Date of Award

2020

Document Type

Dissertation

Degree Name

Ph.D.

Organizational Unit

College of Natual Science and Mathematics, Geography and the Environment

First Advisor

Paul Sutton

Second Advisor

Jing Li

Third Advisor

Jonathan Moyer

Fourth Advisor

Kristopher Kuzera

Keywords

Africa, Development, GDP, Nighttime light, VIIRS

Abstract

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.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Xuantong Wang

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

95 p.

Discipline

Geography, Economics

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