1 Use R! Series Editors: Robert Gentleman Kurt Hornik Giovanni Parmigiani
2 Use R! Albert: Bayesian Computation with R Bivand/Pebesma/Gómez-Rubio: Applied Spatial Data Analysis with R Cook/Swayne: Interactive and Dynamic Graphics for Data Analysis: With R and GGobi Hahne/Huber/Gentleman/Falcon: Bioconductor Case Studies Paradis: Analysis of Phylogenetics and Evolution with R Pfaff: Analysis of Integrated and Cointegrated Time Series with R Sarkar: Lattice: Multivariate Data Visualization with R Spector: Data Manipulation with R
3 Roger S. Bivand Edzer J. Pebesma Virgilio Gómez-Rubio Applied Spatial Data Analysis with R ABC
4 Roger S. Bivand Norwegian School of Economics and Business Administration Breiviksveien Bergen Norway Edzer J. Pebesma University of Utrecht Department of Physical Geography 3508 TC Utrecht Netherlands Series Editors: Robert Gentleman Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N, M2-B876 Seattle, Washington USA Virgilio Gómez-Rubio Department of Epidemiology and Public Health Imperial College London St. Mary s Campus Norfolk Place London W2 1PG United Kingdom Kurt Hornik Department für Statistik und Mathematik Wirtschaftsuniversität Wien Augasse 2-6 A-1090 Wien Austria Giovanni Parmigiani The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins University 550 North Broadway Baltimore, MD USA ISBN e-isbn DOI / Library of Congress Control Number: c 2008 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper springer.com
5 Ewie Voor Ellen, Ulla en Mandus A mis padres, Victorina y Virgilio Benigno
6 Preface We began writing this book in parallel with developing software for handling and analysing spatial data with R (R Development Core Team, 2008). Although the book is now complete, software development will continue, in the R community fashion, of rich and satisfying interaction with users around the world, of rapid releases to resolve problems, and of the usual joys and frustrations of getting things done. There is little doubt that without pressure from users, the development of R would not have reached its present scale, and the same applies to analysing spatial data analysis with R. It would, however, not be sufficient to describe the development of the R project mainly in terms of narrowly defined utility. In addition to being a community project concerned with the development of world-class data analysis software implementations, it promotes specific choices with regard to how data analysis is carried out. R is open source not only because open source software development, including the dynamics of broad and inclusive user and developer communities, is arguably an attractive and successful development model. R is also, or perhaps chiefly, open source because the analysis of empirical and simulated data in science should be reproducible. As working researchers, we are all too aware of the possibility of reaching inappropriate conclusions in good faith because of user error or misjudgement. When the results of research really matter, as in public health, in climate change, and in many other fields involving spatial data, good research practice dictates that someone else should be, at least in principle, able to check the results. Open source software means that the methods used can, if required, be audited, and journalling working sessions can ensure that we have a record of what we actually did, not what we thought we did. Further, using Sweave 1 atoolthatpermits the embedding of R code for complete data analyses in documents throughout this book has provided crucial support (Leisch, 2002; Leisch and Rossini, 2003). 1
7 VIII Preface We acknowledge our debt to the members of R-core for their continuing commitment to the R project. In particular, the leadership and example of Professor Brian Ripley has been important to us, although our admitted muddling through contrasts with his peerless attention to detail. His interested support at the Distributed Statistical Computing conference in Vienna in 2003 helped us to see that encouraging spatial data analysis in R was a project worth pursuing. Kurt Hornik s dedication to keep the Comprehensive R Archive Network running smoothly, providing package maintainers with superb, almost 24/7, service, and his dry humour when we blunder, have meant that the user community is provided with contributed software in an unequalled fashion. We are also grateful to Martin Mächler for his help in setting up and hosting the R-Sig-Geo mailing list, without which we would have not had a channel for fostering the R spatial community. We also owe a great debt to users participating in discussions on the mailing list, sometimes for specific suggestions, often for fruitful questions, and occasionally for perceptive bug reports or contributions. Other users contact us directly, again with valuable input that leads both to a better understanding on our part of their research realities and to the improvement of the software involved. Finally, participants at R spatial courses, workshops, and tutorials have been patient and constructive. We are also indebted to colleagues who have contributed to improving the final manuscript by commenting on earlier drafts and pointing out better procedures to follow in some examples. In particular, we would like to mention Juanjo Abellán, Nicky Best, Peter J. Diggle, Paul Hiemstra, Rebeca Ramis, Paulo J. Ribeiro Jr., Barry Rowlingson, and Jon O. Skøien. We are also grateful to colleagues for agreeing to our use of their data sets. Support