Align data analytics pipeline and risk dashboard

[matamadio]

The objective is to plug country risk outputs from CCDR analytics into a global dashboard.

To do so effectively, we want to do the least amount of changes between the output of the python analytics and the input required by the dashboard.

I think we already agree on the list of output fields to plot, and mostly on the way to plot them.

ADM0_CODE	Unique identifier
ADM1_CODE	Unique identifier
ADM2_CODE	Unique identifier
ADM3_CODE	Unique identifier
ADM3_NAME	ADM unit names
ADM2_NAME	ADM unit names
ADM1_NAME	ADM unit names
ADM0_NAME	ADM unit names
ADM4_pop	Total population count
ADM4_builtup	Total builtup extent (ha)
ADM4_agr	Total agricultural land (ha)
FL_pop_EAI	Expected mortality from river floods (population count)
FL_pop_EAI%	Expected mortality from river floods (% of ADM3 population)
FL_builtup_EAI	Expected damage on builtup from river floods (hectars)
FL_builtup_EAI%	Expected damage on builtup from river floods (% of ADM3 builtup)
FL_EAE_agri	Expected damage on agricultural land from river floods (hectars)
FL_EAE_agri%	Expected damage on agricultural land from river floods (% of ADM3 builtup)
CF_pop_EAI	Expected mortality from river floods (population count)
CF_pop_EAI%	Expected mortality from river floods (% of ADM3 population)
CF_builtup_EAI	Expected damage on builtup from river floods (hectars)
CF_builtup_EAI%	Expected damage on builtup from river floods (% of ADM3 builtup)
CF_EAE_agri	Expected damage on agricultural land from river floods (hectars)
CF_EAE_agri%	Expected damage on agricultural land from river floods (% of ADM3 builtup)
DR_S1_30p	Frequency of agricultural stress affecting at least 30% of arable land during Season 1 (percentage of historical period 1984-2022)
DR_S2_30p	Frequency of agricultural stress affecting at least 30% of arable land during Season 2 (percentage of historical period 1984-2022)
SW_BU_EAI	Expected annual impact from tropical cyclon strong winds over builtiup (hectares)
SW_BU_EAI%	Expected annual impact from tropical cyclon strong winds over builtiup (% of ADM3 builtup)
LS_pop_C3	Population within landslide hazard zones class 3 (high)
LS_builtup_C3	Built-up within landslide hazard zones class 3 (high

[matamadio]

New dashboard under development by Zeeshan: https://szhaider.shinyapps.io/NigeriaDisasterRisks/

[matamadio]

As dashboard developers seems quite herratic, suggest to develop it internally (ourselves) leveraging existing tools:

Shiny - either R or Python

• https://shiny.posit.co/py/docs/overview.html

Greppo - opensource py app

• https://greppo.io/
• https://towardsdatascience.com/build-a-geospatial-dashboard-in-python-using-greppo-60aff44ba6c9

[matamadio]

Superceeded by new workflow and dashboard.