Exploring Spatial Data with GeoDaTM : A Workbook
Luc Anselin
Spatial Analysis Laboratory Department of Geography University of Illinois, Urbana-Champaign Urbana, IL 61801
http:/sal.agecon.uiuc.edu/
Center for Spatially Integrated Social Science
http:/www.
Spatial Autocorrelation
Nilupa Gunaratna, Yali Liu, Junyong Park
1. Definition
Observations made at different locations may not be independent. For example,
measurements made at nearby locations may be closer in value than measurements made
at locations f
Spatial Autocorrelation
Andrea Zuur
Introduction
The goal of this presentation is to provide ecology students with an understandable
primer on spatial autocorrelation within the context of ecology. The assumptions of
spatial and classical statistics are c
Radial Basis Functions
Radial basis interpolation is the name given to a large family of exact interpolators. In many ways
the methods applied are similar to those used in geostatistical interpolation (see Section 6.7), but
without the benefit of prior an
Poisson Regression
Poisson Distribution
A Poisson distribution is given by:
Pr[Y = y ] =
e y
y!
, y = 0,1,2.
Where, is the average number of occurrences in
a specified interval
Assumptions:
Independence
Prob. of occurrence In a short interval is propor
Point Pattern Analysis
using
Spatial Inferential Statistics
1
How Point Pattern Analysis (PPA)
From Centrographic Statistics (previously):
Centrographic Statistics calculates single,
summary measures
PPA analyses the complete set of points
From Spatial Au
Geographic Information Science
Geography 625
Intermediate
Geographic Information Science
Week4: Point Pattern Analysis
Instructor: Changshan Wu
Department of Geography
The University of Wisconsin-Milwaukee
Fall 2006
University of Wisconsin-Milwaukee
Geogr
Multiple testing / Multiple comparison -Overview
From Glantz, Primer of Biostatistics, Chapter 4
Suppose that we perform 5 t-tests, each with alpha = 0.05.
What is the probability that we will get at least one false positive result?
P(at least one false p
Introduction to Point Pattern Analysis
Erum Tariq (2004)
School of Mines & Technology, South Dakota
Abstract
Point Pattern Analysis is a class of techniques that endeavor to identify patterns in spatial
data. We utilize the Quadrant Count Method as an int
Global Measures of
Spatial Autocorrelation
China
1
Briggs Henan University 2010
Last Time
The concept of spatial autocorrelation.
Near things are more similar than distant things
The use of the weights matrix Wij to measure
nearness
The difficulty of
Global Spatial Autocorrelation Indices - Moran's I, Geary's C and the General
Cross-Product Statistic
By M.Sawada
Department of Geography
University of Ottawa
Ottawa ON K1N 6N5
Introduction
The analysis of spatially located data is one of the basic concer
Global Morans I and Global Gearys c
Morans I and Gearys c are well known tests for spatial autocorrelation. They represent
two special cases of the general cross-product statistic that measures spatial
autocorrelation. Morans I is produced by standardizin
geary.test cfw_spdep
R Documentation
Geary's C test for spatial autocorrelation
Description
Geary's test for spatial autocorrelation using a spatial weights matrix in weights list form. The
assumptions underlying the test are sensitive to the form of the
SPATIAL DIFFUSION
ANALYSIS
Example Application Areas
Diffusion of Information
Diffusion of Toxic Wastes
Spread of Infectious Diseases
Product Adoption Example
Tony E. Smith and Sanyoung Song
http:/www.seas.upenn.edu~tesmith
Basic Model
Steady State A
Spatial Statistics
Concepts (O&U Ch. 3)
Centrographic Statistics (O&U Ch. 4 p. 77-81)
single, summary measures of a spatial distribution
Point Pattern Analysis (O&U Ch 4 p. 81-114)
- pattern analysis; points have no magnitude (no variable)
Quadrat Analys
6. Knox Statistic for Space-Time Clustering
The Knox approach is used to test whether there is a significant cluster during a defined
distance and time period. First it counts the number of point pairs as either close or
distant in space and /or time, the
Geographical Analysis ISSN 0016-7363
GeoDa: An Introduction to Spatial Data Analysis
Luc Anselin1, Ibnu Syabri2, Youngihn Kho1
1
Spatial Analysis Laboratory, Department of Geography, University of Illinois, Urbana, IL, 2Laboratory for Spatial Computing an
GeoDa: An Introduction to Spatial Data Analysis
Luc Anselin, Ibnu Syabri and Youngihn Kho Spatial Analysis Laboratory Department of Agricultural and Consumer Economics University of Illinois, Urbana-Champaign Urbana, IL 61801 USA
anselin@uiuc.edu, syabri@
Generalized extreme value distribution - Wikipedia.
http:/en.wikipedia.org/wiki/Extreme_value_distri.
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Generalized extreme value distribution
From Wikipedia, the free encyclopedia
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Spatial analysis of density dependent pattern in coniferous forest stands*
Janet Franklin 1, Joel Michaelsen 1 & Alan H. Strahler2*, *
1Department of Geography, University of California, Santa Barbara, California, 93106; 2Department of Geology and Geograp
An Application of Extreme Value Theory for Measuring Risk
Manfred Gilli, Evis Kllezi e
Department of Econometrics, University of Geneva and FAME CH1211 Geneva 4, Switzerland
Abstract Many fields of modern science and engineering have to deal with events w
EXTREME VALUE THEORY
Richard L. Smith Department of Statistics and Operations Research University of North Carolina Chapel Hill, NC 27599-3260 rls@email.unc.edu AMS Committee on Probability and Statistics Short Course on Statistics of Extreme Events Phoen
SaTScan User Guide
TM
for version 9.0
By Martin Kulldorff July, 2010 http:/www.satscan.org/
Contents
Introduction . 4 The SaTScan Software . 4 Download and Installation. 5 Test Run . 5 Sample Data Sets . 6 Statistical Methodology . 9
Appendix C
Ordinary Least Squares and Poisson Regression Models
by Luc Anselin University of Illinois Champaign-Urbana, IL This note provides a brief description of the statistical background, estimators and model characteristics for a regression specific
Appendix D:
Negative Binomial Regression Models and Estimation Methods
By Dominique Lord Texas A&M University Byung-Jung Park Korea Transport Institute This appendix presents the characteristics of Negative Binomial regression models and discusses their e
Regression Models for Count Data in R
Achim Zeileis
Universitt Innsbruck a
Christian Kleiber
Universitt Basel a
Simon Jackman
Stanford University
Abstract The classical Poisson, geometric and negative binomial regression models for count data belong to th
Exploratory temporal visualization of Massachusetts breast cancer data archives
Alex Brown, Toxics Use Reduction Institute (TURI) and UMass-Lowell Dept of Environmental, Earth & Atmospheric Sciences (Corresponding author: Alexander_Brown@uml.edu) Dr Rich
Continuous Data Analysis
Analysis of Spatially Continuous Data
Bailey and Gatrell Chapter 5
Focus is on patterns in the attribute values not locations as in the analysis of point patterns The locations are simply sites at which attribute values have been
The Bonferonni and Sidk Corrections for Multiple Comparisons
Herv Abdi1
1 Overview
The more tests we perform on a set of data, the more likely we are to reject the null hypothesis when it is true (i.e., a "Type I" error). This is a consequence of the logi