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\begin{document}

\title{A Spatial Feature Engineering Algorithm for Creating Air Pollution
Health Datasets}



\author[1]{Raja Sher Afgun Usmani }%
\author[1]{Thulasyammal Ramiah Pillai }%
\author[1]{Ibrahim Abaker Targio Hashem }%
\author[1]{NZ Jhanjhi }%
\author[1]{Anum Saeed }%
\affil[1]{Affiliation not available}%


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  \date{June 16, 2024}


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\begin{abstract}
Dear Editor: This manuscript is already online in techrxiv
(https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are
not correctly associated with the published preprint so we are
submitting this again by associating the co-authors. We have also
improved the similarity report, the similarity index is 11\% of this
submission. Thank you.\\
Abstract: Air pollution is one of the significant causes of mortality
and morbidity every year. In recent years, many researchers have focused
their attention on the associations of air pollution and health. These
studies used two types of data in their studies, i.e., air pollution
data and health data. Feature engineering is used to create and optimize
air quality and health features. In order to merge these datasets
residential address, community/county/block/city and hospital/school
address are used. Using residence address or any location becomes a
spatial problem when the Air Quality Monitoring (AQM) stations are
concentrated in urban areas within the regions and an overlap in the AQM
stations in urban areas coverage area, which raises the question that
how to associate the patients with the relevant AQM station. Also, in
most of the studies the distance of patients to the AQM stations is also
not taken into account. In this study, we propose a four-part spatial
feature engineering algorithm to find the coordinates for health data,
calculate distances with AQM stations and associate health records to
the nearest AQM station. Hence, removing the limitations of current air
pollution health datasets. The proposed algorithm is applied as a case
study in Klang Valley, Malaysia. The results show that the proposed
algorithm can generate air pollution health dataset efficiently and the
algorithm also provides the radius facility to exclude the patients who
are situated far away from the stations.%
\end{abstract}%



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