All Topics

A B C D E F G H I K L M N O P R S T U V W
FC-30 - Private sector origins
  • Identify some of the key commercial activities that provided an impetus for the development of GIS&T
  • Differentiate the dominant industries using geospatial technologies during the 1980s, 1990s, and 2000s
  • Describe the contributions of McHarg and other practitioners in developing geographic analysis methods later incorporated into GIS
  • Evaluate the correspondence between advances in hardware and operating system technology and changes in GIS software
  • Describe the influence of evolving computer hardware and of private sector hardware firms such as IBM on the emerging GIS software industry
  • Discuss the emergence of the GIS software industry in terms of technology evolution and markets served by firms such as ESRI, Intergraph, and ERDAS
KE-02 - Problem definition
  • Recognize the challenges of implementing and using geospatial technologies
  • Create a charter or hypothesis that defines and justifies the mission of a GIS to solve existing problems
  • Define an enterprise GIS in terms of institutional missions and goals
  • Identify geographic tasks for which particular geospatial technologies are not adequate or sufficient
  • Identify what is typically needed to garner support among managers for designing and/or creating a GIS
AM-87 - Problems of currency, source, and scale
  • Describe the problem of conflation associated with aggregation of data collected at different times, from different sources, and to different scales and accuracy requirements
  • Explain how geostatistical techniques might be used to address such problems
DM-70 - Problems of Large Spatial Databases

Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017).  Sources include satellites, aircraft and drone platforms, vehicles, geosocial networking services, mobile devices, and cameras. The problems in processing these data to extract useful information include query, analysis, and visualization. Data mining techniques and machine learning algorithms, such as deep convolutional neural networks, often are used with geospatial big data. The obvious problem is handling the large data volumes, particularly for input and output operations, requiring parallel read and write of the data, as well as high speed computers, disk services, and network transfer speeds. Additional problems of large spatial databases include the variety and heterogeneity of data requiring advanced algorithms to handle different data types and characteristics, and integration with other data. The velocity at which the data are acquired is a challenge, especially using today’s advanced sensors and the Internet of Things that includes millions of devices creating data on short temporal scales of micro seconds to minutes. Finally, the veracity, or truthfulness of large spatial databases is difficult to establish and validate, particularly for all data elements in the database.

FC-26 - Problems of Scale and Zoning

Spatial data are often encoded within a set of spatial units that exhaustively partition a region, where individual level data are aggregated, or continuous data are summarized, over a set of spatial units. Such is the case with census data aggregated to enumeration units for public dissemination. Partitioning schemes can vary by scale, where one partitioning scheme spatially nests within another, or by zoning, where two partitioning schemes have the same number of units but the unit shapes and boundaries differ. The Modifiable Areal Unit Problem (MAUP) refers to the fact the nature of spatial partitioning can affect the interpretation and results of visualization and statistical analysis. Generally, coarser scales of data aggregation tend to have stronger observed statistical associations among variables. The ecological fallacy refers to the assumption that an individual has the same attributes as the aggregate group to which it belongs. Combining spatial data with different partitioning schemes to facilitate analysis is often problematic. Areal interpolation may be used to estimate data over small areas or ecological inference may be used to infer individual behaviors from aggregate data. Researchers may also perform analyses at multiple scales as a point of comparison.

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. 

DC-01 - Professional Land Surveying

Professional Land Surveyors are the only profession that create the legal description of land parcels, which are then officially recorded to show ownership and rights pertaining to each and every land parcel within a jurisdiction. The Surveyor is skilled at undertaking the physical measurements that are needed to locate accurately land parcels on the ground and to write the unambiguous legal description of the land to create legal title in real estate. These land ownership records are critical for the transfer of ownership in the real estate market. The legal land description provided by Surveyors forms the foundation, and the real estate market provides the mechanism, for real estate to become the largest store of tangible wealth in any free market economy.

AM-85 - Propagation of error in geospatial modeling
  • Compare and contrast error propagation techniques (e.g., Taylor, Monte Carlo)
  • Explain how some operations can exacerbate error while others dampen it (e.g., mean filter)
FC-10 - Properties
  • Formalize attribute values and domains in terms of set theory
  • Develop alternative forms of representations for situations in which attributes do not adequately capture meaning
  • Define Stevens’ four levels of measurement (i.e., nominal, ordinal, interval, ratio)
  • Describe particular geographic phenomena in terms of attributes
  • Determine the proper uses of attributes based on their domains
  • Characterize the domains of attributes in a GIS, including continuous and discrete, qualitative and quantitative, absolute and relative
  • Recognize situations and phenomena in the landscape which cannot be adequately represented by formal attributes, such as aesthetics
  • Compare and contrast the theory that properties are fundamental (and objects are human simplifications of patterns thereof) with the theory that objects are fundamental (and properties are attributes thereof)
  • Recognize attribute domains that do not fit well into Stevens’ four levels of measurement such as cycles, indexes, and hierarchies

Pages