National Academies Press: OpenBook
« Previous: 4 Models and Methods Relevant to NGA
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

References

Alpaydin, E. 2014. Introduction to Machine Learning, 3rd Ed. Cambridge, MA: MIT Press, 640 pp.

Anderson, B.D., and J.B. Moore. 2012. Optimal Filtering. North Chelmsford, MA: Courier Corporation, 368 pp.

Anselin, L., and S.J. Rey. 2012. Spatial econometrics in an age of CyberGIScience. International Journal of Geographical Information Science 26(12):2211-2226.

Axtell, R., R. Axelrod, J.M. Epstein, and M.D. Cohen. 1996. Aligning simulation models: A case study and results. Computational and Mathematical Organization Theory 1(2):123-142.

Banerjee, S., B.P. Carlin, and A.E. Gelfand. 2014. Hierarchical Modeling and Analysis for Spatial Data. Boca Raton, FL: CRC Press, 474 pp.

Barthélemy, M. 2011. Spatial networks. Physics Reports 499(1):1-101.

Bast, H., P. Brosi, and S. Storandt. 2014. Real-time movement visualization of public transit data. Pp. 331-340 in SIGSPATIAL’14, Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems.

Bauer, P., A. Thorpe, and G. Brunet. 2015. The quiet revolution of numerical weather prediction. Nature 525(7567):47-55.

Bayarri, M.J., J.O. Berger, J. Cafeo, G. Garcia-Donato, F. Liu, J. Palomo, R.J. Parthasarathy, R. Paulo, J. Sacks, and D. Walsh. 2007. Computer model validation with functional output. Annals of Statistics 35(5):1874-1906.

Beaumont, M.A. 2010. Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics 41:379-406.

Berkooz, G., P. Holmes, and J.L. Lumley. 1993. The proper orthogonal decomposition in the analysis of turbulent flows. Annual Review of Fluid Dynamics 25:539-575.

Benner, P., S. Gugercin, and K. Willcox. 2015. A survey of projection-based model reduction methods for parametric dynamical systems. SIAM Review 57(4):483-531.

Bishop, C.M. 2006. Pattern Recognition and Machine Learning. New York: Springer, 738 pp.

Bonabeau, E. 2002. Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America 99(Suppl 3):7280-7287.

Borgatti, S., K.M. Carley, and D. Krackhardt. 2006. Robustness of centrality measures under conditions of imperfect data. Social Networks 28(2):124-136.

Box, G.E.P., and D.W. Behnken. 1960. Some new three level designs for the study of quantitative variables. Technometrics 2(4):455-475.

Box, G.E.P., and K.B. Wilson. 1951. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society Series B 13(1):1-45.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Box, G.E., G.M. Jenkins, G.C. Reinsel, and G.M. Ljung. 2015. Time Series Analysis: Forecasting and Control. New York: John Wiley & Sons, 712 pp.

Brandes, U., M. Eiglsperger, J. Lerner, and C. Pich. 2013. Graph Markup Language (GraphML). Pp. 517-541 in Handbook of Graph Drawing and Visualization, edited by R. Tamassia. Boca Raton, FL: CRC Press [online]. Available at https://cs.brown.edu/~rt/gdhandbook/chapters/graphml.pdf [accessed May 4, 2016].

Brooks, S., A. Gelman, G. Jones, and X.L. Meng, eds. 2011. Handbook of Markov Chain Monte Carlo. Boca Raton, FL: CRC Press.

Burton, R.M., and B. Obel. 1995. The validity of computational models in organization science: From model realism to purpose of the model. Computational and Mathematical Organization Theory 1(1):57-71.

Butler, M.P., P.M. Reed, K. Fisher-Vanden, K. Keller, and T. Wagener. 2014. Identifying parametric controls and dependencies in integrated assessment models using global sensitivity analysis. Environmental Modelling & Software 59:10-29.

Butler, T., and D. Estep. 2013. A numerical method for solving a stochastic inverse problem for parameters. Annals of Nuclear Energy 52:86-94.

Caminade, C., S. Kovats, J. Rocklov, A.M. Tompkins, A.P. Morse, F. Jesús Colón-González, H. Stenlund, P. Martens, and S.J. Lloyd. 2014. Impact of climate change on global malaria distribution. Proceedings of the National Academy of Sciences of the United States of America 111(9):3286-3291.

Cao, G., S. Wang, M. Hwang, A. Padmanabhan, Z. Zhang, and K. Soltani. 2015. A scalable framework for spatiotemporal analysis of location-based social media data. Computers, Environment and Urban Systems 51:70-82.

Carley, K.M., J. Diesner, J. Reminga, and M. Tsvetovat. 2007. Toward an interoperable dynamic network analysis toolkit. Decision Support Systems 43:1324-1347.

Carley, K.M., M.K. Martin, and B. Hirshman. 2009. The etiology of social change. Topics in Cognitive Science 1(4):621-650.

Carley, K.M., M.W. Bigrigg, and B. Diallo. 2012a. Data-to-model: A mixed initiative approach for rapid ethnographic assessment. Computational and Mathematical Organization Theory 18(3):300-327.

Carley, K.M., G. Morgan, M. Lanham, and J. Pfeffer. 2012b. Multi-modeling and sociocultural complexity. Pp. 128-137 in Advances in Design for Cross-Cultural Activities, Part II, edited by D.D. Schmorrow, and D.M. Nicholson. Boca Raton, FL: CRC Press.

Carley, K.M., J. Pfeffer, F. Morstatter, and H. Liu. 2014. Embassies burning: Toward a near-real -time assessment of social media using geo-temporal dynamic network analytics. Social Network Analysis and Mining 4:195.

