1 Running head: ABBREVIATED TITLE OF PAPER (50 characters maximum) PAGE 1 Annotated Bibliography Engstrom, R., Hersh, J., & Newhouse, D. (2016). Poverty in HD: What Does High Resolution Satellite Imagery Reveal about Economic Welfare. Working paper. This paper was a joint effort between researchers and seminar participants at the Boston University Development. The researchers got a financial support from the Strategic Research Program and World Bank Innovation. They selected areas for the sample, and they used some statistics of GN poverty and got some estimated from census of population to understand and prediction of the prevalence of poverty. They compared between the data which created spatial features from high resolution satellite imagery. Moreover, the researchers made a classification for the objects such as of density and height of buildings, numbers of cars, type of roads and agriculture. The poverty has many correlates, some in urban areas and other in rural areas. Also, they used many indexes and indicators for modeling. They validated the poverty by using high resolution features and they explained the variation the extent of poverty. Finally, they concluded to many results, which indicated to a strong correlation between satellite indicators and predicted welfare. Also, the variables measuring were the strongest predictors of variation in poverty, Finally, they asserted the valuable of satellite imagery to help governments and stakeholders to elimination the poverty. In sum, it is a useful paper, which has an explanation and analysis of how satellite images were used in poverty research, and what features can be extracted for analysis. Engstrom, R. (2018). Linking pixels and poverty: Using satellite imagery to map poverty, Panel contribution to the population-environment research network cyber seminar,10516. The author presented the importance of this topic and how many researchers in different disciplines have worked on poverty. The main goal for these researches is ending poverty in all its forms everywhere, that by defined its location. Recently, the researchers are starting to map the poverty. They performed mapping in traditional way by either using survey of household data or by combining them with census data. They faced many troubles in time, cost, and labors to collect data in many areas. Furthermore, the safety in the unstable areas. They avoided these problems by using satellite imagery. They started with images in night time lights to recognize the variations in poverty between countries. The researcher found "that areas with greater wealth have higher NTL light emissions and poorer areas have fewer light emissions." Since the results were limited to urban areas only. He focused on using high-resolution spatial images of less than 5 meters although they were expensive for researchers. Also, he used other approaches to map poverty areas including simple visual interpretation. He concluded to that the satelli.

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Running head: ABBREVIATED TITLE OF PAPER (50
characters maximum)
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1
Annotated Bibliography
Engstrom, R., Hersh, J., & Newhouse, D. (2016). Poverty in
HD: What Does High Resolution Satellite Imagery Reveal about
Economic Welfare. Working paper.
This paper was a joint effort between researchers and seminar
participants at the Boston University Development. The
researchers got a financial support from the Strategic Research
Program and World Bank Innovation. They selected areas for
the sample, and they used some statistics of GN poverty and got
some estimated from census of population to understand and
prediction of the prevalence of poverty. They compared between
the data which created spatial features from high resolution
satellite imagery.
Moreover, the researchers made a classification for the objects
such as of density and height of buildings, numbers of cars, type
of roads and agriculture. The poverty has many correlates, some
in urban areas and other in rural areas. Also, they used many
indexes and indicators for modeling. They validated the poverty
by using high resolution features and they explained the
variation the extent of poverty. Finally, they concluded to many
results, which indicated to a strong correlation between satellite
indicators and predicted welfare. Also, the variables measuring
were the strongest predictors of variation in poverty, Finally,
they asserted the valuable of satellite imagery to help
governments and stakeholders to elimination the poverty.

