models Developed for the Inner Bluegrass

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

IBG Results

m8_arc4v62

Probability #IBG_Cells %IBG_Cells #CellTestSite %CellTestSite

KV Gain Statistic (SURV)

VL (1) 3368340 5.403017698 569 0.788984719 (5.8481)

L (2) 37800081 60.63357815 30577 42.39856901 (0.4301)

M (3) 13607899 21.82787935 18444 25.57475249 0.1465

H (4) 4285049 6.873473457 12323 17.08727363 0.5977

VH (5) 3280459 5.262051347 10205 14.15042014 0.6281

Total 62341828 100 72118 100

12% of surface contains 31% of sites

Hazard Hills

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

Hazard Hills Results

m8_hazd4v62

Probability #HH_Cells %HH_Cells #CellTestSite %CellTestSite KV Gain Statistic

(SURV)

VL (1) 3238909 2.06036497 247 0.602468413 (2.4199)

L (2) 146028368 92.89292599 28237 68.87409142 (0.3487)

M (3) 6923570 4.404285855 8356 20.38148202 0.7839

H (4) 287461 0.18286237 593 1.44641202 0.8736

VH (5) 722433 0.459560811 3565 8.695546124 0.9471

Total 157200741 100 40998 100

5% of surface contains 30% of sites

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

State Model

Next Steps

• Variant resolution elevation models• Other factors

– Soils– Floodplain

• Mapping access to habitat diversity• Mapping landforms

– Topographic indices– Feature Analyst

Blackside Dace Habitat Modeling

Factors Chosen for Modeling

• Gradient• Canopy• Riparian vegetation type• Water conductivity• Riparian zone width• Bridges/culvert density• Link Magnitude/Stream

order

Expert Systems Modeling FactorsHabitat Factor 1 2 3 4 Gradient (stream level)

>6% 4 – 6% 2 – 4% <2%

Canopy (%coverage)

0 – 50% 50 – 70% 70 – 90% >90%

Riparian Vegetation

Cultivated, Developed, Barren

Grass, Herbaceous, Pasture (hay)

Shrubs, Scrub Forested

Mine Density (%HUC14 area)

>30% 5 – 30% 1-5% 0%

Riparian Zone Width

<6m 6-12m 12-18m >18m

Bridges/Culverts Density (per 90 sq/meters)

>4 2.5 – 4 1 – 2.5 <1

Stream Order (Strahler)

6 – 7 1 4 – 5 2 - 3

Predictive Weighted Expert Systems Model

Statistical Predictive Model

Model Comparison

Model Comparison