Catlett, C., W.E. Allcock, P. Andrews, and 91 others. 2007. TeraGrid: Analysis of organization, system architecture, and middleware enabling new types of applications. Pp. 225-249 in High Performance Computing and Grids in Action, edited by Lucio Grandinetti. Advances in Parallel Computing Series, Vol. 16. Amsterdam: IOS Press.

Chancellor, E. 1999. Devil Take the Hindmost: A History of Financial Speculation. New York: Plume.

Chesshire, J.H., and A.J. Surrey. 1975. World energy resources and the limitations of computer modelling. Long Range Planning 8(3):54-61.

Chien, A.A., and V. Karamcheti. 2013. Moore’s Law: The first ending and a new beginning. Computer 12(46):48-53.

Chinesta, F., A. Huerta, G. Rozza, and K. Willcox. 2016. Model reduction methods. Encyclopedia of Computational Mechanics, Vol. 2: Solids and Structures, 2nd Ed, Edited by E. Stein, R. de Borst, and Thomas T.J.R. Hughes. New York: John Wiley & Sons.

Clapper, J.R. 2015. Worldwide Threat Assessment of the U.S. Intelligence Community. Statement for the Record, Senate Select Committee on Intelligence, February 26, 2015.

Collins, W.D., A.P. Craig, J.E. Truesdale, A.V. Di Vittorio, A.D. Jones, B. Bond-Lamberty, K.V. Calvin, J.A. Edmonds, S.H. Kim, A.M. Thomson, P. Patel, Y. Zhou, J. Mao, X. Shi, P.E. Thornton, L.P. Chini, and G.C. Hurtt. 2015. The integrated Earth system model version 1: Formulation and functionality. Geoscientific Model Development 8(7):2203-2219.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Conti, S., J.P. Gosling, J.E. Oakley, and A. O’Hagan. 2009. Gaussian process emulation of dynamic computer codes. Biometrika 96(3):663-676.

Courtier, P., J.N. Thépaut, and A. Hollingsworth. 1994. A strategy for operational implementation of 4D-Var, using an incremental approach. Quarterly Journal of the Royal Meteorological Society 120(519):1367-1387.

Cressie, N., and C.K. Wikle. 2015. Statistics for Spatio-Temporal Data. New York: John Wiley & Sons, 512 pp.

Dahan-Dalmedico, A. 2001. History and epistemology of models: Meteorology (1946–1963) as a case study. Archive for History of Exact Sciences 55:395-422.

Das Sarma, A., H. Lee, H. Gonzalez, J. Madhavan, and A. Halevy. 2012. Efficient spatial sampling of large geographical tables. Pp. 193-204 in SIGMOD ‘12, Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data.

Davis, G.B., J. Olson, and K.M. Carley. 2008. OraGIS and Loom: Spatial and Temporal Extensions to the ORA Analysis Platform. Carnegie Mellon University, Institute for Software Research, Technical Report, CMU-ISR-08-121. Reprinted as DTIC ADA486288, June 2008.

Davis, P.K., and R.H. Anderson. 2004. Improving the composability of DoD models and simulations. Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 1(1):5-17.

Davis, P.K., and J.H. Bigelow. 2003. Motivated Metamodels: Synthesis of Cause-Effect Reasoning and Statistical Metamodeling. RAND/MR-1570. Santa Monica, CA: RAND Corporation [online]. Available at http://www.rand.org/pubs/monograph_reports/MR1570.html [accessed September 26, 2016].

Davis, P.K., R.D. Shaver, and J. Beck. 2008. Portfolio-Analysis Methods For Assessing Capability Options. RAND Corporation [online]. Available at http://www.rand.org/content/dam/rand/pubs/monographs/2008/RAND_MG662.pdf [accessed September 26, 2016].

Dean, J., and S. Ghemawat. 2008. MapReduce: Simplified data processing on large clusters. Communications of the ACM 51(1):107-113.

Delli Gatti, D., S. Desiderio, E. Gaffeo, P. Cirillo, and M. Gallegati. 2011. Macroeconomics from the Bottom Up. Milan: Springer.

DOE (U.S. Department of Energy). 2009. Science Challenges and Future Directions: Climate Change Integrated Assessment Research. Report PNNL-18417 [online]. Available at http://science.energy.gov/~/media/ber/pdf/ia_workshop_low_res_06_25_09.pdf [accessed May 5, 2016].

Drouet, L., V. Bosetti, and M. Tavoni. 2015. Selection of climate policies under the uncertainties in the Fifth Assessment Report of the IPCC. Nature Climate Change 5:937-940.

Edwards, P. 2010. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA: MIT Press.

Efron, B., and C. Morris. 1977. Stein’s paradox in statistics. Scientific American 236:119-127.

Egan, J.R., I.M. Hall, D.J. Lemon, and S. Leach. 2011. Modeling Legionnaires’ disease outbreaks: Estimating the timing of an aerosolized release using symptom-onset dates. Epidemiology 22(2):188-198.

Eldawy, A., and M.F. Mokbel. 2015. The era of big spatial data: A survey. Information and Media Technologies 10(2):305-316.

Eldred, M.S., B.M. Adams, D.M. Gay, L.P. Swiler, K. Haskell, W.J. Bohnhoff, J.P. Eddy, W.E. Hart, J.P. Watson, J.D. Griffin, P.D. Hough, T.G. Kolda, P.J. Williams, and M.L. Martinez-Canales. 2007. DAKOTA, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 4.1 Reference Manual. SAND2006-4055. Albuquerque, NM: Sandia National Laboratories [online]. Available at https://dakota.sandia.gov/content/sand-reports [accessed May 5, 2016].

Evensen, G. 2009. Data Assimilation: The Ensemble Kalman Filter. Berlin: Springer.

