Allow for incorporating information on informal settlements

[carlhiggs]

For many cites and regions informal settlements are important for consideration: potentially densely populated, these are locations home to populations who may be most vulnerable to urban inequities. They may also be excluded from census data, and so possibly not included in population estimates. We should find a way to allow for sensitivity analysis of population data to compare it with data on location of informal settlements where available. This could help highlight inequities in population data, and potential limitations in some urban indicators for population access to healthy urban environments. If such limitations were identified, we might be able to work towards methods to mitigate these.

This issue is a placeholder for this intent.

[carlhiggs]

Indirectly, this might now be possible via #295 , or configuring an alternative population data grid -- ie. if one had a prepared dataset that was known to account for informal settlements (that could perhaps be overlooked in larger modelled data relying on census estimates, like GHS-POP, our default), then this could be substituted for the global data, and optionally the results compared as a sensitivity analysis.

As this is now technically possible I will consider this issue technically resolved. It is an important consideration however, so I will change its tag from 'enhancement' to 'user process to document'. It will be great to include some guidance on doing this in our instructional materials.

[carlhiggs]

Some resources that could help inform approaches to deal with this:

• https://appliedsciences.nasa.gov/what-we-do/projects/mapping-missing-millions-developing-global-database-informal-settlement
  • https://www.conflict-ecology.org/research#h.p_mPPOHgiGSXaR
• Van Den Hoek, Jamon, and Hannah K. Friedrich. 2021. "Satellite-Based Human Settlement Datasets Inadequately Detect Refugee Settlements: A Critical Assessment at Thirty Refugee Settlements in Uganda" Remote Sensing 13, no. 18: 3574. https://doi.org/10.3390/rs13183574
• Gram-Hansen, Bradley, Patrick Helber, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, and Piotr Bilinski. ‘Mapping Informal Settlements in Developing Countries Using Machine Learning and Low Resolution Multi-Spectral Data’. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 361–68, 2019. https://doi.org/10.1145/3306618.3314253.
• https://frontierdevelopmentlab.github.io/informal-settlements/
  • ^^^ includes data and code that could be used potentially for sensitivity analyses of configured data that may overlook informal settlements in certain locations - e.g. Medellin, Colombia