from Luc Anselin has been important over a long period, including a very fruitful CSISS workshop in Santa Barbara in Work by colleagues, such as the first book known to us on using R for spatial data analysis (Kopczewska, 2006), provided further incentives both to simplify the software and complete its description. Without John Kimmel s patient encouragement, it is unlikely that we would have finished this book. Even though we have benefitted from the help and advice of so many people, there are bound to be things we have not yet grasped so remaining mistakes and omissions remain our sole responsibility. We would be grateful for messages pointing out errors in this book; errata will be posted on the book website (http://www.asdar-book.org). Bergen Münster London April 2008 Roger S. Bivand Edzer J. Pebesma Virgilio Gómez-Rubio
8 Contents Preface VII 1 Hello World: Introducing Spatial Data Applied Spatial Data Analysis Why Do We Use R In General? for Spatial Data Analysis? R and GIS What is GIS? Service-Oriented Architectures Further Reading on GIS Types of Spatial Data Storage and Display Applied Spatial Data Analysis R Spatial Resources Online Resources Layout of the Book Part I Handling Spatial Data in R 2 Classes for Spatial Data in R Introduction Classes and Methods in R Spatial Objects SpatialPoints Methods Data Frames for Spatial Point Data SpatialLines... 38
9 X Contents 2.6 SpatialPolygons SpatialPolygonsDataFrame Objects Holes and Ring Direction SpatialGrid and SpatialPixel Objects Visualising Spatial Data The Traditional Plot System Plotting Points, Lines, Polygons, and Grids Axes and Layout Elements Degrees in Axes Labels and Reference Grid Plot Size, Plotting Area, Map Scale, and Multiple Plots Plotting Attributes and Map Legends Trellis/Lattice Plots with spplot A Straight Trellis Example Plotting Points, Lines, Polygons, and Grids Adding Reference and Layout Elements to Plots Arranging Panel Layout Interacting with Plots Interacting with Base Graphics Interacting with spplot and Lattice Plots Colour Palettes and Class Intervals Colour Palettes Class Intervals Spatial Data Import and Export Coordinate Reference Systems Using the EPSG List PROJ.4 CRS Specification Projection and Transformation Degrees, Minutes, and Seconds Vector File Formats Using OGR Drivers in rgdal Other Import/Export Functions Raster File Formats Using GDAL Drivers in rgdal Writing a Google Earth Image Overlay Other Import/Export Functions Grass Broad Street Cholera Data Other Import/Export Interfaces Analysis and Visualisation Applications TerraLib and art Other GIS and Web Mapping Systems Installing rgdal
10 Contents XI 5 Further Methods for Handling Spatial Data Support Overlay Spatial Sampling Checking Topologies Dissolving Polygons Checking Hole Status Combining Spatial Data Combining Positional Data Combining Attribute Data Auxiliary Functions Customising Spatial Data Classes and Methods Programming with Classes and Methods S3-Style Classes and Methods S4-Style Classes and Methods Animal Track Data in Package Trip Generic and Constructor Functions Methods for Trip Objects Multi-Point Data: SpatialMultiPoints Hexagonal Grids Spatio-Temporal Grids Analysing Spatial Monte Carlo Simulations Processing Massive Grids Part II Analysing Spatial Data 7 Spatial Point Pattern Analysis Introduction Packages for the Analysis of Spatial Point Patterns Preliminary Analysis of a Point Pattern Complete Spatial Randomness G Function: Distance to the Nearest Event F Function: Distance from a Point to the Nearest Event Statistical Analysis of Spatial Point Processes Homogeneous Poisson Processes Inhomogeneous Poisson Processes Estimation of the Intensity Likelihood of an Inhomogeneous Poisson Process Second-Order Properties Some Applications in Spatial Epidemiology Case Control Studies Binary Regression Estimator
11 XII Contents Binary Regression Using Generalised Additive Models Point Source Pollution Accounting for Confounding and Covariates Further Methods for the Analysis of Point Patterns Interpolation and Geostatistics Introduction Exploratory Data Analysis Non-Geostatistical Interpolation Methods Inverse Distance Weighted Interpolation Linear Regression Estimating Spatial Correlation: The Variogram Exploratory Variogram Analysis Cutoff, Lag Width, Direction Dependence Variogram Modelling Anisotropy Multivariable Variogram Modelling Residual Variogram Modelling Spatial Prediction Universal, Ordinary, and Simple Kriging Multivariable Prediction: Cokriging Collocated Cokriging Cokriging Contrasts Kriging in a Local Neighbourhood Change of Support: Block Kriging Stratifying the Domain Trend Functions and their Coefficients Non-Linear Transforms of the Response Variable Singular Matrix Errors Model Diagnostics Cross Validation Residuals Cross Validation z-scores Multivariable Cross Validation Limitations to Cross Validation Geostatistical Simulation Sequential Simulation Non-Linear Spatial Aggregation and Block Averages Multivariable and Indicator Simulation Model-Based Geostatistics and Bayesian Approaches Monitoring Network Optimization Other R Packages for Interpolation and Geostatistics Non-Geostatistical Interpolation spatial RandomFields geor and georglm fields