FAO (Food and Agriculture Organization). 2012. The State of Food Insecurity in the World: Economic Growth is Necessary but Not Sufficient to Accelerate Reduction of Hunger and Malnutrition. Rome: FAO [online]. Available at http://www.fao.org/docrep/016/i3027e/i3027e.pdf [accessed May 5, 2016].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Fearnhead, P., and D. Prangle. 2012. Constructing summary statistics for approximate Bayesian computation: Semi-automatic approximate Bayesian computation. Journal of the Royal Statistical Society Series B: Statistical Methodology 74(3):419-474.

Ferguson, N.M., M.J. Keeling, W.J. Edmunds, R. Gani, B.T. Grenfell, R.M. Anderson, and S. Leach. 2003. Planning for smallpox outbreaks. Nature 425(6959):681-685.

Fischoff, B., and A.L. Davis. 2014. Communicating scientific uncertainty. Proceedings of the National Academy of Sciences of the United States of America 111:13,664-13,671.

Forrester, J.W. 1961. Industrial Dynamics. Waltham, MA: Pegasus Communications, 464 pp.

Frantz, T.L., M. Cataldo, and K.M. Carley. 2009. Robustness of centrality measures under uncertainty: Examining the role of network topology. Computational and Mathematical Organization Theory 15(4):303-328.

Fryxell, B., K. Olson, P. Ricker, F.X. Timmes, M. Zingale, D.Q. Lamb, P. MacNeice, R. Rosner, J.W. Truran, and H. Tufo. 2000. FLASH: An adaptive mesh hydrodynamics code for modeling astrophysical thermonuclear flashes. Astrophysical Journal Supplement Series 131(1):273-334.

Gallup, J.L., and J.D. Sachs. 2001. The economic burden of malaria. American Journal of Tropical Medicine and Hygiene 64(1-2 Suppl):85-96.

Geanakoplos, J., R. Axtell, D.J. Farmer, P. Howitt, B. Conlee, J. Goldstein, M. Hendrey, N.M. Palmer, and C.-Y. Yang. 2012. Getting at systemic risk via an agent-based model of the housing market. American Economic Review 102(3):53-58.

Gelman, A., and J. Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, UK: Cambridge University Press.

Gelman, A., J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari, and D.B. Rubin. 2013. Bayesian Data Analysis, 3rd Ed. Boca Raton, FL: Chapman and Hall/CRC Press, 675 pp.

Gething, P.W., D.L. Smith, A.P. Patil, A.J. Tatem, R.W. Snow, and S.I. Hay. 2010. Climate change and the global malaria recession. Nature 465(7296):342-345.

Gilbert, N. 2008. Agent-Based Models. Series: Quantitative Applications in the Social Sciences. Los Angeles, CA: SAGE Publications, 112 pp.

Giorgi, F., and L.O. Mearns. 1991. Approaches to the simulation of regional climate change: A review. Reviews of Geophysics 29:191-216.

Gutmann, E.G., T. Pruitt, M.P. Clark, L. Brekke, J.R. Arnold, D.A. Raff, and R.M. Rasmussen. 2014. An inter-comparison of statistical downscaling methods used for water resource assessments in the United States. Water Resources Research 50:7167-7186.

Hanasaki, N., S. Fujimori, T. Yamamoto, S. Yoshikawa, Y. Masaki, Y. Hijioka, M. Kainuma, Y. Kanamori, T. Masui, K. Takahashi, and S. Kanae. 2013. A global water scarcity assessment under shared socio-economic pathways—Part 2: Water availability and scarcity. Hydrological Earth System Science 17(7):2393-2413.

Hannigan, J., G. Hernandez, R.M. Medina, R. Roos, and P. Shakarian. 2013. Mining for spatially-near communities in geo-located social networks. Pp. 16-23 in Association for the Advancement of Artificial Intelligence—Social Networks and Social Contagion: Web Analytics and Computational Social Science, Arlington, VA, November 15-17, Technical Report.

Hansen, J., G. Jacobs, L. Hsu, J. Dykes, J. Dastugue, R. Allard, C. Barron, D. Lalejini, M. Abramson, S. Russell, and R. Mittu. 2011. Information domination: Dynamically coupling METOC and INTEL for improved guidance for piracy interdiction. NRL Review (2011):109-119.

Hastie, T., R. Tibshirani, and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Ed. New York: Springer-Verlag, 763 pp.

Hay, S.I., C.A. Guerra, P.W. Gething, A.P. Patil, A.J. Tatem, A.M. Noor, C.W. Kabaria, B.H. Manh, I.R.F. Elyazar, S. Brooker, D.L. Smith, R.A. Moyeed, and R.W. Snow. 2009. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Medicine 6(3):286-301.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Hejazi, M.I., J. Edmonds, L. Clarke, P. Kyle, E. Davies, V. Chaturvedi, M. Wise, P. Patel, J. Eom, and K. Calvin. 2014. Integrated assessment of global water scarcity over the 21st century under multiple climate change mitigation policies. Hydrology and Earth System Sciences 18(8):2859-2883.

Helton, J.C. 1993. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal. Reliability Engineering & System Safety 42(2-3):327-367.

Hendricks, J.S., G.W. McKinney, M.L. Fensin, M.R. James, R.C. Johns, J.W. Durkee, J.P. Finch, D.B. Pelowitz, L.S. Waters, M.W. Johnson, and F.X. Gallmeier. 2008. MCNPX 2.6.0 Extensions. LA-UR-08-2216. Los Alamos National Laboratory [online]. Available at https://mcnpx.lanl.gov/opendocs/versions/v260/v260.pdf [accessed May 5, 2016].

