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Spatial Analysis
Statistics, Visualization, and Computational Methods




ISBN 9781498707633
Published August 11, 2015 by CRC Press
323 Pages - 96 B/W Illustrations

 
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Book Description

An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis—containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS—as well as detailed illustrations and numerous case studies.

The book enables readers to:

  • Identify types and characterize non-spatial and spatial data
  • Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results
  • Construct testable hypotheses that require inferential statistical analysis
  • Process spatial data, extract explanatory variables, conduct statistical tests, and explain results
  • Understand and interpret spatial data summaries and statistical tests

Spatial Analysis: Statistics, Visualization, and Computational Methods incorporates traditional statistical methods, spatial statistics, visualization, and computational methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Topics covered include: spatial descriptive methods, hypothesis testing, spatial regression, hot spot analysis, geostatistics, spatial modeling, and data science.

Table of Contents

The Context and Relevance of Spatial Analysis
From Data to Information, to Knowledge and Wisdom
Spatial Analysis Using a GIS Timeline
Geographic Data: Properties, Strengths, and Analytical
Challenges
Conclusion
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Scientific Observations and Measurements in Spatial Analysis

Scales of Measurement
Population and Sample
Conclusion
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Using Statistical Measures to Analyze Data Distributions

Descriptive Statistics
Spatial Statistics: Measures for Describing Basic Characteristics of Spatial Data
Spatial Measures of Central Tendency
Spatial Measures of Dispersion
Random Variables and Probability Distribution
Conclusion
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Exploratory Data Analysis, Visualization, and Hypothesis Testing

Exploratory Data Analysis, Geovisualization, and Data
Visualization Methods
Exploratory Approaches for Visualizing Spatial Datasets
Visualizing Multidimensional Datasets: An Illustration Based on the US Educational Achievements Rates, 1970–2012
Hypothesis Testing, Confidence Intervals, and p Values
Computation
Statistical Conclusion
Conclusion
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Analyzing Spatial Statistical Relationships

Engaging in Correlation Analysis
Ordinary Least Squares and Geographically Weighted Regression
Methods
Conclusion
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Engaging in Point Pattern Analysis

Rationale for Studying Point Patterns and Distributions
Exploring Patterns, Distributions, and Trends Associated with Point Features
Conclusions
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Engaging in Areal Pattern Analysis Using Global and Local
Statistics

Rationale for Studying Areal Patterns
The Notion of Spatial Relationships
Quantifying Spatial Autocorrelation Effects in Areal Patterns
Conclusions
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Engaging in Geostatistical Analysis

Rationale for Using Geostatistics to Study Complex Spatial
Patterns
Basic Interpolation Equations
Spatial Structure Functions for Regionalized Variables
Kriging Method and its Theoretical Framework
Conditional Geostatistical Simulation
Inverse Distance Weighting
Conclusions
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

Data Science: Understanding Computing Systems and Analytics for Big Data

Introduction to Data Science
Rationale for a Big Geospatial Data Framework
Data Management
Analytics and Strategies for Big Geospatial Data
Conclusions
Challenge Assignments
Review and Study Questions
Glossary of Key Terms
References

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Author(s)

Biography

Dr. Tonny J. Oyana received his Ph.D and his postdoctoral training from the University of Buffalo, New York, USA. He is currently the director of spatial analytics and informatics, Research Center for Health Disparities, Equity, and the Exposome; and a professor of spatial information systems in the Department of Preventive Medicine at the University of Tennessee Health Science Center, Knoxville, USA. His research focuses on establishing the relationship between environmental health and exposure; advancing GIS methods, algorithm design, and spatial analytical methods; and understanding the factors that contribute toward land systems change. In addition, he has authored more than 80 scientific works.

Dr. Florence M. Margai (now deceased) was a professor in the Department of Geography at Binghamton University, New York, USA, where she taught courses that reflected her areas of specialization: advanced statistics, environmental health hazards, health disparities, and environmental analysis using geospatial and visualization technologies. She also served as the associate dean of Harpur College of Arts and Sciences, Vestal, New York, USA. Margai obtained her Ph.D from Kent State University, Ohio, USA, and worked with nonprofit organizations to assist in the geographic targeting of vulnerable population groups for disease intervention and health promotional campaigns, sustainability, and capacity development initiatives.

Reviews

"Spatial analysis is at the core of quantitative geography and geographic information systems (GIS). Oyana and Margai effectively explain the foundation of spatial analysis and progressively lead readers ways to apply fundamental and advanced methods for geographic problem solving. The book provides a good balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and spatial patterns, and predict spatial processes. The book will be an excellent textbook for undergraduate courses in quantitative geography or spatial analysis. Graduate students new to geospatial sciences will also find the book useful for self-study."
—May Yuan, University of Texas at Dallas

"Right from the first page this book reads differently. It’s not only the writing style which is so different from your run-of-the-mill dry statistical textbook but also the combination of theoretical presentations with study questions and challenge assignments, making the reading so much more enjoyable while forcing the reader to pause and reflect on the content of each Chapter. Another feature of this book is its breadth, encompassing the analysis of point, areal and geostatistical data before ending with a short chapter devoted to the hot topic of Big data, including data management and data mining. The illustration of different concepts using data from environmental and social sciences adds to the general appeal of the presentation. Tonny and Florence must be commended for writing a textbook that should make spatial analysis more accessible to geographers!"
—Pierre Goovaerts, BioMedware, Inc, PGeostat, LLC, University of Florida

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