12 Contents XIII 9 Areal Data and Spatial Autocorrelation Introduction Spatial Neighbours Neighbour Objects Creating Contiguity Neighbours Creating Graph-Based Neighbours Distance-Based Neighbours Higher-Order Neighbours Grid Neighbours Spatial Weights Spatial Weights Styles General Spatial Weights Importing, Converting, and Exporting Spatial Neighbours and Weights Using Weights to Simulate Spatial Autocorrelation Manipulating Spatial Weights Spatial Autocorrelation: Tests Global Tests Local Tests Modelling Areal Data Introduction Spatial Statistics Approaches Simultaneous Autoregressive Models Conditional Autoregressive Models Fitting Spatial Regression Models Mixed-Effects Models Spatial Econometrics Approaches Other Methods GAM, GEE, GLMM Moran Eigenvectors Geographically Weighted Regression Disease Mapping Introduction Statistical Models Poisson-Gamma Model Log-Normal Model Marshall s Global EB Estimator Spatially Structured Statistical Models Bayesian Hierarchical Models The Poisson-Gamma Model Revisited Spatial Models Detection of Clusters of Disease Testing the Homogeneity of the Relative Risks Moran s I Test of Spatial Autocorrelation
13 XIV Contents Tango s Test of General Clustering Detection of the Location of a Cluster Geographical Analysis Machine Kulldorff s Statistic Stone s Test for Localised Clusters Other Topics in Disease Mapping Afterword R and Package Versions Used Data Sets Used References Subject Index Functions Index...371
14 Part I Handling Spatial Data in R
15 Handling Spatial Data The key intuition underlying the development of the classes and methods in the sp package, and its closer dependent packages, is that users approaching R with experience of GIS will want to see layers, coverages, rasters, or geometries. Seen from this point of view, sp classes should be reasonably familiar, appearing to be well-known data models. On the other hand, for statistician users of R, everything is a data.frame, a rectangular table with rows of observations on columns of variables. To permit the two disparate groups of users to play together happily, classes have grown that look like GIS data models to GIS and other spatial data people, and look and behave like data frames from the point of view of applied statisticians and other data analysts. This part of the book describes the classes and methods of the sp package, and in doing so also provides a practical guide to the internal structure of many GIS data models, as R permits the user to get as close as desired to the data. However, users will not often need to know more than that of Chap. 4 to read in their data and start work. Visualisation is covered in Chap. 3, and so a statistician receiving a well-organised set of data from a collaborator may even be able to start making maps in two lines of code, one to read the data and one to plot the variable of interest using lattice graphics. Note that coloured versions of figures may be found on the book website together with complete code examples, data sets, and other support material. If life was always so convenient, this part of the book could be much shorter than it is. But combining spatial data from different sources often means that much more insight is needed into the data models involved. The data models themselves are described in Chap. 2, and methods for handling and combining them are covered in Chap. 5. Keeping track of which observation belongs to which geometry is also discussed here, seen from the GIS side as feature identifiers, and row names from the data frame side. In addition to data import and export, Chap. 4 also describes the use and transformation of coordinate reference systems for sp classes, and integration of the open source GRASS GIS and R. Finally,Chap.6explainshowthemethodsandclasses introduced in Chap. 2 can be extended to suit one s own needs.
16 1 Hello World: IntroducingSpatialData 1.1 Applied Spatial Data Analysis Spatial data are everywhere. Besides those we collect ourselves ( is it raining? ), they confront us on television, in newspapers, on route planners, on computer screens, and on plain paper maps. Making a map that is suited to its purpose and does not distort the underlying data unnecessarily is not easy. Beyond creating and viewing maps, spatial data analysis is concerned with questions not directly answered by looking at the data themselves. These questions refer to hypothetical processes that generate the observed data. Statistical inference for such spatial processes is often challenging, but is necessary when we try to draw conclusions about questions that interest us. Possible questions that may arise include the following: Does the spatial patterning of disease incidences give rise to the conclusion that they are clustered, and if so, are the clusters found related to factors such as age, relative poverty, or pollution sources? Given a number of observed soil samples, which part of a study area is polluted? Given scattered air quality measurements, how many people are exposed to high levels of black smoke or particulate matter (e.g. PM 10 ), 1 and where do they live? Do governments tend to compare their policies with those of their neighbours, or do they behave independently? In this book we will be concerned with applied spatial data analysis, meaning that we will deal with data sets, explain the problems they confront us with, and show how we can attempt to reach a conclusion. This book will refer to the theoretical background of methods and models for data analysis, but emphasise hands-on, do-it-yourself examples using R; readers needing this background should consult the references. All data sets used in this book and all examples given are available, and interested readers will be able to reproduce them. 1 Particulate matter smaller than about 10 µm.