Higdon, D. 2006. A primer on space-time modeling from a Bayesian perspective. In Statistical Methods for Spatio-Temporal Systems. Monographs on Statistics and Applied Probability. B. Finkenstadt, L. Held, and V. Isham, eds. Chapman and Hall, pp. 217-279.

Higdon, D., J. Gattiker, B. Williams, and M. Rightley. 2008. Computer model calibration using high-dimensional output. Journal of the American Statistical Association 103(482):570-583.

Hofmann, M.A. 2004. Challenges of model interoperation in military simulations. Simulation 80(12):659-667.

Holland, J.H. 1992. Complex adaptive systems. Daedalus 121(1):17-33.

Holt, J. 2009. A summary of the primary causes of the housing bubble and the resulting credit crisis: A nontechnical paper. Journal of Business Inquiry 8(1):120-129.

Howard, R.A. 1968. The foundations of decision analysis. IEEE Transactions on Systems Science and Cybernetics 4(3):211-219.

Ide, K., P. Courtier, M. Ghil, and A.C. Lorenc. 1997. Unified notation for data assimilation: Operational, sequential and variational. Journal of the Meteorological Society of Japan 75(1B):181-189.

IPCC (Intergovernmental Panel on Climate Change). 2013. Summary for policymakers. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley. Cambridge, UK: Cambridge University Press.

Jones, A.E., and A.P. Morse. 2012. Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa. Geophysical Research Letters 39:L23707, DOI: 10.1029/2012gl054040.

Joseph, K., K.M. Carley, D. Filonuk, G.P. Morgan, and J. Pfeffer. 2014. Arab Spring: From news data to forecasting. Social Network Analysis and Mining 4(1):177, DOI: 10.1007/s13278-014-0177-5.

Kaipio, J., and E. Somersalo. 2006. Statistical and Computational Inverse Problems. Applied Mathematical Science Vol. 160. New York: Springer.

Kalman, R.E., and R.S. Bucy. 1961. New results in linear filtering and prediction theory. Journal of Basic Engineering 83(1):95-108.

Kas, M., K.M. Carley, and L.R. Carley. 2012. Who was where, when? Spatiotemporal analysis of researcher mobility in nuclear science. In Proceedings of the International Workshop on Spatio Temporal Data Integration and Retrieval (STIR 2012), April 1, 2012, Washington, DC.

Keeney, R.L. 1982. Decision analysis: An overview. Operations Research 30(5):803-838.

Kennedy, M.C., and A. O’Hagan. 2001. Bayesian calibration of computer models. Journal of the Royal Statistical Society Series B: Statistical Methodology 63(3):425-464.

Kiehl, J.T. 2007. Twentieth century climate model response and climate sensitivity. Geophysical Research Letters 34(22):L22710.

Kim, S.H., M. Hejazi, L. Liu, K. Calvin, L. Clarke, J. Edmonds, P. Kyle, P. Patel, M. Wise, and E. Davies. 2016. Balancing global water availability and use at basin scale in an integrated assessment model. Climatic Change 136(2):217-231.

Kirk, M. 2011. Ending Somali Piracy Against American and Allied Shipping [online]. Available at http://kirk.senate.gov/pdfs/KirkReportfinal2.pdf [accessed May 5, 2016].

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Kogge, P., K. Bergman, S. Borkar, D. Campbell, W. Carlson, W. Dally, M. Denneau, P. Franzon, W. Harrod, K. Hill, J. Hiller, S. Karp, S. Keckler, D. Klein, R. Lucas, M. Richards, A. Scarpelli, S. Scott, A. Snavely, T. Sterling, R.S. Willians, and K. Yelick. 2008. Exascale Computing Study: Technology Challenges in Achieving Exascale Systems. DARPA, Information Processing Techniques Office, and Air Force Research Laboratory, 278 pp. [online]. Available at http://staff.kfupm.edu.sa/ics/ahkhan/Resources/Articles/ExaScale%20Computing/TR-2008-13.pdf [accessed May 5, 2016].

Kolaczyk, E.D., and G. Csárdi. 2014. Statistical Analysis of Network Data with R. Use R, Vol. 65. New York: Springer, 207 pp.

Kotamarthi, R., L. Mearns, K. Hayhoe, C.L. Castro, and D. Wuebble. 2016. Use of Climate Information for Decision-Making and Impacts Research: State of Our Understanding. Prepared for the Department of Defense, Strategic Environmental Research and Development Program. 55 pp.

Kou, Y., C.T., Lu, S. Sirwongwattana, and Y.P. Huang. 2004. Survey of fraud detection techniques. Pp. 749-754 in Proceedings of the 2004 IEEE International Conference on Networking, Sensing and Control, March 21-23, 2004, Taipei, Taiwan, Vol. 2. Piscataway, NJ: IEEE.

Krey, V. 2014. Global energy-climate scenarios and models: A review. Wiley Interdisciplinary Reviews Energy and Environment 3(4):363-383.

Lansing, J.S. 2003. Complex adaptive systems. Annual Review of Anthropology 32:183-204.

Lauderdale, J.M., C. Caminade, A.E. Heath, A.E. Jones, D.A. MacLeod, K.C. Gouda, U.S. Murty, P. Goswami, S.R. Mutheneni, and A.P. Morse. 2014. Towards seasonal forecasting of malaria in India. Malaria Journal 13:310.

Levis, A.H., K.M. Carley, and G. Karsai. 2011. Resilient Architectures for Integrated Command and Control in a Contested Cyber Environment. SAL/FR-11-02. Final Technical Report to the Air Force Research Laboratory, by George Mason University, Fairfax, VA [online]. Available at http://www.casos.cs.cmu.edu/publications/papers/RC2withSF298_Final2.pdf [accessed May 5, 2016].

Liu, J.S., and R. Chen. 1998. Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association 93(443):1032-1044.

