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Editorial of Geospatial Data for Research on Economic Development

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Development Engineering
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Geospatial data for research on economic development
It is estimated that in 2016 nearly 38 per cent of workers in the
world's least developed countries were living with their families
below the poverty line (approximately $1.90 per person per day) (UN,
2017), often without access to schools, healthcare, electricity, safe
water and other critical services. Yet, accurate data on the number of
poor people and their living conditions are scarcely existent, espe-
cially in developing countries. Census counts comprise the primary
source of this type of data, but are published infrequently, varying in
resolution and precision, due in part to limited available resources for
data collection.
The gap in the availability of data between the developing and the
developed world is, at least partially, the result of technological ad-
vances and innovative solutions for data collection. Novel sources of
data facilitate the development of new approaches for demographic
modeling and measurement in wealthy nations, while in developing
countries, fewer sources of big data exist (Blumenstock et al., 2015). For
example, while nearly half of today's world population has internet
access, more than 4 billion people - mostly in developing countries - still
don't have access to the internet.
The result is an increasing gap in the
availability of data for developing and developed countries. Consider,
for example, the utilization of data gathered from online social media
for demographic research. Social media platforms such as Facebook,
Twitter and other collaborative virtual spaces are increasingly har-
nessed to track the digital traces (Bruno, 2007) of society, whether for
monitoring stocks of migrants (Zagheni Emilio et al., 2017) or for
generating taxonomies of happiness levels in US states (Mitchell et al.,
2013). This type of data only provides insights about those who parti-
cipate in such virtual spaces and does not always fully capture the social
complexity of the ever-changing developing world.
The lack of accurate and timely data on the distribution of poverty
also often forces policy-makers to make decisions based on limited (and
sometimes inaccurate) data (Blumenstock, 2016). For example, devel-
opment programs and policies are typically designed to change out-
comes (e.g. to raise incomes, to improve learning, or to reduce illness).
An impact evaluation is intended to evaluate the outcome of a program
by answering one specific question: What is the impact of a specific
program on an outcome of interest? (Gertler et al., 2016). Answering
this question requires to track and monitor the impacts of the program -
and this can only be done by a continuous and frequent collection of
data from multiple sources. Such data provides the basis for any evi-
dence-based policy making.
But poverty and other societal characteristics also hold a spatial
dimension. Poverty can often be identified visually and reflected in the
density of residential structures, patters of roads, the material of roofs
or the distribution and extent of green spaces. Many of these features
are observable from space. From the perspective of social science,
capturing this data may provide meaningful information about the
context that shapes social phenomena (Council and Stern, 1988). There
are more than 1400 satellites continuously collecting an unprecedented
amount of data from Earth at ever-improving spatial, spectral and
temporal resolutions, over increasingly short time intervals. Data col-
lected by these satellites and other sensors are progressively shifting the
way we measure and understand our world. These data allow re-
searchers to study and map the physical environment and infer about
social processes in near real time, in almost every corner of the world.
The heightened availability of satellite data has revolutionized our
ability to map human settlements and understand the distribution and
characteristics of the population on Earth. New methods for machine
learning, predictive analytics and remote sensing are reforming the way
we measure and address societal challenges, detect poverty and steer
efforts to reduce it. Researchers are continually discovering new ways
to convert remotely sensed data into meaningful information about the
nature and pace of change of human and physical landscapes.
