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'Unmasking' masked address data: A medoid geocoding solution.

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A new medoid-based geocoding method accurately locates masked spatial data, overcoming limitations of commercial software for open data initiatives.

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Area of Science:

  • Data Science
  • Geospatial Analysis
  • Privacy Preservation

Background:

  • Open data initiatives face challenges balancing data accessibility with individual privacy.
  • Data masking is used to protect privacy but complicates spatial data geocoding.
  • Existing commercial geocoding software often fails with administratively-masked spatial data.

Purpose of the Study:

  • To present a novel medoid-based technique for geocoding administratively-masked spatial data.
  • To minimize spatial uncertainty introduced by data masking.
  • To compare the performance of the medoid approach against commercial geocoding software.

Main Methods:

  • Developed a medoid-based geocoding technique for masked spatial datasets.
  • Implemented and tested the medoid geocoding approach.
  • Compared geocoding accuracy and spatial uncertainty with commercial software.

Main Results:

  • The medoid-based approach successfully geocodes administratively-masked data.
  • This method significantly minimizes spatial uncertainty compared to standard masking.
  • The medoid technique demonstrated superior spatial accuracy over commercial geocoding packages.

Conclusions:

  • Medoid-based geocoding is a mechanically simple and effective solution for masked spatial data.
  • This approach enhances the utility of open spatial datasets while preserving privacy.
  • The proposed method offers improved spatial accuracy and reliability for geocoding masked data.