Author: Shehab D. Ibrahim (Mindanao State University – Iligan Institute of Technology)
Pandemic orders are declared to protect public health. However, some of these orders are more challenging to comply for impoverished or vulnerable groups. Let’s take the staying at home policy as an example. Vulnerable groups have no choice but to go out to work despite the great physical risk.
The researcher will investigate the pandemic resilience of the municipalities with high poverty incidence. Specifically, the researcher will look into the spread of the poverty index across the municipalities in the Philippines, study the correlation between pandemic resilience/ preparedness and social statistics of the municipalities, and determine the important factors (e.g. ICT and healthcare system investments) in developing pandemic resilience and provide data visualizations to aid decision-makers. The data will be taken from the DOH public open data repository, OpenStat database from the Philippines Statistics Authority (PSA), and other Philippines open government data repositories.
The researcher plans to create a Dynamic Poverty Heat Map through Geographical Information System (GIS) to show the poverty levels of every municipality. Spatial autocorrelation can also be performed to measure the autocorrelation of municipalities (through its polygon representations). This is to determine the pattern of the spatial position of municipalities and the poverty rate and evaluate if it is clustered, dispersed, or random. Also, the researchers will determine significant attributes from the DOH and OpenStat Database specifically related to Pandemic Resilience and Social Statistics of municipalities. A correlation heatmap can be visualized to show the relationship between these attributes. Finally, the researcher will create a model that will use the Social Statistics attributes as the features (independent variables) and the Pandemic Resilience attribute as the targeted variable (dependent variable). Models that can determine the important predictor variables, such as Lasso and Ridge regression, will be used. This will be helpful in determining the factors that may lead to developing pandemic resilience.
This article is the author’s reflection on the insight gained from the recently concluded OCDex 2022 Public Data Analytics Fellowship Trainings.
For more information about the article, please reach out to the author: shehab.ibrahim@g.msuiit.edu.ph or Layertech labs support at learning@layertechlab.com