Over the last few decades, researchers have been working to detect
and map poverty and wealth from space, primarily with nighttime light
data. Based on the assumption that poorly lit areas with high popula-
tion counts signify higher percentages of poor people and lower wealth,
researchers have been able to estimate the number of individuals living
in poverty, globally (Elvidge et al., 2009). Nighttime lights are also
being extensively used to map urbanization. According to this ap-
proach, a pixel's nighttime light value exceeding a specified threshold
(which may vary across regions or countries) signifies urban develop-
ment. Moreover, because the intensity of light emitted at night is clo-
sely related to economic development, this proxy is also leveraged to
predict national economic growth (Henderson et al., 2012) as well as
regional economic development (Doll et al., 2006). However, inference
of human activity with Nighttime lights is often inaccurate, particularly
in low-density urban areas (Zhang and Seto, 2013). DMSP-OLS sensors,
which until recently (with the availability of data collected by the
Visible Infrared Imaging Radiometer Suite (VIIRS)) have been the pri-
mary source for nighttime light data can exaggerate the extent of urban
areas (Henderson et al., 2003) while overlooking small or developing
Development Engineering 4 (2019) 100041
Available online 27 February 2019
2352-7285/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
settlements. In addition, the extent and intensity of lit areas cannot
directly delimit urban regions due to the “blooming” effect (the iden-
tification of lit areas as consistently larger than the settlements with
which they are associated) (Imhoff et al., 1997) and “over saturation” of
pixels (i.e., pixels in bright areas, such as in city centers, reach the
highest possible intensity and no further details can be recognized)
(Hsu et al., 2015). As a result, there has been a significant move towards
mapping poverty with daytime satellite data at higher spatial resolu-
tions. For example, Engstrom et al. (2017) showed that object and
texture features extracted from high-resolution satellite imagery over
Sri Lanka (e.g. the number and density of buildings, number of cars,
density and length of roads, roof material, etc.) could explain nearly 60
percent of poverty headcount rates and average log consumption,
compared to models built using nighttime lights that could explain only
15 percent of the variation in poverty or income. Jean et al. (2016)
combined nighttime maps with high-resolution daytime satellite images
to estimate consumption expenditure and asset wealth. The authors
showed, that a convolutional neural network can be trained to identify
image features that could explain up to 75% of the variation in local-
level economic outcomes in Africa. Other studies develop novel
methods designed to map slums and informal settlements in developing
countries by capturing some of their unique spatial features (e.g.
building density, building heights, and sizes), differentiating between
informal from formal settlements (Taubenböck and Kraff, 2014). With
around 1 billion people living today in slums (UN-Habitat, 2015)
lacking access to drinking water, sanitation facilities and durable
housing conditions, accurate and up-to-date information on the extent
of slums and the number of slums dwellers is fundamental for the
preservation of a sustainable human society.
These new sources of geospatial data are transforming our under-
standing of the modern world. Novel machine-learning algorithms are
combined with satellite imagery from multiple sources to estimate road
quality in developing countries, predict consumption expenditure,
measure wealth and inequality, monitor small-holder farmers' yields in
regions affected by drought, and monitor electricity provision in rural
areas. Parallel computing platforms with large storage capacities are
becoming increasingly available, allowing for the extraction of mean-
ingful information about the developing world and encouraging new
design of environmentally responsible approaches to measuring and
monitoring economies.
This special issue of Development Engineering highlights 5 case
studies that illustrate novel use of technology and new sources of big
geospatial data to derive insights pertaining to societal processes,
primarily in developing countries. Augusto Zagatti et al. utilize na-
tionwide de-identified Call Detail Records (CDR) to investigate
nighttime and daytime population densities and commuting patterns
in Haiti. The authors construct origin-destination matrices of
weighted connections between home and work locations to identify
fragmented labor markets in the country. Eitzel et al. evaluate the
potential of ‘medium-tech’ digital mapping technologies as solutions
for empowering disenfranchised communities in rural Zimbabwe. The
authors demonstrate that digital collaborative mapping with rela-
tively simple technology allows spreading information among rural
farmers in Zimbabwe. This case study demonstrates the potential of
geospatial data as a tool to address poverty and sustainability and
improve the well-being of the most vulnerable populations on our
planet. Marty et al. apply a Geographic Simulation and Extrapolation
(GeoSIMEX) approach to mitigate the spatial imprecision inherent in
the geoparsed data when evaluating the impact and success of in-
ternational aid and interventions projects. The authors present ana-
lysis of the impact of Chinese aid on vegetative land cover in Rwanda
and Burundi and find that Chinese investments have slowed the loss
of vegetation in Rwanda. Rivera Ballesteros et al. utilize satellite
imagery collected by Sentinel-2 satellite for supervised image classi-
fication to estimate the extent of tree cover in twelve gazetted forests
in semi-arid Burkina Faso. The authors show this source of imagery
allows to detect patches of forest cover in arid and semi-arid regions
that could not be identified with lower resolution imagery and eval-
uate the extent and characteristics of deforestation in these regions.
Finally, Goldblatt et al. discuss the challenges of measuring urbani-
zation in developing countries that lack the resources and infra-
structure needed to produce reliable data. The authors highlight the
potential use of publicly available remotely sensed data for mapping
changes in the built-up LC/LU in Ho Chi Minh City, Vietnam, in the
period between 2000 and 2015.
The invention of computerized Geographic Information Systems
(GIS) in the 1960s revolutionized how we collect, map and analyze
spatial data and give meaning to the world around us. With advances in
computer processing power and data storage, increased availability of
personal computers, the invention of the internet (the World Wide
Web) and the growing popularity of mobile and “smart” phones, the
volume of data we generate and consume is growing exponentially.
