Authors: Jessica E. Steele, Pål Roe Sundsøy, Carla Pezzulo, Victor A. Alegana, Tomas J. Bird, Joshua Blumenstock, Johannes Bjelland, Kenth Engø-Monsen, Yves-Alexandre de Montjoye, Asif M. Iqbal, Khandakar N. Hadiuzzaman, Xin Lu, Erik Wetter, Andrew J. Tatem and Linus Bengtsson Reference: Steele, Jessica E., et al. “Mapping Poverty Using Mobile Phone and Satellite Data.” Journal of The Royal Society Interface, vol. 14, no. 127, 2017, p. 20160690., doi:10.1098/rsif.2016.0690. Annotation: The authors’ main ideas in this work are how public and private data for low- and middle-income countries can be used to provide understanding and insight into the spatial distribution of poverty. Traditionally, methods used to target and analyze poverty rely on census data, which are often unavailable or out of date in most low- and middle-income countries. This article dives into how cellphone data is collected and paired with satellite information. RS data or remote sensing data collects physical properties of the earth, while CDR data, or call detail records, collects a detailed record of all telephonic calls produced by a telephone exchange. RS data and CDR data are a complementary pair, combined having the ability to capture human living conditions and behaviors. The poverty data in this study was represented using three geographically referenced datasets, including asset, consumption, and income-based measures of wellbeing in Bangladesh. The results of this study found that models with the combination of RS and CDR data provide an advantage over models with just the data source. The data showed that when RS data and CDR data were combined, they produced a higher R-squared value compared to RS-only or CDR-only data. However, the results also demonstrate that CDR-only and RS-only data models perform comparably in their ability to map poverty indicators.
This article relates to Amartya Sen’s definition of human development because they both discuss how analyzing poverty can enhance our freedoms and “allow people to lead lives that they have reason to live.” The dimension of human development being addressed by the authors here is poverty assessment and analysis and the development goals are to measure, target, and end poverty in all dimensions. The geospatial datasets used in the study were CDR data and RS data. Poverty assessment and analysis have grown considerably with the utilization of CDR and RS data. The geospatial methods used by the authors in this study were estimating and monitoring poverty rates at a high spatial resolution and supporting traditional methods of data collection.
Authors: Utz Pape and Luca Parisotto Reference: “Pape, Utz; Parisotto, Luca. 2019. Estimating Poverty in a Fragile Context : The High Frequency Survey in South Sudan. Policy Research Working Paper;No. 8722. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31190 License: CC BY 3.0 IGO.” Annotation: This research paper is about poverty estimation in South Sudan. The paper introduces this topic by discussing the rich and difficult history of Sudan and the reasons for their poverty. After the Civil War broke out in 2013, the South Sudanese conflict continued to escalate, resulting in a humanitarian crisis that displaced a third of their population. Very little was known about the welfare and wellbeing of the country during its independence in 2011. The last survey measuring poverty and consumption was conducted as far back as 2009. The growth and escalation of the civil war also posed a serious challenge to the planning of fieldwork. For this reason, the surveys capitalized on technology and methodological innovations to collect reliable data and provide accurate poverty estimates. To bridge this gap in data, the High-Frequency South Sudan Survey (HFS) implemented by the National Bureau of Statistics conducted several surveys across several states, measuring various characteristics such as consumption, poverty, welfare, currency devaluation, and inflation. As a country going through a period of upheaval, it displayed all the characteristics of a war economy, with soaring inflation and rapid currency devaluation. Due to this, poverty has risen to extremely high levels. The paper continues by stating that the HFS in 2016 estimated that 4 out of 5 people from several states were living below the international poverty line of US $1.90 PPP. Furthermore, in 2017 South Sudan ranked 187 out of 189 countries in the Human Development Index with a 57-year life expectancy. To continue to fill the gaps in data of poverty in inaccessible areas, additional data was captured through satellite imagery and other geospatial characteristics.
This article relates to Amartya Sen’s definition of human development because both discuss using the analysis of data to enhance our quality of life and freedoms. The study discusses using data to alleviate poverty, poverty being an unfreedom Amartya Sen mentions in his book, “Development as Freedom.” By acquiring information from these regions of poverty, policies can be made to allow these developing countries to reach developmental freedom. The dimension being addressed by the authors’ research is poverty assessment and analysis. The development goals of this research are to target and alleviate poverty and providing those in poverty with the resources they need. The geospatial datasets used were satellite data and the High-Frequency South Sudan Survey. The geospatial methods used by the authors in this study were utilizing the Rapid Consumption Methodology combined with geo-spatial data. The development pattern/process being explored in this paper was the estimation and spread of poverty in South Sudan.
