Geospatial Big Data Analytics for Quality Control of Surveys

Main Article Content

Benjamin Leehan
Nathaniel Bastian

Abstract

Geospatial big data analytics allows survey quality control analysts to draw important conclusions about survey data quality that otherwise would take excessive time and resources. In this work, we explored two algorithmic methods that can help ensure reliability of survey interviews by detecting geospatial outliers. Focusing on geospatial data collected from surveys, we implemented outlier detection techniques with two different distance metrics to identify statistical anomalies in real-world datasets that may have qualitative interpretations. We found that one algorithm, which considers the local distribution of points in a dataset, identifies a different set of outliers when compared to another method, which considers the global distribution of points. Since there was a small overlap (10-19%) of flagged points between the two algorithms implemented, it may be helpful for analysts to focus on the fewer “outlier” points that are flagged by both methods rather than all the “outlier” points that are flagged by each algorithm. Finally, analysts should consider the computational costs, as the algorithms differ significantly.

Article Details

How to Cite
Leehan, B., & Bastian, N. (2022). Geospatial Big Data Analytics for Quality Control of Surveys. Industrial and Systems Engineering Review, 10(2), 142-150. https://doi.org/10.37266/ISER.2022v10i2.pp142-150
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Articles

References

Ben-Gal, I. (2005). Outlier Detection. In O. Maimon, & L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA, 2005; 131-146. Retrieved from https://doi.org/10.1007/0-387-25465-X_7.

Brassil, C. (2021a). Quality Control Overview. Presentation on 21 August 2021 from D3 Systems.

Brassil, C. (2021b). GPS Training III – Basic Analysis. Presentation on 21 August 2021 from D3 Systems.

Breunig, M., Kriegel, H. P., Ng, R., and Sander, J. (2000). LOF: Identifying Density-Based Local Outliers. Proceedings of the ACM SIGMOD 2000 International Conference on Management of Data, 2000; 1-12..

Singh, A., and Lalitha, S. (2018). A novel spatial outlier detection technique. Communications in Statistics - Theory and Methods, 2018; 47, 247-257.