University of Denver
Using Multi-Source Data to Assess the Dynamics of Socioeconomic Development in Africa
Abstract
dc:description.abstract<p>Frequent and rapid spatially explicit assessment of socioeconomic development is critical for achieving the Sustainable Development Goals (SDGs) at both national and global levels. In the past decades, scientists have proposed many methods for monitoring human activities on the Earth’s surface on various spatiotemporal scales using Defense Meteorological Satellite Program Operational Line System (DMSP-OLS) nighttime lights (NTL) data. However, the DMSP-OLS NTL data and the associated processing methods have limited their reliability and applicability for systematic measuring and mapping of socioeconomic development. This research utilizes Visible Infrared Imaging Radiometer Suite (VIIRS) NTL and the Isolation Forest (iForest) machine learning algorithm for more intelligent data processing to capture human activities. I use machine learning and NTL data to map gross domestic product (GDP) at 1 km2. I then use these data products to derive inequality indexes like GINI coefficients and 20:20 ratios at nationally aggregate levels. I have also conducted a case study based on agricultural production information to estimate subnational GDP in Uganda. This flexible approach processes the data in an unsupervised manner on various spatial scales. Assessments show that this method produces accurate sub-national GDP data for mapping and monitoring human development uniformly in Uganda and across the globe.</p>
Degree
thesis:*- Name thesis:degree_name
- Ph.D.
- Level thesis:degree_level
- Dissertation
- Year dc:date.available
- 2020
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Wang, Xuantong
- Contributors dc:contributor
-
- Paul Sutton
- Jing Li
- Jonathan Moyer
- Kristopher Kuzera
Subjects
dc:subject × 9Rights
dc:rights- Statement dc:rights
-
- <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>
- Language dc:language
- en
Identifiers
dc:identifier.*- Repository record dc:identifier
- https://digitalcommons.du.edu/etd/1862
- OAI identifier oai:identifier
- oai:digitalcommons.du.edu:etd-2855