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John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

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Page 1: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

John WieczorekMuseum of Vertebrate Zoology

University of California, Berkeley

Georeferencing Introduction: Collaboration to Automation

Page 2: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Georeferencing

Collaborations

Automation

Page 3: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Georeferencing

Collaborations

Automation

Page 4: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

What is a georeference?

Page 5: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

A numerical description of a place that can be mapped.

What is a georeference?

Page 6: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

A numerical description of a place that can be mapped.

What is a georeference?

In other words…

Page 7: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

ID Species Locality1 Lynx rufus Dawson Rd. N Whitehorse2 Pudu puda cerca de Valdivia3 Canis lupus 20 mi NW Duluth

9 Ursus arctos Bear Flat, Haines Junction

4 Felis concolor Pichi Trafúl5 Lama alpaca near Cuzco6 Panthera leo San Diego Zoo7 Sorex lyelli Lyell Canyon, Yosemite8 Orcinus orca 1 mi W San Juan Island

What we have:Localities we can read

Page 8: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Darwin Core Location Terms

–higherGeography–waterbody, island, islandGroup–continent, country, countryCode, stateProvince, county, municipality

– locality–minimumElevationInMeters, maximumElevationInMeters, minimumDepthInMeters, maximumDepthInMeters

Page 9: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

What we want:Localities we can map

Page 10: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Darwin Core Georeference Terms

– decimalLatitude, decimalLongitude– geodeticDatum– coordinateUncertaintyInMeters– coordinatePrecision– pointRadiusSpatialFit– footprintWKT, footprintSRS,

footprintSpatialFit– georeferencedBy, georeferenceProtocol– georeferenceSources – georeferenceVerificationStatus– georeferenceRemarks

Page 11: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

What is a georeference?

A numerical description of a place that can be mapped.

Page 12: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

“Davis, Yolo County, California”

“point method”

Coordinates: 38.5463 -121.7425Horizontal Geodetic Datum: NAD27

Page 13: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Data Quality

• data have the potential to be used in ways unforeseen when collected.

• the value of the data is directly related to the fitness for a variety of uses.

• “as data become more accessible many more uses become apparent.” – Chapman 2005

• the MaNIS/HerpNET/ORNIS guidelines follow best practices (Chapman and Wieczorek 2006) to enhance data quality and value

Page 14: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

What is an acceptable georeference?

A numerical description of a place that can be mapped

and that describes the spatial extent of a locality

and its associated uncertainties.

Page 15: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

“Davis, Yolo County, California”

“bounding-box method”

Coordinates: 38.5486 -121.754238.545 -121.7394

Horizontal Geodetic Datum: NAD27

Page 16: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

“Davis, Yolo County, California”

“point-radius method”

Coordinates: 38.5468 -121.7469Horizontal Geodetic Datum: NAD27Maximum Uncertainty: 8325 m

Page 17: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

What is an ideal georeference?

A numerical description of a place that can be mapped

and that describes the spatial extent of a locality

and its associated uncertaintiesas well as possible.

Page 18: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

“Davis, Yolo County, California”

“shape method”

Page 19: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

“20 mi E Hayfork, California”

“probability method”

Page 20: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

point easy to produce no data quality

bounding-box simple spatial queriesdifficult quality assessment

point-radius easy quality assessmentdifficult spatial queries

shape accurate representationcomplex, uniform

Method Comparison

probability accurate representationcomplex, non-uniform

Page 21: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

MaNIS/HerpNET/ORNIS (MHO) Guidelines

http://manisnet.org/GeorefGuide.html

• uses point-radius representation of georeferences

• circle encompasses all sources of uncertainty about the location

• methodology formalizes assumptions, algorithms, and documentation standards that promote reproducible results

• methods are universally applicable

Page 22: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Darwin Core Georeference Terms

– decimalLatitude, decimalLongitude– geodeticDatum– coordinateUncertaintyInMeters– coordinatePrecision– pointRadiusSpatialFit– footprintWKT, footprintSRS,

footprintSpatialFit– georeferencedBy, georeferenceProtocol– georeferenceSources – georeferenceVerificationStatus– georeferenceRemarks

Page 23: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Georeferencing

Collaborations

Automation

Page 24: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Collaborative DistributedDatabases for Vertebrates

Page 25: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Collaborations

Page 26: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

MaNIS Localities Georeferenced

n = 326k localities (1.4M specimens)r = 14 localities/hr (point-radius method)

t = 3 yrs (~40 georeferencers)

