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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.

AM-62 - Approaches to point, line, and area generalization
  • Describe the basic forms of generalization used in applications in addition to cartography (e.g., selection, simplification)
  • Explain why areal generalization is more difficult than line simplification
  • Explain the logic of the Douglas-Poiker line simplification algorithm
  • Explain the pitfalls of using data generalized for small scale display in a large scale application
  • Design an experiment that allows one to evaluate the effect of traditional approaches of cartographic generalization on the quality of digital data sets created from analog originals
  • Evaluate various line simplification algorithms by their usefulness in different applications
  • Discuss the possible effects on topological integrity of generalizing data sets
CV-40 - Mobile Maps and Responsive Design

Geographic information increasingly is produced and consumed on mobile devices. The rise of mobile mapping is challenging traditional design conventions in research, industry, and education, and cartographers and GIScientists now need to accommodate this mobile context. This entry introduces emerging design considerations for mobile maps. First, the technical enablements and constraints that make mobile devices unique are described, including Global Positioning System (GPS) receivers and other sensors, reduced screensize and resolution, reduced processing power and memory capacity, less reliable data connectivity, reduced bandwidth, and physical mobility through variable environmental conditions. Scholarly influences on mobile mapping also are reviewed, including location-based services, adaptive cartography, volunteered geographic information, and locational privacy. Next, two strategies for creating mobile maps are introduced—mobile apps installed onto mobile operating systems versus responsive web maps that work on mobile and nonmobile devices—and core concepts of responsive web design are reviewed, including fluid grids, media queries, breakpoints, and frameworks. Finally, emerging design recommendations for mobile maps are summarized, with representation design adaptations needed to account for reduced screensizes and bandwidth and interaction design adaptations needed to account for multi-touch interaction and post-WIMP interfaces.

CV-05 - Statistical Mapping (Enumeration, Normalization, Classification)

Proper communication of spatial distributions, trends, and patterns in data is an important component of a cartographers work. Geospatial data is often large and complex, and due to inherent limitations of size, scalability, and sensitivity, cartographers are often required to work with data that is abstracted, aggregated, or simplified from its original form. Working with data in this manner serves to clarify cartographic messages, expedite design decisions, and assist in developing narratives, but it also introduces a degree of abstraction and subjectivity in the map that can make it easy to infer false messages from the data and ultimately can mislead map readers. This entry introduces the core topics of statistical mapping around cartography. First, we define enumeration and the aggregation of data to units of enumeration. Next, we introduce the importance of data normalization (or standardization) to more truthfully communicate cartographically and, lastly, discuss common methods of data classification and how cartographers bin data into groups that simplify communication.

CV-08 - Symbolization and the Visual Variables

Maps communicate information about the world by using symbols to represent specific ideas or concepts. The relationship between a map symbol and the information that symbol represents must be clear and easily interpreted. The symbol design process requires first an understanding of the underlying nature of the data to be mapped (e.g., its spatial dimensions and level of measurement), then the selection of symbols that suggest those data attributes. Cartographers developed the visual variable system, a graphic vocabulary, to express these relationships on maps. Map readers respond to the visual variable system in predictable ways, enabling mapmakers to design map symbols for most types of information with a high degree of reliability.

DM-65 - Spatial Data Uncertainty

Although spatial data users may not be aware of the inherent uncertainty in all the datasets they use, it is critical to evaluate data quality in order to understand the validity and limitations of any conclusions based on spatial data. Spatial data uncertainty is inevitable as all representations of the real world are imperfect. This topic presents the importance of understanding spatial data uncertainty and discusses major methods and models to communicate, represent, and quantify positional and attribute uncertainty in spatial data, including both analytical and simulation approaches. Geo-semantic uncertainty that involves vague geographic concepts and classes is also addressed from the perspectives of fuzzy-set approaches and cognitive experiments. Potential methods that can be implemented to assess the quality of large volumes of crowd-sourced geographic data are also discussed. Finally, this topic ends with future directions to further research on spatial data quality and uncertainty.

