Experimental mapping of the risk of encountering buried archaeology

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Predictive models developed to determine the potential for encountering archaeological remains in alluviated lowland landscapes in the UK.

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Experimental mapping of the risk of encountering buried archaeology

  1. 1. Risk or Opportunity? Developing tools to predict the archaeological potential of Britain's aggregate bearing landscapes Keith Challis IBM Vista, University of Birmingham U B
  2. 2. Outline <ul><li>Background </li></ul><ul><li>Method </li></ul><ul><li>Examples </li></ul><ul><li>Delivery </li></ul><ul><li>Critique </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham Research will work to develop a set of robust algorithms that are based on personal perceptions and empirical scientific data to quantify archaeological risk at the level of individual land parcels.
  3. 3. Background
  4. 4. Study Areas <ul><li>Three, c .300km 2 study areas </li></ul><ul><li>MTV Derbyshire </li></ul><ul><li>MTV Newark </li></ul><ul><li>LTV Gainsborough </li></ul><ul><li>30k ha terrace </li></ul><ul><li>35k ha floodplain </li></ul><ul><li>144 SAM </li></ul><ul><li>11,222 HER records </li></ul><ul><li>2254 Aimee (NMR) records </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  5. 5. Origins <ul><li>Trent Valley GeoArchaeology </li></ul><ul><ul><li>TVG2002: Yorke et all “Fluvial Landform Risk Maps” </li></ul></ul><ul><ul><li>TVG 2002: Spence et al “Development of Risk Maps” </li></ul></ul><ul><ul><li>TVG 2002: Walker “Effectiveness of Evaluation” </li></ul></ul><ul><li>Archaeology of “blank” areas </li></ul><ul><li>Need for interpreted “red flag” mapping </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  6. 6. Goals <ul><li>To provide interpreted information to non-expert users </li></ul><ul><li>Models rooted in knowledge base </li></ul><ul><li>Not to usurp the HER as a source of data or to undermine curatorial prerogative </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  7. 7. Approach: User-focused <ul><li>Understand what users need, how they think and work </li></ul><ul><li>Model the knowledge-based approach of expert users “topsight” (Gelernter 1992) </li></ul><ul><li>Presentation of results structured to fit the real-world and in a user friendly medium </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  8. 8. Approach: Simplify <ul><li>Inductive (data driven) rather than deductive (theory driven) </li></ul><ul><li>Simplify and summarise (the detail is in the HER) </li></ul><ul><li>Validate through user feedback (rather than test and quantify) </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  9. 9. Approach: Model Objectives <ul><li>The completed models provide per parcel scores for: </li></ul><ul><li>The predicted archaeological potential of all land parcels. </li></ul><ul><li>The aggregate bearing potential and value of all land parcels. </li></ul><ul><li>The susceptibility of individual land parcels to field evaluation techniques. </li></ul><ul><li>The likely physical condition of buried cultural remains. </li></ul><ul><li>The risk of encountering buried waterlogged organic remains. </li></ul><ul><li>The level of impact that different forms of extraction may have on the archaeological record </li></ul><ul><li>The importance of archaeology in the light of regional priorities. </li></ul><ul><li>The likely mitigation needs in the light of PPG 16 guidance </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  10. 10. Method
  11. 11. Method: Predictive Models <ul><li>Classic predictive modelling </li></ul><ul><li>Big, empty, heterogeneous areas (2500km 2 / 21 sites) </li></ul><ul><li>Assess and weigh environmental factors </li></ul><ul><li>Weights inform model </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  12. 12. <ul><li>Such models are inappropriate for the TV </li></ul><ul><li>c. 40% of land parcels contain a record </li></ul><ul><li>Eg. Newark, 1254 parcels out of 5012 </li></ul>Method: Predictive Models Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  13. 13. Method: Our Data Model <ul><li>OS MasterMap® as a spatial framework </li></ul><ul><li>Raster based GIS models </li></ul><ul><li>50m grid (200k cells) </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  14. 14. Method: Model Building <ul><li>Source data is rasterised </li></ul><ul><li>Simplified scores are applied or extracted </li></ul><ul><li>Models are based on weighted means of scores </li></ul><ul><li>Blank areas filled using landscape classification and spatial modelling </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham Terrace: Score = 3
  15. 15. Method: Per Parcel Results <ul><li>Calculations reclassified to 5 level scale from low risk to high </li></ul><ul><li>Aggregated model scores devolved to level of an OS MasterMap® TOID </li></ul><ul><li>Built up parcels, water and parcels less than 1ha in extent excluded </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  16. 16. Presentation of Results Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  17. 17. Examples
  18. 18. Example: Organic Remains Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham <ul><li>“ The risk of encountering buried waterlogged organic remains” </li></ul><ul><li>Environmental model based on weighting of existing continuous data </li></ul><ul><li>No “blanks” to fill </li></ul>
  19. 19. Example: Organic Remains Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham <ul><li>Model built from layers representing aspects of environment </li></ul><ul><li>Scores based on assessment of preservation potential from 0 (none) to 5 (high) </li></ul><ul><li>Subjective, but documented </li></ul>Drift Geology Soils Palaeochannels
  20. 20. Example: Organic Remains Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham <ul><li>Final model a weighted mean </li></ul><ul><li>Raster (50 x 50m cell) </li></ul><ul><li>Vector (mean values propagated to MasterMap®) </li></ul><ul><li>Variability scores indicate reliability </li></ul>
  21. 21. Example: Archaeology Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham <ul><li>“ The predicted archaeological potential of all land parcels” </li></ul><ul><li>Archaeological models more complex </li></ul><ul><li>Period, activity, intensity </li></ul><ul><li>“ Site” or landscape? </li></ul>
  22. 22. Example: Late Prehistory Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham <ul><li>Bronze Age </li></ul><ul><li>Existing HER 92 records (Newark) </li></ul><ul><li>Usual data quality issues </li></ul><ul><li>How to populate a whole landscape (fill “blanks”) </li></ul>Example: Late Prehistory Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham Example: Late Prehistory Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  23. 23. Example: Late Prehistory Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham <ul><li>Landscape characteristics are not helpful (too homogenous) </li></ul><ul><li>Informed generalisation / landscape character / potential </li></ul><ul><li>Risk = background potential + actual knowledge </li></ul>
  24. 24. Example: Late Prehistory Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  25. 25. Delivering Data
  26. 26. Delivery <ul><li>Static mapping </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  27. 27. Delivery <ul><li>Data for stakeholder GIS (tables of values for each TOID) </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  28. 28. Delivery <ul><li>Interactive using embedded Google Earth application </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  29. 29. Critique
  30. 30. Critique <ul><li>“ Topsight” is not necessarily the same thing as predictive modelling or risk mapping </li></ul><ul><li>Modelling period based activity and intensity of activity is problematic </li></ul><ul><li>It would be possible model individual classes of monument with clear geographic preferences (eg burnt mounds) </li></ul><ul><li>Perhaps general models are the most helpful </li></ul><ul><li>The meaning of results is imprecise and open to misinterpretation </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  31. 31. Critique <ul><li>Who needs this anyway? </li></ul><ul><li>HER are authoritative and increasingly accessible </li></ul><ul><li>Models provide topsight </li></ul><ul><li>Planning context </li></ul><ul><li>PPS5 </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham
  32. 32. Summary <ul><li>Landscape scale, data driven classification and modelling </li></ul><ul><li>Nationally transferable method and data structure </li></ul><ul><li>Deliverable on-line </li></ul><ul><li>Topsight </li></ul>Experimental Risk Mapping Keith Challis, IBM Vista, University of Birmingham

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