Spatial Data Mining: Theory and Application 2015 1st ed. 2015 [Kõva köide]

  • Formaat: Hardback, 308 pages, kõrgus x laius x paksus: 235x155x21 mm, kaal: 666 g, 94 Tables, color; 81 Illustrations, color; 22 Illustrations, black and white; XXVIII, 308 p. 103 illus., 81 illus. in color.
  • Ilmumisaeg: 24-Mar-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662485362
  • ISBN-13: 9783662485361
  • Kõva köide
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  • Formaat: Hardback, 308 pages, kõrgus x laius x paksus: 235x155x21 mm, kaal: 666 g, 94 Tables, color; 81 Illustrations, color; 22 Illustrations, black and white; XXVIII, 308 p. 103 illus., 81 illus. in color.
  • Ilmumisaeg: 24-Mar-2016
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3662485362
  • ISBN-13: 9783662485361
* This book is an updated version of a well-received book previously published in Chinese by Science Press of China (the first edition in 2006 and the second in 2013). It offers a systematic and practical overview of spatial data mining, which combines computer science and geo-spatial information science, allowing each field to profit from the knowledge and techniques of the other. To address the spatiotemporal specialties of spatial data, the authors introduce the key concepts and algorithms of the data field, cloud model, mining view, and Deren Li methods. The data field method captures the interactions between spatial objects by diffusing the data contribution from a universe of samples to a universe of population, thereby bridging the gap between the data model and the recognition model. The cloud model is a qualitative method that utilizes quantitative numerical characters to bridge the gap between pure data and linguistic concepts. The mining view method discriminates the different requirements by using scale, hierarchy, and granularity in order to uncover the anisotropy of spatial data mining. The Deren Li method performs data preprocessing to prepare it for further knowledge discovery by selecting a weight for iteration in order to clean the observed spatial data as much as possible. In addition to the essential algorithms and techniques, the book provides application examples of spatial data mining in geographic information science and remote sensing. The practical projects include spatiotemporal video data mining for protecting public security, serial image mining on nighttime lights for assessing the severity of the Syrian Crisis, and the applications in the government project `the Belt and Road Initiatives'.
1 Introduction 1(22)
1.1 Motivation for SDM
1(8)
1.1.1 Superfluous Spatial Data
2(2)
1.1.2 Hazards from Spatial Data
4(2)
1.1.3 Attempts to Utilize Data
6(2)
1.1.4 Proposal of SDM
8(1)
1.2 The State of the Art of SDM
9(4)
1.2.1 Academic Activities
9(1)
1.2.2 Theoretical Techniques
10(1)
1.2.3 Applicable Fields
11(2)
1.3 Bottleneck of SDM
13(7)
1.3.1 Excessive Spatial Data
13(1)
1.3.2 High-Dimensional Spatial Data
13(1)
1.3.3 Polluted Spatial Data
14(2)
1.3.4 Uncertain Spatial Data
16(1)
1.3.5 Mining Differences
17(1)
1.3.6 Problems to Represent the Discovered Knowledge
17(1)
1.3.7 Monograph Contents and Structures
18(2)
1.4 Benefits to a Reader
20(1)
References
20(3)
2 SDM Principles 23(34)
2.1 SDM Concepts
23(7)
2.1.1 SDM Characteristics
23(2)
2.1.2 Understanding SDM from Different Views
25(1)
2.1.3 Distinguishing SDM from Related Subjects
26(1)
2.1.4 SDM Pyramid
27(2)
2.1.5 Web SDM
29(1)
2.2 From Spatial Data to Spatial Knowledge
30(5)
2.2.1 Spatial Numerical
