Hello everyone,
I am currently conducting research on socio-environmental justice and the distribution of urban ecosystem services. I am using the Urban Nature Access model to analyze future scenarios for 2030 and 2035.
I have encountered a challenge regarding data resolution: official population projections for those years are only available at a macro-administrative level (large districts/localities). However, my LULC (Land Use/Land Cover) maps and the required analysis for the model demand a much finer spatial grain (at the grid/pixel or block level), which is unavailable for future dates.
I would like to ask the community’s opinion on the following:
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Is it methodologically sound within the InVEST framework to distribute the projected macro-unit population proportionally, based on the current population density of the LULC pixels?
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If I assume that the built-up areas in my 2030/2035 LULC maps will absorb this growth, is a dasymetric mapping approach (weighting population distribution by urban land-use classes) considered a robust proxy for the model’s demand requirements?
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Are there any known statistical biases or “red flags” I should be aware of when performing this type of downscaling from large administrative units to a high-resolution grid for future scenarios?
I want to ensure that the “demand” side of the model remains consistent with the “supply” side, even if the population data starts at a much coarser scale than the biophysical data.
Thank you very much for your guidance and suggestions.