From Maps to Models Augmenting the Nation's Geospatial Intelligence Capabilities (2016) / Chapter Skim
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4 Models and Methods Relevant to NGA
4 Models and Methods Relevant to NGA
Pages 57-92
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From page 57... ...
These needs place a premium on the following types of models and methods: • Models of physical processes that affect human activities (e.g., weather and water flow) ; • Social system models of human behavior in a geospatial context; • Models of combined physical and social systems; • Inverse methods to infer uncertain model parameters from measurements of the real-world system; • Spatial statistics, data mining, and machine learning to discover trends, patterns, and associations in disparate data; and • Spatial network analysis to examine how patterns of relations affect behavior at the individual to state level.
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From page 58... ...
Physical process models developed or used by NGA include those used to generate high-resolution representations of Earth's magnetic and gravitational potential (Pavlis et al., 2012; see Figure 1.2)
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From page 59... ...
Many physical process models involve simulating fluids that are governed by Navier-Stokes and continuity equations, which represent conservation of momentum and mass, and they are solved numerically through finite or spectral discretization approaches. Accurate and representative observations of the natural system are critical both for creating the physical process models themselves and for setting the correct initial and boundary conditions that constrain the physical processes in a model-based investigation.
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From page 60... ...
Climate models are mature for the questions and scales they were designed to address and are a good example of the state of the art in physical process models. The models are complex -- involving multiple processes (see Figure 3.4)
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From page 61... ...
; and 3. Dynamical, which uses the coarse information as input to high-resolution computer models to dynamically simulate phenomena at much finer temporal and spatial scales.
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From page 62... ...
. • Uncertainty analysis support: In physical process models, uncertainty characterization is a method for conveying the uncertainties that are inherent when simulating continuous environments with discrete grid points and time steps, using imperfect observations and models.
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From page 63... ...
The results of physical process models are increasingly being adapted for use in geospatial tools such as geographic information systems (GISs) , which could facilitate their use in geospatial intelligence.
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From page 64... ...
source location, dispersion rate, and damage estimations from the release of hazardous materials in densely populated urban environments; • Improved simulations to anticipate regional extreme events and environmental threats -- such as droughts, relentless heat (sustained high heat and humidity) events, disease vector precursors, or rapid sea-level rise -- that could lead to large-scale social unrest, disruption, or migration; • System emulators and reduced-order models of large complex physical systems -- such as climate, water, or chemical processes -- to allow rapid exploration of scenarios of interest to NGA; • Methods to rapidly and inexpensively predict the importance of physical processes on intelligence applications; • Design of robust frameworks, couplers, and application program interfaces to include physical process models in intelligence applications; and • Refinement of physical models to facilitate their combination with social system models to gain additional understanding and potentially predictive capability of relevant coupled physical–social system stresses and behaviors.
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From page 65... ...
These types of models support what-if reasoning, and they can be used to help NGA think through possible social responses to events embedded in particular geographic conditions, frame arguments, describe the interplay of complex processes, explain a behavior outcome to a physical process, or discuss alternative courses of action. These classes of models are described below.
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From page 66... ...
Toolkits for agent-based modeling are not as mature, but they facilitate model development and support integration with the common statistical platforms and network analysis tools used for analysis, validation, and system testing. Common toolkits for the more cognitive models include ACT-R and SOAR, and toolkits for the least cognitive models include Repast, Mason, and NetLogo.
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From page 67... ...
Most social system models never receive more than face validation because they are typically used for reasoning rather than prediction. Validation and assessment are generally easier when the models are built so that the inputs and outputs are in formats that can be used by standard social network tools and statistical packages (e.g., in CSV format)
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From page 68... ...
• Software/code availability: Multiple modeling toolkits exist for many classes of models. No toolkits have all the findings regarding cognitive biases, social network biases, or game-theoretic considerations built in.
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From page 69... ...
• Improving understanding of how human behavior is constrained or enabled by the geography of the natural and built environment. Intelligence questions are time sensitive, and so decreasing the amount of time or personnel required for model development, testing, and use is critical if social system models are to be used more routinely for geospatial intelligence.
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From page 70... ...
would likely require coupled physical process–social system models to examine how an urban population may respond to an environmental stressor, such as a heat wave, water shortage, vector-borne disease, or air pollution. Coupled models can focus on individual sectors, such as the transportation system and its effect on the environment,3 the effect of climate on the energy demands of buildings,4 or the effects of climate on agriculture (e.g., Figure 2.10)
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From page 71... ...
uses a higher-resolution climate component, resulting in a 105-fold increase in computational expense. • Data requirements: Integrated assessment models require large sets of internally consistent data, including information on energy production, consumption, agriculture, land use and land cover, emissions, and the economy.
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From page 72... ...
are widely used, the use of formal uncertainty techniques is somewhat nascent. INVERSE METHODS A forward model, such as the physical process or social system models discussed above, requires input parameters to produce outputs (i.e., predictions)
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From page 73... ...
As such, they are fundamental to any modeling endeavor. They are most commonly applied to computationally demanding physical process models, such as hydrologic models, where observations of flow and pressure, taken at various spatial locations over time, are used to estimate porosity and permeability of an aquifer.
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From page 74... ...
State-of-the-art probabilistic inverse methods use the Bayesian paradigm for statistical modeling, producing a posterior probability distribution for the unknown parameters. This posterior distribution describes the probability of parameters given the data, model, and any prior knowledge on model parameters.
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From page 75... ...
