FOSS4G 2021 - A tool for machine learning based dasymetric mapping approaches in GRASS GIS
Channel: FOSS4G
Date: 2021-11-01

Socio-economic and demographic data is usually collected at the individual or household level, and numbers are then aggregated and released at the level of administrative units. The spatial extent of many phenomena, however, do not correspond to any existing administrative limits, making them difficult to exploit. Additionally, geospatial information has started to be available at more and more detailed spatial resolutions, thanks to progress made using high-resolution EO data. Consequently, 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. Dasymetric mapping can be used to create a more meaningful gridded layer of disaggregated socio-economic data, but the major challenge resides in determining the spatial distribution of a variable within aggregated spatial units.
The dasymetric mapping approach has been made more accessible with an existing GRASS GIS addon “v.area.weigh" (Metz, Grass Development Team, 2013), available on the official GRASS GIS add-on repository. It provides a tool for dasymetric mapping, however requires that the user provide their own weighted layer. Grippa et al. (2019) published a replicable approach that implements the random forest algorithm for the creation of a weighting layer for dasymetric mapping with the related computer code. While this code allows replicating the method, it is very specific to the experiments presented in the paper and may not fit the needs of other scientists. Moreover, since it is computer code, potential users not skilled enough in Python and R programming could be reluctant to use it.
An important step of the approach has already been implemented in a GRASS GIS add-on, “r.zonal.classes” (Grippa, Grass Development Team, 2019), which consists of the zonal extraction of class proportions from categorical raster data. The tool presented today completes the implementation of this approach, in a more generic and user-friendly manner. To our knowledge, there is no other existing open-source and ready-to-use tool, with a Graphical User Interface (GUI) for creation of dasymetric mapping weighting layers, using a ML approach.
References
Metz, M., GRASS Development Team, 2013. Addon v.area.weigh, Geographic Resources Analysis Support System (GRASS) Software, Version 7.8, Open Source Geospatial Foundation. https://grass.osgeo.org/grass78/manuals/addons/v.area.weigh.html (7 June 2021)
Grippa, T., Linard, C., Lennert, M., Georganos, S., Mboga, N., Vanhuysse, S., Gadiaga, A., Wolff, E. 2019. Improving urban population distribution models with very-high resolution satellite information, Data, 4(1), 1–17. doi.org/10.3390/data4010013.
Grippa, T., GRASS Development Team, 2019. Addon r.zonal.classes. Geographic Resources Analysis Support System (GRASS) Software, Version 7.8. Open Source Geospatial Foundation. https://grass.osgeo.org/grass78/manuals/addons/r.zonal.classes.html (7 June 2021)

Authors and Affiliations –
Flasse, Charlotte (1)
Grippa, Taïs (1)
Fennia, Safa (1)
(1) Université Libre de Bruxelles, Belgium
Track –
Academic
Topic –
FOSS4G implementations in strategic application domains: land management, crisis/disaster response, smart cities, population mapping, climate change, ocean and marine monitoring, etc.
Level –
2 - Basic. General basic knowledge is required.
Language of the Presentation –
English