CV-03 - Vector Formats and Sources
  • List the data required to explore a specified problem
  • Discuss the extent, classification, and currency of government data sources and their influence on mapping
  • List the data required to compile a map that conveys a specified message
  • Discuss the issue of conflation of data from different sources or for different uses as it relates to mapping
  • Describe a situation in which it would be acceptable to use smaller-scale data sources for compilation to compile a larger scale map
  • Describe the copyright issues involved in various cartographic source materials
  • Explain how data acquired from primary sources, such as satellite imagery and GPS, differ from data compiled from maps, such as DLGs
  • Explain how digital data compiled from map sources influences how subsidiary maps are compiled and used
  • Explain how geographic names databases (i.e., gazetteer) are used for mapping
  • Explain how the inherent properties of digital data, such as Digital Elevation Models, influence how maps can be compiled from them
  • Identify the types of attributes that will be required to map a particular distribution for selected geographic features
  • Determine the standard scale of compilation of government data sources
  • Assess the data quality of a source dataset for appropriateness for a given mapping task, including an evaluation of the data resolution, extent, currency or date of compilation, and level of generalization in the attribute classification
  • Compile a map using at least three data sources
AM-10 - Spatial Interaction

Spatial interaction (SI) is a fundamental concept in the GIScience literature, and may be defined in numerous ways. SI often describes the "flow" of individuals, commodities, capital, and information over (geographic) space resulting from a decision process. Alternatively, SI is sometimes used to refer to the influence of spatial proximity of places on the intensity of relations between those places. SI modeling as a separate research endeavor developed out of a need to mathematically model and understand the underlying determinants of these flows/influences. Proponents of SI modeling include economic geographers, regional scientists, and regional planners, as well as climate scientists, physicists, animal ecologists, and even some biophysical/environmental researchers. Originally developed from theories of interacting particles and gravitational forces in physics, SI modeling has developed through a series of refinements in terms of functional form, conceptual representations of distances, as well as a range of analytically rigorous technical improvements.
 

CV-13 - User Interface and User Experience (UI/UX) Design

Advances in personal computing and information technologies have fundamentally transformed how maps are produced and consumed, as many maps today are highly interactive and delivered online or through mobile devices. Accordingly, we need to consider interaction as a fundamental complement to representation in cartography and visualization. UI (user interface) / UX (user experience) describes a set of concepts, guidelines, and workflows for critically thinking about the design and use of an interactive product, map or otherwise. This entry introduces core concepts from UI/UX design important to cartography and visualization, focusing on issues related to visual design. First, a fundamental distinction is made between the use of an interface as a tool and the broader experience of an interaction, a distinction that separates UI design and UX design. Norman’s stages of interaction framework then is summarized as a guiding model for understanding the user experience with interactive maps, noting how different UX design solutions can be applied to breakdowns at different stages of the interaction. Finally, three dimensions of UI design are described: the fundamental interaction operators that form the basic building blocks of an interface, interface styles that implement these operator primitives, and recommendations for visual design of an interface.

CV-19 - Big Data Visualization

As new information and communication technologies have altered so many aspects of our daily lives over the past decades, they have simultaneously stimulated a shift in the types of data that we collect, produce, and analyze. Together, this changing data landscape is often referred to as "big data." Big data is distinguished from "small data" not only by its high volume but also by the velocity, variety, exhaustivity, resolution, relationality, and flexibility of the datasets. This entry discusses the visualization of big spatial datasets. As many such datasets contain geographic attributes or are situated and produced within geographic space, cartography takes on a pivotal role in big data visualization. Visualization of big data is frequently and effectively used to communicate and present information, but it is in making sense of big data – generating new insights and knowledge – that visualization is becoming an indispensable tool, making cartography vital to understanding geographic big data. Although visualization of big data presents several challenges, human experts can use visualization in general, and cartography in particular, aided by interfaces and software designed for this purpose, to effectively explore and analyze big data.

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