17 2 1 Hello World: Introducing Spatial Data In this chapter we discuss the following: (i) Why we use R for analysing spatial data (ii) The relation between R and geographical information systems (GIS) (iii) What spatial data are, and the types of spatial data we distinguish (iv) The challenges posed by their storage and display (v) The analysis of observed spatial data in relation to processes thought to have generated them (vi) Sources of information about the use of R for spatial data analysis and the structure of the book. 1.2 Why Do We Use R In General? The R system 2 (R Development Core Team, 2008) is a free software environment for statistical computing and graphics. It is an implementation of the S language for statistical computing and graphics (Becker et al., 1988). For data analysis, it can be highly efficient to use a special-purpose language like S, compared to using a general-purpose language. For new R users without earlier scripting or programming experience, meeting a programming language may be unsettling, but the investment 3 will quickly pay off. The user soon discovers how analysis components written or copied from examples can easily be stored, replayed, modified for another data set, or extended. R can be extended easily with new dedicated components, and can be used to develop and exchange data sets and data analysis approaches. It is often much harder to achieve this with programs that require long series of mouse clicks to operate. R provides many standard and innovative statistical analysis methods. New users may find access to both well-tried and trusted methods, and speculative and novel approaches, worrying. This can, however, be a major strength, because if required, innovations can be tested in a robust environment against legacy techniques. Many methods for analysing spatial data are less frequently used than the most common statistical techniques, and thus benefit proportionally more from the nearness to both the data and the methods that R permits. R uses well-known libraries for numerical analysis, and can easily be extended by or linked to code written in S, C,C++,Fortran,orJava.Links to various relational data base systems and geographical information systems exist, many well-known data formats can be read and/or written. The level of voluntary support and the development speed of R are high, and experience has shown R to be environment suitable for developing professional, mission-critical software applications, both for the public and the 2 3 A steep learning curve the user learns a lot per unit time.
18 1.2 Why Do We Use R 3 private sector. The S language can not only be used for low-level computation on numbers, vectors, or matrices but can also be easily extended with classes for new data types and analysis methods for these classes, such as methods for summarising, plotting, printing, performing tests, or model fitting (Chambers, 1998). In addition to the core R software system, R is also a social movement, with many participants on a continuum from users just beginning to analyse data with R to developers contributingpackagestothecomprehensiver Archive Network 4 (CRAN) for others to download and employ. Just as R itself benefits from the open source development model, contributed package authors benefit from a world-class infrastructure, allowing their work to be published and revised with improbable speed and reliability, including the publication of source packages and binary packages for many popular platforms. Contributed add-on packages are very much part of the R community, and most core developers also write and maintain contributed packages. A contributed package contains R functions, optional sample data sets, and documentation including examples of how to use the functions for Spatial Data Analysis? For over 10 years, R has had an increasing number of contributed packages for handling and analysing spatial data. All these packages used to make different assumptions about how spatial data were organised, and R itself had no capabilities for distinguishing coordinates from other numbers. In addition, methods for plotting spatial data and other tasks were scattered, made different assumptions on the organisation of the data, and were rudimentary. This was not unlike the situation for time series data at the time. After some joint effort and wider discussion, a group 5 of R developers have written the R package sp to extend R with classes and methods for spatial data (Pebesma and Bivand, 2005). Classes specify a structure and define how spatial data are organised and stored. Methods are instances of functions specialised for a particular data class. For example, the summary method for all spatial data classes may tell the range spanned by the spatial coordinates, and show which coordinate reference system is used (such as degrees longitude/latitude, or the UTM zone). It may in addition show some more details for objects of a specific spatial class. A plot method may, for example create a map of the spatial data. The sp package provides classes and methods for points, lines, polygons, and grids (Sect. 1.4, Chap. 2). Adopting a single set of classes for spatial data offers a number of important advantages: 4 CRAN mirrors are linked from 5 Mostly the authors of this book with help from Barry Rowlingson and Paulo J. Ribeiro Jr.
