Uganda is one of the poorest nations in the world. It is important to obtain accurate, timely data on socio-economic characteristics sub-nationally, so as to target poverty reduction strategies to those most in need. Many studies have demonstrated that nighttime lights (NTL) can be used to measure human activities. Nevertheless, the methods developed from these studies (1) suffer from coarse resolutions, (2) fail to capture the nonlinearity and multi-scale variability of geospatial data, and (3) perform poorly for agriculture-dependent regions. This study proposes a new enhanced light intensity model (ELIM) to estimate the gross domestic product (GDP) for sub-national units within Uganda. This model is developed by combining the NTL data from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the population data from the Global Human Settlement Layer (GHSL), and information on agricultural production and market prices across several commodity types. This resulted in a gridded dataset for Uganda’s GDP at sub-national levels, to capture the spatial heterogeneity in the economic activity.

Frequent and rapid spatially explicit assessment of socioeconomic development is critical for achieving the Sustainable Development Goals (SDGs) at both national and global levels. Over the past decades, scientists have proposed many methods for estimating human activity on the Earth’s surface at various spatiotemporal scales using Defense Meteorological Satellite Program Operational Line System (DMSP-OLS) nighttime light (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 study utilized Visible Infrared Imaging Radiometer Suite (VIIRS) NTL and the Isolation Forest machine learning algorithm for more intelligent data processing to capture human activities. We used machine learning and NTL data to map gross domestic product (GDP) at 1 km². We then used these data products to derive inequality indexes (e.g., Gini coefficients) at nationally aggregate levels. This flexible approach processes the data in an unsupervised manner at various spatial scales. Our assessments show that this method produces accurate subnational GDP data products for mapping and monitoring human development uniformly across the globe.

Predicting traffic flow is critical in efficient maritime transportation management, coordination, and planning. Scientists have proposed many prediction methods, most of which are designed for specific locations or for short-term prediction. For the purpose of management, methods that enable long-term prediction for large areas are highly desirable. Therefore, we propose developing a spatiotemporal approach that can describe and predict traffic flows within a region. We designed the model based on a multiple hexagon-based convolutional neural network (mh-CNN) model that takes both the flow dynamics and environmental conditions into account. This model is highly flexible in that it predicts zonal traffic flow within variable time windows. We applied the method to measure and predict the daily and hourly traffic flows in the South Atlantic States region by taking the impacts of extreme weather events into consideration. Results show that our method outperformed other methods in daily prediction during normal days and hourly prediction during hurricane events. Based on the results, we also provide some recommendations regarding the future usage and customization of the model.

The outbreak of the unprecedented Coronavirus Disease 2019 (COVID-19) pandemic calls for innovative risk assessment and mapping approaches to prompt public messaging. Most of the existing approaches aim to present population risks associated with geographic areas (e.g., county), thus providing limited values to guide individuals to take proactive measures against COVID-19. To better facilitate the general public to make informed decisions on daily activity plans, we propose an activity-based spatiotemporal risk mapping approach to capture and represent exposure risk at a personal level. This approach leverages the classical space-time representations to capture personal activity space and measures exposure risk in such activity space. This approach further implements geovisualization designs to communicate measured exposure information. To illustrate the usability of the approach, we have conducted a case study in Denver, Colorado with COVID-19 data from October 2020 and four representative travel profiles.

Education is a human right, and equal access to education is important for achieving sustainable development. Measuring socioeconomic development, especially the changes to education inequality, can help educators, practitioners, and policymakers with decision- and policy-making. This article presents an approach that combines population distribution, human settlements, and nighttime light (NTL) data to assess and explore development and education inequality trajectories at national levels across multiple time periods using latent growth models (LGMs). Results show that countries and regions with initially low human development levels tend to have higher levels of associated education inequality and uneven distribution of urban population. Additionally, the initial status of human development can be used to explain the linear growth rate of education inequality, but the association between trajectories becomes less significant as time increases.