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BioGeomancer: Semi-automated Georeferencing Engine John Wieczorek, Aaron Steele, Dave Neufeld, P. Bryan Heidorn, Robert Guralnick, Reed Beaman, Chris Frazier,

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Presentation on theme: "BioGeomancer: Semi-automated Georeferencing Engine John Wieczorek, Aaron Steele, Dave Neufeld, P. Bryan Heidorn, Robert Guralnick, Reed Beaman, Chris Frazier,"— Presentation transcript:

1 BioGeomancer: Semi-automated Georeferencing Engine John Wieczorek, Aaron Steele, Dave Neufeld, P. Bryan Heidorn, Robert Guralnick, Reed Beaman, Chris Frazier, Paul Flemons, Nelson Rios, Greg Hill, Youjun Guo

2 Spatially Challenged Occurrence Data LA PEÑITA; 5.5. KM N Baird Mtns.; Salmon R. headwaters CALIENTE MOUNTAIN 10 MI SW CANAS, RIO HIGUERON near Sedan 4.4 MI N, 6.2 MI W SEMINOLE

3 Spatially Enabled Occurrence Data

4 Georeferencing Engine

5 GeoLocate

6 Input - Verbatim Locality Strings LA PEÑITA; 5.5. KM N Baird Mtns.; Salmon R. headwaters CALIENTE MOUNTAIN 10 MI SW CANAS, RIO HIGUERON near Sedan 4.4 MI N, 6.2 MI W SEMINOLE

7 Legacy Locality Data Issues Treat locality description as accurate Treat locality description as complete

8 Legacy Locality Data Issues Treat locality description as accurate Treat locality description as complete We need these to start processing.

9 Legacy Locality Data Issues Treat locality description as accurate Treat locality description as complete We need these to start processing. These are assumptions we should not hold to be true.

10 Legacy Locality Data Issues Treat locality description is accurate Treat locality description as complete Apply rules for locality string interpretation

11 Georeferencing Engine - Locality Interpretation Components

12 Legacy Locality Data Issues Treat locality description is accurate Treat locality description as complete Apply rules for locality string interpretation There is more than one way to accomplish string interpretation.

13 Locality Interpretation Methods Regular expression analysis –GeoLocate - Tulane –Enhanced BioGeomancer Classic – Yale Machine Learning/Natural Language Processing –U. Illinois, Urbana-Champagne –Inxight Software, Inc.

14 Locality Types F – feature P – path FO – offset from a feature, sans heading FOH – offset from feature at a heading FO+ – orthogonal offsets from a feature FPOH – offset at a heading from a feature along a path 31 other locality types known so far

15 Five Most Common Locality Types* 51.0% - feature 21.4% - locality not recorded 17.6% - offset from feature at a heading 8.6% - path 5.8% - undefined *based on 500 records randomly selected from the 296k records georeferenced manually in the MaNIS Project.

16 Clause Subset of a locality description to which a locality type can be applied.

17 Step 1: Define Clause Boundaries LA PEÑITA; 5.5. KM N Baird Mtns.; Salmon R. headwaters CALIENTE MOUNTAIN 10 MI SW CANAS, RIO HIGUERON near Sedan 4.4 MI N, 6.2 MI W SEMINOLE

18 Step 1: Define Clause Boundaries

19 Step 1: Define Clause Boundaries

20 Step 1: Define Clause Boundaries

21 Step 1: Define Clause Boundaries

22 Step 2: Determine Locality Types LA PEÑITA; 5.5. KM N

23 Step 2: Determine Locality Types LA PEÑITA; 5.5. KM N Baird Mtns.;

24 Step 2: Determine Locality Types LA PEÑITA; 5.5. KM N Baird Mtns.; Salmon R. headwaters

25 Step 2: Determine Locality Types LA PEÑITA; 5.5. KM N Baird Mtns.; Salmon R. headwaters CALIENTE MOUNTAIN 10 MI SW CANAS, RIO HIGUERON near Sedan 4.4 MI N, 6.2 MI W SEMINOLE

26 Step 3: Interpret Clauses LA PEÑITA; 5.5. KM N Feature: LA PEÑITA Offset: 5.5 Offset Units: KM Heading: N

27 Step 4: Find Feature Descriptions LA PEÑITA; 5.5. KM N Feature: LA PEÑITA Offset: 5.5 Offset Units: KM Heading: N

28 Georeferencing Engine - Spatial Description Components

29 Legacy Locality Data Issues Treat locality description is accurate Treat locality description as complete Apply rules for locality string interpretation Treat spatial data references as accurate

30 Legacy Locality Data Issues Treat locality description is accurate Treat locality description as complete Apply rules for locality string interpretation Treat spatial data references as accurate This is another assumption we should not hold to be true.

31 “Davis, Yolo County, California”

32

33

34 Legacy Locality Data Issues Treat locality description is accurate Treat locality description as complete Apply rules for locality string interpretation Treat spatial data references as accurate Apply rules for spatial description building

35 Step 5: Construct Spatial Description for Each Clause

36 West of B

37 Step 6: Construct Final Spatial Interpretation 10 MI SW CANAS, RIO HIGUERON Clause 1: 10 MI SW CANAS, Clause 2: RIO HIGUERON

38 Step 6: Construct Final Spatial Interpretation 10 MI SW CANAS, RIO HIGUERON Clause 1: 10 MI SW CANAS, Clause 2: RIO HIGUERON We hold these clauses to be simultaneously true.

39 Step 6: Construct Final Spatial Interpretation 10 MI SW CANAS, RIO HIGUERON Clause 1: 10 MI SW CANAS, Clause 2: RIO HIGUERON We hold these clauses to be simultaneously true. The final spatial description is the intersection of the spatial descriptions of all clauses.

40 Legacy Locality Data Issues Treat locality description is accurate Treat locality description as complete Apply rules for locality string interpretation Treat spatial data references as accurate Apply rules for spatial description building Apply criteria to reject unwanted hypotheses

41 Additional Input - Preferences  Assume terrestrial locations  Assume aquatic locations  marine only  freshwater only  Assume direct offsets  Assume offsets by road, if possible

42 Output Original data Zero, one, or more spatial interpretations - spatial footprint - point-radius description Process metadata –preferences (e.g., GeoLocate method, assume by road) –omissions (e.g., unused information) –confidence values

43 Conclusion Georeferences are hypotheses Hypotheses require testing Tested hypotheses should be so noted

44


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