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9780471969914

Artificial Intelligence in Geography

by ;
  • ISBN13:

    9780471969914

  • ISBN10:

    0471969915

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 1997-07-07
  • Publisher: Wiley
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Supplemental Materials

What is included with this book?

Summary

This unique work introduces the basic principles of artificial intelligence with applications in geographical teaching and research, GIS, and planning. Written in an accessible, non-technical and witty style, this book marks the beginning of the Al revolution in geography with major implications for teaching and research. The authors provide an easy to understand basic introduction to Al relevant to geography. There are no special mathematical and statistical skills needed, indeed these might well be a hindrance. Al is a different way of looking at the world and it requires a willingness to experiment, and readers who are unhindered by the baggage of obsolete technologies and outmoded philosophies of science will probably do best. The text provides an introduction to expert systems, neural nets, genetic algorithms, smart systems and artificial life and shows how they are likely to transform geographical enquiry. A major methodological milestone in geography The first geographical book on artificial intelligence (Al) No need for previous mathematical or statistical skills/knowledge Accessible style makes a difficult subject available to a wide audience Stan Openshaw is one of the world s leading researchers into geographical computing, spatial analysis and GIS.

Author Biography

Stan Openshaw is a retired British geographer. His last post was professor of human geography based in the School of Geography at the University of Leeds. After eighteen years at Newcastle University, including three years as professor of quantitative geography, he moved to work in Leeds in 1992. Christine Openshaw is the author of Artificial Intelligence in Geography, published by Wiley.

