Dynamic Poverty Prediction with Vegetation Index
Anonymous Author(s)
AfﬁliationAddress email
Abstract
Accurate and timely estimates of economic status are critical to policy-makersin the world’s poorest countries.
Previous work has applied convolutional neuralnetworks (CNNs) to high-resolution satellite imagery to perform poverty prediction.
Although promising, such imagery has limited access and lacks a temporal signal.
We show that publicly available, moderate-resolution vegetation index can beused with CNNs to produce equally accurate poverty estimates for developingcountries that are heavily dependent on agriculture.
In contrast to previous work,the continuous streaming of well known vegetation indices also allows us to updateour estimates in light of weather shocks, opening up the possibility of makingdynamic poverty mapping at minimal cost.
Introduction
Despite global economic growth, 330 million people are still living in extreme poverty in Africa [2].
The United Nations has acknowledged poverty as one of the greatest challenges facing humanity andaims to end extreme poverty in all forms by 2030 [11].
To achieve the goal, policy makers often relyon complex household surveys to measure poverty and allocate resources accordingly [1].
Since thedata collection process is costly and time consuming, there is a lack of good-quality data to assesspoverty regularly [8].
Inexpensive and scalable approaches to poverty prediction are therefore neededto complement household surveys.
Recent advances in remote sensing and machine learning have opened up a new path for povertyprediction.
High resolution satellite images are rich in content and available globally, providingan objective view on the economic conditions of various developing countries [4, 7, 10].
Theincreasing abundance of these images lends themselves to convolutional neural networks (CNNs), adeep learning approach that has recently seen tremendous success in many computer vision tasks[15, 16, 18].
In a critically acclaimed paper [8], Jean et al.
applied CNNs on Google’s daytimesatellite images to measure regional poverty in Africa, yielding results comparable to estimationbased on past surveys.
Although promising, most current studies with satellite images are limited to providing ﬁxed estimatesof poverty maps.
The poverty predictions in Jean et al.
[8], for example, were constant scalarsregardless of the time of prediction, simply due to the lack of continuous access to Google’s proprietarydata.
Given the unprecedented climate changes anticipated in sub-Saharan countries [13], dynamicpoverty prediction is critical to timely interventions and policy evaluation.
In this paper, we leverage the continuous streaming of the normalized difference vegetation index(NDVI), one of the widely known satellite measurements of Earth’s vegetation greenness, to estimatepoverty indicators more frequently.
NDVI measures the difference in red and near-infrared lightreﬂectance resulted from photosynthesis; areas of barren rock have very low NDVI values whiledense croplands often have high NDVI values during peak periods (see Figure 1).
As ultra-poor regions heavily depend on agriculture [5], NDVI provides an uninterrupted signal for crop heath andpoverty tracking in general.
Our contribution is twofold: (1) we demonstrate that publicly-available, moderate-resolution
NDVIcan help predict poverty in Malawi, Nigeria, Rwanda, Tanzania, and Uganda as well as state-of-artmethods, and (2) we perform poverty prediction for an out-of-sample period and capture changes inpoverty measures for ultra-poor regions in Uganda. Based on the availability of survey data, thesecountries are chosen for direct comparison with [8].
Related Work
Recent studies on poverty prediction rely on passively collected data and statistical methods tocircumvent the scarcity of household surveys.
Researchers have ﬁt linear models to nighttime lightluminosities and found they strongly correlate with the gross domestic product of various countries[4, 7, 10].
Proprietary cell phone records of millions of subscribers in Rwanda and Senegal have alsobeen used with tree-based classiﬁers and Gaussian process regressors to provide asset wealth indexestimates [3, 14].
In another line of research, CNNs help extract predictive features from Google’shigh-resolution daytime images, providing accurate estimates of both consumption expenditure andasset wealth in multiple African countries [8, 21].
Although existing technologies can only measure NDVI at a lower resolution, recent work on povertyand health in Africa has proven its signiﬁcant predictive power.
Through increased crop yields, NDVIhas been found to be positively correlated with child survival, nutrition, and anthropometric variablessuch as wasting [9].
Using spatial statistics techniques, Sedda et al.
also [17] showed that the intensityof poverty varies inversely with NDVI in West Africa.
Data and Methods
Inspired by previous work, we propose applying CNNs to publicly available NDVI images to learnfeatures useful for poverty prediction.
Following Jean et al.
