Mapping the Dimensions of Poverty Through Big Data, Socioeconomic Surveys and Machine Learning in Cambodia

Theara Khoun (), Ate Poortinga, Nyein Soe Thwal, Iván González de Alba, Andrea McMahon and Carlos Mendez
Additional contact information
Theara Khoun: United Nations Development Programme
Ate Poortinga: SERVIR Southeast Asia
Nyein Soe Thwal: SERVIR Southeast Asia
Iván González de Alba: United Nations Development Programme
Andrea McMahon: SERVIR Southeast Asia
Carlos Mendez: Nagoya University

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2025, vol. 180, issue 3, No 12, 1593-1618

Abstract: Abstract Cambodia has witnessed rapid economic growth in recent years; however, it remains one of the most economically vulnerable nations in Southeast Asia, grappling with persistent poverty challenges. Accurately understanding the multiple dimensions of poverty is essential for promoting sustainable development and guiding targeted policy interventions. Yet, traditional poverty data are often outdated and lack the granularity needed for effective subnational planning. To address this gap, this study leverages new big data sources, machine learning techniques, and the Cambodia Socio-Economic Survey (CSES) to predict and map multidimensional poverty across 10 indicators in three dimensions based on the Global Multidimensional Poverty Index (MPI): education, health, and living standard dimensions at fine spatial scales. By integrating deprivation probabilities across a gridded landscape with building footprint information, the study estimates household-level deprivations. Using a random forest algorithm, the study achieves high predictive accuracy for indicators such as clean water, sanitation, food consumption, housing materials, cooking fuel, and access to electricity. However, challenges remain, including the need for unbiased training data and the limited capacity to capture disparities within regional aggregates (provinces, districts, townships). Despite these limitations, the study identifies nighttime lights, population density, and road network data as key predictors of poverty. The findings demonstrate the feasibility of using big-earth observation data and machine learning to complement traditional socioeconomic surveys, enabling a more detailed and dynamic understanding of multidimensional poverty at various geographical scales.

Keywords: Poverty mapping; Multidimensional poverty; Machine learning; Big data; Socioeconomic surveys; Cambodia (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11205-025-03718-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:soinre:v:180:y:2025:i:3:d:10.1007_s11205-025-03718-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11205-025-03718-3

Access Statistics for this article

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement is currently edited by Filomena Maggino

More articles in Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().