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A B C D E F G H I K L M N O P R S T U V W
DA-11 - GIS&T and the Digital Humanities

This entry reviews the use of GIS&T in the digital humanities and in the spatial humanities, highlighting opportunities for interdisciplinary collaborations between GIScientists and humanities scholars, including in history, archeology, and literary studies. Challenges are highlighted as well, including epistemological and ontological differences between the spatial, abstract, and quantitative view of the world of GIS&T and GIScience and the humanities emphasis on place and qualitative methods. The potential of mixed methods to bring together different epistemological perspectives is discussed in this context. Scale is identified as a promising geographical framework for humanities research, both in its metaphorical aspects and as intended in cartography. Examples of the use of GIS&T and GIScience in the humanities are provided, including historical GIS, geohistorical gazetteers, archeology and GIS, and GIS in literary studies. The entry is framed historically, with reference to the work of Bakhtin, Braudel, and Hägerstrand, who are early influencers of the spatial turn in the humanities. Among the research directions briefly explored are the GIS of place, deep maps, and qualitative GIS, which exemplify how the collaboration between GIScience and the humanities can be strengthened.

KE-25 - GIS&T Education and Training

GIS education and training have their roots both in formal educational settings and in professional development.  Methods and approaches for teaching and learning about and with geospatial technologies have evolved in tight connection with the advances in the internet and personal computers.  The adoption and integration of GIS and related geospatial technologies into dozens of academic disciplines has led to a high demand for instruction that is targeted and timely, a combination that is challenging to meet consistently with diverse audiences and in diverse settings. Academic degrees, concentrations, minors, certificates, and numerous other programs abound within formal and informal education.

DA-33 - GIS&T in Urban and Regional Planning

Professionals within the urban and regional planning domain have long utilized GIS&T to better understand cities through mapping urban data, representing new proposals, and conducting modeling and analysis to help address urban problems. These activities include spatial data collection and management, cartography, and a variety of applied spatial analysis techniques. Urban and regional planning has developed the sub-fields of planning support systems and Geodesign, both of which describe a combination of technologies and methods to incorporate GIS&T into collaborative planning contexts. In the coming years, shifting patterns of global urbanization, smart cities, and urban big data present emerging opportunities and challenges for urban planning professionals.

KE-24 - GIS&T Positions and Qualifications

Workforce needs tied to geospatial data continue to evolve.  Along with expansion in the absolute number of geospatial workers employed in the public and private sectors is greater diversity in the fields where their work has become important.  Together, these trends generate demand for new types of educational and professional development programs and opportunities. Colleges and universities have responded by offering structured academic programs ranging from minors and academic certificates to full GIS&T degrees.  Recent efforts also target experienced GIS&T professionals through technical certifications involving software applications and more comprehensive professional certifications designed to recognize knowledge, experience, and expertise.

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.

AM-22 - Global Measures of Spatial Association

Spatial association broadly describes how the locations and values of samples or observations vary across space. Similarity in both the attribute values and locations of observations can be assessed using measures of spatial association based upon the first law of geography. In this entry, we focus on the measures of spatial autocorrelation that assess the degree of similarity between attribute values of nearby observations across the entire study region. These global measures assess spatial relationships with the combination of spatial proximity as captured in the spatial weights matrix and the attribute similarity as captured by variable covariance (i.e. Moran’s I) or squared difference (i.e. Geary’s C). For categorical data, the join count statistic provides a global measure of spatial association. Two visualization approaches for spatial autocorrelation measures include Moran scatterplots and variograms (also known as semi-variograms).

CP-23 - Google Earth Engine

Google Earth Engine (GEE) is a cloud-based platform for planetary scale geospatial data analysis and communication.  By placing more than 17 petabytes of earth science data and the tools needed to access, filter, perform, and export analyses in the same easy to use application, users are able to explore and scale up analyses in both space and time without any of the hassles traditionally encountered with big data analysis.  Constant development and refinement have propelled GEE into one of the most advanced and accessible cloud-based geospatial analysis platforms available, and the near real time data ingestion and interface flexibility means users can go from observation to presentation in a single window.

PD-13 - GPU Programming for GIS Applications

Graphics processing units (GPUs) are massively parallel computing environments with applications in graphics and general purpose programming. This entry describes GPU hardware, application domains, and both graphics and general purpose programming languages.

CP-06 - Graphics Processing Units (GPUs)

Graphics Processing Units (GPUs) represent a state-of-the-art acceleration technology for general-purpose computation. GPUs are based on many-core architecture that can deliver computing performance much higher than desktop computers based on Central Processing Units (CPUs). A typical GPU device may have hundreds or thousands of processing cores that work together for massively parallel computing. Basic hardware architecture and software standards that support the use of GPUs for general-purpose computation are illustrated by focusing on Nvidia GPUs and its software framework: CUDA. Many-core GPUs can be leveraged for the acceleration of spatial problem-solving.  

DC-19 - Ground Verification and Accuracy Assessment

Spatial products such as maps of land cover, soil type, wildfire, glaciers, and surface water have become increasingly available and used in science and policy decisions.  These maps are not without error, and it is critical that a description of quality accompany each product.  In the case of a thematic map, one aspect of quality is obtained by conducting a spatially explicit accuracy assessment in which the map class and reference class are compared on a per spatial unit basis (e.g., per 30m x 30m pixel).  The outcome of an accuracy assessment is a description of quality of the end-product map, in contrast to conducting an evaluation of map quality as part of the map production process.  The accuracy results can be used to decide if the map is of adequate quality for an intended application, as input to uncertainty analyses, and as information to improve future map products.

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