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A B C D E F G H I J K L M N O P R S T U V W
FC-31 - Academic Developments of GIS&T in English-speaking Countries: a Partial History

The constellation of science and technology that is now considered a unit (Geographic Information Science and Technology – GIS&T) has emerged from many source disciplines through many divergent and convergent pasts in different times and places. This narrative limits itself to the perspective of the English-speaking community, leaving other regions for a separate chapter As in the case of many technical developments in the second half of the twentieth century, academic institutions played a key (though far from exclusive) role in innovation and risk-taking. In a number of locations, academic innovators tried out new technology for handling geographic information, beginning as early as the 1960s. Three institutions (University of Washington, Laboratory for Computer Graphics – Harvard University, and Experimental Cartography Unit – Royal College of Art (UK)) deserve particular treatment as examples of the early innovation process. Their innovations may look crude by current standards, but they laid some groundwork for later developments. Academic institutions played a key role in innovation over the past decades, but the positioning of that role has shifted as first government, then commercial sectors have taken the lead in certain aspects of GIS&T. Current pressures on the academic sector may act to reduce this role.

FC-18 - Adjacency and connectivity
  • List different ways connectivity can be determined in a raster and in a polygon dataset
  • Explain the nine-intersection model for spatial relationships
  • Demonstrate how adjacency and connectivity can be recorded in matrices
  • Calculate various measures of adjacency in a polygon dataset
  • Create a matrix describing the pattern of adjacency in a set of planar enforced polygons
  • Describe real world applications where adjacency and connectivity are a critical component of analysis
DC-12 - Aerial photography image interpretation
  • Use photo interpretation keys to interpret features on aerial photographs
  • Calculate the nominal scale of a vertical aerial image
  • Calculate heights and areas of objects and distances between objects shown in a vertical aerial image
  • Produce a map of land use/land cover classes using a vertical aerial image
  • Describe the elements of image interpretation
DC-10 - Aerial Photography: History and Georeferencing

In 1903, Julius Neubranner, a photography enthusiast, designed and patented a breast-mounted aerial camera for carrier pigeons. Weighing only 70 grams, the camera took automatic exposures at 30-second intervals along the flight line flown by the bird. Although faster than balloons, they were not always reliable in following their flight paths. Today the pigeon corps has been replaced by unmanned aerial vehicles, but aerial photography continues to be an important source of data for use in a wide range of geospatial applications. Processing of the imagery to remove various types of distortion is a necessary step before the images can be georeferenced and used for mapping purposes. 

AM-79 - Agent-based Modeling

Agent-based models are dynamic simulation models that provide insight into complex geographic systems. Individuals are represented as agents that are encoded with goal-seeking objectives and decision-making behaviors to facilitate their movement through or changes to their surrounding environment. The collection of localized interactions amongst agents and their environment over time leads to emergent system-level spatial patterns. In this sense, agent-based models belong to a class of bottom-up simulation models that focus on how processes unfold over time in ways that produce interesting, and at times surprising, patterns that we observe in the real world.

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.

AM-97 - An Introduction to Spatial Data Mining

The goal of spatial data mining is to discover potentially useful, interesting, and non-trivial patterns from spatial data-sets (e.g., GPS trajectory of smartphones). Spatial data mining is societally important having applications in public health, public safety, climate science, etc. For example, in epidemiology, spatial data mining helps to nd areas with a high concentration of disease incidents to manage disease outbreaks. Computational methods are needed to discover spatial patterns since the volume and velocity of spatial data exceed the ability of human experts to analyze it. Spatial data has unique characteristics like spatial autocorrelation and spatial heterogeneity which violate the i.i.d (Independent and Identically Distributed) assumption of traditional statistic and data mining methods. Therefore, using traditional methods may miss patterns or may yield spurious patterns, which are costly in societal applications. Further, there are additional challenges such as MAUP (Modiable Areal Unit Problem) as illustrated by a recent court case debating gerrymandering in elections. In this article, we discuss tools and computational methods of spatial data mining, focusing on the primary spatial pattern families: hotspot detection, collocation detection, spatial prediction, and spatial outlier detection. Hotspot detection methods use domain information to accurately model more active and high-density areas. Collocation detection methods find objects whose instances are in proximity to each other in a location. Spatial prediction approaches explicitly model the neighborhood relationship of locations to predict target variables from input features. Finally, spatial outlier detection methods find data that differ from their neighbors. Lastly, we describe future research and trends in spatial data mining.

DA-07 - Applications in federal government
  • List and describe the types of data maintained by federal governments
  • Explain how geospatial information might be used in a taking of private property through a government’s claim of its right of eminent domain
  • Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications
  • Explain the concept of a “spatial decision support system”
DA-06 - Applications in state government
  • List and describe the types of data maintained by state governments
  • Explain how geospatial information might be used in a taking of private property through a government’s claim of its right of eminent domain
  • Describe how geospatial data are used and maintained for land use planning, property value assessment, maintenance of public works, and other applications
  • Explain the concept of a “spatial decision support system”
FC-16 - Area and Region
  • List reasons why the area of a polygon calculated in a GIS might not be the same as the real world object it describes
  • Demonstrate how the area of a region calculated from a raster data set will vary by resolution and orientation
  • Outline an algorithm to find the area of a polygon using the coordinates of its vertices
  • Explain how variations in the calculation of area may have real world implications, such as calculating density
  • Delineate regions using properties, spatial relationships, and geospatial technologies
  • Exemplify regions found at different scales
  • Explain the relationship between regions and categories
  • Identify the kinds of phenomena commonly found at the boundaries of regions
  • Explain why general-purpose regions rarely exist
  • Differentiate among different types of regions, including functional, cultural, physical, administrative, and others
  • Compare and contrast the opportunities and pitfalls of using regions to aggregate geographic information (e.g., census data)
  • Use established analysis methods that are based on the concept of region (e.g., landscape ecology)
  • Explain the nature of the Modifiable Areal Unit Problem (MAUP)

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