Authors: Pape,Utz Johann Wollburg,Philip Randolph Reference: “Pape, Utz; Wollburg, Philip. 2019. Estimation of Poverty in Somalia Using Innovative Methodologies. Policy Research Working Paper;No. 8735. World Bank, Washington, DC. © World Bank. https://openknowledge.worldbank.org/handle/10986/31267 License: CC BY 3.0 IGO.” Annotation: This paper discusses the estimation of poverty in Somalia. It begins by mentioning Somalia’s history, including the challenges the country has faced. Like most low socio-economic countries, Somalia is a highly data-deprived country, leaving policymakers to operate in a statistical vacuum. The government had previously conducted the last population census in 1975. To overcome this gap in data, the World Bank implemented a Somali High Frequency Survey to better estimate poverty indicators. This relates back to the High Frequency South Sudan survey discussed in the previous paper about the estimation of poverty in South Sudan. However, in this case, Somalia’s insecurity and lack of statistical infrastructure pose several challenges for implementing a survey and measuring poverty. Due to inaccessibility to certain areas because of the presence of terrorist groups, lack of a recent census, and the nature of a nomadic population, acquiring accurate information about the poverty indicators in Somalia proved to be challenging. This paper outlines how the SHFS estimated poverty indicators and overcame these challenges through methodological and technological adaptations. Using geospatial techniques and high-resolution imagery, they modeled the spatial population distribution, built a probability-based population sampling frame, and generated enumeration areas to overcome the lack of a recent population census. Poverty estimation in inaccessible areas relied on satellite imagery and other geospatial data. The geospatial data that was collected was used to create a security assessment access map, which gave researchers the ability to access the data from the inaccessible and unsafe regions in Somalia. After reading this paper, I found many similarities between the SHFS and HFS in terms of their solutions to estimating poverty indicators; that being satellite imagery and geospatial data.
This paper relates to Amartya Sen’s definition of human development. The production of security assessment maps and poverty maps would allow Somalia and its citizens a better quality of life. With these maps, we can target the regions with high poverty and provide aid, benefits, and education to reduce the poverty rates. The dimension of human development being addressed by the authors’ research in this paper is poverty assessment and analysis. The development goals of the authors in this paper were to refine the models to predicting poverty patterns through satellite imagery by including predictors with higher spatial frequencies and building footprints. The geospatial datasets used by the authors in this study were satellite imagery and the Somali High-Frequency Survey, while the geospatial methods include sampling strategies using micro-listing and questionnaire design.
Authors: Unknown Reference: Service, Indo-Asian News. “Mobile Phones Can Create High-Resolution Poverty Map.” India Today, 9 Feb. 2017, www.indiatoday.in/technology/news/story/mobile-phones-can-create-high-reolution-poverty-map-959791-2017-02-09. Annotation: The author’s main idea in this article is how mobile phones can create high-resolution poverty maps in India. Researchers from the University of Southampton and the Flowminder Foundation found that when combining mobile data and geospatial data from satellites, they were able to create poverty predictions with significant advantages when compared to those made from traditional sources. The article continues by saying that census and household surveys are often used as data sources to estimate poverty rates. However, they aren’t updated regularly, especially in low-income countries. Since information on mobile phones is everchanging and constantly updating, it can track changes on an ongoing basis. Jessica Steele, the author of the study, states that when paired with satellite data, it can provide a more dynamic view of poverty and its geographic spread. Mobile phones send information to receiving towers, giving an approximate location of the mobile user. It also contains information about data usage, number of texts sent and received, times of calls and their durations, etc. This anonymized data helps build a picture of poverty. As we learned from the previous studies, satellite data can provide significant information about living conditions in rural areas. However, in tightly packed cities it becomes more challenging. It’s the reverse for mobile phones, with bigger cities providing more information. The researchers in this study also noted that there are individuals, the poorest ones, who may not own a mobile phone. Despite this, they were still able to find distinct differences between the lower-income and higher-income areas.
This article relates to Amartya Sen and his definition of human development. Amartya Sen believed that human development was the process of expanding human freedoms and enhancing the quality of life. By using satellite imagery and mobile data to create poverty maps, more information can be collected to better analyze and understand those suffering from the unfreedoms of poverty. By creating a dynamic view of poverty, economic aid can be distributed more efficiently. The dimension being addressed by the author’s research in this article is poverty assessment and analysis with the goal to reduce poverty. The geospatial datasets that were used by the authors were satellite imagery and RS data, while the geospatial data science methods used were the assessment and analysis of mobile data.
Overall, I believe that the digital economy is transforming how data is collected, processed, and used to make decisions. New methods, such as combining traditional household survey data with non-traditional data sources (satellite imagery and mobile phone data), are allowing researchers to map poverty at a higher scale and resolution and hopefully, helping countries continue to reduce poverty.