Detecting interpolation errors in infant mortality counts in 20th Century England and Wales

TL;DR

This paper introduces a novel changepoint method to detect and correct interpolation errors in historical infant mortality data, improving data consistency and analysis accuracy over changing administrative boundaries.

Contribution

The paper presents a new changepoint technique specifically designed to identify poor interpolation instances in historical datasets with boundary changes.

Findings

The method successfully detects interpolation errors in original data.

Correcting errors influences clustering of infant mortality curves.

Improves data reliability for historical demographic analysis.

Abstract

Understanding historical datasets, such as the England and Wales infant mortality data, for local government districts can provide valuable insights into our changing society. Such analyses can prove challenging in practice, due to frequent changes in the boundaries of local government districts for which records are collected. One solution adopted in the literature to overcome such practical challenges is to pre-process data using areal interpolation to render the units consistent over the time period of focus. However, such methods are prone to errors. In this paper we introduce a novel changepoint method to detect instances where interpolation performs poorly. We demonstrate the utility of our method on original data, and also demonstrate how correcting interpolation errors can affect the clustering of the infant mortality curves.