Assignment 1

Annotated Bibliography

Brianna Williams

February 23, 2020

Problem Statement

​ The problem I am investigating is child undernutrition in Haiti, especially following the 2010 earthquake. Undernutrition is the main source of mortality within developing countries. Currently, 1 in 5 children in Haiti suffer from malnutrition (Meds & Food for Kids in Haiti). The effects of child malnutrition include irreversible physical, mental, and emotional detriments. According to researchers at World Bank, 29.7% of children in Haiti are experiencing “moderate malnutrition,” while 18.9% are categorized as “severely malnourished.” Following the 2010 earthquake, various geospatial datasets have been able to track population disbursement in order to dispatch intense relief efforts. My sources below have outlined the basis of my problem statement on investigating child malnutrition in Haiti, and how to overcome these intense statistics that span across all developing countries.

Source 1

“Spatial Analysis of Undernutrition of Children in Léogâne Commune, Haiti”

Spray, A. L., Eddy, B., Hipp, J. A., & Iannotti, L. (2013). Spatial Analysis of Undernutrition of Children in Léogâne Commune, Haiti. Food and Nutrition Bulletin, 34(4), 444–461. https://doi.org/10.1177/156482651303400410

​ For my problem statement, this source directly relates to my problem statement of how child malnutrition is evident in developing countries, specifically Haiti following the earthquake. This article explains how undernutrition in Haiti the leading cause of child mortality due to both socioecological and socioeconomic factors. Haiti is ranked among the poorest countries in the world, all ascribed by natural disasters, disease, and political distress. After the record-breaking earthquake in 2010, Haiti has struggled to resolve their food insecurities. The author of the article focuses on the epicenter of the 2010 earthquake, Léogâne, and its child net malnutrition. The author notes that various factors contribute to this morbid fatality, including “clean water, sanitation, and health services.” He expresses that the lack in “diversity” in Haitian children’s diet allows them to become more susceptible to illnesses such as Diarrheal Disease, the lead cause of child mortality in the area. Alongside food insecurity, healthcare in Haiti is severely inadequate. The article notes that approximately 40% of the population lacks adequate healthcare services, and 50% lack the ability to retrieve basic prescriptions. Within the article, it is concluded that the populations hardships, including child undernutrition, are a result of the earthquake that occurred a decade ago.

​ Furthermore, the article relates to Amartya Sen’s idea that healthcare is essential within a person’s life. In order to be able to have sustainable growth within a nation, available healthcare services are imperative. The sustainable development goals considered within the study align with the Haitian Government goals of improving water, sanitation, and food security. Through analyzing the undernutrition of children in Léogâne, Haiti with geographical weighted datasets (GWS) and geographic information systems (GIS), the study concluded that there is promising development in Haiti’s child malnutrition issue. The authors use these geospatial datasets and methodically hold cross-sectional household surveys of the region. GWR was specifically used to evaluate how “undernutrition and its household variants vary across the region.” These geospatial datasets have given a more profound insight on how to improve nutrition within various regions. The study aimed to characterize child undernutrition in Haiti through the usage of GWR and concluded with their methods that undernutrition in children were often found in “pockets” of the area, rather than being evenly distributed in the region’s entirety.

Source 2

“Mapping of Malnutrition from EMR Data in Southern Haiti”

Coston, A. (2015). Mapping of Malnutrition from EMR Data in Southern Haiti. http://hdl.handle.net/2152.5/1525

​ This research assesses the malnutrition in Southern Haiti using EMR data. Alex Coston gives a brief background of Haiti’s malnutrition issue, especially following the 2010 earthquake and a severe rain season. He explains that by using geographical information systems, data scientists will be able to understand the underlying conditions and reasoning for malnutrition and the “geospatial risk factors of a region.” Coston then explains how in Port Salut, Klinik Timoun Nou Yo (KTNY) developed a nutrition program for children in the region, which directly relates to the issue I am evaluating. Coston evaluates KTNY tracker, a GIS tool developed to aid KTNY and their studies surrounding patient distribution. To support this tracker, data from 2013 and 2014 was plugged into the software and yielded results that concluded that most children affected by malnutrition reside along the coast of Haiti, as well as less dense areas. Coston then discusses how the KTNY tracker alongside QGIS will be useful and easy to analyze clinical data as well as serve as a “visual advocate” towards increasing funding for children malnutrition program efforts.

