High-Resolution Poverty Maps in Sub-Saharan Africa

• URL: http://arxiv.org/abs/2009.00544v5
• Date: Mon, 10 May 2021 02:00:52 GMT
• Title: High-Resolution Poverty Maps in Sub-Saharan Africa
• Abstract summary: We propose a generalizable prediction methodology to produce poverty maps at the village level. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Up-to-date poverty maps are an important tool for policy makers, but until now, have been prohibitively expensive to produce. We propose a generalizable prediction methodology to produce poverty maps at the village level using geospatial data and machine learning algorithms. We tested the proposed method for 25 Sub-Saharan African countries and validated them against survey data. The proposed method can increase the validity of both single country and cross-country estimations leading to higher precision in poverty maps of 44 Sub-Saharan African countries than previously available. More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.

Related papers

• A satellite foundation model for improved wealth monitoring [45.63770015866594]
  Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costly, infrequent, quickly outdated, and sometimes error-prone.<n>Here we introduce Tempov, a satellite foundation model pretrained by self-supervision on three million bi-temporal Landsat pairs and adapted with parameter-efficient fine-tuning to sparse survey labels.<n>The model enables large-scale, high-resolution wealth mapping and dynamic measurement, including zero-shot nowcasting up to a decade after observed labels, retrospective hindcasting, and decadal change tracking.
  arXiv  Detail & Related papers  (2026-04-25T06:37:51Z)
• Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy [5.456665139074406]
  This paper investigates disparities in representation, systematic biases in prediction errors, and fairness concerns in satellite-based poverty mapping across urban and rural lines. Our findings highlight the importance of careful error and bias analysis before using satellite-based poverty maps in real-world policy decisions.
  arXiv  Detail & Related papers  (2023-05-02T21:07:35Z)
• Learning and Reasoning Multifaceted and Longitudinal Data for Poverty Estimates and Livelihood Capabilities of Lagged Regions in Rural India [22.98110639419913]
  The project aims to examine the poverty situation of rural India for the period of 1990-2022 based on the quality of life and livelihood indicators. The project also intends to examine causation and longitudinal analysis to examine the reasons for poverty.
  arXiv  Detail & Related papers  (2023-04-27T05:33:08Z)
• Novel Machine Learning Approach for Predicting Poverty using Temperature and Remote Sensing Data in Ethiopia [0.0]
  A lack of poverty data prevents critical humanitarian organizations from responding to large-scale crises. We propose a transfer learning model based on surface temperature change and remote sensing data to extract features useful for predicting poverty rates.
  arXiv  Detail & Related papers  (2023-02-28T18:32:16Z)
• Graph-based Village Level Poverty Identification [52.12744462605759]
  The development of the Web infrastructure and its modeling tools provides fresh approaches to identifying poor villages. By modeling the village connections as a graph through the geographic distance, we show the correlation between village poverty status and its graph topological position. We propose the first graph-based method to identify poor villages.
  arXiv  Detail & Related papers  (2023-02-14T06:58:40Z)
• Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
  We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery. Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
  arXiv  Detail & Related papers  (2023-01-04T05:19:33Z)
• IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling [90.97859312029615]
  We propose an approach to estimate object count statistics over large geographies through sampling. We show empirically that the proposed framework achieves strong performance on estimating the number of buildings in the United States and Africa, cars in Kenya, brick kilns in Bangladesh, and swimming pools in the U.S.
  arXiv  Detail & Related papers  (2021-12-16T18:59:29Z)
• Micro-Estimates of Wealth for all Low- and Middle-Income Countries [14.717329936263672]
  We develop the first micro-estimates of wealth and poverty that cover the populated surface of all 135 low and middle-income countries at 2.4km resolution. The estimates are built by applying machine learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, topographic maps, as well as aggregated and de-identified connectivity data from Facebook.
  arXiv  Detail & Related papers  (2021-04-15T20:37:46Z)
• Interpretable Poverty Mapping using Social Media Data, Satellite Images, and Geospatial Information [0.0]
  We present a interpretable and cost-efficient approach to poverty estimation using machine learning and readily accessible data sources. We achieve an $R2$ of 0.66 for wealth estimation in the Philippines, compared to 0.63 using satellite imagery.
  arXiv  Detail & Related papers  (2020-11-27T05:24:53Z)
• Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient [72.44260113860061]
  We propose an interactive method to support and optimize the work of volunteers in OpenStreetMap. The proposed method greatly reduces the amount of data that the volunteers of OSM need to verify/correct.
  arXiv  Detail & Related papers  (2020-09-17T10:05:30Z)
• Predicting Livelihood Indicators from Community-Generated Street-Level Imagery [70.5081240396352]
  We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health.
  arXiv  Detail & Related papers  (2020-06-15T18:12:12Z)
• Generating Interpretable Poverty Maps using Object Detection in Satellite Images [80.35540308137043]
  We demonstrate an interpretable computational framework to accurately predict poverty at a local level by applying object detectors to satellite images. Using the weighted counts of objects as features, we achieve 0.539 Pearson's r2 in predicting village-level poverty in Uganda, a 31% improvement over existing (and less interpretable) benchmarks.
  arXiv  Detail & Related papers  (2020-02-05T02:50:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.