Lorenz, E.N. 1963. On the predictability of hydrodynamic flow. Transactions of the New York Academy of Sciences 25(4):409-432.

Lorenz, J., H. Rauhut, F. Schweitzer, and D. Helbing. 2011. How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences of the United States of America 108(22):9020-9025.

MacLeod, D.A., A. Jones, F. Di Giuseppe, C. Caminade, and A.P. Morse. 2015. Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model. Environmental Research Letters 10(4), DOI: 10.1088/1748-9326/10/4/044005.

Manabe, S., and K. Bryan. 1969. Climate calculations with a combined ocean-atmosphere model. Journal of the Atmospheric Sciences 26(4):786-789.

Manabe, S., and R.T. Wetherald. 1975. The effects of doubling the CO2 concentration on the climate of a general circulation model. Journal of Atmospheric Sciences 32(1):3-15.

Marchuk, G.I. 1995. Adjoint Equations and Analysis of Complex Systems. New York: Springer, 468 pp.

Martin, J., L.C. Wilcox, C. Burstedde, and O. Ghattas. 2012. A stochastic Newton MCMC method for large-scale statistical inverse problems with application to seismic inversion. SIAM Journal on Scientific Computing 34(3):A1460-A1487.

Marzouk, Y.M., and H.N. Najm. 2009. Dimensionality reduction and polynomial chaos acceleration of Bayesian inference in inverse problems. Journal of Computational Physics 228(6):1862-1902.

McGuire, T.R. 1997. The last northern cod. Journal of Political Ecology 4(1):41-54.

Medina, R.M., and G.F. Hepner. 2011. Advancing the understanding of sociospatial dependencies in terrorist networks. Transactions in GIS 15(5):577-597.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Medina, R.M., and G.F. Hepner. 2015. A note of the state of geography and geospatial intelligence research. NGA Pathfinder 13(1):8-9.

Mell, P., and T. Grance. 2011. The NIST Definition of Cloud Computing. NIST Special Publication 800-145. National Institute of Standards and Technology, 7 pp. [online]. Available at http://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-145.pdf [accessed May 5, 2016].

Merrill, J.A., B. Sheehan, K.M. Carley, and P.D. Stetson. 2015. Transition networks in a cohort of patients with congestive heart failure. A novel application of informatics methods to inform care coordination. Applied Clinical Informatics 6(3):548-564.

Mignolet, M.P., and C. Soize. 2008. Stochastic reduced order models for uncertain geometrically nonlinear dynamical systems. Computer Methods in Applied Mechanics and Engineering 197(45-48):3951-3963.

Miller, J.H. 1998. Active nonlinear tests (ANTs) of complex simulations models. Management Science 44(6):820-830.

Moore, L.M. 1981. Principle component analysis in linear systems: Controllability, observability, and model reduction. IEEE Transactions on Automatic Control 26(1):17-31.

Morgan, G.P., and K.M. Carley. 2012. Modeling formal and informal ties within an organization: A multiple model integration. Pp. 253-292 in The Garbage Can Model of Organizational Choice: Looking Forward at Forty, edited by A. Lomi and R. Harrison. Research in the Sociology of Organizations Vol. 36. Bingley, UK: Emerald Group Publishing.

Morgan, M.G., and M. Henrion. 1990. Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. Cambridge, U.K.: Cambridge University Press, 332 pp.

Moss, R.H., J.A. Edmonds, K.A. Hibbard, M.R. Manning, S.K. Rose, D.P. van Vuuren, T.R. Carter, S. Emori, M. Kainuma, T. Kram, G.A. Meehl, G.F. Mitchell, N. Nakicenovic, K. Riahl, S.J. Smith, R.J. Stouffer, A.M. Thomson, J.P. Weyant, and T.J. Wilbanks. 2010. The next generation of scenarios for climate change research and assessment. Nature 463(7282):747-756.

Myers, M.F., D.J. Rogers, J. Cox, A. Flahault, and S.I. Hay. 2000. Forecasting disease risk for increased epidemic preparedness in public health. Advances in Parasitology 47:309-330.

NAC (NASA Advisory Council). 1986. Earth System Science: A Program for Global Change. Washington, DC: NASA.

NASEM (The National Academies of Sciences, Engineering, and Medicine). 2016a. Next Generation Earth System Prediction: Strategies for Sub-Seasonal to Seasonal Forecasts. Washington, DC: The National Academies Press.

NASEM. 2016b. Fostering Transformative Research in the Geographical Sciences. Washington, DC: The National Academies Press.

NIC (National Intelligence Council). 2012. Global Trends 2030: Alternative Worlds. NIC 2012-001 [online]. Available at https://cgsr.llnl.gov/content/assets/docs/Global_Trends_2030-NIC-US-Dec12.pdf [accessed May 9, 2016].

North, M.J., and C.M. Macal. 2007. Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation. Oxford: Oxford University Press.

NRC (National Research Council). 1979. Carbon Dioxide and Climate: A Scientific Assessment. Washington, DC: National Academy Press, 22 pp.

NRC. 2006. Completing the Forecast: Characterizing and Communicating Uncertainty for Better Decisions Using Weather and Climate Forecasts. Washington, DC: The National Academies Press, 124 pp.

NRC. 2007a. Review of the U.S. Climate Change Science Program’s Synthesis and Assessment Product 5.2, “Best Practice Approaches for Characterizing, Communicating, and Incorporating Scientific Uncertainty in Climate Decision Making.” Washington, DC: The National Academies Press, 64 pp.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

NRC. 2007b. Models in Environmental Regulatory Decision Making. Washington, DC: The National Academies Press, 286 pp.

NRC. 2012. Assessing the Reliability of Complex Models: Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification. Washington, DC: The National Academies Press, 184 pp.

