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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.  

KE-31 - Professional Certification

Professional Certification has been a part of the GIS enterprise for over two decades. There are several different certification programs and related activities now in operation within GIS, though there has been much debate over its merits, how it should be done and by whom. 

PD-11 - Python for GIS

Figure 1. PySAL within QGIS Processing Toolbox: Hot-spot analysis of Homicide Rates in Southern US Counties.

 

Python is a popular language for geospatial programming and application development. This entry provides an overview of the different development modes that can be adopted for GIS programming with Python and discusses the history of Python adoption in the GIS community. The different layers of the geospatial development stack in Python are examined giving the reader an understanding of the breadth that Python offers to the GIS developer. Future developments and broader issues related to interoperability and programming ecosystems are identified.

CV-18 - Representing Uncertainty

Using geospatial data involves numerous uncertainties stemming from various sources such as inaccurate or erroneous measurements, inherent ambiguity of the described phenomena, or subjectivity of human interpretation. If the uncertain nature of the data is not represented, ill-informed interpretations and decisions can be the consequence. Accordingly, there has been significant research activity describing and visualizing uncertainty in data rather than ignoring it. Multiple typologies have been proposed to identify and quantify relevant types of uncertainty and a multitude of techniques to visualize uncertainty have been developed. However, the use of such techniques in practice is still rare because standardized methods and guidelines are few and largely untested. This contribution provides an introduction to the conceptualization and representation of uncertainty in geospatial data, focusing on strategies for the selection of suitable representation and visualization techniques.

AM-68 - Rule Learning for Spatial Data Mining

Recent research has identified rule learning as a promising technique for geographic pattern mining and knowledge discovery to make sense of the big spatial data avalanche (Koperski & Han, 1995; Shekhar et al., 2003). Rules conveying associative implications regarding locations, as well as semantic and spatial characteristics of analyzed spatial features, are especially of interest. This overview considers fundamentals and recent advancements in two approaches applied on spatial data: spatial association rule learning and co-location rule learning.

CV-04 - Scale and Generalization

Scale and generalization are two fundamental, related concepts in geospatial data. Scale has multiple meanings depending on context, both within geographic information science and in other disciplines. Typically it refers to relative proportions between objects in the real world and their representations. Generalization is the act of modifying detail, usually reducing it, in geospatial data. It is often driven by a need to represent data at coarsened resolution, being typically a consequence of reducing representation scale. Multiple computations and graphical modication processes can be used to achieve generalization, each introducing increased abstraction to the data, its symbolization, or both.

FC-15 - Shape

Shape is important in GI Science because the shape of a geographical entity can have far-reaching effects on significant characteristics of that entity. In geography we are mainly concerned with two-dimensional shapes such as the outlines of islands, lakes, and administrative areas, but three-dimensional shapes may become important, for example in the treatment of landforms. Since the attribute of shape has infinitely many degrees of freedom, there can be no single numerical measure such that closely similar shapes are assigned close numerical values. Therefore different shape descriptors have been proposed for different purposes. Although it is generally desirable for a shape descriptor to be scale invariant and rotation invariant, not all proposed descriptors satisfy both these requirements. Some methods by which a shape is described using a single number are described, followed by a discussion of moment-based approaches. It is often useful to represent a complex shape by means of a surrogate shape of simpler form which facilitates storage, manipulation, and comparison between shapes; some examples of commonly used shape surrogates are presented. Another important task is to compare different shapes to determine how similar they are. The article concludes with a discussion of a number of such measures of similarity.

CP-10 - Social Media Analytics

Social media streams have emerged as new sources to support various geospatial applications. However, traditional geospatial tools and systems lack the capacities to process such data streams, which are generated dynamically in extremely large volumes and with versatile contents. Therefore, innovative approaches and frameworks should be developed to detect an emerging event discussed over the social media, understand the extent, consequences of the event, as well as it time-evolving nature, and eventually discover useful patterns. In order to harness social media for geospatial applications, this entry introduces social media analytics technologies for harvesting, managing, mining, analyzing and visualizing the spatial, temporal, text, and network information of social media data.

FC-37 - Spatial Autocorrelation

The scientific term spatial autocorrelation describes Tobler’s first law of geography: everything is related to everything else, but nearby things are more related than distant things. Spatial autocorrelation has a:

  • past characterized by scientists’ non-verbal awareness of it, followed by its formalization;
  • present typified by its dissemination across numerous disciplines, its explication, its visualization, and its extension to non-normal data; and
  • an anticipated future in which it becomes a standard in data analytic computer software packages, as well as a routinely considered feature of space-time data and in spatial optimization practice.

Positive spatial autocorrelation constitutes the focal point of its past and present; one expectation is that negative spatial autocorrelation will become a focal point of its future.

DM-60 - Spatial Data Infrastructures

Spatial data infrastructure (SDI) is the infrastructure that facilitates the discovery, access, management, distribution, reuse, and preservation of digital geospatial resources. These resources may include maps, data, geospatial services, and tools. As cyberinfrastructures, SDIs are similar to other infrastructures, such as water supplies and transportation networks, since they play fundamental roles in many aspects of the society. These roles have become even more significant in today’s big data age, when a large volume of geospatial data and Web services are available. From a technological perspective, SDIs mainly consist of data, hardware, and software. However, a truly functional SDI also needs the efforts of people, supports from organizations, government policies, data and software standards, and many others. In this chapter, we will present the concepts and values of SDIs, as well as a brief history of SDI development in the U.S. We will also discuss the components of a typical SDI, and will specifically focus on three key components: geoportals, metadata, and search functions. Examples of the existing SDI implementations will also be discussed.  

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