Proceedings of the International Conference on Science and Engineering (ICSE-UIN-SUKA 2021)

GIS Mapping Based on Spatial-Temporal Model Estimation Affecting COVID-19 Outbreak in Kalimantan

Authors
Sifriyani1, *, Idris Mandang2, Fidia DenyTisnaAmijaya3
1Statistics Study Program, Mathematics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University Samarinda, Indonesia
2Geophysics Study Program, Physics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University Samarinda, Indonesia
3Mathematics Study Program, Mathematics Department, Faculty of Mathematics and Natural Sciences, Mulawarman University Samarinda, Indonesia
*Corresponding author. Email: sifriyani@fmipa.unmul.ac.id
Corresponding Author
Sifriyani
Available Online 23 December 2021.
DOI
https://doi.org/10.2991/aer.k.211222.035How to use a DOI?
Keywords
GIS Mapping; Spatio-temporal; Geographically Weighted Panel Regression; COVID-19 Outbreak; Applied Geography
Abstract

Innovation of Spatio-temporal analysis specifically for the geographically Weighted Panel Regression model with the development of geographic-weighted functions for spatial and temporal interactions. Map the GIS based on Spatio-temporal model estimation for the factors that may provide influence on the increase in the number of positive cases of COVID-19 in Kalimantan. Observation data consisted of 56 which was based on the Regency/City scale in Kalimantan. The variables used in this study were COVID-19 cases, number of doctors, number of hospitals, number of health care centers, number of tuberculosis cases, percentage of the elderly population, population density, percentage of the lower class population, and gross regional domestic income as regional economic indicators. COVID-19 data used in this study were the data from 2020 to August 10, 2021. The study utilized Spatio-temporal analysis with the Geographically Weighted Panel Regression model by involving the development geographic weighting of Gaussian, Bisquare, and Tricube kernel functions. The GWPR model is able to provide better estimator results than the Geographically Weighted Regression (GWR) model because it considers location and time aspects simultaneously. The results of this study, the GWPR model with Improve geographic weighting of the Bisquare kernel function was considered the most acceptable method. The model criteria were based on the coefficient of determination and RMSE. The results of the significance test of the GWPR model parameters on 56 Regency/City data in Kalimantan had succeeded in mapping the area into 24 categories based on the significant variables of each region.

Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

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Volume Title
Proceedings of the International Conference on Science and Engineering (ICSE-UIN-SUKA 2021)
Series
Advances in Engineering Research
Publication Date
23 December 2021
ISBN
978-94-6239-494-0
ISSN
2352-5401
DOI
https://doi.org/10.2991/aer.k.211222.035How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press International B.V.
Open Access
This is an open access article under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Sifriyani
AU  - Idris Mandang
AU  - Fidia DenyTisnaAmijaya
PY  - 2021
DA  - 2021/12/23
TI  - GIS Mapping Based on Spatial-Temporal Model Estimation Affecting COVID-19 Outbreak in Kalimantan
BT  - Proceedings of the International Conference on Science and Engineering (ICSE-UIN-SUKA 2021)
PB  - Atlantis Press
SP  - 217
EP  - 225
SN  - 2352-5401
UR  - https://doi.org/10.2991/aer.k.211222.035
DO  - https://doi.org/10.2991/aer.k.211222.035
ID  - 2021
ER  -