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.

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.

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.

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.