A bootstrapping approach for generating an inverse distance weight matrix when multiple observations have an identical location in large health surveys

Sung Wook Kim,Stavros Petrou,Felix Achana

doi:10.1186/s12942-019-0189-5

Sung Wook Kim, Stavros Petrou + Show 1 more

Open Access

https://doi.org/10.1186/s12942-019-0189-5

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Abstract

Spatial weight matrices play a key role in econometrics to capture spatial effects. However, these constructs are prone to clustering and can be challenging to analyse in common statistical packages such as STATA. Multiple observations of survey participants in the same location (or cluster) have traditionally not been dealt with appropriately by statistical packages. It is common that participants are assigned Geographic Information System (GIS) data at a regional or district level rather than at a small area level. For example, the Demographic Health Survey (DHS) generates GIS data at a cluster level, such as a regional or district level, rather than providing coordinates for each participant. Moreover, current statistical packages are not suitable for estimating large matrices such as 20,000 × 20,000 (reflective of data within large health surveys) since the statistical package limits the N to a smaller number. In addition, in many cases, GIS information is offered at an aggregated level of geographical areas. To alleviate this problem, this paper proposes a bootstrap approach that generates an inverse distance spatial weight matrix for application in econometric analyses of health survey data. The new approach is illustrated using DHS data on uptake of HIV testing in low and middle income countries.

Highlights

Spatial weight matrices play an important role in econometrics to capture spatial effects [1]
The sign for the coefficients for the simulated results is identical to that for the original data and the result provides a small bias. These results suggest that the bootstrapping simulation offers close results to those based on the original data despite the weak spatial autocorrelation
Spatial regression using a matrix programming language, Mata, was carried out and the result was compared with the result of spatial regression based on bootstrapping

Summary

Introduction

Spatial weight matrices play an important role in econometrics to capture spatial effects [1]. These matrices are used to generate spatial lag variables and spatial error models [2]. Geographic Information System (GIS) data are commonly provided at an aggregated geographic level in many national and international health surveys. Participants are generally assigned a GIS location at a regional or district rather than small area level. It is a common procedure that data surveyors attempt to aggregate collected data at a higher level in order to conceal the identity of survey participants.

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