As an intelligence agency, little information on NGA’s current activities, future plans, or the workforce needed to carry them out is publicly available. At the request of the committee, NGA provided the most essential information needed to carry out this study, including the following:
• NGA occupation descriptions (includ ing education. knowledge. and skill requirements) for current scientrst and analyst positrons
• The total number of screntists and analysts currently working rn each geospatral intellrgence occupation and then umber hired each year over the past few years.
• The ages and hrghest degrees held by the current scientrst and analyst workforce
• The courses offered at the NGA College.
• The unrversrties where NGA recruits or sends employees for training
• The occupations tracked by the Bureau of Labor Statistrcs that are most relevant to NGA
These data were provided in 2011, trends may have shrfted significan tly srnce the data were collected.
NGA did not provide strategrc information. such as NGA hr ring prioritres. problems finding ski lis or expertrse. or the basis for the NGA College curriculum. When such information was needed to support the analysis. the report states the assumptrons made by the committee so readers can follow the reasoning.
offer education or training in the disciplines, methods, and/or technologies underlying geospatial intelligence. Few of these programs are targeted to NGA’s needs. For Task 4, the committee identified a short list of actions, of varying scope, that NGA can take to help build a skilled geospatial intelligence workforce in the future.
Military intelligence has always required mapping, cartographic analysis, and the collection of geographic information (Sweeney, 1924). The United States has supported mapping and charting for military intelligence purposes since 1804, when the Army’s Lewis and Clark expedition began exploring the Louisiana Territory (Table 1.1). Mapping and charting efforts advanced significantly during World War I, in part because of the extensive use of aerial photography for battlefield intelligence (e.g., MacLeod, 1919; Collier, 1994). In the World War II era, technological improvements in aircraft and cameras greatly expanded military applications of aerial photography, and maps began to be combined with analyzed imagery (e.g., Monmonier, 1985). The development of high-altitude aircraft in the mid-1950s enabled detailed maps of military bases, shipyards, and other strategic targets to be made, revealing, for example, the presence of Soviet medium-range ballistic missiles in Cuba in 1962 (e.g., Richelson, 1999). The advent of satellites in the late 1950s provided the capability to photograph the Earth, measure its physical properties, and accurately determine positions of objects on the surface (Table 1.1).
NGA Scientist and Analyst Occupations
Geospatial intelligence is produced by scientists (including mathematicians) and analysts. Scientists are experts in a particular discipline, and they define NGA’s research strategy, oversee scientific activities, apply new technologies, and develop expertise and tradecraft for the agency. Analysts acquire, process, and analyze data from government and commercial sources; ensure the quality, accuracy, and currency of geospatial information; populate databases; and produce information products for military and intelligence applications. NGA distinguishes more than 30 types of geospatial intelligence analysts, based on scientific discipline (e.g., geodetic earth science, nautical cartography, political geography) or function (e.g., data analysis, development of analysis methods, crossdisciplinary issues). Some analysts address agency-wide issues, such as developing multisource strategies to address intelligence problems, discovering and evaluating new open-source data, and tasking data collection systems. Descriptions of current NGA science and analyst occupations are given in Appendix B.
In the decades following World War II, the collection and handling of intelligence information from photogrammetry, geodesy, mapping, and charting became increasingly automated (Clarke, 2009). With automation came an improved ability to integrate different types of information and to carry out new types of analyses useful to decision makers, including time-space