Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)

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Modeling and Mapping on Bayesian Spatio-Temporal CAR Localized for Poverty in Sulawesi Island, Indonesia

Authors
Sukarna1, 2, Anang Kurnia2, *, Kusman Sadik2
1Department of Mathematics, Universitas Negeri Makassar, Makassar, Indonesia
2Department of Statistics, IPB University, Dramaga, Indonesia
*Corresponding author. Email: anangk@apps.ipb.ac.id
Corresponding Author
Anang Kurnia
Available Online 18 December 2023.
DOI
10.2991/978-94-6463-332-0_21How to use a DOI?
Keywords
Bayesian Spatio-temporal Localized model; Relative risk; Sulawesi Island Poverty
Abstract

Spatial modeling can identify locations with high or low risk of disease effect, but it cannot explain the temporal shift in risk, which may be as relevant or more important. As a result, mapping modeling should consider both geographical and temporal components. Some research has utilized Bayesian Spatio-Temporal Conditional Autoregressive (BST CAR) models. However, no research has been conducted on using BST CAR Localized model for poverty on Sulawesi Island, Indonesia. This research aims to find the best BST CAR localized model for poverty in 81 regencies/cities on Sulawesi Island. The BST CAR localized model with different number of clusters G=2, G=3, and G=5 was used to model the relative risk (RR) of poverty in each of 81 regencies and cities. The results suggest that BST CAR Localized with G=2 is the best model for modeling the relative risk of poverty on Sulawesi Island. Variables such as Gender Development Index (IPG), Women’s Income Contribution (SPP), Adjusted Per Capita Expenditure (PKD), and Human Development Index (IPM) have a significant impact on poverty. SPP has a positive influence on poverty, while the other three components have a negative impact.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
Series
Advances in Computer Science Research
Publication Date
18 December 2023
ISBN
978-94-6463-332-0
ISSN
2352-538X
DOI
10.2991/978-94-6463-332-0_21How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sukarna
AU  - Anang Kurnia
AU  - Kusman Sadik
PY  - 2023
DA  - 2023/12/18
TI  - Modeling and Mapping on Bayesian Spatio-Temporal CAR Localized for Poverty in Sulawesi Island, Indonesia
BT  - Proceedings of the 5th International Conference on Statistics, Mathematics, Teaching, and Research 2023 (ICSMTR 2023)
PB  - Atlantis Press
SP  - 185
EP  - 197
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-332-0_21
DO  - 10.2991/978-94-6463-332-0_21
ID  - 2023
ER  -