Cleaning up georeference errors using iDigBio flags
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Cleaning up georeference errors using iDigBio flags Heather Appleby was an undergraduate intern for the Tri-Trophic Thematic Collection Network (TTD-TCN) the summer of 2015. Her internship was to learn fundamentals of R, while producing a useful product to help the TTD-TCN review georeferenced data for over 1 million specimen records. She accomplished her goals utilizing the iDigBio portal recordset correction feature (, providing both feedback to iDigBio about those error reports, and creating publicly available R-scripts for simplifying the view of the error reports on GitHub (

One of the major objectives of the Tri-Trophic Thematic Collection Network is to make available one million North American Hemiptera specimen records through the iDigBio data portal. At the American Museum of Natural History, the principal database used for this effort is Arthropod Easy Capture (AEC; Arthropod Easy Capture, 2014; Schuh, Hewson-Smith, & Ascher, 2010) Database. A group of 50+ paid digitizers and volunteers at 11 institutions used AEC to capture species and collection event information from specimen labels. A total of 902,000 specimens are entered in AEC, with a project wide total reaching near 1.3 million records (and still growing).

The specimen databasing procedure begins with a group of specimen digitizers who are assigned a number of drawers of insect specimens pinned to collection labels. The labels denote species, collector name, collection number, date, and location, and sometimes latitude and longitude coordinates. After being sexed, the data corresponding to each specimen are transcribed into the AEC database. If coordinates are not provided on the collection label, the specimen record is reviewed a second time, by different group of digitizers to be georeferenced. Georeferencing, or the process of assigning latitude and longitude coordinates to a collection site locality string, involves searching for this collection location information in GeoLocate web software and Google Earth. Our georeferencing method follows the best practices outlined by iDigBio as well as a “how to” guide specifically designed for the AEC database (see links below). Once coordinates are retrieved they are entered into the AEC database, along with the previously entered label information for each species and locality. Occasionally records are georeferenced incorrectly during the digitization process, typically due to human error. Latitude and longitude coordinates may be read incorrectly, localities may be written poorly on the label and, thus, typed incorrectly in the database, or the wrong state, county or municipality may be applied to a locality string.

Completed specimen records are shared with the iDigBio portal through a Darwin Core Archive. iDigBio detects incorrect locality data, based on latitude and longitude coordinate data and county, state, or country bounds, and proposes corrections to those records. The latest version of the iDigBio portal (released August, 2015) comes with the ability to download their analysis of every provided record set, allowing data providers direct feedback about the record sets they are submitting. This new feature categorizes bad entries and their corrections based on the detected errors, making database cleanup more organized and straightforward.

rev_geocode_eez The lat/lon point in in the ocean, or in a location that is not considered part of an Exclusive Economic Zone, and should be moved inland. dwc_stateprovince_replaced The lat/lon does not correspond to the state. Either the coordinates must be re-georeferenced or the state was entered incorrectly. dwc_country_replaced The lat/lon does not correspond to the country. Either the coordinates must be re-georeferenced or the country was entered incorrectly. rev_geocode_mismatch The latitude and longitude values are switched. The reversed latitude and longitude should be checked in GeoLocate or Google maps and regeoreferenced. rev_geocode_lon_sign The longitude sign is flipped. The sign should be switched to negative or positive and checked on GeoLocate or Google Maps and re-georeferenced. rev_geocode_lat_sign The latitude sign is flipped. The sign should be switched to negative or positive and checked on GeoLocate or Google Maps and re-georeferenced. geopoint_similar_coord The latitude or longitude may have been copied twice, resulting in the same coordinate for both. The locality should be re-georeferenced.

Each type of flag is packaged up in a downloadable folder. Every folder contains a list of the incorrectly georeferenced data (occurrence_raw.csv) and a list of and the iDigBio corrected records (occurrence.csv). All files are in the Comma Separated Value (CSV) format. The uncorrected file list (occurrence_raw.csv) is useful to search for "bad localities" in our data. Different flags indicate different kinds of errors (see table above). For example, the flag "rev_geocode_eez" indicates the coordinates represent a location in the ocean, not a likely place to collect an insect. Once a bad locality is identified, the incorrect latitude and longitude values are inserted into GeoLocate to verify the error. If it is an error, new coordinates are obtained and the AEC database updated.

After correcting a few errors, I had come upon a number of duplicate localities in the dataset and some correctly georeferenced entries in the bad locality files. iDigBio files contain all of the Darwin Core fields for all specimens associated with the bad localities, making extraction of only the locality data from these files time consuming. I realized that it would make sense for me to reformat the iDigBio output using R into a list of unique localities with only the locality information fields (i.e. dwc:locality, dwc:county, dwc:decimalLatitude, dwc:decimalLongitude). In addition, I filtered out all localities except US, Mexican, and Canadian localities and made a unique list, removing duplicates. Duplicate localities in the raw files exist because iDigBio outputs specimen data, not specifically locality data, and many specimens may be collected at the same locality. Finally, in an attempt to determine the cause of false positives, I organized the file by country and state, as it appeared that these errors were occurring because the centrad was placed in a different county than what is directly recorded in the Darwin Core county (dwc:county) field directly.

Another objective of this project was to provide feedback for the new recordset evaluation feature in iDigBio. While accessing the iDigBio correction records, and working with the output to fix our incorrectly georeferenced data, I reported several observations to the iDigBio Advanced Computing and Information Systems (ACIS) team. These observations were utilized to help refine the iDigBio correction output. We were able to help identify two bugs in the flagged files through our correspondence. Initially many of the localities showing up in the reverse longitude sign flag appeared to be false positives. When checked in GeoLocate, the coordinates seemed to be very close to a county or state border. Immediately the ACIS team recognized an issue with the designation of boundaries and fixed the problem. We came across another issue while cleaning up the files in the “dwc_stateprovince_replaced” flagged list. All of the georeferenced localities labeled “Baja California Norte” in our database were corrected to “California” in the iDigBio Portal. While “Baja California Norte” is an incorrect state name for the region, we learned that the correction should have been made to “Baja California”.

As a result of her 10 week summer internship, we provided useful feedback that helped to guide development of this important community resource. Also, her introduction to R has been an asset to her current position at the School of Visual Arts NATLab (, where she has been collecting NYC soil sample data and analyzing them for nutrient content.

Important Links: Project GitHub: School of Visual Arts NATLab: TCN Summary: iDigBio Portal: GeoLocate: iDigBio georeferencing practices: Georeferencing how to:

References: Arthropod Easy Capture. (2014). Arthropod Easy Capture. Retrieved October 14, 2014, from Schuh, R. T., Hewson-Smith, S., & Ascher, J. S. (2010). Specimen databases: A case study in entomology using Web-based software. American Entomologist, 56, 206–216.