Abstract
Abstract. Socio-economic and demographic data is often released at the level of census administrative units. However, there is often a need for data available at a higher spatial resolution. Dasymetric mapping is an approach that can be used to disaggregate such data into finer levels of detail. It relies on the assumption that proxies available at a higher spatial resolution, along with knowledge of an area, can be used to produce weights in order to spatially reallocate the data to a finer scale layer. The power and efficiency of machine learning (ML) approaches can be taken advantage of when producing weighted layers for dasymetric mapping. Less advanced users, however, may find these approaches too complex. To encourage a wider uptake of such approaches, easy-to-use tools are necessary. GRASS GIS is a free and open-source GIS software that contains many modules for processing geographic data. The existing GRASS GIS add-on “v.area.weigh” already makes the dasymetric mapping approach more accessible, however users must provide their own weighted layer. This paper presents the development of a GRASS GIS add-on, “r.area.createweight”, which provides a simple and convenient tool to facilitate the implementation of a ML-based approach to produce weighted layers for dasymetric mapping. The tool will be available on the official GRASS GIS add-on repository to encourage a more widespread uptake of these approaches.
Highlights
Socio-economic and demographic data is usually collected at the individual or household level, and numbers are aggregated and released at the level of administrative units (Su et al, 2010)
Scientists often aim to perform spatial analyses at a fine resolution, but face issues related to the fact that the spatial resolution of administrative units, on which socio-economic and demographic data are aggregated, is too coarse and does not fit their needs and specifications
The major challenge in dasymetric mapping resides in determining the spatial distribution of the socio-economic or demographic variable within the administrative units, based on a set of ancillary geoinformation data
Summary
Socio-economic and demographic data is usually collected at the individual or household level, and numbers are aggregated and released at the level of administrative units (Su et al, 2010). The dasymetric mapping approach (Wu et al, 2005; Langford, 2007) has increasingly received attention in order to exploit socio-economic and demographic data for spatially detailed analysis and/or to explore spatial phenomena that do not follow existing administrative units (the modifiable areal unit problem MAUP). This modelling technique relies on the assumption that the knowledge of an area can be used to unequally spatially disaggregate (or redistribute) socio-economic data provided at the administrative level, to create a more realistic, finer scale, gridded layer of disaggregated socio-economic data (Su et al, 2010). The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLVI-4/W2-2021 FOSS4G 2021 – Academic Track, 27 September–2 October 2021, Buenos Aires, Argentina development of simple and convenient methods or tools are necessary to encourage a more widespread uptake of these approaches by potential user communities, for example by remote sensing and GIS scientists
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