NRC. 2013. Future U.S. Workforce for Geospatial Intelligence. Washington, DC: The National Academies Press, 172 pp.

Oakley, J., and A. O’Hagan. 2002. Bayesian inference for the uncertainty distribution of computer model outputs. Biometrika 89(4):769-784.

Oberkampf, W.L., and C.J. Roy. 2010. Verification and Validation in Scientific Computing. Cambridge, UK: Cambridge University Press, 790 pp.

Oberkampf, W.L., T.G. Trucano, and C. Hirsch. 2004. Verification, validation, and predictive capability in computational engineering and physics. Applied Mechanical Reviews 57(5):345-384.

O’Hagan, A. 2006. Bayesian analysis of computer code outputs: A tutorial. Reliability Engineering & System Safety 91(10-11):1290-1300.

Oliver, D., S. Shekhar, J.M. Kang, R. Laubscher, V. Carlan, and A. Bannur. 2014. A K-Main Routes approach to spatial network activity summarization. IEEE Transactions on Knowledge and Data Engineering 26(6):1464-1478.

Oliver, T.A., G. Terejanu, C.S. Simmons, and R.D. Moser. 2015. Validating predictions of unobserved quantities. Computer Methods in Applied Mechanics and Engineering 283(1):1310-1335.

Olson, J.F., and K.M. Carley. 2008. Summarization and information loss in network analysis. In Workshop on Link Analysis, Counter-terrorism, and Security, held in conjunction with the SIAM International Conference on Data Mining (SDM), April 2008.

Olson, J., and K.M. Carley. 2009. Visualizing Spatial Dependencies in Network Topology. Carnegie Mellon University, Institute for Software Research, Technical Report CMU-ISR-09-127. Reprinted as DTIC ADA525370, July 12, 2010.

Oort, A.H., and E.M. Rasmusson. 1970. On the annual variation of the monthly mean meridional circulation. Monthly Weather Review 98:423-442.

Ott, E., B.R. Hunt, I. Szunyogh, A.V. Zimin, E.J. Kostelich, M. Corazza, E. Kalnay, D.J. Patil, and J.A. Yorke. 2004. A local ensemble Kalman filter for atmospheric data assimilation. Tellus A 56(5):415-428.

Pace, D.K. 2004. Modeling and simulation verification and validation challenges. Johns Hopkins APL Technical Digest 25(2):163-172.

Pascual, M., and M.J. Bouma. 2009. Do rising temperatures matter? Ecology 90(4):906-912.

Pavlis, N.K., S.A. Holmes, S.C. Kenyon, and J.K. Factor. 2012. The development and evaluation of the Earth Gravitational Model 2008 (EGM2008). Journal of Geophysical Research 117: B04406, DOI:10.1029/2011JB008916.

Phillips, N.A. 1956. The general circulation of the atmosphere: A numerical experiment. Quarterly Journal of the Royal Meteorological Society 82(352):123-164.

Platzman, G.W. 1979. The ENIAC computations of 1950: Gateway to numerical weather prediction. Bulletin of the American Meteorological Society 60(4):302-312.

Raftery, A.E., T. Gneiting, F. Balabdaoui, and M. Polakowski. 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review 133(5):1155-1174.

Raiffa, H. 1968. Decision Analysis: Introductory Lectures on Choices under Uncertainty. Reading, MA: Addison-Wesley.

Reed, D.A., and J. Dongarra. 2015. Exascale computing and big data. Communications of the ACM 58(7):56-68.

Reichler, T., and J. Kim. 2008. How well do coupled models simulate today’s climate? Bulletin of the American Meteorological Society 89(3):303-312.

Richardson, L.F. 1922. Weather Prediction by Numerical Process. Cambridge, UK: Cambridge University Press.

Ristic, B., S. Arulampalam, and N.J. Gordon. 2004. Beyond the Kalman Filter: Particle Filters for Tracking Applications. Boston, MA: Artech House.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Roache, P.J. 2002. Code verification by the method of manufactured solutions. Journal of Fluids Engineering 124(1):4-10.

Rosoff, H., and D. von Winterfeldt. 2007. A risk and economic analysis of dirty bomb attacks on the ports of Los Angeles and Long Beach. Risk Analysis 27(3):533-546.

Rutter, M. 2007. Proceeding from observed correlation to causal inference: The use of natural experiments. Perspectives on Psychological Science 2(4):377-395.

Ruttimann, J. 2006. 2020 computing: Milestones in scientific computing. Nature 440(7083):399-405.

Sacks, J., W.J. Welch, T.J. Mitchell, and H.P. Wynn. 1989. Design and analysis of computer experiments. Statistical Science 4(4):409-423.

Sadilek, A., H.A. Kautz, and V. Silenzio. 2012. Predicting disease transmission from geo-tagged micro-blog data. In Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence, pp. 136-142.

Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. 2008. Global Sensitivity Analysis: The Primer. Chichester, UK: John Wiley & Sons, 304 pp.

Schapire, R.E. 2003. The boosting approach to machine learning: An overview. Pp. 149-171 in Nonlinear Estimation and Classification, edited by D.D. Denison, M.H. Hansen, C.C. Holmes, B. Mallick, and B. Yu. Lecture Notes in Statistics Vol. 171. New York: Springer.

Schlosser, C.A., K.M. Strzepek, X. Gao, A. Gueneau, C. Fant, S. Paltsev, B. Rasheed, T. Smith-Greico, É. Blanc, H.D. Jacoby, and J.M. Reilly. 2014. The future of global water stress: An integrated assessment. Earths Future 2(8):341-361.

Scott, J. 2013. Social Network Analysis, 3rd Ed. Los Angeles, CA: SAGE, 216 pp.

Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys (CSUR) 34(1):1-47.

Shekhar, S., S. Ravada, V. Kumar, D. Chubb, and G. Turner. 1996. Parallelizing a GIS on a shared address space architecture. IEEE Computer 29(12):42-48.

Shekhar, S., D. Chubb, and G. Turner. 1998. Declustering and load-balancing methods for parallelizing geographic information systems. IEEE Transactions on Knowledge and Data Engineering 10(4):632-655.

Shekhar, S., M.R. Evans, J.M. Kang, and P. Mohan. 2011. Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(3):193-214.

Shekhar, S., Z. Jiang, R.Y. Ali, E. Eftelioglu, X. Tang, V.M.V. Gunturi, and X. Zhou. 2015. Spatiotemporal data mining: A computational perspective. ISPRS International Journal on Geo-Information 4(4):2306-2338.

Shivaji, T., C. Sousa Pinto, A. San-Bento, L.A. Oliveira Serra, J. Valente, J. Machado, T. Marques, L. Carvalho, P.J. Nogueira, B. Nunes, and P. Vasconcelos. 2014. A large community outbreak of Legionnaires’ disease in Vila Franca de Xira, Portugal, October to November 2014. Eurosurveillance 19(50):Article 3 [online]. Available at http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId=20991 [accessed May 9, 2016].

Simmons, A.J., and A. Hollingsworth. 2002. Some aspects of the improvement in skill of numerical weather prediction. Quarterly Journal of the Royal Meteorological Society Part B 128(580):647-677.

Siraj, A.S., M. Santos-Vega, M.J. Bouma, D. Yadeta, D. Ruiz Carrascal, and M. Pascual. 2014. Altitudinal changes in malaria incidence in highlands of Ethiopia and Colombia. Science 343(6175):1154-1158.

Slingo, J., and T. Palmer. 2011. Uncertainty in weather and climate prediction. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 369(1956):4751-4767.

Slootmaker, L.A., E. Regnier, J.A. Hansen, and T.W. Lucas. 2013. User focus and simulation improve predictions of piracy risk. Interfaces 43(3):256-267.

Smith, R.C. 2013. Uncertainty Quantification: Theory, Implementation, and Applications. Philadelphia: Society for Industrial and Applied Mathematics, 383 pp.

Smith, R.L., C. Tebaldi, D. Nychka, and L.O. Mearns. 2009. Bayesian modeling of uncertainty in ensembles of climate models. Journal of the American Statistical Association 104(485):97-116.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Snow, J. 1855. On the Mode of Communication of Cholera. London: John Churchill [online]. Available at http://www.ph.ucla.edu/epi/snow/snowbook.html [accessed May 9, 2016].

Spanos, P.D., and R. Ghanem. 1989. Stochastic finite element expansion for random media. Journal of Engineering Mechanics 115(5):1035-1053.

Sterman, J.D. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World. New York: McGraw-Hill, 982 pp.

Stigler, S.M. 1986. The English breakthrough: Galton. Pp. 265-299 in The History of Statistics: The Measurement of Uncertainty before 1900. Cambridge, MA: Harvard University Press.

Stroeve, J.C., V. Kattsov, A. Barrett, M. Serreze, T. Pavlova, M. Holland, and W.N. Meier. 2012. Trends in Arctic sea ice extent from CMIP5, CMIP3 and observations. Geophysical Research Letters 39, L16502, DOI:10.1029/2012GL052676.

Sung, K.K., and T. Poggio. 1998. Example-based learning for view-based human face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(1):39-51.

Tang, L.-A., Y. Zheng, J. Yuan, J. Han, A. Leung, C.-C. Hung, and W.-C. Peng. 2012. On discovery of traveling companions from streaming trajectories. In ICDE 2012, Proceedings of the IEEE 28th International Conference on Data Engineering. April 1-5, 2012, Washington, DC.

Tang, W., and S. Wang. 2009. HPABM: A hierarchical parallel simulation framework for spatially-explicit agent-based models. Transactions in GIS 13(3):315-333.

Tarantola, A. 2005. Inverse Problem Theory and Methods for Model Parameter Estimation. Philadelphia, PA: Society for Industrial and Applied Mathematics [online]. Available at http://www.ipgp.fr/~tarantola/Files/Professional/Books/InverseProblemTheory.pdf [accessed May 9, 2016].

Taylor, J.B. 2009. The Financial Crisis and the Policy Responses: An Empirical Analysis of What Went Wrong. Working Paper No. 14631. Cambridge, MA: National Bureau of Economic Research.

Tebaldi, C., and R. Knutti. 2007. The use of the multi-model ensemble in probabilistic climate projections. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 365(1857):2053-2075.

Thomson, M.C., F.J. Doblas-Reyes, S.J. Mason, R. Hagedorn, S.J. Connor, T. Phindela, A.P. Morse, and T.N. Palmer. 2006. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439(7076):576-579.

Tippett, M.K., J.L. Anderson, C.H. Bishop, T.M. Hamill, and J.S. Whitaker. 2003. Ensemble square root filters. Monthly Weather Review 131(7):1485-1490.

Tolk, A., and J.A. Muguira. 2003. The levels of conceptual interoperability model. In Proceedings of the 2003 Fall Simulation Interoperability Workshop 7, pp. 1-11.

Tompkins, A.M., and F. Di Giuseppe. 2015. Potential predictability of malaria in Africa using ECMWF monthly and seasonal climate forecasts. Journal of Applied Meteorology and Climatology 54(3):521-540.