19 4 1 Hello World: Introducing Spatial Data (i) It is much easier to move data across spatial statistics packages. The classes are either supported directly by the packages, reading and writing data in the new spatial classes, or indirectly, for example by supplying data conversion between the sp classes and the package s classes in an interface package. This last option requires one-to-many links between the packages, which are easier to provide and maintain than many-to-many links. (ii) The new classes come with a well-tested set of methods (functions) for plotting, printing, subsetting, and summarising spatial objects, or combining (overlaying) spatial data types. (iii) Packages with interfaces to geographical information systems (GIS), for reading and writing GIS file formats, and for coordinate (re)projection code support the new classes. (iv) The new methods include Lattice plots, conditioning plots, plot methods that combine points, lines, polygons, and grids with map elements (reference grids, scale bars, north arrows), degree symbols (as in 52 N) in axis labels, etc. Chapter 2 introduces the classes and methods provided by sp, and discusses some of the implementation details. Further chapters will show the degree of integration of sp classes and methods and the packages used for statistical analysis of spatial data. Figure 1.1 shows how the reception of sp classes has already influenced the landscape of contributed packages; interfacing other packages for handling and analysing spatial data is usually simple as we see in Part II. The shaded nodes of the dependency graph are packages (co)-written and/or maintained by the authors of this book, and will be used extensively in the following chapters. 1.3 R and GIS What is GIS? Storage and analysis of spatial data is traditionally done in Geographical Information Systems (GIS). According to the toolbox-based definition of Burrough and McDonnell (1998, p. 11), a GIS is...a powerful set of tools for collecting, storing, retrieving at will, transforming, and displaying spatial data from the real world for a particular set of purposes. Another definition mentioned in the same source refers to...checking, manipulating, and analysing data, which are spatially referenced to the Earth. Its capacity to analyse and visualise data makes R agoodchoiceforspatial data analysis. For some spatial analysis projects, using only R may be sufficient for the job. In many cases, however, R will be used in conjunction with GIS software and possibly a GIS data base as well. Chapter 4 will show how spatial data are imported from and exported to GIS file formats. As is often the case in applied data analysis, the real issue is not whether a given problem can be
20 1.3 R and GIS 5 sp maptools rgdal splancs geor gstat spsurvey trip aspace spdep spgwr surveillance GeoXp spgrass6 GEOmap ecespa StatDA georglm simba DCluster svcr BARD RTOMO VIM Fig Tree of R contributed packages on CRAN depending on or importing sp directly or indirectly; others suggest sp or use it without declaration in their package descriptions (status as of )
Use R! Series Editors: Robert Gentleman Kurt Hornik Giovanni Parmigiani Use R! Albert: Bayesian Computation with R Bivand/Pebesma/Gómez-Rubio: Applied Spatial Data Analysis with R Cook/Swayne: Interactive
Introduction to GIS (Basics, Data, Analysis) & Case Studies 13 th May 2004 Content Introduction to GIS Data concepts Data input Analysis Applications selected examples What is GIS? Geographic Information
A Process and Environment for Embedding The R Software into TerraLib Pedro Ribeiro de Andrade Neto 1, Paulo Justiniano Ribeiro Junior 1 1 Laboratório de Estatística e Geoinformação (LEG) Statistics Department
Introduction An Introduction to Point Pattern Analysis using CrimeStat Luc Anselin Spatial Analysis Laboratory Department of Agricultural and Consumer Economics University of Illinois, Urbana-Champaign
Statistics for Biology and Health Series Editors M. Gail, K. Krickeberg, J.M. Samet, A. Tsiatis, W. Wong For further volumes: http://www.springer.com/series/2848 David G. Kleinbaum Mitchel Klein Survival
INTERNATIONAL SCIENTIFIC CONFERENCE AND XXIV MEETING OF SERBIAN SURVEYORS PROFESSIONAL PRACTICE AND EDUCATION IN GEODESY AND RELATED FIELDS 24-26, June 2011, Kladovo -,,Djerdap upon Danube, Serbia. APPLICATION
Classes and Methods for Spatial Data: the sp Package Edzer Pebesma Roger S. Bivand Feb 2005 Contents 1 Introduction 2 2 Spatial data classes 2 3 Manipulating spatial objects 3 3.1 Standard methods..........................