​ By increasing access to child malnutrition programs, Coston’s solution aligns with Amartya Sen’s definition of human development. Providing the resources to areas where child undernutrition is relevant will improve living conditions for the people of Haiti. Through his research, Coston analyses the social and economic means of human development by introducing KTNY to affected children. Although it is not explicitly stated, by analyzing the population distribution of affected children, it enables these programs to help the economic and social status of the affected regions. Similarly, the sustainable development goal most relevant to the research are goal 2 (zero hunger) and goal 3 (good health and wellbeing). By using this tracker, it enables researchers to understand where to distribute clinics and how to improve the undernutrition dilemma. Coston uses the innovative geospatial dataset KTNY as well as the electronic medical record (EMR) to efficiently map their malnutrition data. Finally, by mapping the information from 2013 and 2014, Coston was able to answer his question of whether KTNY would be a valuable GIS dataset to analyze malnutrition in Haiti. He concludes that, while it proved to be efficient and valuable in analyzing these trends, there still needs to be future development in the program itself and access to care disparities. As I continue my research, this source provides minimal research and evidence surrounding my problem topic. Because it lacks content, I will look for a similar source that provides more information about how these programs aid Haitian children.

Source 3

“Child Hunger in the Developing World: An Analysis of Environmental and Social Correlates”

Balk, Deborah, et al. “Child Hunger in the Developing World: An Analysis of Environmental and Social Correlates.” Food Policy, Pergamon, 10 Nov. 2005, http://www.sciencedirect.com/science/article/pii/S0306919205000886

​ This article correlates undernutrition of children in developing countries with environmental and household factors. As a broad overview of the subject in terms of the entirety of the developing world, this source analyses each specific country, the year the data was recorded, the number of children studied, and their aggregate-level regression. The authors state that social scientists have shown that household factors, such as birth order and school all have influences on child undernutrition in the developing world. Likewise, the authors note that there are also hereditary, environmental, and gender differences amongst the various countries’ child nutritional outcomes. The authors begin their study by “converting spatial information into survey units and vise-versa to analyze these various demographic determinants of child malnutrition. They ultimately conclude that a country’s environment can be an indication of how nutrition affects children; however, they were unable to identify the “full-suite” of the individual and house-hold indicators. Despite this, they were able to identify household characteristics that were a result of undernutrition for children, such as whether or not a child is breastfed. They also concluded that environmental differences like soil fertility, water availability, and access to food markets impact the pattern of poverty levels, thus creating a suboptimal living environment for children.

​ The authors conclusion that household differences, environment, and socio-economics all have an impact on the availability of food coincide with Amartya’s idea of how an income increase can benefit the development of a society. Specifically, their study addresses socio-economic and cultural differences among countries. Haiti specifically is not addressed although they provide data for the country. In totality though, Haiti’s sustainable goal of improving socioeconomics is addressed in the research paper. The authors use external geospatial datasets like GPS-point locations and DHS data to analyze the prevalence of malnutrition in Africa with population and road density mapping. With these datasets, the authors “undertook a simple ordinary least squares (OLS) regression analysis to test hypotheses from the UNICEF framework” to measure the compatibility of the data. By calculating the Z-scores of the children as well as the standard deviations, they were able to observe weight trends of children aged 1-3. Overall, the authors were able to answer their hypothesis that household and environmental factors affect the nutritional availability for children in developing countries. This source provides excellent reasoning and research on child malnutrition in Haiti.