30(1)
2.2.2 Spatial Data
31(1)
2.2.3 Spatial Concept
31(1)
2.2.4 Spatial Information
32(1)
2.2.5 Spatial Knowledge
33(1)
2.2.6 Unified Action
34(1)
2.3 SDM Space
35(3)
2.3.1 Attribute Space
35(1)
2.3.2 Feature Space
35(1)
2.3.3 Conceptual Space
36(1)
2.3.4 Discovery State Space
36(2)
2.4 SDM View
38(7)
2.4.1 SDM User
38(1)
2.4.2 SDM Method
39(1)
2.4.3 SDM Application
39(1)
2.4.4 SDM Hierarchy
40(2)
2.4.5 SDM Granularity
42(1)
2.4.6 SDM Scale
43(1)
2.4.7 Discovery Mechanism
43(2)
2.5 Spatial Knowledge to Discover
45(6)
2.5.1 General Geometric Rule and Spatial Association Rule
45(3)
2.5.2 Spatial Characteristics Rule and Discriminate Rule
48(1)
2.5.3 Spatial Clustering Rule and Classification Rule
48(1)
2.5.4 Spatial Predictable Rule and Serial Rule
49(1)
2.5.5 Spatial Exception or Outlier
50(1)
2.6 Spatial Knowledge Representation
51(4)
2.6.1 Natural Language
51(1)
2.6.2 Conversion Between Quantitative Data and Qualitative Concept
52(1)
2.6.3 Spatial Knowledge Measurement
53(1)
2.6.4 Spatial Rules Plus Exceptions
54(1)
References
55(2)
3 SDM Data Source 57(62)
3.1 Contents and Characteristics of Spatial Data
57(8)
3.1.1 Spatial Objects
57(1)
3.1.2 Contents of Spatial Data
58(2)
3.1.3 Characteristics of Spatial Data
60(1)
3.1.4 Diversity of Spatial Data
61(1)
3.1.5 Spatial Data Fusion
62(2)
3.1.6 Seamless Organization of Spatial Data
64(1)
3.2 Spatial Data Acquisition
65(6)
3.2.1 Point Acquisition
66(1)
3.2.2 Area Acquisition
67(2)
3.2.3 Mobility Acquisition
69(2)
3.3 Spatial Data Formats
71(4)
3.3.1 Vector Data
72(1)
3.3.2 Raster Data
72(1)
3.3.3 Vector-Raster Data
72(3)
3.4 Spatial Data Model
75(6)
3.4.1 Hierarchical Model and Network Model
76(1)
3.4.2 Relational Model
76(2)
3.4.3 Object-Oriented Model
78(3)
3.5 Spatial Databases
81(5)
3.5.1 Surveying and Mapping Database
81(2)
3.5.2 DEM Database with Hierarchy
83(1)
3.5.3 Image Pyramid
84(2)
3.6 Spatial Data Warehouse
86(4)
3.6.1 Data Warehouse
87(1)
3.6.2 Spatial Data Cubes
87(2)
3.6.3 Spatial Data Warehouse for Data Mining
89(1)
3.7 National Spatial Data Infrastructure
90(12)
3.7.1 American National Spatial Data Infrastructure
90(6)
3.7.2 Geospatial Data System of Great Britain Ordnance Survey
96(1)
3.7.3 German Authoritative Topographic-Cartographic Information System
96(1)
3.7.4 Canadian National Topographic Data Base (NTDB)
97(1)
3.7.5 Australian Land and Geographic Information System
98(1)
3.7.6 Japanese Geographic Information System
98(1)
3.7.7 Asia-Pacific Spatial Data Infrastructure
99(1)
3.7.8 European Spatial Data Infrastructure
100(2)
3.8 China's National Spatial Data Infrastructure
102(5)
3.8.1 CNSDI Necessity and Possibility
102(1)
3.8.2 CNSDI Contents
102(2)
3.8.3 CNGDF of CNSDI
104(1)
3.8.4 CSDTS of CNSDI
105(2)
3.9 From GGDI to Big Data
107(6)
3.9.1 GGDI
107(2)
3.9.2 Digital Earth
109(1)
3.9.3 Smart Planet
110(1)
3.9.4 Big Data
111(2)
3.10 Spatial Data as a Service
113(4)
References
117(2)
4 Spatial Data Cleaning 119(38)
4.1 Problems in Spatial Data
119(10)
4.1.1 Polluted Spatial Data
120(3)
4.1.2 Observation Errors in Spatial Data
123(3)
4.1.3 Model Errors on Spatial Data
126(3)
4.2 The State of the Art
129(4)
4.2.1 Stages of Spatial Data Error Processing
129(2)
4.2.2 The Underdevelopment of Spatial Data Cleaning
131(2)
4.3 Characteristics and Contents of Spatial Data Cleaning
133(1)
4.3.1 Fundamental Characteristics
133(1)
4.3.2 Essential Contents
133(1)
4.4 Systematic Error Cleaning
134(3)
4.4.1 Direct Compensation Method
135(1)
4.4.2 Indirect Compensation Method
136(1)
