Spatial Dimensions of Algorithmic Transparency: A Summary

Jayant Gupta, Alexander Long, Corey Kewei Xu, Tian Tang, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Spatial data brings an important dimension to AI's quest for algorithmic transparency. For example, data driven computer-Aided policy-decisions use measures of segregation (e.g., dissimilarity index) or income-inequality (e.g., Gini index), and these measures are affected by space partitioning choice. This may lead policymakers to underestimate the level of inequality or segregation within a region. The problem stems from the fact that many segregation based analyses use aggregated census data but do not report result sensitivity to choice of spatial partitioning (e.g., census block, tract). Beyond the well-known Modifiable Areal Unit Problem, this paper shows (via mathematical proofs as well as case studies with census data and census based synthetic micro-population data) that values of many measures (e.g., Gini index, dissimilarity index) diminish monotonically with increasing spatial-unit size in a hierarchical space partitioning (e.g., block, block-group, tract), however the ranking based on spatially aggregated measures remain sensitive to the scale of spatial partitions (e.g., block, block group). This paper highlights the need for social scientists to report how rankings of inequality are affected by the choice of spatial partitions.

Original languageEnglish (US)
Title of host publicationProceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021
PublisherAssociation for Computing Machinery
Number of pages10
ISBN (Electronic)9781450384254
StatePublished - Aug 23 2021
Event17th International Symposium on Spatial and Temporal Databases, SSTD 2021 - Virtual, Online, United States
Duration: Aug 23 2021Aug 25 2021

Publication series

Name17th International Symposium on Spatial and Temporal Databases


Conference17th International Symposium on Spatial and Temporal Databases, SSTD 2021
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant No. 1737633. We thank spatial computing research group for their helpful comments and refinements. Finally, we thank the anonymous reviewers for their insightful comments to further refine the paper.

Publisher Copyright:
© 2021 Owner/Author.


  • Accountability
  • Fairness
  • Public policy
  • Spatial data science
  • Urban Planning
  • and Transparency


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