Can Dasymetric Mapping Significantly Improve Population Data Reallocation in a Dense Urban Area?

Abstract

The issue of reallocating population figures from a set of geographical units onto another set of units has received a great deal of attention in the literature. Every other day, a new algorithm is proposed, claiming that it outperforms competitor procedures. Unfortunately, when the new (usually more complex) methods are applied to a new data set, the improvements attained are sometimes just marginal. The relationship cost‐effectiveness of the solutions is case‐dependent. The majority of studies have focused on large areas with heterogeneous population density distributions. The general conclusion is that as a rule more sophisticated methods are worth the effort. It could be argued, however, that when we work with a variable that varies gradually in relatively homogeneous small units, simple areal weighting methods could be sufficient and that ancillary variables would produce marginal improvements. For the case of reallocating census data, our study shows that, even under the above conditions, the most sophisticated approaches clearly yield the better results. After testing fourteen methods in Barcelona (Spain), the best results are attained using as ancillary variable the total dwelling area in each residential building. Our study shows the 3‐D methods as generating the better outcomes followed by multiclass 2‐D procedures, binary 2‐D approaches and areal weighting and 1‐D algorithms. The point‐based interpolation procedures are by far the ones producing the worst estimates.

Dasymetric distribution of votes in a dense city

“…Although in urban areas and mainly in big cities, it is expected that dasymetric approaches shows limited improvement in accuracy over simple areal weighting approaches, in a recent paper Pavía and Cantarino (2016) have shown for the same breakdowns handled in this research that dasymetric refinement clearly beats simple methods for the customary problem of reallocating census figures. This paper analyses whether this is also true for the case for reallocating votes.…”

“…They are used as baseline to gauge the value of four other alternatives based on dasymetric techniques, and therefore more complex (areal interpolation with ancillary data). We have followed Pavía and Cantarino (2016) to choose the dasymetric methods considered in this research. For exactly the same breakdowns analysed in this paper, Pavía and Cantarino (2016) have shown that dasymetric refinements clearly outperform simple methods when dealing with the customary problem of reallocating census figures.…”

“…We have followed Pavía and Cantarino (2016) to choose the dasymetric methods considered in this research. For exactly the same breakdowns analysed in this paper, Pavía and Cantarino (2016) have shown that dasymetric refinements clearly outperform simple methods when dealing with the customary problem of reallocating census figures. They find the expected hierarchy: 3-D procedures producing the better outcomes, followed by multiclass 2-D methods, binary 2-D approaches, and areal weighting and 1-D algorithms, with the pointbased interpolation procedures generating by far the worst estimates.…”

“…Although dasymetric mapping has been proved being more robust and accurate for the spatial reallocation of census data, we explore here whether this superiority is maintained for voting data. The comparison between approaches is customary (see, e.g., Ruther, Leyk and Buttenfield, 2015;Buttenfield, Ruther and Leyk, 2015;Pavía and Cantarino, 2016) an relevant because depending on the final objectives of the redistribution process, the analyst should weight if it is worth enough to apply more time consuming and complex methods or just to consider simple approaches.…”

“…In addition to the baseline approaches of point-based spatial interpolation and areal weighting, four additional methods based on dasymetric techniques, are proposed as alternatives. In particular, we compare to four dasymetric procedures that have proven to be superior to the simplest methods using exactly the same breakdowns for the customary problem of reallocating census data (Pavía and Cantarino, 2016). Section 3 focuses on the challenge that individual secret ballots entails in terms of disposing of a standard for validation and how this can be solved.…”