Geospatial computing in mobile devices

Geospatial computing includes utilizing computing devices and sensors to acquire, process, analyze, manage, and visualize geospatial data, which users can then interact with via a large variety of smart geospatial applications. Geospatial computing is a computational-demanding task, in terms of computation power, data storage capacity, and memory space. Therefore, it has primarily been performed on non-mobile computers. Recent developments allow smartphones to meet many of the demanded requirements for geospatial computing. This book addresses the topic of geospatial computing in smartphones, including positioning, mobile Geographic Information Systems (GIS) and smart mobile applications. You are provided with aspects related to positioning methods, as well as solutions for geospatial data acquisition, processing, and visualization. This resource also covers various aspects of the application technologies, such as context detection and context intelligence.

Author Notes

Ruizhi Chen is currently an endowed chair and professor at the Conrad Blucher Institute of Surveying Science, Texas AM University Corpus Christi. He earned his Ph.D. in Geodesy from University of Helsinki.
Robert E. Guinness is a researcher in the department of navigation and positioning at the Finnish Geodetic Institute. He earned his master of science in space studies from the International Space University in Strasbourg, France.

Preface	p. ix
Acknowledgments	p. xi
Chapter 1 Introduction	p. 1
1.1 The Mobile Revolution	p. 1
1.1.1 Terminology of the Mobile Revolution	p. 2
1.1.2 Challenges and Opportunities of the Mobile Revolution	p. 3
1.2 Introduction to Geospatial Computing	p. 5
1.2.1 What Is Geospatial Computing?	p. 5
1.2.2 Related Disciplines	p. 6
1.2.3 The Geospatial Computing Era	p. 6
1.2.4 Important Concepts and Tasks in Geospatial Computing	p. 8
1.3 Introduction to Mobile Device Positioning	p. 10
1.3.1 Predecessors to Global Satellite Navigation Systems	p. 11
1.3.2 The Beginning of the GNSS Era	p. 12
1.3.3 The E-911 Initiative	p. 12
1.3.4 Using WLANs for Positioning	p. 13
1.4 Organization of the Book	p. 14
References	p. 14
Chapter 2 Fundamentals of Mobile Positioning	p. 17
2.1 Coordinate Systems	p. 18
2.1.1 The ECI Coordinate System	p. 18
2.1.2 The ECEF Coordinate System	p. 18
2.1.3 The Local Geodetic Coordinate System	p. 22
2.1.4 The Height System	p. 23
2.2 Positioning Observables	p. 25
2.2.1 Range	p. 26
2.2.2 Range Difference	p. 28
2.2.3 Acceleration, Speed, and Traveled Distances	p. 29
2.2.4 Angles and Angle Rates	p. 31
2.2.5 Images	p. 33
2.2.6 Proximity	p. 33
2.2.7 Fingerprints	p. 34
2.3 Positioning Methods	p. 35
2.3.1 GNSS Positioning	p. 35
2.3.2 Positioning Based on RF Signals of Wireless Networks	p. 40
2.3.3 Hybrid Positioning	p. 43
2.4 Summary	p. 47
References	p. 47
Chapter 3 GNSS Positioning in Mobile Devices	p. 49
3.1 Standalone GNSS Positioning	p. 50
3.1.1 Calculation of Satellite Positions and Clock Offsets	p. 50
3.1.2 Observation Equations of the Pseudoranges	p. 52
3.1.3 Linear Least Squares Estimate Based on the Taylor Series Expansion	p. 52
3.1.4 Closed-Form Least Squares Solution	p. 55
3.1.5 The Kalman Filter Solution	p. 57
3.2 Differential GNSS	p. 60
3.3 SBAS	p. 63
3.4 A-GNSS	p. 67
3.5 Summary	p. 69
References	p. 69
Chapter 4 Wireless Positioning in Mobile Devices	p. 71
4.1 Positioning Based on Radio Signal Coverage Area	p. 72
4.2 Positioning Based on Radio Signal Pattern	p. 74
4.2.1 The Pattern Recognition Approach	p. 75
4.2.2 The Probabilistic Approach	p. 76