4.5 Stochastic Error Cleaning
137(5)
4.5.1 Function Model
137(1)
4.5.2 Random Model
137(1)
4.5.3 Estimation Equation
138(1)
4.5.4 Various Special Circumstances
139(3)
4.6 Gross Error Cleaning
142(9)
4.6.1 The Reliability of the Adjustment System
143(2)
4.6.2 Data Snooping
145(1)
4.6.3 The Iteration Method with Selected Weights
145(1)
4.6.4 Iteration with the Selected Weights from Robust Estimation
146(3)
4.6.5 Iteration Supervised by Posteriori Variance Estimation
149(2)
4.7 Graphic and Image Cleaning
151(4)
4.7.1 The Correction of Radiation Deformation
151(3)
4.7.2 The Correction of Geometric Deformation
154(1)
4.7.3 A Case of Image Cleaning
155(1)
References
155(2)
5 Methods and Techniques in SDM 157(18)
5.1 Crisp Set Theory
157(5)
5.1.1 Probability Theory
157(2)
5.1.2 Evidence Theory
159(1)
5.1.3 Spatial Statistics
160(1)
5.1.4 Spatial Clustering
161(1)
5.1.5 Spatial Analysis
161(1)
5.2 Extended Set Theory
162(3)
5.2.1 Fuzzy Sets
163(1)
5.2.2 Rough Sets
164(1)
5.3 Bionic Method
165(2)
5.3.1 Artificial Neural Network
165(1)
5.3.2 Genetic Algorithms
166(1)
5.4 Others
167(2)
5.4.1 Rule Induction
167(2)
5.4.2 Decision Trees
169(1)
5.4.3 Visualization Techniques
169(1)
5.5 Discussion
169(2)
5.5.1 Comparisons
170(1)
5.5.2 Usability
170(1)
References
171(4)
6 Data Field 175(12)
6.1 From a Physical Field to a Data Field
175(3)
6.1.1 Field in Physical Space
176(1)
6.1.2 Field in Data Space
177(1)
6.2 Fundamental Definitions of Data Fields
178(4)
6.2.1 Necessary Conditions
178(1)
6.2.2 Mathematical Model
179(1)
6.2.3 Mass
179(1)
6.2.4 Unit Potential Function
180(1)
6.2.5 Impact Factor
181(1)
6.3 Depiction of Data Field
182(3)
6.3.1 Field Lines
182(1)
6.3.2 Equipotential Line (Surface)
182(2)
6.3.3 Topological Cluster
184(1)
References
185(2)
7 Cloud Model 187(16)
7.1 Definition and Property
187(2)
7.1.1 Cloud and Cloud Drops
187(1)
7.1.2 Properties
188(1)
7.1.3 Integrating Randomness and Fuzziness
188(1)
7.2 The Numerical Characteristics of a Cloud
189(1)
7.3 The Types of Cloud Models
190(2)
7.4 Cloud Generator
192(4)
7.4.1 Forward Cloud Generator
192(2)
7.4.2 Backward Cloud Generator
194(2)
7.4.3 Precondition Cloud Generator
196(1)
7.5 Uncertainty Reasoning
196(5)
7.5.1 One-Rule Reasoning
197(1)
7.5.2 Multi-rule Reasoning
198(3)
References
201(2)
8 GIS Data Mining 203(54)
8.1 Spatial Association Rule Mining
203(12)
8.1.1 The Mining Process of Association Rule
204(1)
8.1.2 Association Rule Mining with Apriori Algorithm
205(2)
8.1.3 Association Rule Mining with Concept Lattice
207(4)
8.1.4 Association Rule Mining with a Cloud Model
211(4)
8.2 Spatial Distribution Rule Mining with Inductive Learning
215(7)
8.3 Rough Set-Based Decision and Knowledge Discovery
222(9)
8.3.1 Attribute Importance
223(1)
8.3.2 Urban Temperature Data Mining
224(7)
8.4 Spatial Clustering
231(14)
8.4.1 Hierarchical Clustering with Data Fields
233(2)
8.4.2 Fuzzy Comprehensive Clustering
235(8)
8.4.3 Mathematical Morphology Clustering
243(2)
8.5 Landslide Monitoring
245(10)
8.5.1 SDM Views of Landslide Monitoring Data Mining
245(3)
8.5.2 Pan-Concept Hierarchy Tree
248(1)
8.5.3 Numerical Characters and Rules
248(5)
8.5.4 Rules Plus Exceptions
253(2)
References
255(2)
9 Remote Sensing Image Mining 257(42)
9.1 RS Image Preprocessing
257(3)
9.1.1 Rough Set-Based Image Filter
258(1)
9.1.2 Rough Set-Based Image Enhancement
258(2)
9.2 RS Image Classification
260(8)
9.2.1 Inductive Learning-Based Image Classification
260(4)
9.2.2 Rough Set-Based Image Classification
264(3)
9.2.3 Rough Set-Based Thematic Extraction
