Overview: Spatial Quality Anomalies Diagnosis (SQUAD) Tool

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Author(s): MEASURE Evaluation

Year: 2017

Overview: Spatial Quality Anomalies Diagnosis (SQUAD) Tool Abstract:

Knowledge about health facility locations is important when addressing HIV, maternal and child mortality, and other issues. As a result, there has been a rapid growth in large geospatial data sets, such as master facility lists (MFLs). An MFL and other similar lists typically contain locations of health facilities as well as attributes of the facilities—including name, address, or which administrative unit the facility is located in.

Assessing the quality of these data sets can be challenging, because there are two types of possible errors: spatial errors and attribute errors. Assessing for spatial errors involves looking at such things as the presence of a coordinate, whether it is properly recorded, and the accuracy of its location. Assessing attribute errors involves determining whether attributes such as site name or site ID are correct.

When you work with large spatial data sets, manually reviewing each record to validate both location and attribute information can be prohibitively time-consuming. A more effective approach would be to look for anomalies in the data that may indicate data quality issues. MEASURE Evaluation’s Spatial Quality Anomalies Diagnosis (SQUAD) Tool identifies six different types of anomalies in spatial data.

This brief provides an overview of the SQUAD tool. Access additional SQUAD tool resources, including the downloadable tool package, a PowerPoint presentation, and the SQUAD tool manual.

This document is not available in print from MEASURE Evaluation.

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