Source 4

“Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti”

Bengtsson L, Lu X, Thorson A, Garfield R, von Schreeb J (2011) Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLOS Medicine 8(8). https://doi.org/10.1371/journal.pmed.1001083

​ This article is similar to other articles reviewed in class concerning population disbursement tracked by cell phone data following a natural disaster. In this case, the 2010 earthquake is being researched. The researchers explain that by not knowing the location of the people affected, relief efforts will be poorly distributed. The researchers used SIM card data from Digicel, the most used cell phone company in Haiti to study the trends of population movements following the earthquake. Methodically, the researchers tracked the geographical locations of the SIM cards using cell phone towers within the country. They tracked locations roughly a month prior to the earthquake and five months following. They found that roughly 630,000 people left within 20 days post-earthquake in Port-au-Prince. They found that, while various population groups such as women, children, and elders have little to no mobile data use, it is safe to assume they are generalized with the rest of the population. To eliminate the bias, they compared the SIM card data with other population-based data to receive the same conclusions. Ultimately, they concluded that with this mobile data, estimating population distribution following a natural disaster can be “delivered rapidly.”

​ Millions of people are affected by natural disasters annually causing environmental, economic, and human loss. Throughout the article, they relate to Amartya Sen’s definition of human development by improving government intervention during natural disasters. Through their data analysis, distributing aid during a crisis, like the 2010 earthquake, can be more accurate and valuable. A human instinct following such a disaster is to flee the scene, and while this is beneficial immediately, this instinct makes it difficult and complicated to distribute aid and relief efforts. Unfortunately, there is no way to track immediate data distribution; however, with their SIM card data, processing the distribution becomes easier and faster. While the article does not directly correlate with my intended research of childhood malnutrition within Haiti following the earthquake, it provides valuable information surrounding population distribution following. This data, in turn, correlates with where child malnutrition is most evident. Geospatia data present in the research, such as SIM card data and satellite imagery, have supplied a faster way to see population trends following these disasters. The results of this data allowed researchers to find a way to estimate population distribution faster than before. This source overall does not directly relate to my problem topic, but adds reasoning and evidence on how the earthquake affected Haitians directly.

Source 5

“Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination”

Aardt, J. A. V., Mckeown, D., Faulring, J., Raqueño, N., Casterline, M., Renschler, C., … Gill, S. (2011). Geospatial Disaster Response during the Haiti Earthquake: A Case Study Spanning Airborne Deployment, Data Collection, Transfer, Processing, and Dissemination. Photogrammetric Engineering & Remote Sensing, 77(9), 943–952. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.388.2622&rep=rep1&type=pdf

​ Similar to the previous source, this case study analyses airborne imagery of Haiti following the 2010 earthquake. This data aided efforts to create “rapid turnaround damage assessment products” as well as supported response to natural disasters that create long-term effects on a country’s economy. The earthquake ultimately affected over one displaced million Haitians. Due to this, the country desperately needed accurate and high-resolution imagery as the disaster unfolded to identify debris location, building damage, and assessment of the fault line. The researchers used geospatial organizations such as National Oceanic and Atmospheric Administration, Google, Inc., Pictometry International, DigitalGlobe, and Geoeye, Inc. to analyse the “high spatial resolution multispectral imagery” over the areas that were most impacted by the earthquake. They used RIT WASP, a sensor used to map wildfire, to investigate the affected sites. Likewise, researchers used WASP imaging systems to “enable accurate georeferencing of imagery.” Upwards of 200 GB of this raw imagery was collected post-earthquake. Similar to the previous article, the researchers also used crowd-sourcing to review damage surveys through “social networking.” They quantified the extent of various building’s damage by reaching out to engineers. By using sources such as Google, these engineers were able to identify numerous damages within the greater Port-au-Prince area. The researchers concluded that ImageCat-RIT-Kucera International airborne response alongside crowd-surfing was useful in identifying building damage following the 2010 Haiti earthquake.

​ This article is also not directly about my problem topic of child malnutrition; however, I think it is important to evaluate certain issues that were relevant following the 2010 earthquake because it will support my research topic. The article analyzes the structural integrity of buildings in Haiti through the usage of data science. Predominantly, the article relates to Amartya Sen’s idea that removing “unfreedoms,” such as poverty, poor economic opportunities, and social deprivation, enables human development. Because the structural integrity of buildings is so important to families, especially following the earthquake, freedoms were absent from Haitian citizens. While the case study does not explicitly identify any sustainable development goals, it is implied that they are trying to achieve sustainable cities and communities (SDG 11). Overall, this case study studies how geospatial data sets can help benefit a society affected by a natural disaster, and how these data sets can further support in relief efforts.

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