Other GIS databases provide national, state, and local boundaries paths of waterways and locations and extents of lakes and boundaries of forests. GIS databases also provide geolocated access to names, addresses, and uses, and information about roads, bridges, buildings, and other urban features. Tax assessment records and other geolocated records provide information about the uses of individual sectors of urban geography. There are photographs at 1M resolution or better that cover most major cities, with insets at even higher resolution often available. Higher-resolution aerial or satellite imagery for selected areas can be obtained. These sources also provide multispectral imagery at similar resolutions that distinguishes land use, vegetation cover, soil type, urban areas, and other elements. Satellite imagery and elevation data at 30 M resolution are readily available for most of the Earth via Landsat and other sources. Geospatial data are growing in diversity and size. WILLIAM RIBARSKY, in Visualization Handbook, 2005 23.1 Introduction
SPACIAL MEDIA META DATA HOW TO
Finally, the article explains how to optimize metadata and spatial data infrastructure strategy for a successful and sustainable system as well as highlights some emerging trends in the geospatial and general information technology fields that will likely impact future use of these concepts. The focus for the spatial data infrastructure is discoverability and dissemination of geospatial data. The article then builds on the foundation of good metadata to describe the components of a spatial data infrastructure and how each part is designed and integrated. Specific guidance is provided in the text for development of metadata requirements, use of metadata standards, and implementing best practices and automation in creation of metadata. This article describes the mechanism for describing and organizing geospatial data through the use of metadata as the descriptive element and spatial data infrastructure as the organizational framework. Geospatial data is most useful when it can be discovered, shared, and used. Scott Simmons, in Comprehensive Geographic Information Systems, 2018 Abstract We then present two specialized case studies to illustrate the use of geospatial reasoning with open data: (1) the use of fuzzy reasoning for map buffering and (2) the automated learning of nonclassical geospatial ontologies. We begin by describing specific aspects of the open geospatial data environment as background, and then we discuss a number of different types of reasoning that have been applied to geospatial data, including classical reasoning and probabilistic, fuzzy, rough, and heuristic reasoning approaches. We define geospatial reasoning as both reasoning about the location of objects on the earth (e.g., relating to inference of spatial relationships) and reasoning about geospatial data (e.g., relating to the attributes of data that is geospatial in nature). In this chapter, we discuss the ways in which geospatial reasoning has been applied to open data. The development and use of open standards within the geospatial community have been heavily supported because of the wide range of uses to which geospatial data can be applied, and because of the large numbers of agencies both globally and locally that are involved in collecting such data. This means that it can be accessed freely by users, and is made available through open standards. For this reason, whether collected by public or private organizations, large amounts of geospatial data are available as open data. For example, roads, localities, water bodies, and public amenities are useful as reference information for a number of purposes. Much geospatial data is of general interest to a wide range of users. Geospatial data combines location information (usually coordinates on the earth), attribute information (the characteristics of the object, event, or phenomena concerned), and often also temporal information (the time or life span at which the location and attributes exist).
The location may be static in the short-term (e.g., the location of a road, an earthquake event, children living in poverty), or dynamic (e.g., a moving vehicle or pedestrian, the spread of an infectious disease). Geospatial data is data about objects, events, or phenomena that have a location on the surface of the earth.
Kristin Stock, Hans Guesgen, in Automating Open Source Intelligence, 2016 Introduction