Table of Contents

Foreword xiii(4)
Acknowledgements xvii
CHAPTER 1 Artificial intelligence and geography
1(32)
1.1 An emerging crisis in a world full of dumb computer systems and non-smart analysis and modelling tools
1(5)
1.2 AI is important to geography
6(3)
1.3 Why AI is so important to human geography
9(3)
1.4 So what do geographers need to know about AI?
12(1)
1.5 What is AI?
13(7)
1.6 Can machines think?
20(2)
1.7 AI is widely viewed as being future essential
22(1)
1.8 What does AI consist of?
23(1)
1.9 AI is a future essential toolkit for geography
24(2)
1.10 Questions and Answers
26(3)
Appendix 1 Structure of the Introduction to the AI Course in the School of Geography, University of Leeds
29(2)
References
31(2)
CHAPTER 2 A brief history of artificial intelligence
33(22)
2.1 Historical overview
33(1)
2.2 The early years, 1930s and 1940s
34(1)
2.3 The first neural nets era: 1950-1970
35(2)
2.4 Heuristic search in the 1960s and 1970s
37(1)
2.5 Knowledge-based systems 1970-1980s
37(2)
2.6 Neural nets 2: 1986 onwards
39(1)
2.7 Evolutionary programming and artificial life
39(1)
2.8 Fuzzy Logic and Hybrid Intelligent Systems
40(1)
2.9 Into an AI-rich and increasingly AI-dependent future
41(2)
2.10 What might geographers expect from AI?
43(1)
2.11 Principal geographical applications of AI
44(5)
2.12 Conclusions
49(1)
2.13 Questions and Answers
49(2)
References
51(4)
CHAPTER 3 Heuristic search in geography
55(53)
3.1 Playing games
55(4)
3.1.1 Spatial search
57(2)
3.2 Heuristic search methods of the traditional AI kind
59(4)
3.3 Some illustrations of heuristic search
63(1)
3.4 Heuristic search to optimise a function
64(8)
3.5 Some applications of blind search in geography
72(10)
3.5.1 Case study 1: finding nuclear power station sites
73(4)
3.5.2 Case study 2: an optimal nuclear bombing strategy
77(1)
3.5.3 Making spatial searching more intelligent
77(3)
3.5.4 Map searching as a parallel process
80(2)
3.6 More sophisticated examples of heuristic search
82(5)
3.6.1 Case study 3: spatial network optimisation
82(1)
3.6.2 Case study 4: zone design
83(4)
3.7 Exploratory spatial analysis as a map search process
87(7)
3.7.1 Case study 5: the geographical analysis machine
87(4)
3.7.2 Case study 6: the geographical correlates exploration machine
91(3)
3.8 Conclusions
94(2)
3.9 Questions and Answers
96(6)
Appendix 1 A simple Monte Carlo search procedure
102(1)
Appendix 2 A simple Monte Carlo focused search for numerical optimization
103(1)
Appendix 3 A simple Monte Carlo focused search heuristic suitable for zone design
104(1)
Appendix 4 A simulated annealing search procedure
104(1)
Appendix 5 A tabu search procedure
105(1)
References
106(2)
CHAPTER 4 Expert systems and intelligent knowledge-based systems
108(29)
4.1 Introduction
108(2)
4.2 Definitions of expert systems
110(1)
4.3 What are expert systems so potentially useful?
111(2)
4.4 What is an intelligent knowledge-based system?
113(1)
4.5 What does a simple ES consist of?
114(2)
4.6 Building a simple expert system
116(4)
4.6.1 The knowledge-base (rule base)
116(1)
4.6.2 The inference engine
117(1)
4.6.3 Handling uncertainty
118(1)
4.6.4 Knowledge acquisition and knowledge engineering
119(1)
4.6.5 Some ethical issues
119(1)
4.7 Geographic examples of expert systems
120(2)
4.8 Automatic rule making from examples
122(6)
4.8.1 Knowledge discovery systems
123(1)
4.8.2 Memory-based reasoning (MBR)
124(4)
4.9 Other types of intelligent knowledge-based systems
128(1)
4.10 Conclusions
129(2)
4.11 Questions and Answers
131(4)
References
135(2)
CHAPTER 5 Neurocomputing
137(26)
5.1 Introduction
137(4)
5.1.1 Solving problems that were previously impossible
139(2)
5.2 So what is a neural net?
141(5)
5.3 A brief look at the history of neural nets
146(4)
5.3.1 The first neural networks 1943-69
146(1)
5.3.2 The years of gloom and dormancy: 1969-82
147(1)
5.3.3 The new neural nets: 1982-86
148(1)
5.3.4 Take off: 1987-
148(1)
5.3.5 The promise of neurocomputing
149(1)
5.4 A geographical appreciation
150(4)
5.5 What are the potential neural network modelling applications in geography?
154(2)
5.6 Conclusions
156(1)
5.7 Questions and Answers
157(4)
References
161(2)
CHAPTER 6 Applying artificial neural networks
163(38)
6.1 Introduction
163(1)
6.2 Supervised neural networks
164(15)
6.2.1 The black box
164(3)
6.2.2 How does it work?
167(7)
6.2.3 Pros and cons
174(1)
6.2.4 Spatial interaction modelling using a neural net
174(3)
6.2.5 Neural net modelling of telecommunication flows
177(1)
6.2.6 Try it out!
178(1)
6.2.7 Some neural benefits
178(1)
6.3 Unsupervised neural networks
179(13)
6.3.1 Unsupervised training
179(2)
6.3.2 Review of potential neuroclassifier architectures
181(3)
6.3.3 Comparisons with conventional classifiers
184(1)
6.3.4 Kohonen's self-organising maps as the basis for a spatial data classifier
185(4)
6.3.5 Kohonen's self-organising map as a modeller
189(1)
6.3.6 Some empirical evaluations and case studies
190(2)
6.4 Conclusions
192(1)
6.5 Questions and Answers
193(5)
References
198(3)
CHAPTER 7 Evolutionary computation, genetic algorithms, evolution strategies and genetic programming
201(35)
7.1 Introduction
201(1)
7.2 Genetic algorithms
202(8)
7.2.1 What is a genetic algorithm?
202(2)
7.2.2 A basic genetic algorithm
204(3)
7.2.3 So how does the genetic algorithm work?
207(1)
7.2.4 Genetic algorithm problems and advantages
208(2)
7.3 Other types of evolution programming
210(6)
7.3.1 Non-binary genetic algorithms
210(1)
7.3.2 Evolution strategies
210(2)
7.3.3 An empirical comparison of evolutionary strategies with genetic algorithms
212(4)
7.4 Building model breeding machines
216(6)
7.4.1 Background
216(1)
7.4.2 Model breeding: modelling census relationships
217(3)
7.4.3 Model breeding: building spatial interaction models
220(1)
7.4.4 Model breeding problems
221(1)
7.5 Genetic programming and evolving computer programs
222(5)
7.5.1 A basic algorithm for genetic programming
222(2)
7.5.2 An example of genetic programming
224(1)
7.5.3 More algorithmic details
224(1)
7.5.4 Goods and bads
225(2)
7.6 A genetic programming approach to building new spatial interaction models
227(2)
7.7 Conclusions
229(1)
7.8 Questions and Answers
230(4)
References
234(2)
CHAPTER 8 Artificial life
236(32)
8.1 Introduction
236(1)
8.2 What is artificial life?
237(3)
8.3 Why is artificial life an important technology?
240(1)
8.4 Primitive artificial life
241(10)
8.4.1 Cellular automaton
241(1)
8.4.2 A one-dimensional or linear cellular automaton
242(2)
8.4.3 Two-dimensional cellular automaton
244(3)
8.4.4 Cellular automaton as a modelling tool
247(1)
8.4.5 Building artificial bugs
248(3)
8.5 Some examples of artificial life in GIS
251(11)
8.5.1 MAPEX
251(4)
8.5.2 More sophisticated GIS trispace explorers
255(6)
8.5.3 Some other possibilities
261(1)
8.5.4 Distributed AI
261(1)
8.6 Conclusions
262(1)
8.7 Questions and Answers
263(3)
References
266(2)
CHAPTER 9 Fuzzy logic, fuzzy systems and soft computing
268(41)
9.1 Why it is important?
268(2)
9.2 What is fuzzy logic?
270(9)
9.2.1 We live in a fuzzy word
270(1)
9.2.2 So what is fuzzy set theory?
271(7)
9.2.3 Fuzziness versus probability
278(1)
9.3 Building a fuzzy systems model
279(22)
9.3.1 When might this be useful?
279(22)
9.3.2 Fuzzy spatial interaction modelling
301(1)
9.4 Conclusions
301(3)
9.4.1 Fuzzy modelling is in principle simple
301(1)
9.4.2 Some advantages
302(1)
9.4.3 Some disadvantages
302(1)
9.4.4 So what!
303(1)
9.5 Questions and Answers
304(3)
References
307(2)
CHAPTER 10 Conclusions and epilogue
309(12)
10.1 An emerging era of the smart machine
309(2)
10.2 Geographical hopes
311(1)
10.3 Doubts and questions
312(2)
10.4 A research agenda
314(3)
10.4.1 AI in spatial analysis relevant to GIS
314(1)
10.4.2 Spatial modelling in GIS
315(1)
10.4.3 Applications in human geography
315(1)
10.4.4 New applications
316(1)
10.4.5 Real-world demonstrators
316(1)
10.4.6 Teaching
317(1)
10.4.7 Social consequences
317(1)
10.4.8 Geocomputation
317(1)
10.5 Conclusions
317(1)
10.6 Questions and Answers
318(1)
References
319(2)
Index 321

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