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AM-84 - Simulation Modeling

Advances in computational capacity have enabled dynamic simulation modeling to become increasingly widespread in scientific research. As opposed to conceptual or physical models, simulation models enable numerical experimentation with alternative parametric assumptions for a given model design. Numerous design choices are made in model development that involve continuous or discrete representations of time and space. Simulation modeling approaches include system dynamics, discrete event simulation, agent-based modeling, and multi-method modeling. The model development process involves a shift from qualitative design to quantitative analysis upon implementation of a model in a computer program or software platform. Upon implementation, model analysis is performed through rigorous experimentation to test how model structure produces simulated patterns of behavior over time and space. Validation of a model through correspondence of simulated results with observed behavior facilitates its use as an analytical tool for evaluating strategies and policies that would alter system behavior.

AM-81 - GIS-Based Computational Modeling

GIS-based computational models are explored. While models vary immensely across disciplines and specialties, the focus is on models that simulate and forecast geographical systems and processes in time and space. The degree and means of integration of the many different models with GIS are covered, and the critical phases of modeling: design, implementation, calibration, sensitivity analysis, validation and error analysis are introduced. The use of models in simulations, an important purpose for implementing models within or outside of GIS, is discussed and the context of scenario-based planning explained. To conclude, a survey of model types is presented, with their application methods and some examples, and the goals of modeling are discussed.

FC-24 - Conceptual Models of Error and Uncertainty

Uncertainty and error are integral parts of science and technology, including GIS&T, as they are of most human endeavors. They are important characteristics of knowledge, which is very seldom perfect. Error and uncertainty both affect our understanding of the present and the past, and our expectations from the future. ‘Uncertainty’ is sometimes used as the umbrella term for a number of related concepts, of which ‘error’ is the most important in GIS and in most other data-intensive fields. Very often, uncertainty is the result of error (or suspected error).  As concepts, both uncertainty and error are complex, each having several different versions, interpretations, and kinds of impacts on the quality of GIS products, and on the uses and decisions that users may make on their basis. This section provides an overview of the kinds of uncertainty and common sources of error in GIS&T, the role of a number of additional related concepts in refining our understanding of different forms of imperfect knowledge, the problems of uncertainty and error in the context of decision-making, especially regarding actions with important future consequences, and some standard as well as more exploratory approaches to handling uncertainties about the future. While uncertainty and error are in general undesirable, they may also point to unsuspected aspects of an issue and thus help generate new insights.

DA-37 - GIS&T and Epidemiology

Location plays an important role in human health. Where we live, work, and spend our time is associated with different exposures, which may influence the risk of developing disease. GIS has been used to answer key research questions in epidemiology, which is the study of the distribution and determinants of disease. These research questions include describing and visualizing spatial patterns of disease and risk factors, exposure modeling of geographically varying environmental variables, and linking georeferenced information to conduct studies testing hypotheses regarding exposure-disease associations. GIS has been particularly instrumental in environmental epidemiology, which focuses on the physical, chemical, biological, social, and economic factors affecting health. Advances in personal exposure monitoring, exposome research, and artificial intelligence are revolutionizing the way GIS can be integrated with epidemiology to study how the environment may impact human health.

DA-16 - GIS&T and Forestry

GIS applications in forestry are as diverse as the subject itself. Many foresters match a common stereotype as loggers and firefighters, but many protect wildlife, manage urban forests, enhance water quality, provide for recreation, and plan for a sustainable future.  A broad range of management goals drives a broad range of spatial methods, from adjacency functions to zonal analysis, from basic field measurements to complex multi-scale modeling. As such, it is impossible to describe the breadth of GIS&T in forestry. This review will cover core ways that geospatial knowledge improves forest management and science, and will focus on supporting core competencies.  

AM-69 - Cellular Automata

Cellular automata (CA) are simple models that can simulate complex processes in both space and time. A CA consists of six defining components: a framework, cells, a neighborhood, rules, initial conditions, and an update sequence. CA models are simple, nominally deterministic yet capable of showing phase changes and emergence, map easily onto the data structures used in geographic information systems, and are easy to implement and understand. This has contributed to their popularity for applications such as measuring land use changes and monitoring disease spread, among many others.

AM-10 - Spatial Interaction

Spatial interaction (SI) is a fundamental concept in the GIScience literature, and may be defined in numerous ways. SI often describes the "flow" of individuals, commodities, capital, and information over (geographic) space resulting from a decision process. Alternatively, SI is sometimes used to refer to the influence of spatial proximity of places on the intensity of relations between those places. SI modeling as a separate research endeavor developed out of a need to mathematically model and understand the underlying determinants of these flows/influences. Proponents of SI modeling include economic geographers, regional scientists, and regional planners, as well as climate scientists, physicists, animal ecologists, and even some biophysical/environmental researchers. Originally developed from theories of interacting particles and gravitational forces in physics, SI modeling has developed through a series of refinements in terms of functional form, conceptual representations of distances, as well as a range of analytically rigorous technical improvements.

DA-46 - Computational Geography

Computational Geography emerged in the 1980s in response to the reductionist limitations of early GIS software, which inhibited deep analyses of rich geographic data. Today, Computational Geography continues to integrate a wide range of domains to facilitate spatial analyses that require computational resources or ontological paradigms beyond that made available in traditional GIS software packages. These include novel approaches for the mass creation of geospatial data, large-scale database design for the effective storage and querying of spatial identifiers (i.e., distributed spatial databases), and methodologies which enable simulations and/or analysis in the context of large-scale, frequently near-real-time, spatially-explicit sources of information. The topics studied within Computational Geography directly enable many of the world’s largest public databases, including Google Maps and Open Street Map (OSM), as well as many modern analytic pipelines designed to study human behavior with the integration of large volumes of location information (e.g., mobile phone data) with other geospatial sources (e.g., satellite imagery).