New Approaches to Spatial AnalysisChapter 12

1
New Approaches to Spatial AnalysisChapter 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 powers impact on GIS field.
• Outline briefly the implications of complexity
  with this
• Describe emerging geographic analysis techniques
• Describe cellular automation and agent based
  modelsapplications
• 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
  wrongespecially 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 toolsarrays of datadiscovery of new
  ways of analyzing relationships and
  processesmethods have no mathematical
  assumptions about underlying causes of
  patternscan be used for investigation of
  non-linear phenomena.
• Computer modeling and Simulation important in
  Geography and distinct from statistical
  processesrepresent 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
  approachhumans 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 ApplicationsExpert Systems

• Expert Systemsearliest 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 complexweights or
  probabilities before final action
• Inference systemguides the expert system through
  its knowledge baserules to apply and order to
  apply them
• Knowledge acquisition system and output
  deviceacquiring 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 ApplicationsArtificial Neural
Networks

• Artificial Neural Networks (ANNs)
• brain-like structure intelligence
• simple model of braininterconnected 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 methodsmaps
  combinations of input X onto combinations of
  Ymay 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 classesnumerous 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 observationsproduces a much more
  accurate classificationno way of knowing this is
  going to perform better, but results show that
  ANN handles larger, more complex problems (scale
  up better)
• problem of overtrainingmatched too well to
  training data set, learned idiosyncrasies too
  well
• black-box solutionsonly see the solution not
  whats on the inside. OK for land cover maps,
  not so good for fire risk maps.

11
Geocomputation AI ApplicationsGenetic Algorithms

• Another AI techniquegenerate answers without the
  how or why.
• Loosely modeled on Evolutiongenetic adaptation
  and mutations that have evolved because they are
  successful
• Coding scheme devised to represent candidate
  solutionssimplest level, string of binary digits
  1001001010001
• Potential solution scored on fitness
  criteriasuccessful solutions allowed to breed
• Crossover or mutationrandom 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 ApplicationsAgent-Based
Systems

• Also called Agent Technologyan agent is a
  computer program with various properties
• Autonomyhas the capacity for independent action
• Reactivitycan react in various ways to its
  current environment
• Goal Directionmakes 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 MacGills 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 worldsometimes
  this is necessary because to use an open system
  would be impractical.

14
Spatial Modelscellular Automata

• Applicable to Raster GISa 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 latticeagain biology.
• Classic CA, John Conways 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 Modelsprediction of
  permanent landscape features versus movement of
  people across a landscapeimportant for future
  research
• Predicting the past versus predicting the
  futurehow 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
  plausibilityneeds to be tested again and again.

18
FinallyCoupling Models and GIS

• Importanthow 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 significantGIS 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 Couplingfiles transferred between a GIS
  and the modeldynamics are calculated in model
  and displayed in GIS
• Tight Couplingeach system write files that can
  be read by the otherstill 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 GISharder
• - 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