Assessing Spatial Data Quality Using Five Data Anomalies: Speeding the Process for Master Facility Lists and Other Large Data Sets

Author(s): John Spencer, Becky Wilkes

Year: 2019

Abstract:

With the increased ease of the collection of geographic data coordinates and the desire for accurate country master facility lists (MFLs) comes the need for tools and methods with which to rapidly assess the quality of large spatial data sets. Global health professionals who have had limited training in the use of geographic information systems may need guidance in assessing spatial data. Identifying data quality issues in data sets of this size is challenging, because of the complex relationship between the spatial components and the attributes of the data.

Informed by spatial data quality literature, this paper presents a framework for assessing common issues with spatial data and identifies five specific potential data anomalies that can be identified and further investigated to increase the quality of a spatial data set, such as an MFL. Focusing on these five anomalies will provide quantifiable results, which help in planning a practical, effective strategy for corrections. This approach yields not only a list of the locations that need to be corrected, but also feedback on what may be wrong with the data.