Optimisation of the T-square sampling method to estimate population sizes

Abstract

Population size and density estimates are needed to plan resource requirements and plan health related interventions. Sampling frames are not always available necessitating surveys using nonstandard household sampling methods. These surveys are time-consuming, difficult to validate, and their implementation could be optimised. Here, we discuss an example of an optimisation procedure for rapid population estimation using T-Square sampling which has been used recently to estimate population sizes in emergencies. A two-stage process was proposed to optimise the TSquare method wherein the first stage optimises the sample size and the second stage optimises the pathway connecting the sampling points. The proposed procedure yields an optimal solution if the distribution of households is described by a spatially homogeneous Poisson process and can be sub-optimal otherwise. This research provides the first step in exploring how optimisation techniques could be applied to survey designs thereby providing more timely and accurate information for planning interventions.

Public health information in crisis-affected populations: a review of methods and their use for advocacy and action

“…[24][25][26] The T-square method requires two distances to be measured at each sampling point. 27 From each starting point the distance (d1) to the nearest shelter is measured; standing at this shelter, the distance (d2) to the next nearest shelter falling within a half-plane situated on the other side of the half-plane 'T' is measured" (Figure A2). 25 The occupants in both shelters are counted.…”

“…In statistical terms this is described as a spatially homogeneous Poisson process. 27 The ratio of distances, d1 to d2, depends on this spatial distribution pattern. The statistical robustness of the T-square method is verified through two tests.…”

Methods for health surveys in difficult settings: charting progress, moving forward

“…Grais et al's report from Niger [13] offers promising improvements to the "spin-the-pen" selection of households in urban areas. Bostoen et al [14] take a more fundamental approach, and explore the use of mathematical programming as a tool for optimising household sampling designs. They use the example of population estimation, a key prerequisite for meaningful health planning in any setting without reliable census data.…”

Spatial Variation in Tree Density and Estimated Aboveground Carbon Stocks in Southern Africa

“…To deal with errors associated with plot sizes, we estimate density and biomass at cluster level to increase the degrees of freedom (especially for sparsely populated areas), thereby having an unbiased estimate [9]. In a cluster, the unbiased mean search area (assuming there are no vacant subplots) occupied by nearest tree for the distance point to tree1 is: Global Positioning System was used to navigate to sentinel sites-clusters-plots-subplots.…”

“…The T-square method, as used in ecological surveys and lately in health, is more robust in estimating populations [9,10]. The errors associated with sample size and topographic differences can be resolved by estimating distribution and density at larger The sampled areas fall within the Zambezian phytochoria in Southern Africa [17] with dominant woody vegetation assemblages including not only the miombo, Burkea/Terminalia/Combretum, Mopane woodlands (Plates 1a,b,f respectively), and Acacia/Combretum, but also the transition to the Guinea-Congolia in the west and Zanzibar-Inhambane to the east.…”