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KE-19 - Managing GIS&T Operations and Infrastructure

This article discusses the key role of effective management practices to derive expected benefits from the infrastructure and operations of enterprise GIS, including needs assessment, data evaluation and management, and stakeholder involvement. It outlines management factors related to an emerging application of enterprise GIS.  How to configure GIS infrastructure and operations to support enterprise business needs is the focus. When appropriate, additional information is provided for programs, projects, and activities specifically relevant for equity and social justice.

DA-45 - GIS&T in Business

Geographic Information Systems and Technology are utilized extensively in the business sector and have become a strategic element for competition and partnering.  Although the traditional digital map layers and tables remain at the core of business GIS, the spatial architecture in firms now includes location analytics, location intelligence, AI, machine learning, imagery, social media linkages.  Cloud-based solutions provide platform flexibility, centralized data, and potential to roll out user-friendly webGIS across large segments of business users and customers. GIS is well suited to the digital transformations that are essential for firms, large and small.  With these advances, GIS has become prominent and its function has moved upwards in companies’ organizational hierarchies, with enterprise GIS even being recognized in the C-suite.  UPS is an example in which GIS is now a critical corporate competitive factor. In spite of these successes, a gap remains in the supply of skilled spatial workforce for companies. Business schools can contribute by changing by school leadership “getting it” about spatial, bringing GIS into the mainstream curricula, developing training for business faculty in teaching, conducting research in location analytics, and populating student body and alumni base with knowledge and enthusiasm for spatial thinking and management.

AM-23 - Local Measures of Spatial Association

Local measures of spatial association are statistics used to detect variations of a variable of interest across space when the spatial relationship of the variable is not constant across the study region, known as spatial non-stationarity or spatial heterogeneity. Unlike global measures that summarize the overall spatial autocorrelation of the study area in one single value, local measures of spatial association identify local clusters (observations nearby have similar attribute values) or spatial outliers (observations nearby have different attribute values). Like global measures, local indicators of spatial association (LISA), including local Moran’s I and local Geary’s C, incorporate both spatial proximity and attribute similarity. Getis-Ord Gi*another popular local statistic, identifies spatial clusters at various significance levels, known as hot spots (unusually high values) and cold spots (unusually low values). This so-called “hot spot analysis” has been extended to examine spatiotemporal trends in data. Bivariate local Moran’s I describes the statistical relationship between one variable at a location and a spatially lagged second variable at neighboring locations, and geographically weighted regression (GWR) allows regression coefficients to vary at each observation location. Visualization of local measures of spatial association is critical, allowing researchers of various disciplines to easily identify local pockets of interest for future examination.

PD-10 - Natural Language Processing in GIScience Applications

Natural Language Processing (NLP) has experienced explosive growth in recent years. While the field has been around for decades, recent advances in NLP techniques as well as advanced computational resources have re-engaged academics, industry, and the general public. The field of Geographic Information Science has played a small but important role in the growth of this domain. Combining NLP techniques with existing geographic methodologies and knowledge has contributed substantially to many geospatial applications currently in use today. In this entry, we provide an overview of current application areas for natural language processing in GIScience. We provide some examples and discuss some of the challenges in this area.

GS-28 - GIS&T and Community Engagement

URISA’s GISCorps is a case study in community engagement by members of the GIS&T community, whether for purposes of community service or service learning. Since 2004, GISCorps volunteers have contributed their GIS&T expertise to organizations and communities in need all over the world. In doing so, volunteers make a positive difference to the broader community while gaining experience, developing skills, and expanding professional networks.

AM-66 - Watersheds and Drainage Networks

This topic is an overview of basic concepts about how the distribution of water on the Earth, with specific regard to watersheds, stream and river networks, and waterbodies are represented by geographic data. The flowing and non-flowing bodies of water on the earth’s surface vary in extent largely due to seasonal and annual changes in climate and precipitation. Consequently, modeling the detailed representation of surface water using geographic information is important. The area of land that collects surface runoff and other flowing water and drains to a common outlet location defines a watershed. Terrain and surface features can be naturally divided into watersheds of various sizes. Drainage networks are important data structures for modeling the distribution and movement of surface water over the terrain.  Numerous tools and methods exist to extract drainage networks and watersheds from digital elevation models (DEMs). The cartographic representations of surface water are referred to as hydrographic features and consist of a snapshot at a specific time. Hydrographic features can be assigned general feature types, such as lake, pond, river, and ocean. Hydrographic features can be stored, maintained, and distributed for use through vector geospatial databases, such as the National Hydrography Dataset (NHD) for the United States.

CP-08 - Spatial Cloud Computing

The scientific and engineering advancements in the 21st century pose grand computing challenges in managing big data, using complex algorithms to extract information and knowledge from big data, and simulating complex and dynamic physical and social phenomena. Cloud computing emerged as new computing model with the potential to address these computing challenges. This entry first introduces the concept, features and service models of cloud computing. Next, the ideas of generalized architecture and service models of spatial cloud computing are then elaborated to identify the characteristics, components, development and applications of spatial cloud computing for geospatial sciences. 

FC-10 - GIS Data Properties

Data properties are characteristics of GIS attribute systems and values whose design and format impacts analytical and computational processing.  Geospatial data are expressed at conceptual, logical, and physical levels of database abstraction intended to represent geographical information. The appropriate design of attribute systems and selection of properties should be logically consistent and support appropriate scales of measurement for representation and analysis. Geospatial concepts such as object-field views and dimensional space for relating objects and qualities form data models based on a geographic matrix and feature geometry. Three GIS approaches and their attribute system design are described: tessellations, vectors, and graphs.

FC-14 - Directional Operations

In the same manner as distance, direction plays an equally important role in GIS. This article first summarizes different ways of measuring direction, either quantitatively or qualitatively. Formulas and examples are provided. In the following discussion, fundamental differences between distance and direction in describing spatial relations is examined; properties of angles are emphasized in the context of GIS; and the classification of both cardinal and projective direction is illustrated. With a focus on quantitative operations, various directional operations are categorized and elaborated based on factors such as the underlying data model (vector or raster) and whether direction effect is explicitly or implicitly embedded in the data.

FC-32 - Semantic Information Elicitation

The past few decades have been characterized by an exponential growth of digital information resources. A considerable amount of this information is semi-structured, such as XML files and metadata records and unstructured, such as scientific reports, news articles, and historical archives. These resources include a wealth of latent knowledge in a form mainly intended for human use. Semantic information elicitation refers to a set of related processes: semantic information extraction, linking, and annotation that aim to make this knowledge explicit to help computer systems make sense of the content and support ontology construction, information organization, and knowledge discovery.

In the context of GIScience research, semantic information extraction aims at processing unstructured and semi-structured resources and identifying specific types of information: places, events, topics, geospatial concepts, and relations. These may be further linked to ontologies and knowledge bases to enrich the original unstructured content with well-defined meaning, provide access to information not explicit in the original sources, and support semantic annotation and search. Semantic analysis and visualization techniques are further employed to explore aspects latent in these sources such as the historical evolution of cities, the progression of phenomena and events and people’s perception of places and landscapes.

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