267(1)
9.3 RS Image Retrieval
268(6)
9.3.1 Features for Image Retrieval
268(1)
9.3.2 Semivariogram-Based Parameter to Describe Image Similarity
269(2)
9.3.3 Image Retrieval for Detecting Train Deformation
271(3)
9.4 Facial Expression Image Mining
274(7)
9.4.1 Cloud Model-Based Facial Expression Identification
275(3)
9.4.2 Data Field-Based Human Facial Expression Recognition
278(3)
9.5 Brightness of Nighttime Light Images as a Proxy
281(9)
9.5.1 Brightness of Nighttime Lights As a Proxy for Freight Traffic
282(2)
9.5.2 Evaluating the Syrian Crisis with Nighttime Light Images
284(4)
9.5.3 Nighttime Light Dynamics in the Belt and Road
288(2)
9.6 Spatiotemporal Video Data Mining
290(6)
9.6.1 Technical Difficulties in Spatiotemporal Video Data Mining
291(1)
9.6.2 Intelligent Video Data Compression and Cloud Storage
292(1)
9.6.3 Content-Based Video Retrieval
292(1)
9.6.4 Video Data Mining Under Spatiotemporal Distribution
293(3)
References
296(3)
10 SDM Systems 299
10.1 GISDBMiner for GIS Data
299(1)
10.2 RSImageMiner for Image Data
300(4)
10.3 Spatiotemporal Video Data Mining
304(1)
10.4 EveryData
305(3)
References
308
Deren Li,a scientist in photogrammetry and remote sensing, is the membership of the Chinese Academy of Sciences, membership of the Chinese Academy of Engineering, membership of the Euro-Asia International Academy of Science, Professor and PhD supervisor of Wuhan University, Vice-President of the Chinese Society of Geodesy, Photogrammetry and Cartography, Chairman of the Academic Commission of Wuhan University and the National Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS). He has concentrated on the research and education in spatial information science and technology represented by remote sensing (RS), global positioning system (GPS) and geographic information system (GIS). His majors are the analytic and digital photogrammetry, remote sensing, mathematical morphology and its application in spatial databases, theories of object-oriented GIS and spatial data mining in GIS as well as mobile mapping systems, etc. Prof. Deren Li served as Comm. III and Comm. VI president of ISPRS in 1988-1992 and 1992-1996, worked for CEOS in 2002-2004 and president of Asia GIS Association in 2003-2006. He got Dr.h.c. from ETH in 2008. In 2010 he has been elected ISPRS fellow. Shuliang Wang, PhD, a scientist in data science and software engineering, is a professor in Beijing Institute of Technology in China. His research interests include spatial data mining, and software engineering. For his innovatory study of spatial data mining, he was awarded the Fifth Annual InfoSci-Journals Excellence in Research Awards of IGI Global, IEEE Outstanding Contribution Award for Granular Computing, and one of China's National Excellent Doctoral Thesis Prizes. Deyi Li, PhD, a scientist in computer science and artificial intelligence, is the founder of cloud model. He is now a professor in Tsinghua University in China, a membership of Chinese Academy of Engineering and a membership of the Euro-Asia International Academy of Science. His research interests include networked data mining, artificial intelligence with uncertainty, cloud computing, and cognitive physics. For his contribution, he was awarded many international and national prizes or awards, e.g. the Premium Award by IEE Headquarters, the IFAC World Congress Outstanding Paper Award, National Science and Technology Progress Award and so on.

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