Shedding Light on Philippine Poverty · National Economic Development Authority (NEDA) Jean, Neal,...
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Shedding Light on Philippine Poverty
Isabelle Tingzon | February 17, 2019
Mapping Poverty using Machine Learning, Satellite Images, and Open Geospatial Data
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We use data to solve real-worldproblems
PH Department of Science and Technology
P U B L I C S E C TO RABOUT US
Manila | Singapore
Founded 2015
University of the Philippines
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OUR WORK
Example: The Language Landscape of the Philippines in 4 Maps
Geospatial Analysis and Visualization
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Map of HIV Center Accessibility across the Philippines
OUR WORK
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Our social impact mission is to empower evidence-based policy and action by:
◆ Filling critical data gaps◆ Making data open and useful◆ Innovating with purpose
OUR MISSION
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$ 4.1 M INVESTED | 58 PROJECTS | 38 COUNTRIES
The UNICEF Innovation Fund provides financial and technological support for companies that are using technology in innovative ways to improve children’s lives.
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Sources: Philippine Statistical Authority 2015 Survey, National Economic Development Authority
22M or 21.6% of Filipinos live below the national poverty line.
MOTIVATION
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Child Poverty in the PhilippinesMOTIVATION
3 in every 10 children belong to poor families
Source: Official Poverty Statistics among the Basic Sectors 2015, PSA
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Can cost
millions of dollars
Conducted only
once every 3-5 years
Granularity is on a regional/provincial level
Conducting Household Surveys
MOTIVATION
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Use unconventional, readily-available data sources to predict socioeconomic indicators
SOLUTIONS
Satellite Imagery
Social Media
Data
Other Geospatial
Datasets
Education Level
Electricity Access
Wealth Index
Machine Learning Models
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Machine Learning for Wealth & Poverty Prediction
*Jean, Neal, et al. "Combining satellite imagery and machine learning to predict poverty." Science 353.6301 (2016): 790-794.
✦ Goal: Faster, cheaper, and more granular reconstruction of poverty measures in the Philippines
✦ Replicated a study by Jean et al.* from the Stanford Sustainability and AI Lab
○ Estimated asset-based wealth for five sub-Saharan African countries
✦ Crowdsourced geospatial information for poverty prediction
SOLUTION Provincial-level Wealth Index(Ground truth)
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Using satellite images to predict wealthAPPROACH
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Problem: Data ScarcityNeed a lot of labeled training data for an end-to-end deep learning approach
Poverty Predictions
Wealth Index, Education, Access to Water, Electricity,
Child Mortality
Model
Data Gap: Not enough labeled training data!
APPROACH
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Nighttime lights can be used as a proxy for economic developmentMetro Manila (Daytime)
SOLUTION
Metro Manila (Nighttime)
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Tagbilaran, Bohol (Daytime) Tagbilaran, Bohol (Nighttime)
SOLUTION
Nighttime lights can be used as a proxy for economic development
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Wealthier places are brighter at nightSOLUTION
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Nighttime Light Intensity
Low, Medium, High based on pixel brightness
Daytime Satellite Images
Convolutional Neural Network
Predict nighttime light intensity as a proxy task
SOLUTION
*Jean, Neal, et al. "Combining satellite imagery and machine learning to predict poverty." Science 353.6301 (2016): 790-794.
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Nighttime Light IntensityDaytime Satellite
Images
Convolutional Neural Network
Step 1: Predict Nighttime light intensity from Daytime Satellite Images
Step 2: Wealth Prediction using Cluster-level Feature Embeddings
SOLUTION
*Jean, Neal, et al. "Combining satellite imagery and machine learning to predict poverty." Science 353.6301 (2016): 790-794.
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The model is able to explain 62.5% of the variance
How accurate are our wealth predictions?RESULTS
*Using CNN feature embeddings with regional indicators
Predictions and reported r2 values are from five-fold nested cross-validation.
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Reconstructing Provincial-level MapsRESULTS
Actual Wealth Index
Predictions and reported r2 values are from five-fold nested cross-validation.
Predicted Wealth Index
0 (poor)
Normalized Wealth Index
1 (wealthy)
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RESULTS
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✦ Difficult to distinguish between different levels of extreme poverty for areas with little to no electrification
✦ Difficult to predict other aspects of human development (e.g. access to water, child mortality) using nighttime lights
✦ Poverty estimates are not meant to replace on-the-ground surveys.
LimitationsCAVEATS
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✦ Research Collaboration: Partner with research institutes on exploring alternative methodologies and data sources for poverty prediction:○ Using social media, traffic data, and other geospatial data sources○ Explore unsupervised representation learning methods
✦ Data Sharing: Model additional indicators including○ Socioeconomic resilience to natural disasters○ Child malnutrition rates○ Challenge: Lack of reliable granular ground-truth data
✦ Real-world Impact: Collaborate with the public sector (NGOs, LGUs) for possible applications in humanitarian aid and resource allocation, both in the Philippines and other countries
Moving ForwardNEXT STEPS
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Thank [email protected]
Special Thanks
Dohyoung Kim (UNICEF)Vedran Sekara (UNICEF)Priscilla Moraes (Google)Ingmar Weber (QCRI)Masoomali Fatehkia (QCRI)Neal Jean (Stanford Sustainability & AI Lab)Sherrie Wang (Stanford Sustainability & AI Lab)
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✦ Philippine Statistical Authority (PSA) 2015 Survey
✦ National Economic Development Authority (NEDA)
✦ Jean, Neal, et al. "Combining satellite imagery and machine learning to predict poverty." Science 353.6301 (2016): 790-794.
✦ Xie, Michael, et al. "Transfer learning from deep features for remote sensing and poverty mapping." Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Press, 2016.
✦ Head, Andrew, et al. "Can Human Development be Measured with Satellite Imagery?." Proceedings of the Ninth International Conference on Information and Communication Technologies and Development. ACM, 2017.
✦ Perez, Anthony, et al. "Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning." arXiv preprint arXiv:1711.03654 (2017).
References