14 Spatial Data Analysis OVERVIEW This chapter is the first in a set of three dealing with geographic analysis and modeling methods. The chapter begins with a review of the relevant terms, and an outlines
A HYBRID APPROACH FOR AUTOMATED AREA AGGREGATION Zeshen Wang ESRI 380 NewYork Street Redlands CA 92373 Zwang@esri.com ABSTRACT Automated area aggregation, which is widely needed for mapping both natural
GEOGRAPHIC INFORMATION SYSTEMS CERTIFICATION GIS Syllabus - Version 1.2 January 2007 Copyright AICA-CEPIS 2009 1 Version 1 January 2007 GIS Certification Programme 1. Target The GIS certification is aimed
Future Landscapes Research report June 2005 CONTENTS 1. Introduction 2. Original ideas for the project 3. The Future Landscapes prototype 4. Early usability trials 5. Reflections on first phases of development
Using Spatial Statistics In GIS K. Krivoruchko a and C.A. Gotway b a Environmental Systems Research Institute, 380 New York Street, Redlands, CA 92373-8100, USA b Centers for Disease Control and Prevention;
Applied Multivariate Statistical Modelling Prof. J. Maiti Department of Industrial Engineering and Management Indian Institute of Technology, Kharagpur Lecture - 32 Regression Modelling Using SPSS (Refer
Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model Xavier Conort email@example.com Motivation Location matters! Observed value at one location is
Introduction to GIS 1 Introduction to GIS http://www.sli.unimelb.edu.au/gisweb/ Dr F. Escobar, Assoc Prof G. Hunter, Assoc Prof I. Bishop, Dr A. Zerger Department of Geomatics, The University of Melbourne
R Graphics Cookbook Winston Chang Beijing Cambridge Farnham Koln Sebastopol O'REILLY Tokyo Table of Contents Preface ix 1. R Basics 1 1.1. Installing a Package 1 1.2. Loading a Package 2 1.3. Loading a
SPSS-SA Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Training Brochure 2009 TABLE OF CONTENTS 1 SPSS TRAINING COURSES FOCUSING
New URL: http://www.r-project.org/conferences/dsc-2001/ DSC 2001 Proceedings of the 2nd International Workshop on Distributed Statistical Computing March 15-17, Vienna, Austria http://www.ci.tuwien.ac.at/conferences/dsc-2001
EXPLORING SPATIAL PATTERNS IN YOUR DATA OBJECTIVES Learn how to examine your data using the Geostatistical Analysis tools in ArcMap. Learn how to use descriptive statistics in ArcMap and Geoda to analyze
Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In
1) Introduction Marketing Mix Modelling and Big Data P. M Cain Big data is generally defined in terms of the volume and variety of structured and unstructured information. Whereas structured data is stored
Data Visualization Techniques and Practices Introduction to GIS Technology Michael Greene Advanced Analytics & Modeling, Deloitte Consulting LLP March 16 th, 2010 Antitrust Notice The Casualty Actuarial
Policy Discussion Briefing January 27 Composite performance measures in the public sector Rowena Jacobs, Maria Goddard and Peter C. Smith Introduction It is rare to open a newspaper or read a government
Engineering Problem Solving and Excel EGN 1006 Introduction to Engineering Mathematical Solution Procedures Commonly Used in Engineering Analysis Data Analysis Techniques (Statistics) Curve Fitting techniques
User s Guide to ArcView 3.3 for Land Use Planners in Puttalam District Dilhari Weragodatenna IUCN Sri Lanka, Country Office Table of Content Page No Introduction...... 1 1. Getting started..... 2 2. Geo-referencing...
MapInfo Professional Version 12.5 Printing Guide The purpose of this guide is to assist you in getting the best possible output from your MapInfo Professional software. We begin by covering the new print,
Longitudinal and Life Course Studies 2009 Volume 1 Issue 1 Pp 63-72 Handling attrition and non-response in longitudinal data Harvey Goldstein University of Bristol Correspondence. Professor H. Goldstein
Time Series Analysis: Basic Forecasting. As published in Benchmarks RSS Matters, April 2015 http://web3.unt.edu/benchmarks/issues/2015/04/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD firstname.lastname@example.org
Enhancing Information and Methods for Health System Planning and Research, Institute for Clinical Evaluative Sciences (ICES), January 19-20, 2004, Toronto, Canada Workshop: Using Spatial Analysis and Maps
Practical Time Series Analysis Using SAS Anders Milhøj Contents Preface... vii Part 1: Time Series as a Subject for Analysis... 1 Chapter 1 Time Series Data... 3 1.1 Time Series Questions... 3 1.2 Types
A Geographic Information System (GIS) integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of geographically referenced information. GIS allows us to view,
An Introduction to Open Source Geospatial Tools by Tyler Mitchell, author of Web Mapping Illustrated GRSS would like to thank Mr. Mitchell for this tutorial. Geospatial technologies come in many forms,
Vector Data Analysis I: Buffering Today we will use ArcMap and ArcToolbox to manipulate vector-based geographic data. The results of these simple analyses will allow us to visualize complex spatial relationships.
