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New Approaches to Spatial Analysis—Chapter 12 Theoretical chapter— biological and ecological processes and models basis for spatial models Recent Changes.

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Presentation on theme: "New Approaches to Spatial Analysis—Chapter 12 Theoretical chapter— biological and ecological processes and models basis for spatial models Recent Changes."— Presentation transcript:

1 New Approaches to Spatial Analysis—Chapter 12 Theoretical chapter— biological and ecological processes and models basis for spatial models Recent Changes in GIS-- Technical and Theoretical Geocomputation-– developing field Spatial Modeling Developments— recent advances and linking of models to GIS

2 Chapter points: Describe computer power’s impact on GIS field. Outline briefly the implications of complexity with this Describe emerging geographic analysis techniques Describe cellular automation and agent based models—applications Outline ways of coupling spatial models to GIS

3 Advent of GIS and Spatial Analysis Spatial Analysis existed long before- 1911 Journal of GIS emerged in 1987 Contemporary GIS materialized and became popular during the information age, huge expansion of computers, while Spatial Analysis came about much earlier.

4 Complexity of Computers New scientific non-linear view of the world Complex systems analysis— biology and thermodynamics Limit to power of prediction in nonlinear systems -- Why are weather forecasts always wrong— especially in Denver? Computers are critical to describe relationships in complex analysis–- crucial to development of theories of complexity.

5 Huge array of new computer tools Automated tools—arrays of data— discovery of new ways of analyzing relationships and processes– methods have no mathematical assumptions about underlying causes of patterns—can be used for investigation of non- linear phenomena. Computer modeling and Simulation– important in Geography and distinct from statistical processes—represent world as its, actual causal mechanisms

6 Geocomputation Definition — ”the use of computers to tackle geographical problems that are too complex for manual techniques”. Vague — what would be a better definition? Computational complexity? Stan Openshaw-- Centre for computational Geography at the University of Leeds. Asks “can we use cheap computer power in place of brain power to help us discover patterns in geospatial data? Leads into idea of Artificial Intelligence...

7 Geocomputation— Artificial Intelligence Artificial Intelligence-- attempt to endow a computer with some of the intelligence capabilities of intelligent life forms… without imitating exactly the same information processing steps of humans or biological systems… First things first— need a intelligent approach—humans versus computers: GAM discussed in Chapter 5.. Adaptability and effective use of information are key to a human investigation approach. AI being applied to Geographical problems— discussed next….

8 Geocomputation– AI Applications Expert Systems Expert Systems–earliest approach (knowledge + reasoning = intelligence) construct a formal representation of the human-expert knowledge in some field of data that is of interest, knowledge base is stored in a set of production rules: if then conditional statements. May be more complex…weights or probabilities before final action Inference system—guides the expert system through its knowledge base—rules to apply and order to apply them Knowledge acquisition system and output device—acquiring the knowledge and storage of rules on why the particular conclusion was obtained. Limited applications in geography, suited best for narrowly defined, well-understood fields of application

9 Geocomputation– AI Applications Artificial Neural Networks Artificial Neural Networks (ANNs) brain-like structure = intelligence simple model of brain—interconnected set of neurons, a neuron being a simple element with an input and output Value of output signal = to weighted sum of input signals, signal values are usually 0 or 1 Hidden layers connecting neurons Supervised (known data)—adjusted connection weights to activity, classify input data by learning the subtle patterns in data set; example signal levels in remote sensing data unsupervised mode (traditional)--similar to clustering analysis solution. Similar to multivariate statistical methods—maps combinations of input X onto combinations of Y—may take any form, not limited to logistic regression.

10 ANN Examples  Linear classifier can only draw straight lines though the cases as boundaries between the two classes—numerous wrong classifications clear on knowledge base and how one arrived at solution  Neural Network ANN has potential to draw any line shape through the cloud of observations—produces a much more accurate classification—no way of knowing this is going to perform better, but results show that ANN handles larger, more complex problems (“scale up better”) problem of overtraining—matched too well to training data set, learned idiosyncrasies too well black-box solutions—only see the solution not what’s on the inside. OK for land cover maps, not so good for fire risk maps.

11 Geocomputation– AI Applications Genetic Algorithms Another AI technique—generate answers without the how or why. Loosely modeled on Evolution—genetic adaptation and mutations that have evolved because they are successful Coding scheme devised to represent candidate solutions—simplest level, string of binary digits 1001001010001 Potential solution scored on fitness criteria— successful solutions allowed to “breed” Crossover or mutation—random exchanges between strings to produce new strings or randomly flipping bits on current pop, slight changes are better than huge randomization. Now rare in spatial analysis and GIS literature.

12 Geocomputation– AI Applications Agent-Based Systems Also called Agent Technology— an agent is a computer program with various properties: Autonomy— has the capacity for independent action Reactivity— can react in various ways to its current environment Goal Direction— makes use of its capabilities to pursue the current tasks at hand Intelligent/communicate with other agents solve problems in multiagent systems, example internet search engines Openshaw and MacGill’s space-time attribute creature

13 Spatial Models Instead of random models, develop process models that explicitly represent the real processes and mechanisms that operate to produce the observable geographical world action Possible to use in 3 different ways: - as a basis for pattern measurement and hypothesis testing - for prediction - to enable exploration and understanding of how processes works in the real world Judgment about plausibility becomes as important as results, especially crucial for prediction and exploration— example using closed models to describe open systems of the real world…sometimes this is necessary because to use an open system would be impractical.

14 Spatial Models— cellular Automata Applicable to Raster GIS—a grid consisting of nominal variable, a finite number of discrete states. Cell states changes/evolves according to model time step, current state of the cell and neighbors in the lattice—again biology. Classic CA, John Conway’s “Game of Life”, can be used to represent a geographical process

15 Cellular Automata

16 Two-dimensional automata

17 Spatial Models— Agent Models Agents represent humans in a real simulated environment Model Building Tools and Programs– Star Logo (MIT media lab); Ascape (Brookings Institute); Swarm (Santa Fe Institute). Cellular Models versus Agent Models…prediction of permanent landscape features versus movement of people across a landscape—important for future research Predicting the past versus predicting the future—how well does a model that predicts historical records be used to predict future occurrences? Equifinality problem-Open and closed model problem again, what is the theoretical plausibility—needs to be tested again and again.

18 Finally—Coupling Models and GIS Important—how the different spatial models can be connected to the vast range of geospatial data? Models used in GIS for geographical data types are different than those used in spatial modeling. Most significant—GIS data are static whereas in spatial models are dynamic… Software design problem of how to make spatio- temporal data rapidly accessible....

19 Three Approaches to GIS Modeling Loose Coupling—files transferred between a GIS and the model— dynamics are calculated in model and displayed in GIS Tight Coupling—each system write files that can be read by the other…still difficult to view moving images in a GIS, each image requires a new files and still a slow process. Integrated Model and GIS systems do exist - putting the required GIS functions in the model - putting model functions in a GIS—harder - develop a generic language for building models in a GIS environment. Latter two approaches being explored by researchers - Magical - GRASS GIS (Geographic Resources Analysis Support System) - PC Raster

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