Micro-Estimates of Wealth for all Low- and Middle-Income Countries

• URL: http://arxiv.org/abs/2104.07761v1
• Date: Thu, 15 Apr 2021 20:37:46 GMT
• Title: Micro-Estimates of Wealth for all Low- and Middle-Income Countries
• Abstract summary: 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.
• Score: 14.717329936263672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop the first micro-estimates of wealth and poverty that cover the populated surface of all 135 low and middle-income countries (LMICs) 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. We train and calibrate the estimates using nationally-representative household survey data from 56 LMICs, then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each micro-estimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for new insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of the Sustainable Development Goals.

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and middle-income countries, censuses and household surveys that collect these data are costly, infrequent, quickly outdated, and sometimes error-prone.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)
• The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region [64.30214722988666]
  This paper describes the survey design, collection protocol, processing methodology, and characteristics of the released dataset.<n>The dataset includes three components: (i) an Individuals database describing demographic, socioeconomic, and household characteristics; (ii) a Trips database with over 80,000 annotated displacements including timestamps, transport modes, and trip purposes; and (iii) a Raw GPS Traces database comprising about 500 million high-frequency points.
  arXiv  Detail & Related papers  (2025-06-06T09:22:21Z)
• BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response [50.76124284445902]
  Building damage assessment (BDA) is an essential capability in the aftermath of a disaster to reduce human casualties.<n>Recent research focuses on the development of AI models to achieve accurate mapping of unseen disaster events.<n>We present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
  arXiv  Detail & Related papers  (2025-01-10T14:57:18Z)
• 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)
• Interpreting wealth distribution via poverty map inference using multimodal data [0.0]
  We propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple populated places. These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media. Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them.
  arXiv  Detail & Related papers  (2023-02-17T11:35:44Z)
• 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)
• Releasing survey microdata with exact cluster locations and additional privacy safeguards [77.34726150561087]
  We propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under re-identification attempts.
  arXiv  Detail & Related papers  (2022-05-24T19:37:11Z)
• 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)
• High-Resolution Poverty Maps in Sub-Saharan Africa [0.0]
  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.
  arXiv  Detail & Related papers  (2020-09-01T16:23:43Z)
• 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)
• When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes [111.69190108272133]
  coronavirus disease 2019 (COVID-19) global pandemic has led many countries to impose unprecedented lockdown measures. Data-driven models that predict COVID-19 fatalities under different lockdown policy scenarios are essential. This paper develops a Bayesian model for predicting the effects of COVID-19 lockdown policies in a global context.
  arXiv  Detail & Related papers  (2020-05-13T18:21:50Z)
• 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.