LIBER QUARTERLY, ISSN 1435-5205 LIBER 1999. All rights reserved K.G. Saur, Munich. Printed in Germany Digital Cadastral Maps in Land Information Systems by PIOTR CICHOCINSKI ABSTRACT This paper presents
MapInfo Pro Version 15.0 The purpose of this guide is to assist you in getting the best possible output from your MapInfo Pro software. We begin by covering the new print, import, and export features and
, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal
Institute of Natural Resources Departament of General Geology and Land use planning Work with a MAPS Lecturers: Berchuk V.Y. Gutareva N.Y. Contents: 1. Qgis; 2. General information; 3. Qgis desktop; 4.
GIS 120 Ticket 70552 03:55pm to 6:50pm MW first class meeting August 20th last class meets December 5th Syllabus Introduction to GIS 120 Instructor: Warren Roberts Rio Hondo College GIS Los Angeles County
Advanced Spatial Statistics Fall 2012 NCSU Fuentes Lecture notes Areal unit data 2 Areal Modelling Areal unit data Key Issues Is there spatial pattern? Spatial pattern implies that observations from units
My presentation is about data visualization. How to use visual graphs and charts in order to explore data, discover meaning and report findings. The goal is to show that visual displays can be very effective
Geography 4203 / 5203 GIS Modeling Class (Block) 9: Variogram & Kriging Some Updates Today class + one proposal presentation Feb 22 Proposal Presentations Feb 25 Readings discussion (Interpolation) Last
Visualization methods for patent data Treparel 2013 Dr. Anton Heijs (CTO & Founder) Delft, The Netherlands Introduction Treparel can provide advanced visualizations for patent data. This document describes
A GENERAL TAXONOMY FOR VISUALIZATION OF PREDICTIVE SOCIAL MEDIA ANALYTICS Stacey Franklin Jones, D.Sc. ProTech Global Solutions Annapolis, MD Abstract The use of Social Media as a resource to characterize
Spreadsheet software for linear regression analysis Robert Nau Fuqua School of Business, Duke University Copies of these slides together with individual Excel files that demonstrate each program are available
Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2
4 Programming Languages & Tools Almost any programming language one is familiar with can be used for computational work (despite the fact that some people believe strongly that their own favorite programming
Geographically weighted visualization interactive graphics for scale-varying exploratory analysis Chris Brunsdon 1, Jason Dykes 2 1 Department of Geography Leicester University Leicester LE1 7RH email@example.com
15.1 Lesson 15 - Fill Cells Plugin This lesson presents the functionalities of the Fill Cells plugin. Fill Cells plugin allows the calculation of attribute values of tables associated with cell type layers.
STEPS Epi Info Training Guide Department of Chronic Diseases and Health Promotion World Health Organization 20 Avenue Appia, 1211 Geneva 27, Switzerland For further information: www.who.int/chp/steps WHO
Paper 156-2010 The Forgotten JMP Visualizations (Plus Some New Views in JMP 9) Sam Gardner, SAS Institute, Lafayette, IN, USA Abstract JMP has a rich set of visual displays that can help you see the information
GIS: Geographic Information Systems A short introduction Outline The Center for Digital Scholarship What is GIS? Data types GIS software and analysis Campus GIS resources Center for Digital Scholarship
PGR Computing Programming Skills Dr. I. Hawke 2008 1 Introduction The purpose of computing is to do something faster, more efficiently and more reliably than you could as a human do it. One obvious point
Geography 4203 / 5203 GIS & Spatial Modeling Class 2: Spatial Doing - A discourse about analysis and modeling in a spatial context Updates Class homepage at: http://www.colorado.edu/geography/class_homepages/geog_4203
A comparison of the OpenGIS TM Abstract Specification with the CIDOC CRM 3.2 Draft Martin Doerr ICS-FORTH, Heraklion, Crete Oct 4, 2001 1 Introduction This Mapping has the purpose to identify, if the OpenGIS
The Center for Astrophysical Thermonuclear Flashes VisIt Visualization Tool Randy Hudson firstname.lastname@example.org Argonne National Laboratory Flash Center, University of Chicago An Advanced Simulation and Computing
Objectives Tutorial 8 Raster Data Analysis This tutorial is designed to introduce you to a basic set of raster-based analyses including: 1. Displaying Digital Elevation Model (DEM) 2. Slope calculations
Huffman 1 The Use of GPS and GIS to Analyze Access Near Intersections Presented to the Urban Street Symposium July 28 th 30 th, 2003 Anaheim, CA Chris Huffman, P.E. Corridor Management Administrator Kansas
Assessment of Workforce Demands to Shape GIS&T Education Gudrun Wallentin, Barbara Hofer, Christoph Traun email@example.com University of Salzburg, Dept. of Geoinformatics Z_GIS, Austria www.gi-n2k.eu
SESSION 8: GEOGRAPHIC INFORMATION SYSTEMS AND MAP PROJECTIONS KEY CONCEPTS: In this session we will look at: Geographic information systems and Map projections. Content that needs to be covered for examination
Natural Neighbour Interpolation DThe Natural Neighbour method is a geometric estimation technique that uses natural neighbourhood regions generated around each point in the data set. The method is particularly
SAS R IML (Introduction at the Master s Level) Anton Bekkerman, Ph.D., Montana State University, Bozeman, MT ABSTRACT Most graduate-level statistics and econometrics programs require a more advanced knowledge
Page 1 of 10 GIS 101 - Introduction to Geographic Information Systems Last Revision or Approval Date - 9/8/2011 College of the Canyons SECTION A 1. Division: Mathematics and Science 2. Department: Earth,
Matthias Beck Ross Geoghegan The Art of Proof Basic Training for Deeper Mathematics ! "#$$%'!()*+!!,-''!.)-/%)/#0! 1)2#3$4)0$!-5!"#$%)4#$&*'!! 1)2#3$4)0$!-5!"#$%)4#$&*#6!7*&)0*)'! 7#0!83#0*&'*-!7$#$)!90&:)3'&$;!
