Dynamic Poverty Prediction with Vegetation Index
Abstract
Accurate and timely estimates of economic status are critical to policy-makers in the world’s poorest countries.
Previous work has applied convolutional neural networks (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 be used with CNNs to produce equally accurate poverty estimates for developing countries that are heavily dependent on agriculture.
In contrast to previous work, the continuous streaming of well known vegetation indices also allows us to update our estimates in light of weather shocks, opening up the possibility of making dynamic 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 and aims to end extreme poverty in all forms by 2030 [11].
To achieve the goal, policy makers often rely on complex household surveys to measure poverty and allocate resources accordingly [1].
Since the data collection process is costly and time consuming, there is a lack of good-quality data to assess poverty regularly [8].
Inexpensive and scalable approaches to poverty prediction are therefore needed to complement household surveys.
Recent advances in remote sensing and machine learning have opened up a new path for poverty prediction.
High resolution satellite images are rich in content and available globally, providing an objective view on the economic conditions of developing countries [4, 7, 10].
The increasing abundance of these images lends themselves to convolutional neural networks (CNNs), a deep 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 daytime satellite images to measure regional poverty in Africa, yielding results comparable to estimation based on past surveys.
Although promising, most current studies with satellite images are limited to providing ﬁxed estimates of poverty maps.
The poverty predictions in Jean et al.
[8], for example, were constant scalars regardless of time prediction simply data.
Given the unprecedented climate changes anticipated in sub-Saharan countries [13], dynamic poverty 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 estimate poverty indicators more frequently.
NDVI measures the difference in red and near-infrared light reﬂectance resulted from photosynthesis; areas of barren rock have very low NDVI while dense croplands often have high NDVI values (see Figure 1).
As ultra-poor regions heavily depend on agriculture [5], NDVI provides an uninterrupted signal for crop heath and poverty tracking in general.
Our contribution is twofold: (1) we demonstrate that publicly-available, moderate-resolution NDVI can help predict poverty in Malawi, Nigeria, Rwanda, Tanzania, and Uganda as well as state-of-art methods, and (2) we perform poverty prediction for an out-of-sample period and capture changes in poverty measures for ultra-poor regions in Uganda.
Based on the availability of survey data, these countries are chosen for direct comparison with [8].
Related Work
Recent studies on poverty prediction rely on passively collected data and statistical methods to circumvent the scarcity of household surveys.
Researchers have ﬁt linear models to nighttime light luminosities 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 also been used with tree-based classiﬁers and Gaussian process regressors to provide asset wealth index estimates [3, 14].
In another line of research, CNNs help extract predictive features from Google’s high-resolution daytime images, providing accurate estimates of both consumption expenditure and asset wealth in multiple African countries [8, 21].
Although existing technologies can only measure NDVI at a lower resolution, recent work on poverty and health in Africa has proven its signiﬁcant predictive power.
Through increased crop yields, NDVI has been found to be positively correlated with child survival, nutrition, and anthropometric variables such as wasting [9].
Using spatial statistics techniques, Sedda et al.
also [17] showed that the intensity of poverty varies inversely with NDVI in West Africa.
Data and Methods
Inspired by previous work, we apply CNNs to publicly available NDVI images to learn features useful for poverty prediction.
Following Jean et al.
[8], we use transfer learning and a two-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 using NDVI features to predict poverty indicators.
The combination of NDVI images and nighttime lights allows vegetation features indicative of economic activity to be learned and generalized to the poverty prediction task.
Country
Consumption
(LSMS)
Asset
[Table]
Prediction
[Table]
Malawi
[Table]
Rwanda
[Table]
[Figure]
bottom
In the ﬁrst step of our procedure, we start with a VGG-16 network pre-trained on ImageNet and adapt its fully connected layers to ﬁt our input image sizes.
Our inputs are annual average NDVI images, each 64 × 64 pixels in size and at a spatial resolution of 250 square meters per pixel.
The images are sampled from a dataset produced by NASA’s Terra satellite [6] and represent areas that are evenly spaced at 0.025 degree intervals.
The network learns to map each NDVI image to the average value of annual nighttime light intensities that describe the same geographical region, as provided by the National Oceanic and Atmospheric Administration [12].
In contrast to [8, 21], we take a log transformation, but we do not discretize nightime light intensities.
In the second step, we extract features from NDVI images and ﬁt regression models to predict two survey variables - logarithm of consumption expenditure and asset index.
For direct comparison, we select the same surveys and follow the same preprocessing steps as in [8] (see Table 1 for the list of surveys).
The conv5-2 layer of the ﬁne-tuned network outputs a feature map of size 512 × 1 for each NDVI image, and we average feature maps of images whose centers are within ﬁve kilometers of a surveyed community.
For each survey, we then train random forests on the 512-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 maps can potentially capture and reﬂect these events in poverty predictions.
Hence, we also ﬁne-tune the network 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 and testing it on the updated ones.
[Figure]
n /
[Figure] [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 and 50 to 75% of the variation in average asset wealth across surveyed countries.
In general, our models perform comparably to Jean et al.
[8] when ﬁt using data from individual countries.
When trained on pooled consumption or asset observations across all countries, our models perform 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 poverty line, which is set at $1.9 per person per day by the World Bank.
This observation agrees with our intuition that extremely poor communities depend most heavily on crop production.
The modest improvements in consumption prediction can be partly explained by the fact that consumption data is noisier [8, 20].
In the second experiment, we study whether temporal changes in NDVI are indicative of poverty changes.
Because surveys from different years are generally conducted at different locations, we limit 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, the consumption 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 to 2013 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 truth when tested on the 2013 data.
Conclusion
In this paper, we have leveraged CNNs to extract features from NDVI images that are highly predictive of poverty.
We demonstrate that publicly available, moderate-resolution NDVI can predict poverty as well as high-resolution images constrained by Google’s licensing terms.
Our model based on NDVI can also produce dynamic poverty estimates, potentially helping policy-makers make more informed and timely decisions.
Acknowledgement
This work was supported by the Cornell University Atkinson Center for a Sustainable Future (AVF2017).