Finding Outliers in Models of Spatial Data

Statistical models fit to data often require extensive and challenging re-estimation before achieving final form. For example, outliers can adversely affect fits. In other cases involving spatial data, a cluster may exist for which the model is incorrect, also adversely affecting the fit to the “good” data. In both cases, estimate residuals must be checked and rechecked until the data are cleaned and the appropriate model found. In this article, we demonstrate an algorithm that fits models to the largest subset of the data that is appropriate. Specifically, if a hypothesized linear regression model fits ninety percent of the data, our algorithm can not only find an excellent fit as if only that “good” data were presented, but will also highlight the ten percent of the “bad” data that is not fit. Our work in digital government has focused on mapping data. Thus we illustrate how models fit to census track data work, and how the data in the “bad” set can be v...

• Type: Conference
• Year: 2003
• Where: DGO
• Authors: David W. Scott, J. Blair Christian
• Keywords: Census Track Data, DGO 2003, DGO 2007, Model, Spatial Data