[8], we use transfer learning and atwo-step procedure to bypass the lack of labeled responses: (1) ﬁne-tune a VGG-16 network [19] on
NDVI images to predict nighttime light intensities, and (2) ﬁt random forest regression models usingthese NDVI features to predict poverty indicators.
The combination of NDVI images and nighttimelights allows vegetation features indicative of economic activity to be learned and generalized to thepoverty prediction task.
In the ﬁrst step of our procedure, we start with a VGG-16 network pre-trained on ImageNet andadapt its fully connected layers to ﬁt our input image sizes.
Our inputs are annual average NDVI
Country
Consumption
(LSMS)
Asset
[Table]
Rwanda
[Table]
[Figure]
included.
The bottom show similar r 2 values for estimating asset index .)
images, each 64 × 64 pixels in size and at a spatial resolution of 250 square meters per pixel.
Theimages are sampled from a dataset produced by NASA’s Terra satellite [6] and represent areas thatare evenly spaced at 0.025 degree intervals.
The network learns to map each NDVI image to theaverage value of annual nighttime light intensities that describe the same geographical region, asprovided by the National Oceanic and Atmospheric Administration [12].
In contrast to [8, 21], wetake a log transformation, but we do not discretize nightime light intensities, making the ﬁne-tuningtask a bit more challenging.
In the second step, we extract features from NDVI images and ﬁt regression models to predict twosurvey variables - logarithm of consumption expenditure and asset index.
For direct comparison, weselect the same countries and surveys and follow the same preprocessing steps as in [8] (see Table 1for the list of countries and surveys).
The conv5-2 layer of the ﬁne-tuned network outputs a featuremap of size 512 × 1 for each NDVI image, and we average feature maps of images whose centers arewithin ﬁve kilometers of a surveyed community.
For each survey, we then train random forests on the512-dimensional feature maps, using nested 5-fold spatial cross-validation to select hyperparameters,and output predictions.
In response to weather shocks, NDVI often changes over time (see Figure 1), and its feature mapscan potentially capture and reﬂect these events in poverty predictions.
Hence, we also ﬁne-tune thenetwork in the ﬁrst step with updated NDVI images and nighttime lights.
Predictions for out-ofsample periods are obtained by ﬁrst training a random forest on the previous NDVI feature maps andtesting it on the updated ones.
[Figure] [Figure]
Results
Our ﬁrst experiment is to evaluate the predictive power of NDVI for poverty estimation.
Following[8], we use expenditure data from the World Bank’s Living Standards Measurement Study (LSMS)and asset index data from the Demographic and Health Surveys (DHS).
Table 1 shows that
our
NDVI models are highly predictive of both average household consumption and average asset wealth.
Spatially cross-validated predictions explain 34 to 60% of the variation in average consumption and50 to 75% of the variation in average asset wealth across surveyed countries.
In general, our modelsperform comparably to Jean et al.
[8] when ﬁt using data from individual countries.
When trained on pooled consumption or asset observations across all countries, however, our modelsperform signiﬁcantly and consistently better than the state-of-art method (see Figure 2).
We see an improvement of more than 100% in r 2 for asset index predictions for regions below the 2x internationalpoverty line, which is set at $1.9 per person per day by the World Bank.
This observation agrees withour intuition that extremely poor communities depend most heavily on crop production.
The modestimprovements in consumption prediction can be partly explained by the fact that consumption data isnoisier due to recall error [8, 20].
In the second experiment, we study whether temporal changes in NDVI are indicative of povertychanges.
Because surveys from different years are generally conducted at different locations, welimit this experiment to 209 communities in Uganda that are part of both the 2011-2012 and 20132014 LSMS surveys.
As the 2011-2012 East Africa drought affected a large area of Uganda, theconsumption distribution for these communities changes quite signiﬁcantly between the surveys.
Figure 3 shows that our random forest model can translate the increase in annual NDVI from 2011 to2013 to reﬂect increased consumption in the poorest communities following the drought.
In contrast,models that rely on static inputs such as et al.
[8] can only perform as well as the 2011 ground truthwhen tested on the 2013 data.
Conclusion
In this paper, we have leveraged CNNs to extract features from NDVI images that are highly predictiveof poverty.
We demonstrate that publicly available, moderate-resolution NDVI can predict poverty aswell as high-resolution images constrained by Google’s licensing terms.
Our model based on NDVIcan also produce dynamic poverty estimates, potentially helping policy-makers make more informedand timely decisions.