4.3 Positioning Based on Range and Range Difference	p. 79
4.3.1 Positioning Based on Range	p. 79
4.3.2 Positioning Based on Range Difference	p. 82
4.4 Positioning Based on DoA	p. 84
4.5 Summary	p. 85
References	p. 86
Chapter 5 Hybrid Positioning in Mobile Devices	p. 89
5.1 Measurements of the Built-in Sensors in Mobile Devices	p. 90
5.2 PDR	p. 91
5.2.1 The Generic Position Propagation Model	p. 92
5.2.2 Step Detection	p. 93
5.2.3 Heading Determination	p. 95
5.2.4 Position Propagation	p. 97
5.3 Multisensor Multisignal Positioning	p. 99
5.3.1 Integration Strategy	p. 99
5.3.2 Integration Algorithm	p. 100
5.4 Visual-Based and Visual-Aided Positioning	p. 106
5.4.1 Visual-Based Positioning	p. 106
5.4.2 Visual-Aided Positioning	p. 107
5.4.3 Hybrid Integration	p. 108
5.5 Summary	p. 109
References	p. 109
Chapter6 Mobile GIS	p. 111
6.1 Mobile Devices as a Platform for Mobile GIS	p. 112
6.1.1 Mobile Communications	p. 113
6.1.2 Mobile Computing Capabilities	p. 114
6.1.3 Positioning Capabilities	p. 114
6.1.4 Support for Application Development	p. 115
6.2 Crowdsourcing Geospatial Data Using Mobile Devices	p. 117
6.2.1 The GPX Data Format	p. 119
6.2.2 OpenStreetMap	p. 119
6.3 ArcGIS for Mobile	p. 120
6.4 Emerging Mobile GIS Applications	p. 123
6.4.1 Geotagged Photos Using Mobile Devices	p. 124
6.4.2 Collecting Waypoints and Tracks Using Mobile Devices	p. 126
6.5 Summary	p. 127
References	p. 128
Chapter 7 Mobile LBSs	p. 129
7.1 What Is a LBS?	p. 129
7.2 History of LBSs	p. 131
7.2.1 Three Converging Technologies: Mobile, Internet, Location	p. 133
7.2.2 The Birth of Commercial LBSs	p. 134
7.3 Types of LBSs	p. 136
7.4 Important LBS Concepts	p. 137
7.4.1 LBS Data Types	p. 138
7.4.2 LBS Functions	p. 140
7.5 Current LBS Marketplace	p. 142
7.5.1 LBS Market Size and Overall Trends	p. 142
7.5.2 User Behavior and Attitudes Toward LBS	p. 143
7.6 Summary	p. 145
References	p. 146
Chapter 8 Context Awareness	p. 149
8.1 Defining Context and Context Awareness	p. 150
8.1.1 What Is Context?	p. 150
8.1.2 What Is Context Awareness?	p. 152
8.2 Why Is Context Awareness Important?	p. 154
8.3 History of Context-Aware Computing	p. 155
8.4 Examining Context in Detail	p. 158
8.4.1 What: The Activity Context	p. 158
8.4.2 Who: The User and the Social Context	p. 159
8.4.3 Where: The Location Context	p. 160
8.4.4 When: The Time and Date Context	p. 162
8.4.5 Why: The Motivational Context	p. 162
8.4.6 In What Manner: Motion Context and Other Details of Context	p. 163
8.4.7 By What Means: The Context Aware Device and the Methods of Sensing Context	p. 163
8.5 How to Use Context	p. 165
8.6 Summary	p. 168
References	p. 169
Chapter 9 Contextual Reasoning	p. 171
9.1 What is Contextual Reasoning?	p. 171
9.2 A Hypothetical Example	p. 172
9.3 What Are the Methods of Contextual Reasoning?	p. 174
9.3.1 Introduction to Machine Learning	p. 175
9.3.2 Nai've Bayes' Classifiers	p. 176
9.3.3 Hidden Markov Model (HMM)-Based Classifiers	p. 179
9.3.4 The Sliding Window Method	p. 188
9.3.5 Bayesian Networks	p. 189
9.3.6 Decision Trees	p. 190
9.3.7 Support Vector Machines (SVMs)	p. 191
9.4 Summary	p. 194
References	p. 194
Chapter 10 Future Directions in Mobile Geospatial Computing	p. 199
10.1 Three-dimensional Visualization of Geospatial Data in Mobile Devices	p. 200
10.2 Advanced Mobile Sensing and Contextual Thinking	p. 200
10.3 Geospatial Computing Using Wearable Sensors	p. 202
10.4 Cloud-Based Mobile Geospatial Computing	p. 203
10.5 Summary and Conclusions	p. 204
References	p. 205
About the Authors	p. 207
Index	p. 209

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