Devices on satellites, airplanes and UAVs collect massive amounts of
imagery that capture the characteristics of Earth. All these studies
featured in this special section illustrate novel research methods to
analyze these unique sources of data to achieve a better understanding
of the nature of developing countries.
Blumenstock, J.E., 2016. Fighting poverty with data. Science 353, 753–754. https://doi.
Blumenstock, J., Cadamuro, G., On, R., 2015. Predicting poverty and wealth from mobile
phone metadata. Science 350, 1073–1076.
Bruno, L., 2007. Beware, your imagination leaves digital traces. Times High. Lit.(6).
Council, R.R., Stern, P.C., 1988. Linking remote sensing and social science: the need and
the challenges. In: People and Pixels: Linking Remote Sensing and Social Science.
National Academies Press, pp. 1–27.
Doll, C.N.H., Muller, J.-P., Morley, J.G., 2006. Mapping regional economic activity from
night-time light satellite imagery. Ecol. Econ. 57, 75–92.
Elvidge, C.D., Sutton, P.C., Ghosh, T., Tuttle, B.T., Baugh, K.E., Bhaduri, B., Bright, E.,
2009. A global poverty map derived from satellite data. Comput. Geosci. 35,
Emilio, Zagheni, Weber, Ingmar, Krishna, Gummadi, 2017. Leveraging Facebook's ad-
vertising platform to monitor stocks of migrants. Popul. Dev. Rev. 43, 721–734.
Engstrom, R., Hersh, J., Newhouse, D., 2017. Poverty from Space: Using High-Resolution
Satellite Imagery for Estimating Economic Well-Being (Policy Research Working
Paper No. 8284). Poverty and Equity Global Practice Group, World Bank,
Washington DC.
Gertler, P.J., Martinez, S., Premand, P., Rawlings, L.B., Vermeersch, C.M., 2016. Impact
Evaluation in Practice. The World Bank.
Henderson, M., Yeh, E.T., Gong, P., Elvidge, C., Baugh, K., 2003. Validation of urban
boundaries derived from global night-time satellite imagery. Int. J. Remote Sens. 24,
Henderson, J.V., Storeygard, A., Weil, D.N., 2012. Measuring economic growth from
outer space. Am. Econ. Rev. 102, 994–1028.
Hsu, F.-C., Baugh, K.E., Ghosh, T., Zhizhin, M., Elvidge, C.D., 2015. DMSP-OLS radiance
calibrated nighttime lights time series with intercalibration. Rem. Sens. 7,
Imhoff, M., Lawrence, W.T., Stutzer, D.C., Elvidge, C.D., 1997. A technique for using
Development Engineering 4 (2019) 100041
composite DMSP/OLS “City Lights” satellite data to map urban area. Remote Sens.
Environ. 61, 361–370.
Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S., 2016. Combining sa-
tellite imagery and machine learning to predict poverty. Science 353, 790–794.
Mitchell, L., Frank, M.R., Harris, K.D., Dodds, P.S., Danforth, C.M., 2013. The geography
of happiness: connecting twitter sentiment and expression, demographics, and ob-
jective characteristics of place. PLoS One 8, e64417.
Taubenböck, H., Kraff, N.J., 2014. The physical face of slums: a structural comparison of
slums in Mumbai, India, based on remotely sensed data. J. Hous. Built Environ. 29,
UN, 2017. Progress towards the Sustainable Development Goals: Report of the Secretary-
General (Economic and Environmental Questions: Sustainable Development).
(United Nations).
UN-Habitat, 2015. Slum Almanac 2015/2016: Tracking Improvement in the Lives of Slum
Dewllers. Participatory Slum Upgrading Programme, UN-Habitat.
Zhang, Q., Seto, K.C., 2013. Can night-time light data identify typologies of urbanization?
A global assessment of successes and failures. Rem. Sens. 5, 3476–3494. https://doi.
Ran Goldblatt
, Madeline Jones, Brad Bottoms
New Light Technologies Inc., 1440 G Street Northwest, Washington, DC
20005, USA
E-mail address: (R. Goldblatt).
Corresponding author.
