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CV-11 - Common Thematic Map Types

Thematic maps cover a wide variety of mapping solutions, and include choropleth, proportional symbol, isoline, dot density, dasymetric, and flow maps as well as cartograms, among others. Each thematic map type requires a different data processing method and employs different visual variables, resulting in representations that are either continuous or discrete and smooth or abrupt. As a result, each solution highlights different aspects of the mapped phenomena and shapes the message for the map readers differently. Thematic maps are tools for understanding spatial patterns, and the choice of thematic map type should support this understanding. Therefore, the main consideration when selecting a thematic map type is the purpose of the map and the nature of the underlying spatial patterns.

This entry reviews the common types of thematic maps, describes the visual variables that are applied in them, and provides design considerations for each thematic map type, including their legends. It also provides an overview of the relative strengths and limitations of each thematic map type.

DM-85 - Point, Line, and Area Generalization

Generalization is an important and unavoidable part of making maps because geographic features cannot be represented on a map without undergoing transformation. Maps abstract and portray features using vector (i.e. points, lines and polygons) and raster (i.e pixels) spatial primitives which are usually labeled. These spatial primitives are subjected to further generalization when map scale is changed. Generalization is a contradictory process. On one hand, it alters the look and feel of a map to improve overall user experience especially regarding map reading and interpretive analysis. On the other hand, generalization has documented quality implications and can sacrifice feature detail, dimensions, positions or topological relationships. A variety of techniques are used in generalization and these include selection, simplification, displacement, exaggeration and classification. The techniques are automated through computer algorithms such as Douglas-Peucker and Visvalingam-Whyatt in order to enhance their operational efficiency and create consistent generalization results. As maps are now created easily and quickly, and used widely by both experts and non-experts owing to major advances in IT, it is increasingly important for virtually everyone to appreciate the circumstances, techniques and outcomes of generalizing maps. This is critical to promoting better map design and production as well as socially appropriate uses.

DC-25 - Changes in Geospatial Data Capture Over Time: Part 1, Technological Developments

Geographic Information Systems (GIS) are fueled by geospatial data.  This comprehensive article reviews the evolution of procedures and technologies used to create the data that fostered the explosion of GIS applications. It discusses the need to geographically reference different types of information to establish an integrated computing environment that can address a wide range of questions. This includes the conversion of existing maps and aerial photos into georeferenced digital data.  It covers the advancements in manual digitizing procedures and direct digital data capture. This includes the evolution of software tools used to build accurate data bases. It also discusses the role of satellite based multispectral scanners for Earth observation and how LiDAR has changed the way that we measure and represent the terrain and structures. Other sections deal with building GIS data directly from street addresses and the construction of parcels to support land record systems. It highlights the way Global Positioning Systems (GPS) technology coupled with wireless networks and cloud-based applications have spatially empowered millions of users. This combination of technology has dramatically affected the way individuals search and navigate in their daily lives while enabling citizen scientists to be active participants in the capture of spatial data. For further information on changes to data capture, see Part 2: Implications and Case Studies. 

AM-12 - Cartographic Modeling

Cartographic modeling is an integrated sequence of data processing tasks that organize, combine, analyze and display information to answer a question. Cartographic modeling is effective in GIS environments because they rely heavily upon visualization, making it easy to show input and output layers in map form. In many GIS platforms, the sequence of tasks can be created and modified graphically as well. The modeling is visual, intuitive, and requires some knowledge of GIS commands and data preparation, along with curiosity to answer a particular question about the environment. It does not require programming skill. Cartographic modeling has been used in applications to delineate habitats, to solve network routing problems, to assess risk of storm runoff across digital terrain, and to conserve fragile landscapes. Historical roots emphasize manual and later automated map overlay. Cartographic models can take three forms (descriptive, prescriptive and normative). Stages in cartographic modeling identify criteria that meet an overarching goal; collect data describing each criterion in map form; design a flowchart showing data, GIS operations and parameters; implement the model; and evaluate the solution. A scenario to find a suitable site for biogas energy production walks through each stage in a simple demonstration of mechanics.

KE-19 - Managing GIS&T Operations and Infrastructure

This article discusses the key role of effective management practices to derive expected benefits from the infrastructure and operations of enterprise GIS, including needs assessment, data evaluation and management, and stakeholder involvement. It outlines management factors related to an emerging application of enterprise GIS.  How to configure GIS infrastructure and operations to support enterprise business needs is the focus. When appropriate, additional information is provided for programs, projects, and activities specifically relevant for equity and social justice.

AM-66 - Watersheds and Drainage Networks

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.

FC-09 - Relationships Between Space and Time

Relationships between space and time evoke fundamental questions in the sciences and humanities. Many disciplines, including GIScience, consider that space and time extend in separate dimensions, are interchangeable, and form co-equal parts of a larger thing called space-time.  Our perception of how time operates in relation to space or vice verso influences how we represent space, time, and their relationships in GIS. The chosen representation, furthermore, predisposes what questions we can ask and what approaches we can take for analysis and modeling. There are many ways to think about space, time, and their relationships in GIScience. This article synthesizes five broad categories: (1) Time is independent of space but relates to space by movement and change; (2) Time collaborates with space to probe relationships, explanations, and predictions; (3) Time is spatially constructed and constrained; (4) Time and space are mutually inferable; and (5) Time and space are integrated and co-equal in the formation of flows, events, and processes. Concepts, constructs, or law-like statements arise in each of the categories as examples of how space, time, and their relationships help frame scientific inquiries in GIScience and beyond.

DM-44 - Earth's Shape, Sea Level, and the Geoid

C. F. Gauss set the modern definition of the shape of the Earth, being described as the shape the oceans would adopt if they were entirely unperturbed and, thus, placid—a surface now called the geoid.  This surface cannot be observed directly because the oceans have waves, tides, currents, and other perturbations. Nonetheless, the geoid is the ideal datum for heights, and the science of determining the location of the geoid for practical purposes is the topic of physical geodesy. The geoid is the central concept that ties together what the various kinds of height mean, how they are measured, and how they are inter-related.

AM-86 - Theory of error propagation
  • Describe stochastic error models
  • Exemplify stochastic error models used in GIScience