Page 27: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

ORNIS Localities Georeferenced

n = 267k localities (1.4M specimens)r = 30 localities/hr (point-radius method)

t = 2 yrs (~30 georeferencers)

Page 28: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Scope of the Problem for Natural History Collections

~2.5 Giga-records

Page 29: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Scope of the Problem for Natural History Collections

~2.5 Giga-records

~6 records per locality*

~14 localities per hour*

* based on the MaNIS Project

Page 30: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Scope of the Problem for Natural History Collections

~2.5 Giga-records

~6 records per locality*

~14 localities per hour*

~15,500 years

* based on the MaNIS Project

Page 31: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Scope of the Problem for Natural History Collections

~2.5 Giga-records

~6 records per locality*

~14 (30) localities per hour*

~15,500 (7233) years

* based on the MaNIS (ORNIS) Project

Page 32: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Georeferencing

Collaborations

Automation

Page 33: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

http://www.biogeomancer.org

Page 34: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Automation

Combining the Best in Georeferencing

GeoLocate

DIVA-GIS

MaNIS Georeferencing Calculator

BioGeomancer Classic

Page 35: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation
Page 36: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

• Geodetic Datum:defines the position of the origin, scale, shape, and orientation of a 3-dimensional model of the earth. Example: WGS84.

• Coordinate System: defines the “units of measure” of position with respect to the datum. Example: latitude, longitude in degrees, minutes, seconds

Geographical Concepts:

Page 37: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Map Projections:• mathematical

transformations of the 3-D model of the surface of the earth onto a 2-D map.

• there are many (e.g., conical, cylindrical, azimuthal) - they all suffer from distortions (area, shape, distance, or direction), but some preserve areas or distances.

• When measuring distances on paper maps, use an equal distance projection, if available, otherwise understand the implications.

Page 38: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Named place: a place of reference in a locality description. Example: “Davis” in “5 mi N of Davis”

Areal extent: the geographic area covered by a named place (feature). Example: the area inside the boundaries of a town.

Linear extent: the distance from the geographic center to the furthest point of the areal extent of a named place.

Georeferencing Concepts

Page 39: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

• Offset: the distance from a named place. Example: “5 mi” in “5 mi NE of Beatty”.

• Heading: the direction from a named place. Example: “NE” in “5 mi NE of Beatty”.

Georeferencing Concepts

Page 40: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

• coordinateUncertaintyInMeters:“The horizontal distance (in meters) from the given decimalLatitude and decimalLongitude describing the smallest circle containing the whole of the Location. Leave the value empty if the uncertainty is unknown, cannot be estimated, or is not applicable (because there are no coordinates). Zero is not a valid value for this term.” (from Darwin Core)

• Maximum Error Distance: same as coordinateUncertaintyInMeters, except the units are the same is in the locality description, not necessarily meters.

Georeferencing Concepts

Page 41: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Sources of uncertainty:

Coordinate Uncertainty

Map scale

The extent of the locality

GPS accuracy

Unknown datum

Imprecision in direction measurements

Imprecision in distance measurements (1km vs. 1.1 km)

20° 30’ N 112° 36’ WScale Uncertainty (ft) Uncertainty (m)

1:1,200 3.3 ft 1.0 m

1:2,400 6.7 ft 2.0 m

1:4,800 13.3 ft  4.1 m

1:10,000 27.8 ft 8.5 m

1:12,000 33.3 ft 10.2 m

1:24,000 40.0 ft  12.2 m

1:25,000 41.8 ft 12.8 m

1:63,360 106 ft 32.2 m

 1:100,000 167 ft 50.9 m

1:250,000 417 ft 127 m

Page 42: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Georeferencing Calculator Example

Locality:0.5 km N of Little mermaid, Copenhagen,

DK

Start with original coordinates for the mermaid:

55° 41' 34.18" N 12° 35' 56.73" E

Then use the Georeferencing Error Calculator to determine the final coordinates AND the uncertainty.

Page 43: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Georeferencing Error Calculator:0.5 km N of Little mermaid, Copenhagen, DK

55° 41' 34.18" N 12° 35' 56.73" E

Page 44: John Wieczorek Museum of Vertebrate Zoology University of California, Berkeley Georeferencing Introduction: Collaboration to Automation

Capture georeferences in database or spreadsheet (we will use an Excel template for examples)

Georeferencing Templates