Development Engineering 4 (2019) 100041
... An important area for Development Engineering brings together engineering and development economics by documenting new tools and methods to facilitate evaluation. In evaluations of development solutions, an important question is how to measure one's primary outcome and impact variables; and engineering research has created a wealth of tools with use cases for economic evaluations, including the use of remote and in-situ sensing, for instance using geospatial data for development (Goldblatt et al., 2019), sensory to monitor air quality (Kelpet al., 2018), monitoring cookstove and fuel use (Ventrella et al., 2020), or wearable sensors for agricultural and rural energy expenditure (Zanello et al., 2017). We also welcome manuscripts documenting when a new measurement method does not work, as was illustrated by a study on radio frequency identification (De Mel et al., 2016). ...
To design energy access solutions for rural households in developing countries it is important to have an accurate estimation of what their electricity consumption is. Studies reveal that they mainly use electricity to meet their lighting needs, as they cannot afford high power-consuming appliances. However, the scarce data availability and modeling complexity are a challenge to compute correctly the load profiles without collecting data on-site. This paper presents a methodology that computes the hourly lighting load profiles of rural households in East Africa requiring a small amount of publicly available input data. Combining data from household surveys, climate, and satellite imagery, the methodology applies machine learning for determining occupant behavior patterns, and lamps ownership for indoor and outdoor usage. For this, an average prediction accuracy of 80% is reached. After applying lighting requirement functions, load profiles are generated and then validated using measured data from 13 households in Kenya. Results show that the methodology is able to compute the load profiles with an average normalized root mean squared error of 0.7%, which is less compared to existing simulation approaches using on-site data. To demonstrate a broad application, the monthly lighting consumption is computed and projected geospatially for households in Kenya.
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Clearing of forests and burning of oil-origin fuels have enhanced the Earth’s natural greenhouse effect on our planet. The motivation behind this work is to control such an effect that is widely deemed to be crucial in maintaining life on earth. This effect causes global warming besides increasing environmental pollution due to the harmful carbon dioxide (CO2) emission. Remarkable applications of geospatial technologies have thus been clearly noticed in the relevant field of remote sensing. Such application has been justifiably witnessed in the rapid spatiotemporal monitoring of forest resources. This in turn has participated in the formulation of substantial strategy outlines to set the sustainable management of these forests. Many countries of the world are now heavily working to protect trees and conserve forests. Satellite imaging can hereby be a very efficient way for this purpose. The objective of this paper is to propose a model that uses satellite imaging with Google Earth application to protect forests from being cleared. The pertinent methodology of this model is conceptually based on the method of edge detection. The paper discusses the issues of satellite image acquisition and processing besides the international REDD+ forest conservation program for such protection. The governmental role in providing resources to protect this natural resource has further been highlighted. Using satellite imagery for the conservation of forest and protecting trees from being cut, the anticipated outcome of the proposed model is envisaged to achieve better results that are much “faster” and more “accurate” than the other available approaches. In addition to forests’ conservation, the application field of this work encompasses diverse issues like overcoming environmental and climate changes, diminishing deforestation, and defeating desertification.
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Given the importance of demographic data for monitoring development, the lack of appropriate sources and indicators for measuring progress toward the achievement of targets—like the United Nations’ “2030 Agenda for Sustainable Development”—is a significant cause of uncertainty. As part of a larger effort to tackle the issue, in 2014 the United Nations asked an independent expert advisory group to make recommendations to bring about a data revolution in sustainable development.1 Data innovation, like new digital traces from a variety of technologies, is seen as a significant opportunity to inform policy evaluation and to improve estimates and projections. In this article, we contribute to the development of tools and methods that leverage new data sources for demographic research. We present an innovative approach to estimate stocks of migrants using a previously untapped data source: Facebook’s advertising platform. This freely available source allows advertisers and researchers to query information about socio-demographic characteristics of Facebook users, aggregated at various levels of geographic granularity. We have three main goals: i) to present a new data source that is relevant for demographers; ii) to discuss how demographers can avoid some of the problems related to the analysis of on representative Web and social media data; and iii) to lay the foundations on which demographers and data scientists can build in the future.
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The Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) stable lights products are made using operational OLS data collected at high gain settings, resulting in sensor saturation on brightly lit areas, such as city centers. This has been a paramount shortcoming of the DMSP-OLS stable lights time series. This study outlines a methodology that greatly expands the dynamic range of the OLS data using observations made at different fixed-gain settings, and by incorporating the areas not affected by saturation from the stable lights product. The radiances for the fixed-gain data are computed based on each OLS sensor's pre-flight calibration. The result is a product known as the OLS radiance calibrated nighttime lights. A total of eight global datasets have been produced, representing years from 1996 to 2010. To further facilitate the usefulness of these data for time-series analyses, corrections have been made to counter the sensitivity differences of the sensors, and coefficients are provided to adjust the datasets to allow inter-comparison.