Tsvetovat, M., J. Reminga, and K.M. Carley. 2003. DyNetML: Interchange format for rich social network data. Proceedings of the NAACSOS (North American Association for Computational Social and Organizational Sciences) Conference 2003, June 22-25, Pittsburgh, PA [online]. Available at http://www.casos.cs.cmu.edu/events/conferences/2003/proceedings.html. http://www.casos.cs.cmu.edu/publications/papers/tsvetovat_2003_dynet-mlinterchange.pdf [accessed May 10, 2016].

Van Holt, T., J.C. Johnson, J. Brinkley, K.M. Carley, and J. Caspersen. 2012. Structure of ethnic violence in Sudan: An automated content, meta-network and geospatial analytical approach. Computational and Mathematical Organization Theory 18:340-355.

Wang, S., and M.P. Armstrong. 2009. A theoretical approach to the use of cyberinfrastructure in geographical analysis. International Journal of Geographical Information Science 23(2):169-193.

Wang, S., and X.-G. Zhu. 2008. Coupling cyberinfrastructure and Geographic Information Systems to empower ecological and environmental research. BioScience 58(2):94-95.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

Wang, S., H. Hu, T. Lin, Y. Liu, A. Padmanabhan, and K. Soltani. 2014. CyberGIS for data-intensive knowledge discovery. ACM SIGSPATIAL Newsletter 6(2):26-33.

Wang, S., Y. Liu, and A. Padmanabhan. In press. Open cyberGIS software for geospatial research and education in the big data era. SoftwareX, DOI:10.1016/j.softx.2015.10.003.

Washington, W.M., L. Buja, and A. Craig. 2009. The computational future for climate and Earth system models: On the path to petaflop and beyond. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367(1890):833-846.

Wasserman, S., and K. Faust. 1994. Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences, Vol. 8. Cambridge, UK: Cambridge University Press.

Weinberger, S. 2011. Web of war. Nature 471:566-568.

West, M., and J. Harrison. 1999. Bayesian Forecasting and Dynamic Models, 2nd Ed. New York: Springer, 682 pp.

WHO (World Health Organization). 2012. World Malaria Report 2012. Geneva: WHO, 249 pp.

Wikle, C.K., and M.B. Hooten. 2010. A general science-based framework for dynamical spatio-temporal models. Test 19(3):417-451.

Wilby, R.L., S.P. Charles, E. Zorita, B. Timbal, P. Whetton, and L.O. Mearns. 2004. Guidelines for the use of climate scenarios developed from statistical downscaling methods. Available at http://ipcc-ddc.cru.uea.ac.uk/guidelines/dgm_no2_v1_09_2004.pdf [accessed September 26, 2016].

Wilhelmi, O., J. Boehnert, and K. Sampson. 2016. Visualizing the climate’s future. Eos 97, DOI:10.1029/2016EO042207.

Willcox, K., and J. Peraire. 2002. Balanced model reduction via the proper orthogonal decomposition. AIAA Journal 40(11):2323-2330.

Wright, C., and T. Cheetham. 1999. The strengths and limitations of parental heights as a predictor of attained height. Archives of Disease in Childhood 81(3):257-260.

Yelick, K., S. Coghlan, B. Draney, and R.S. Canon. 2011. The Magellan Report on Cloud Computing for Science. U.S. Department of Energy [online]. Available at http://science.energy.gov/~/media/ascr/pdf/program-documents/docs/Magellan_Final_Report.pdf [accessed May 10, 2016].

Zeigler, B.P., H. Praehofer, and T.G. Kim. 2000. Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems, 2nd Ed. New York: Academic Press, 510 pp.

Zhong, C., S.M. Arisona, X. Huang, M. Batty, and G. Schmitt. 2014. Detecting the dynamics of urban structure through spatial network analysis. International Journal of Geographical Information Science 28(11):2178-2199.

Zhou, X., S. Shekhar, and R.Y. Ali. 2014. Spatio-temporal change footprint pattern discovery: An interdisciplinary survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4:1-23.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×

This page intentionally left blank.

Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 93
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 94
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 95
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 96
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 97
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 98
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 99
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 100
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 101
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 102
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 103
Suggested Citation:"References." National Academies of Sciences, Engineering, and Medicine. 2016. From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities. Washington, DC: The National Academies Press. doi: 10.17226/23650.
×
Page 104
Next: Appendix A Combining Models »
From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities Get This Book
×
 From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities
Buy Ebook | $34.99
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

The United States faces numerous, varied, and evolving threats to national security, including terrorism, scarcity and disruption of food and water supplies, extreme weather events, and regional conflicts around the world. Effectively managing these threats requires intelligence that not only assesses what is happening now, but that also anticipates potential future threats. The National Geospatial-Intelligence Agency (NGA) is responsible for providing geospatial intelligence on other countries—assessing where exactly something is, what it is, and why it is important—in support of national security, disaster response, and humanitarian assistance. NGA’s approach today relies heavily on imagery analysis and mapping, which provide an assessment of current and past conditions. However, augmenting that approach with a strong modeling capability would enable NGA to also anticipate and explore future outcomes.

A model is a simplified representation of a real-world system that is used to extract explainable insights about the system, predict future outcomes, or explore what might happen under plausible what-if scenarios. Such models use data and/or theory to specify inputs (e.g., initial conditions, boundary conditions, and model parameters) to produce an output.

From Maps to Models: Augmenting the Nation's Geospatial Intelligence Capabilities describes the types of models and analytical methods used to understand real-world systems, discusses what would be required to make these models and methods useful for geospatial intelligence, and identifies supporting research and development for NGA. This report provides examples of models that have been used to help answer the sorts of questions NGA might ask, describes how to go about a model-based investigation, and discusses models and methods that are relevant to NGA’s mission.

READ FREE ONLINE

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    Switch between the Original Pages, where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.

    « Back Next »
  6. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  7. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  8. ×

    View our suggested citation for this chapter.

    « Back Next »
  9. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!