Such methods have proven effective in physical process models that also produce first-, second-, and even third-order derivative information in the course of the forward model run. These highly specific MCMC algorithms are generally developed in concert with the computational forward model and require a substantial amount of expertise in modeling and high-performance computing.
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From page 76... ...
Some specialization of the inverse methodology is often required to deal with the properties of the forward model. Generally, inverse methods can be hundreds to mil lions of times more expensive to solve than the corresponding forward model.
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From page 77... ...
would likely benefit from the development of inverse methods that use observations to constrain the state of financial, health, transportation, and other urban social systems over time. Specific research and development efforts that could prove beneficial for NGA include the following: • Developing research partnerships with appropriate collaborators to develop and carry out inverse methods for social and coupled social–technical or social–physical system models and data; • Facilitating the development of inverse methodology for constraining the plausible states of social system simulation models to be consistent with available data up to the current time; and • Facilitating the development of inverse methodology to integrate the diverse forms of data that NGA uses and collects (e.g., satellite data, sensor data, geospatial data, and open-source data)
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From page 78... ...
and an inverse method (combining systems observations with a physical process model)
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From page 79... ...
, using the data to estimate model parameters. In other cases (e.g., network models or deep learning methods for image classification)
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From page 80... ...
How to Make Useful for NGA To be useful to NGA, the rather large body of methodology available from data mining, statistics, and machine learning will have to be aligned to NGA data sources, applications, and computational resources. This alignment might best be explored and guided using pilot studies, likely in partnership with researchers from academia and industry, so that NGA can better understand strengths and weaknesses of different empirical methods for NGA applications.
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From page 81... ...
• Data requirements: Empirical models require data to estimate model parameters and model structure. The properties of data (e.g., size, type, cadence, resolution, and accuracy)
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From page 82... ...
; • Developing capabilities for accessing and formatting disparate data in ways that enable analysis products to be generated quickly for decision making; • Advancing unsupervised learning approaches for NGA-specific data and applications; • Developing new parallel formulations of spatial database management systems (e.g., SQL/OGIS standards) , spatial statistics, and spatial data-mining tasks on current platforms (e.g., GPU, clusters, HDFS, MapReduce, SPARK)
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From page 83... ...
Models and methods that take network dependencies into account are referred to as social network analysis, social media analytics, network science, link analysis, dynamic network analysis, or high-dimensional network analysis. They are a flexible class of graphical and statistical models that explain behavior in terms of the relations among entities.
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From page 84... ...
These modern models and associated tools are more useful, because they can represent and use a wider range of geospatial data. Network analysis models can be developed rapidly and are often used to assess open-source data (including social media)
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From page 85... ...
Active areas of network modeling research of particular relevant to NGA include the following: • High-dimensional and dynamic network analysis, with particular attention to n-mode clustering techniques; • New scalable routines and or approximation techniques for large dense networks, particularly when they take into account spatial relations; • Linking social networks with other networks and/or node attributes (e.g., spatial network analysis, where the nodes have connections to each other and positions on maps) ; and • Assessing the robustness of measures for filling in missing data, and the inference of missing links (e.g., using temporal and spatial dependencies to infer social interactions, and using social interactions to infer geographic location)
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From page 86... ...
. The current state of the art in spatial network analysis is summarized in Box 4.7.
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From page 87... ...
by obtaining spatial network data to use for training and for developing more advanced methods. Few analysts have strong skills in network or spatial network analysis.
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From page 88... ...
; • Developing joint spatial network models to predict the spread of information, technology, and activities; the engagement of entities in activities of interest in regions of interest, given existing social networks; and the development of networks and activities of interest, given geographic constraints such as transportation and communication barriers; • Developing spatiotemporal-network-based sentiment-mining techniques for informing predictive spatial network models; • Developing data sets with multiple levels of spatial and social network data for use in developing theories and testing metrics; • Commissioning review papers that build a compendium of findings about the relation between spatial factors and network factors; and • Improving training in joint spatial network analytics and visualization. Joint spatiotemporal-network techniques are needed because spatial statistical models and methods are limited to isotropic Euclidean spaces, which are inadequate both for spatiotemporal data (e.g., trajectories)
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From page 89... ...
may trigger political, economic, or security problems across different geoterrains; • Coupled physical process–social system models to examine how an urban population may respond to an environmental stressor, such as a heat wave, water shortage, vector-borne disease, or air pollution; • Inverse methods that use observations to constrain the state of financial, health, transportation, and other urban social systems over time; • Empirical methods to discover crime hotspots, increases in unemployment, degradation of neighborhoods, and other urbanization trends that could trigger political, economic, or security problems; and • Social network models to analyze agreements and trade networks among organizations that could help or hinder a response to natural, economic, or political disasters. Models and methods needed to answer the Chinese water transfer intelligence questions (How do agriculture and energy production and consumption change over time?
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From page 90... ...
Collaborate with Outside Experts NGA will need partners to help develop, adapt, and use more sophisticated models and methods (e.g., process models, coupled models, agent-based models, inverse methods, and spatial network models) as well as geospatial models that are not well supported by cutting-edge computational infrastructure.
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From page 91... ...
Substantial research is required to adapt physical process models, social system models, and combined physical–social system models to deal more reliably with these less common settings. Improving understanding of human behavior.
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From page 92... ...
Research is also needed to develop spatial and spatiotemporal methods that use advanced data-intensive computational resources, such as formulating spatial processes as parallel processes that can be mapped naturally to parallel computing architecture.
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