MATLAB Fundamentals and Programming Techniques Course Number 68201 40 Hours Overview MATLAB Fundamentals and Programming Techniques is a five-day course that provides a working introduction to the MATLAB
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
Analysis of Financial Time Series Analysis of Financial Time Series Financial Econometrics RUEY S. TSAY University of Chicago A Wiley-Interscience Publication JOHN WILEY & SONS, INC. This book is printed
Edzer Pebesma firstname.lastname@example.org Tom s UvA summer school, 18 Aug 2008 Edzer Pebesma ifgi, Universität Münster, 1 The R user s conference, August 12-14, Technische Universität Dortmund, Germany
Tutorial 3 - Map Symbology in ArcGIS Introduction ArcGIS provides many ways to display and analyze map features. Although not specifically a map-making or cartographic program, ArcGIS does feature a wide
Data Visualization Principles and Practice Second Edition Alexandru Telea First edition published in 2007 by A K Peters, Ltd. Cover image: The cover shows the combination of scientific visualization and
Exercise 1: Basic visualization of LiDAR Digital Elevation Models using ArcGIS Introduction This exercise covers activities associated with basic visualization of LiDAR Digital Elevation Models using ArcGIS.
The Value of Visualization 2 G Janacek -0.69 1.11-3.1 4.0 GJJ () Visualization 1 / 21 Parallel coordinates Parallel coordinates is a common way of visualising high-dimensional geometry and analysing multivariate
European Petroleum Survey Group EPSG Guidance Note Number 5 Coordinate Reference System Definition - Recommended Practice Revision history: Version Date Amendments 1.0 April 1997 First release. 1.1 June
Review Your Thesis or Dissertation This document shows the formatting requirements for UBC theses. Theses must follow these guidelines in order to be accepted at the Faculty of Graduate and Postdoctoral
A Introduction to Matrix Algebra and Principal Components Analysis Multivariate Methods in Education ERSH 8350 Lecture #2 August 24, 2011 ERSH 8350: Lecture 2 Today s Class An introduction to matrix algebra
Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are
NJDEP GPS Data Collection Standards For GIS Data Development Bureau of Geographic Information Systems Office of Information Resource Management June 8, 2011 1.0 Introduction... 3 2.0 GPS Receiver Hardware
Student Learning Development Presenting numerical data This guide offers practical advice on how to incorporate numerical information into essays, reports, dissertations, posters and presentations. The
Tutorial ID: IGET_WEBGIS_002 This tutorial has been developed by BVIEER as part of the IGET web portal intended to provide easy access to geospatial education. This tutorial is released under the Creative
Map overlay and spatial aggregation in sp Edzer Pebesma June 5, 2015 Abstract Numerical map overlay combines spatial features from one map layer with the attribute (numerical) properties of another. This
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts
IBM SPSS Statistics How to get more value from your survey data Discover four advanced analysis techniques that make survey research more effective Contents: 1 Introduction 2 Descriptive survey research
Application of GIS in Transportation Planning: The Case of Riyadh, the Kingdom of Saudi Arabia Mezyad Alterkawi King Saud University, Kingdom of Saudi Arabia * Abstract This paper is intended to illustrate
THE SELECTION OF RETURNS FOR AUDIT BY THE IRS John P. Hiniker, Internal Revenue Service BACKGROUND The Internal Revenue Service, hereafter referred to as the IRS, is responsible for administering the Internal
MicroStrategy Analytics Express User Guide Analyzing Data with MicroStrategy Analytics Express Version: 4.0 Document Number: 09770040 CONTENTS 1. Getting Started with MicroStrategy Analytics Express Introduction...
Your consent to our cookies if you continue to use this website.