Convergence Curriculum for Geospatial Data Science

Convergence problems are hard. What if scientists and decision makers were ready for the challenge? The Convergence Curriculum for Geospatial Data Science is an integrative framework to prepare next-generation and current-generation students, scholars, and professionals to build the necessary knowledge, skills, and competencies to tackle convergent problems without requiring a series of 15-week courses. The multi-tiered curriculum starts with 5 foundational knowledge threads to establish a common basis for individuals coming from diverse backgrounds. Individuals begin to integrate skills, knowledge, methods, and technologies as they move up through Knowledge Connections and Knowledge Frames. The pinnacle of the curriculum is Knowledge Convergence, which combines the previous competencies with pre-existing domain knowledge. Each component in the curriculum can be exposed to individuals at varying depths: 3 sentences, 3 slides, a 3 hour module, or a 3 week unit. This configuration allows individuals to adapt their learning experience to match their own learning pathway.

This draft curriculum will continue to be developed, tested, and evaluated. Stay tuned for releases of content early in 2023.

 

 

Foundational Knowledge Threads (selected examples)

Ethics

Ethics is, first, about how becoming aware of what moral values are at play in a particular situation, and second, about how to choose actions that best promote pursuit of these moral values. For instance, the question of how should Twitter address hate speech targeting specific groups of people on their platform involves many, sometimes conflicting, values. Banning users who promote hate speech can improve the welfare of people who would be hurt by hate speech and may help counter discrimination, but will also impinge on the freedom of speech of those who are banned.

Credit: Peter Darch

Geospatial

Geospatial knowledge is indicated by or related to a geographic location, in addition to any other characteristics or information. Geospatial knowledge is important because all natural, environmental, social, and cultural phenomena are affected by where the events or features occur and by their spatial relations with neighboring phenomena. Examples of geospatial knowledge and its data include stream flow or air temperature measurements, the slope angle of a hillside, the median income in a state’s counties, soil characteristics in a study plot, the geographic extent of a flood plain, or a network of hiking trails.

Credit: Diana Sinton

Computing

Computing is the process of defining computational problems, decomposing them, and then expressing solutions to the problem as computational steps or algorithms that can be carried out by a computer. Once a problem is decomposed into smaller components, and the component solutions are provided, the idea of abstractions helps to tie the pieces together to express solutions to solve larger problems. Modeling a watershed for a flood management system is a computational problem that takes into account digital terrain data, land use and soil data, position and setting of dams, gates, and pumps, data from rain gauges, and much more, to determine water levels and flows at any given point in the watershed. 

Credit: Giri Narasimhan

Visualization

Visualization is the process of transforming data into insight as a viewer extracts meaning from a graphic representation. Visualization is an iterative, multi-stage process used to identify subtle patterns and relationships that exist in data. As a skill, visualization enables a deeper understanding of data, and as a tool, visualization facilitates making sense of data.

Credit: Vetria Byrd

 

Knowledge Connections (selected examples)

Geoethics

While ethics deal with how people act towards themselves and one another, geoethics deals with how people (through their activities) intersect the Earth. It helps us all grapple with how posting pictures online could compromise spatial privacy through potentially giving away the location of the owner and their whereabouts. For example, the site https://iknowwhereyourcatlives.com hosts geotagged images of cats, which have been uploaded to social media platforms such as Flickr, Instagram, and Twitpic, while the website identifies the location of cats, they also give away the location of the owner, which can be a threat to their spatial privacy.

Credit: Jayakrishnan Ajayakumar

 

Computational Statistics

Computational statistics is situated at the intersection of computer science and statistics. “One can think of Computational statistics as aiming at the design of algorithm for implementing statistical methods on computers including the ones unthinkable before the computer age (e.g. Bootstrap, Simulation), as well as to cope with analytically intractable problems.” The suite of methods such as Monte Carlo simulations and Artificial Neural Networks have provided scientists new tools and techniques to examine diverse phenomena.
Quote Credit: Carlo Lauro in “Computational Statistics or Statistical Computing, is that the question.”
 

Geovisualization

Geovisualization (aka geographic visualization) refers to a set of tools and techniques supporting geospatial communication and information analysis through the use of interactive maps. The visualization of real or simulated 2D or 3D geographical information. Computer-based methods for mapping and visual presentation of geographical and social phenomena

Credit: https://www.igi-global.com/dictionary/communicating-geoinformation-effectively-virtual-city/12162

 

Interactive Visualization

Interactive visualization is the ability to show different layers of a graphic map/visualization, to zoom in or out, and to change the symbology. Enables the exploration of data via the manipulation of chart images, with color, size, etc. Enables users to analyze data by interacting with the visual representation of it.
Credit: https://www.gartner.com/en/information-technology/glossary/interactive-visualization

 

Knowledge Frames (selected examples)

GeoAI (Geospatial Artificial Intelligence)

GeoAI is an interdisciplinary field of geography and artificial intelligence. Most of today’s AI models are developed based on a training dataset, and as a result, they naturally inherit the potential bias in the data. In geographic research, training data are often collected from a certain geographic area, and consequently, it can be difficult for a model trained using the data from one geographic area to perform well on the data from other areas. An important direction, therefore, is to improve model architectures (or the training process) so that the obtained GeoAI models can be transferred across different geographic areas. 

Credit: Hu, Y. et al. in https://gistbok.ucgis.org/bok-topics/artificial-intelligence-approaches
 

Domain Knowledge (selected examples)

Hydrology

Hydrology is the study of occurrence, distribution and movement of water on earth’s surface, sub-surface and atmosphere. Changing climate and human actions such as urbanization all affect the hydrologic system. Warmer climate is causing more frequent and intense storms, which is creating more surface runoff that is overburdening the water handling capacity of natural and engineered systems.
Credit: Venkatesh Merwade

Knowledge Convergence

Aging Dams

The potential for the failure of aging dams requires an understanding of how changing climate affects the chance of high precipitation events that could lead to the failure by overtopping of the dams. It requires a knowledge of statistics and machine learning to quantify the risk of dam failure and to predict which areas or critical infrastructure or populations downstream of the dam. Finally, an understanding of the social and economic impacts of dam failure, and of the governance and response planning of the dams is needed through social sciences for proper communication, understanding the chain of events that could lead to actual impacts and for risk mitigation.
Credit: Upmanu Lall