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The world is rapidly urbanizing, but there is no single urbanization process. Rather, urban areas in different regions of the world are undergoing myriad types of transformation processes. The purpose of this paper is to examine how well data from DMSP/OLS nighttime lights (NTL) can identify different types of urbanization processes. Although data from DMSP/OLS NTL are increasingly used for the study of urban areas, to date there is no systematic assessment of how well these data identify different types of urban change. Here, we randomly select 240 sample locations distributed across all world regions to generate urbanization typologies with the DMSP/OLS NTL data and use Google Earth imagery to assess the validity of the NTL results. Our results indicate that where urbanization occurred, NTL have a high accuracy (93%) of characterizing these changes. There is also a relatively high error of commission (42%), where NTL identified urban change when no change occurred. This leads to an overestimation of urbanization by NTL. Our analysis shows that time series NTL data more accurately identifies urbanization in developed countries, but is less accurate in developing countries, suggesting the need to exert caution when using or interpreting NTL in developing countries.
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A Tresholding technique was used to convert a prototype “city lights” data set from the National Oceanic and Atmospheric Administration's National Geophysical Data Center (NOAAINGDC) into a map of “urban areas” for the continental United States. Thresholding was required to adapt the Defense Meteorological Satellite Program's Operational Linescan System (DMSPIOLS)-based NGDC data set into an urban map because the values reported in the prototype represent a cumulative percentage lighted for each pixel extracted from hundreds of nighttime cloud screened orbits, rather than any suitable land-cover classification. The cumulative percentage lighted data could not be used alone because the very high gain of the OLS nighttime photomultiplier configuration can. lead to a pixel (2.7X2.7 km) appearing “lighted” even with very low intensity, nonurban light sources. We found that a threshold of %89% yielded the best results, removing ephemeral light sources and “blooming” of light onto water when adjacent to cities while still leaving the dense urban core intact. This approach gave very good results when compared with the urban areas as defined by the 1990 U. S. Census; the “urban” area from our analysis being only 5% less than that of the Census. The Census was also used to derive population.- and housing-density statistics for the continent-wide “city lights” analysis; these averaged 1033 persons/km2 and 426 housing units/ king, respectively. The use of a nighttime sensor to determine the location and estimate the density of population based on light sources has proved feasible in this exploratory effort. However, issues concerning the use of census data as a benchmark for evaluating the accuracy of remotely sensed imagery are discussed, and potential improvements in the sensor regarding spatial resolution, instrument gain, and pointing accuracy are addressed.
Measuring consumption and wealth remotely Nighttime lighting is a rough proxy for economic wealth, and nighttime maps of the world show that many developing countries are sparsely illuminated. Jean et al. combined nighttime maps with high-resolution daytime satellite images (see the Perspective by Blumenstock). With a bit of machine-learning wizardry, the combined images can be converted into accurate estimates of household consumption and assets, both of which are hard to measure in poorer countries. Furthermore, the night- and day-time data are publicly available and nonproprietary. Science , this issue p. 790 ; see also p. 753
Policy-makers in the world's poorest countries are often forced to make decisions based on limited data. Consider Angola, which recently conducted its first postcolonial census. In the 44 years that elapsed between the prior census and the recent one, the country's population grew from 5.6 million to 24.3 million, and the country experienced a protracted civil war that displaced millions of citizens. In situations where reliable survey data are missing or out of date, a novel line of research offers promising alternatives. On page 790 of this issue, Jean et al. ( 1 ) apply recent advances in machine learning to high-resolution satellite imagery to accurately measure regional poverty in Africa.
Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist.We show that an individual's past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.
The term “slum” is difficult to define, but if we see one, we know it. Definitions for slums are qualitative such as “areas of people lacking, for example, durable housing or easy access to safe water”. This study aims at identifying characteristic physical features of the built environment that allows defining slum areas based on quantitative and measurable parameters. In general, spatial data on slums are generalized, outdated, or even nonexistent. The bird’s eye view of remotely sensed data is capable to provide an independent, area-wide spatial overview, to capture the complex morphological pattern and at the same time capture the large-scale individual objects typical for slums. Using high-resolution optical satellite data, parameters such as building density, building heights, and sizes are used to differentiate between slums and formal settlements. From it, the physical features are used to analyze structural homogeneity and heterogeneities within and across slums and to suggest characteristic physical features for spatial slum delineation at three study sites in Mumbai, India.