2. In sum, it is a useful paper, which has an explanation and
analysis of how satellite images were used in poverty research,
and what features can be extracted for analysis.
Engstrom, R. (2018). Linking pixels and poverty: Using satellite
imagery to map poverty, Panel contribution to the population-
environment research network cyber seminar,10516.
The author presented the importance of this topic and how many
researchers in different disciplines have worked on poverty. The
main goal for these researches is ending poverty in all its forms
everywhere, that by defined its location. Recently, the
researchers are starting to map the poverty. They performed
mapping in traditional way by either using survey of household
data or by combining them with census data. They faced many
troubles in time, cost, and labors to collect data in many areas.
Furthermore, the safety in the unstable areas. They avoided
these problems by using satellite imagery. They started with
images in night time lights to recognize the variations in
poverty between countries. The researcher found "that areas
with greater wealth have higher NTL light emissions and poorer
areas have fewer light emissions." Since the results were
limited to urban areas only. He focused on using high-resolution
spatial images of less than 5 meters although they were
expensive for researchers. Also, he used other approaches to
map poverty areas including simple visual interpretation. He
concluded to that the satellite data a very rich field for research
and exploration and its helping in mapping of poverty. The
successful of night lights to extract the information over large
areas.
This research is useful to learn other type of data that can be
used to identify poverty. Also, the researcher has proven the
ability of night light imagery to measure the poverty areas.

3. 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. Computers & Geosciences, 35(8),
1652-1660.
This research was carried out in cooperation with several
specialists, which are geographers, geophysicists, and scholars
in environment and energy. Their work relies on satellite data
and World Bank data, which are contributed to draw the global
poverty map. That has led to define the local poverty line and
the international poverty line. Although all countries do not
conduct surveys on poverty data on social and economic
measures, population density, living conditions, and economic
activities. This is not for developed countries, but for
developing countries, which is useful for efforts to reduce
poverty. Satellite data has proven effective in global mapping.
These maps are annual and semi-annual.
This study defined the term “poverty” and it gave many
examples from multiple aspects of poverty. Also, it illustrated
how to use satellite imageries in poverty research and mapping,
by analyzing and exploring any correlation between features.
Engstrom, R., Hersh, J., & Newhouse, D. (2017). Poverty from
space: Using high-resolution satellite imagery for estimating
economic well-being, World Bank Group.
This work was done by the authors based on the efforts of the
World Bank on development issues, in particular poverty and
equity. They said that despite the efforts of the statistical
offices of all countries, local estimates of poverty and economic
well-being are rare or almost non-existent. They noted the use
of satellite images contributes to filling large gaps in data
shortages. With the development of technology and advanced
companies will expand coverage ranges and accurate imaging.
They referred to their studies and the features they extracted to

4. predict poverty through them such as: type of roofs, number of
buildings, size, height, and density. Also, the size and type of
agricultural land, and number of vehicles and cars, the form of
roads and paving materials. They are linking these elements
through special software for the assessment of well-being. Also,
by using nightlights they can know the concentration of
economic activity.
In sum, this work was an explanation and analysis of how
satellite images were used in research, and what features can be
extracted for analysis.
Ferreira, T. (2017) The extension of existing data and methods
to measure poverty and mobility in data-poor, Agrarian Sub-
Saharan Africa, Dissertation presented for the degree of Doctor
of Philosophy (Economics) in the Faculty of Economic and
Management Sciences at the University of Stellenbosch.
The author presented this dissertation for the degree of Doctor
in Economics Philosophy at the University of Stellenbosch. This
research relies on poverty and what the methods are to measure
it. The researcher tried to fill the gap of lack of data. He gave
an overview about study area, economic, and social situation in
it. Then, he began analyzing the satellite data to come up with
results. He used daylight data to measure socio-economic
outcomes. Also, he tried to identify rural agricultural areas,
where the presence of plants enables the researcher to measure
the use of the earth. It also contributes to detect droughts,
which increase poverty. Finally, Ferreira found that the images
in the nightlights could not measure economic activity in Sub-
Saharan Africa.
This research and other research contribute effectively to
alleviating poverty and focusing the efforts of organizations in
poor places only. Also, it is a useful research to learn other
methods for measuring the poverty.

5. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., &
Ermon, S. (2016). Combining satellite imagery and machine
learning to predict poverty. Science, 353(6301), 790-794.
These authors contributed equally to this work. They are in
various fields from Stanford University in California. Moreover,
they cooperated with National Bureau of Economic Research in
Boston, MA. Their research on poverty, and it targeted
developing countries.
They relied on high-resolution satellite imagery for five African
countries, which are Nigeria, Tanzania, Uganda, Malawi and
Rwanda. They pointed to the evolution of technology, and they
explained their approach. The nightlights are not quite
effective, as they have little ability to distinguish between poor
and poorer areas, but it is effective in distinguishing between
densely populated areas from other low-population areas. Also,
that nightlights are good for estimating economic well-being.
They explained through the analysis of satellite images that
daylight contributes to the detection of the advantages related to
the materials used in the construction of roofs and roads.
The research was a joint effort, based on the analysis of satellite
imageries and the spatial features which became apparent
through it. It is useful and offers a good explanation for satellite
imagery analysis.
Morikawa, R. (2014). Remote sensing tools for evaluating
poverty alleviation projects: A case study in Tanzania. Procedia
Engineering. 78.
The purpose of this article was to alleviate poverty and its
effects on rural areas, and preserving the agricultural
environment, which is related to the economic situation of
farmers. The researcher used plant reflection to know the
ecology of crops and determine the types of vegetation. It can
also detect if there any future problems such as drought and soil

6. erosion, and work to find solutions early. That contributes to
measuring the conditions of the local environment in the long
term. All this is done by the so-called “NDVI” the normalized
difference vegetation index. Thus, serving communities and
poverty alleviation projects, and developing their work to
improve living conditions. For example, communities suffering
from famine will predict this in advance and therefore take their
precautions early by reforestation, avoiding logging, protecting
water collection sites and adopting sustainable cultivation
techniques.
In sum, this research uses spatial analysis and remote sensing to
detect any future problems, which are helping to save time,
efforts, and human life.
Steele, J. E., Sundsøy, P. R., Pezzulo, C., Alegana, V. A., Bird,
T. J., Blumenstock, J., ... & Hadiuzzaman, K. N. (2017).
Mapping poverty using mobile phone and satellite data. Journal
of The Royal Society Interface, 14(127), 20160690.
This study represented the first attempt to created predictive
maps of poverty that by using a collection of CDR and RS data.
It further provided an example of processed the CDR data and
created detailed maps without revealing the user or the
information. The remote sensing data captured the
environmental metrics and physical properties like vegetation
indices, night-time lights, climatic conditions. In addition,
captured other data that related to human living conditions and
behavior like distance to roads or major urban areas. All these
data were obtained from many sources especially for this study
and were processed to ensure that resolutions, extents, and
projections matched. They used geographically referenced
datasets representing asset, consumption and income-based
measures of wellbeing. The concluded their study to important
results. They found that the models employing a combination of
CDR and RS data generally provided an advantage more than

7. the models only based on either data source. Also, they found
the spatial covariance in the data was very important. They
found also the consumption has been shown to be lower than the
predictive power for assets. Further, there are some changes in
the income and consumption by day or week, related to changes
in the family size, get or loss the job, and piecework or harvest
outcomes.
It is a useful study that demonstrated the data sources that can
be used to the spatial distribution of poverty. In addition, their
findings provide further support for correlations between socio-
economic measures and night-time light intensity, access to
roads and cities, entropy of contacts and mobility features.
Principle of Business Management
Week 7 Assignment
Importance of Communication
Research define, and describe communication styles, the types
of communication, and
why communication is important to an organization.
The requirements below must be met for your paper to be
accepted and graded:
– 750 words (approximately 2 – 3 pages)
using Microsoft Word.

8. page.
from references.
preferably from
EBSCOhost. Text book, lectures, and other materials in the
course may be used,
but are not counted toward the two reference requirement.
Reference material (data, dates, graphs, quotes, paraphrased
words, values, etc.) must
be identified in the paper and listed on a reference page.
Reference material (data,
dates, graphs, quotes, paraphrased words, values, etc.) must
come from sources such
as, scholarly journals found in EBSCOhost, online newspapers
such as The Wall Street
Journal, government websites, etc. Sources such as Wikis,
Yahoo Answers, eHow, etc.
are not acceptable.