A simple classification and clustering of poverty in rural areas using machine learning

Deddy Barnabas Lasfeto, Tuti Setyorini, James Josias Mauta, Melchior Bria, Obed Oktafianus Nego Nenobais

Article ID: 5938
Vol 8, Issue 8, 2024

VIEWS - 164 (Abstract) 64 (PDF)

Abstract


This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.


Keywords


rural community; poverty; data clustering; data classification; machine learning

Full Text:

PDF


References


ADB. (2021). Mapping the Spatial Distribution of Poverty Using Satellite Imagery in Thailand. Asian Development Bank.

Ahn, D., Cha, M., Han, S., et al. (2020). Teaching Machines to Measure Economic Activities from Satellite Images: Challenges and Solutions. Machine Learning and Satellite Imagery.

Alsharkawi, A., Al-Fetyani, M., Dawas, M., et al. (2021). Poverty classification using machine learning: The case of Jordan. Sustainability (Switzerland), 13(3), 1–16. https://doi.org/10.3390/su13031412

Ayush, K., Uzkent, B., Tanmay, K., et al. (2020). Efficient Poverty Mapping using Deep Reinforcement Learning. Available online: http://arxiv.org/abs/2006.04224 (accessed on 22 January 2024).

BPS. (2018). SUSENAS Modul 2018. BPS.

BPS. (2021). Sabu Raijua in Figures 2021 (Indonesian). BPS.

Castro, D. A., & Álvarez, M. A. (2022). Predicting socioeconomic indicators using transfer learning on imagery data: an application in Brazil. GeoJournal, 88(1), 1081–1102. https://doi.org/10.1007/s10708-022-10618-3

Chitturi, V., & Nabulsi, Z. (2021). Predicting Poverty Level from Satellite Imagery using Deep Neural Networks. arXiv.

Forero-Corba, W., & Bennasar, F. N. (2024). Techniques and applications of Machine Learning and Artificial Intelligence in education: a systematic review. RIED-Revista Iberoamericana de Educacion a Distancia, 27(1), 209–253. https://doi.org/10.5944/ried.27.1.37491

Hernawati, I. (2017). The measurement of poverty construct in Indonesia (Indonesian). Media Informasi Penelitian Kesejahteraan Sosial, 41(3), 269–284.

Isnin Hamdan, R., Bakar, A. A., & Sani, N. S. (2020). Does Artificial Intelligence Prevail in Poverty Measurement? Journal of Physics: Conference Series, 1529(4), 042082. https://doi.org/10.1088/1742-6596/1529/4/042082

Kota, K., Iii, B., Art, B., et al. (2017). National socio-economic survey 2017 (Indonesian). BPS.

Min, P. P., Gan, Y. W., Hamzah, S. N. B., et al. (2022). Poverty Prediction Using Machine Learning Approach. Journal of Southwest Jiaotong University, 57(1), 136–146. https://doi.org/10.35741/issn.0258-2724.57.1.12

Ochoa Guaraca, M. E., Castro, R., Arias Pallaroso, A., et al. (2021). Machine learning approach for multidimensional poverty estimation. Revista Tecnológica—ESPOL, 33(2), 205–225. https://doi.org/10.37815/rte.v33n2.853

Omae, O. J. (2020). University of Nairobi Using Random Forest (RF) to Identify Key Determinants of Poverty in Kenya School of Mathematics [Marster’s thesis]. School of Mathematics.

Poreddy, D., Reddy, E. V. V., Prasad, S. V., et al. (2020). Classification of Poverty Levels Using Machine Learning. Journal of Xi’an University of Architecture & Technology, 12(4), 5723–5728.

Repollo, M. P., & Robielos, R. A. C. (2021). Applying Clustering Algorithm on Poverty Analysis in a Community in the Philippines. In: Proceedings of the International Conference on Industrial Engineering and Operations Management Monterrey; 3–5 November 2021; Mexico. pp. 1511–1521.

Rozenberg, J., & Hallegatte, S. (2016). Model and Methods for Estimating the Number of People Living in Extreme Poverty Because of the Direct Impacts of Natural Disasters. World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-7887

Sihombing, P. R., & Arsani, A. M. (2021). Comparison of Machine Learning Methods in Classifying Poverty in Indonesia in 2018. Jurnal Teknik Informatika (Jutif), 2(1), 51–56. https://doi.org/10.20884/1.jutif.2021.2.1.52

Singh, Y., & Bhatia, P. K. (2007). A Review of Studies on Machine Learning Techniques. International Journal of Computer Science and Security, 1(1), 70.

Tingzon, I., Orden, A., Go, K. T., et al. (2019). Mapping poverty in the Philippines using machine learning, satellite imagery, and crowd-sourced geospatial information. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W19, 425–431. https://doi.org/10.5194/isprs-archives-xlii-4-w19-425-2019

Tran, H., Mining, D., Multimedia, M., & Times, L. S. (2019). A survey of machine learning and data mining. Available online: https://www.researchgate.net/publication/333457161_Survey_of_Machine_Learning_and_Data_Mining_Techniques_used_in_Multimedia_System?channel=doi&linkId=5d88ee6c299bf1996f987f9f&showFulltext=true (accessed on 14 May 2023).

Utmal, D. M. (2021). Machine Learning Its Applications, Challenges & Tools: A Review. International Journal of Computer Science and Mobile Computing, 10(3), 32–38. https://doi.org/10.47760/ijcsmc.2021.v10i03.004

Verma, D. P. K., & Preety. (2020). Application of K-Means Algorithm to Mapping Poverty Outline by Province in India. International Journal of Recent Technology and Engineering (IJRTE), 8(6), 1045–1049. https://doi.org/10.35940/ijrte.f7357.038620

Yoon, H. (2019). Artificial Intelligent Technology in Public and Private Sector: The case of Korea. In: Proceedings of the Artificial Intelligence (AI): Overview and Applications; 17 September 2019; Bangkok, Thailand.




DOI: https://doi.org/10.24294/jipd.v8i8.5938

Refbacks

  • There are currently no refbacks.


Copyright (c) 2024 Deddy Barnabas Lasfeto, Tuti Setyorini, James Josias Mauta, Melchior Bria, Obed Oktafianus Nego Nenobais

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License.