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DC-16 - Nature of Multispectral Image Data

A multispectral image comprises a set of co-registered images, each of which captures the spatially varying brightness of a scene in a specific spectral band, or electromagnetic wavelength region. An image is structured as a raster, or grid, of pixels. Multispectral images are used as a visual backdrop for other GIS layers, to provide information that is manually interpreted from images, or to generate automatically-derived thematic layers, for example through classification. The scale of multispectral images has spatial, spectral, radiometric and temporal components. Each component of scale has two aspects, extent (or coverage), and grain (or resolution). The brightness variations of an image are determined by factors that include (1) illumination variations and effects of the atmosphere, (2) spectral properties of materials in the scene (particularly reflectance, but also, depending on the wavelength, emittance), (3) spectral bands of the sensor, and (4) display options, such as the contrast stretch, which affect the visualization of the image. This topic review focuses primarily on optical remote sensing in the visible, near infrared and shortwave infrared parts of the electromagnetic spectrum, with an emphasis on satellite imagery.  

AM-44 - Modelling Accessibility

Modelling accessibility involves combining ideas about destinations, distance, time, and impedances to measure the relative difficulty an individual or aggregate region faces when attempting to reach a facility, service, or resource. In its simplest form, modelling accessibility is about quantifying movement opportunity. Crucial to modelling accessibility is the calculation of the distance, time, or cost distance between two (or more) locations, which is an operation that geographic information systems (GIS) have been designed to accomplish. Measures and models of accessibility thus draw heavily on the algorithms embedded in a GIS and represent one of the key applied areas of GIS&T.

GS-11 - Professional and Practical Ethics of GIS&T

Geospatial technologies are often and rightly described as “powerful.” With power comes the ability to cause harm – intentionally or unintentionally - as well as to do good. In the context of GIS&T, Practical Ethics is the set of knowledge, skills and abilities needed to make reasoned decisions in light of the risks posed by geospatial technologies and methods in a wide variety of use cases. Ethics have been considered from different viewpoints in the GIS&T field. A practitioner's perspective may be based on a combination of "ordinary morality," institutional ethics policies, and professional ethics codes. By contrast, an academic scholar's perspective may be grounded in social or critical theory. What these perspectives have in common is reliance on reason to respond with integrity to ethical challenges. This entry focuses on the special obligations of GIS professionals, and on a method that educators can use to help students develop moral reasoning skills that GIS professionals need. The important related issues of Critical GIS and Spatial Law and Policy are to be considered elsewhere.  

AM-32 - Spatial Autoregressive Models

Regression analysis is a statistical technique commonly used in the social and physical sciences to model relationships between variables. To make unbiased, consistent, and efficient inferences about real-world relationships a researcher using regression analysis relies on a set of assumptions about the process generating the data used in the analysis and the errors produced by the model. Several of these assumptions are frequently violated when the real-world process generating the data used in the regression analysis is spatially structured, which creates dependence among the observations and spatial structure in the model errors. To avoid the confounding effects of spatial dependence, spatial autoregression models include spatial structures that specify the relationships between observations and their neighbors. These structures are most commonly specified using a weights matrix that can take many forms and be applied to different components of the spatial autoregressive model. Properly specified, including these structures in the regression analysis can account for the effects of spatial dependence on the estimates of the model and allow researchers to make reliable inferences. While spatial autoregressive models are commonly used in spatial econometric applications, they have wide applicability for modeling spatially dependent data.

DM-90 - Hydrographic Geospatial Data Standards

Coastal nations, through their dedicated Hydrographic Offices (HOs), have the obligation to provide nautical charts for the waters of national jurisdiction in support of safe maritime navigation. Accurate and reliable charts are essential to seafarers whether for commerce, defense, fishing, or recreation. Since navigation can be an international activity, mariners often use charts published from different national HOs. Standardization of data collection and processing, chart feature generalization methods, text, symbology, and output validation becomes essential in providing mariners with consistent and uniform products regardless of the region or the producing nation. Besides navigation, nautical charts contain information about the seabed and the coastal environment useful in other domains such as dredging, oceanography, geology, coastal modelling, defense, and coastal zone management. The standardization of hydrographic and nautical charting activities is achieved through various publications issued by the International Hydrographic Organization (IHO). This chapter discusses the purpose and importance of nautical charts, the establishment and role of the IHO in coordinating HOs globally, the existing hydrographic geospatial data standards, as well as those under development based on the new S-100 Universal Hydrographic Data Model.

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.

DM-03 - Relational DBMS and their Spatial Extensions

The relational Database Management System (DBMS) is widely used in modern business systems. Entities and relationships from a data model are presented as relational tables. To store data in a relational database, a relation schema should be defined to specify the design and structure of relations. The schema design generally uses database normalization to reduce data redundancy and maintain data integrity. Users can retrieve and manage data in a relational database using Structured Query Language (SQL). To make spatial data fit the relational model, spatial vector geometry or raster data type can be customized by extending basic data types in relational databases. This further helps derive the so-called spatial object-relational DBMS, by manipulating vector geometry and/or raster data types as spatial objects using SQL queries. The performance of queries is improved by adding spatial indexes in relational databases.

CP-04 - Artificial Intelligence Tools and Platforms for GIS

Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.

GS-20 - Aggregation of Spatial Entities and Legislative Redistricting

The partitioning of space is an essential consideration for the efficient allocation of resources. In the United States and many other countries, this parcelization of sub-regions for political and legislative purposes results in what is referred to as districts. A district is an aggregation of smaller, spatially bound units, along with their statistical properties, into larger spatially-bound units. When a district has the primary purpose of representation, individuals who reside within that district make up a constituency. Redistricting is often required as populations of constituents shift over time or resources that service areas change. Administrative challenges with creating districts have been greatly aided by increasing utilization of GIS. However, with these advances in geospatial methods, political disputes with the way in which districts increasingly snare the process in legal battles often centered on the topic of gerrymandering. This chapter focuses on the redistricting process within the United States and how the aggregation of representative spatial entities presents a mix of political, technical and legal challenges.

DC-26 - Remote Sensing Platforms

Remote sensing means acquiring and measuring information about an object or phenomenon via a device that is not in physical or direct contact with what is being studied (Colwell, 1983).To collect remotely sensed data, a platform – an instrument that carries a remote sensing sensor – is deployed. From the mid 1800’s to the early 1900’s, various platforms such as balloons, kites, and pigeons carried mounted cameras to collect visual data of the world below. Today, aircraft (both manned and unmanned) and satellites collect the majority of remotely sensed data. The sensors typically deployed on these platforms include film and digital cameras, light-detection and ranging (LiDAR) systems, synthetic aperture radar (SAR) systems, and multi-spectral and hyper-spectral scanners. Many of these instruments can be mounted on land-based platforms, such as vans, trucks, tractors, and tanks. In this chapter, we will explore the different types of platforms and their resulting remote sensing applications.