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Non-standard Spatial Statistics and Spatial Econometrics

:	Daniel A. Griffith, Jean H. Paul Paelinck
:	Non-standard Spatial Statistics and Spatial Econometrics
:	Springer-Verlag
:	9783642160431
:	Advances in Geographic Information Science
:	1
:	CHF 135.20
:

:	Geografie
:	English

:	264
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

Despite spatial statistics and spatial econometrics both being recent sprouts of the general tree 'spatial analysis with measurement'-some may remember the debate after WWII about 'theory without measurement' versus 'measurement without theory'-several general themes have emerged in the pertaining literature. But exploring selected other fields of possible interest is tantalizing, and this is what the authors intend to report here, hoping that they will suscitate interest in the methodologies exposed and possible further applications of these methodologies. The authors hope that reactions about their publication will ensue, and they would be grateful to reader(s) motivated by some of the research efforts exposed hereafter letting them know about these experiences.

	Preface	4
	Prologue	5
	Contents	28
	Part I Non-standard Spatial Statistics	33
	1 Introduction: Spatial Statistics	34
	2 Individual Versus Ecological Analyses	35
	2.1 Introduction	35
	2.2 Spatial Autocorrelation Effects	35
	2.3 Aggregation Impacts	36
	2.3.1 The Syracuse Data	38
	2.3.2 Previous Findings for Syracuse	40
	2.4 Spatial Autocorrelation in the Syracuse Data	41
	2.4.1 Spatial Autocorrelation in the Syracuse Data: LN(BLL + 1) Values	41
	2.4.2 Spatial Autocorrelation in the Syracuse Data: Appraised House Value	43
	2.5 Spatial Autocorrelation in the Syracuse Data: Other Sources	46
	2.6 Bayesian Analysis Using Gibbs Sampling (BUGS) and Model Prediction Experiments	47
	2.6.1 Results for the 2000 Census Tracts	50
	2.7 Discussion and Implications	52
	3 Statistical Models for Spatial Data: Some Linkages and Communalities	54
	3.1 Introduction	54
	3.2 Background: Quantifying Spatial Autocorrelation	55
	3.2.1 The Moran Scatterplot	56
	3.2.2 The Semivariogram Plot	57
	3.3 Specifications of Spatial Autoregressive and Geostatistical Models	57
	3.3.1 Spatial Autoregressive Models	58
	3.4 Geostatistical Models	60
	3.5 Linkages Between Spatial Autoregression and Geostatistics	61
	3.6 A Major Commonality of Spatial Autoregression and Geostatistics	62
	3.7 Implications for Quantitative Human Geography	64
	4 Frequency Distributions for Simulated Spatially Autocorrelated Random Variables	65
	4.1 Introduction	65
	4.2 The Normal Probability Model	66
	4.2.1 Simulating Spatially Autocorrelated Normal RVs	67
	4.2.2 Simulation Results for an Ideal Regular Hexagonal Surface Partitioning	69
	4.2.3 Simulation Results for the China County Geographic Configuration	73
	4.2.4 Implications	76
	4.3 The Poisson Probability Model	78
	4.3.1 Simulating Spatially Autocorrelated Poisson RVs	80
	4.3.1.1 MCMC Map Simulation	81
	4.3.1.2 SF Map Simulation	83
	4.3.2 Simulation Results for an Ideal Regular Hexagonal Surface Partitioning	83
	4.3.3 Simulation Results for the China County Geographic Configuration	84
	4.3.4 Implications	88
	4.4 The Binomial Probability Model, N	88
	90	88
	4.4.1 Simulating Spatially Autocorrelated Binomial RVs	91
	4.4.2 Simulation Results for an Ideal Regular Hexagonal Surface Partitioning	93
	4.4.3 Simulation Results for the China County Geographic Configuration	96
	4.4.4 Implications	98
	4.5 Discussion	99
	5 Understanding Correlations Among Spatial Processes	102
	5.1 Introduction	102
	5.2 Two Illustrative Examples	102
	5.3 Geostatistical Semivariogram Model Implications	104
	5.4 Spatial Autoregressive Model Implications	109
	5.4.1 Variance and Covariance Inflation Attributable to Spatial Autocorrelation	112
	5.4.2 Effective Sample Size as a Function of .X and .Y	114
	5.5 Spatial Filtering Model Implications	116
	5.5.1 Correlation Coefficient Decomposition	117
	5.5.2 Variance Inflation	120
	5.6 Discussion	120
	6 Spatially Structured Random Effects: A Comparison of Three Popular Specifications	123
	6.1 Introduction	123
	6.2 Modeling Spatial Structure	123
	6.3 Linear Mixed Models	125
	6.4 Generalized Linear Mixed Models	131
	6.5 Degrees of Freedom for GLMM Random Effects	136
	6.6 Extensions to Space-Time Data Sets	137
	6.7 Discussion and Implications	140
	7 Spatial Filter Versus Conventional Spatial Model Specifications: Some Comparisons	142
	7.1 Introduction	142
	7.1.1 Background	142
	7.2 Variation and Covariation Considerations for Poisson Random Variables	145
	7.2.1 Heterogeneity in Counts Data	146
	7.2.2 Spatial Autocorrelation in Poisson Random Variables	149
	7.2.3 Spatial Autocorrelation-induced Correlation Inflation	151
	7.3 Principal Spatial Statistical Model Specifications	155
	7.3.1 The Log-normal Approximation	155
	7.3.2 A Winsorized Auto-Poisson Model	156
	7.3.3 A Proper CAR Model Specification via GeoBUGS	159
	7.4 Spatial Filter Model Specifications	161
	7.4.1 The Log-normal Approximation Spatial Filter Model	161
	7.4.2 A Poisson Spatial Filter Model	162
	7.4.3 A Spatial Filter Model Specification via BUGS	164
	7.5 Discussion	165
	7.5.1 Cross-validation Results for the Poisson Spatial Filter Model	166
	7.5.2 A Simulation Experiment Based Upon the Poisson Spatial Filter Model	166
	7.5.3 Impacts of Incorporating Additional Information	168
	7.5.4 Implications for Data Mapping	169
	7.6 Concluding Comments	172
	8 The Role of Spatial Autocorrelation in Prioritizing Sites Within a Geographic Landscape	175
	8.1 Introduction: The Problem	175
	8.2 The Murray Superfund Site: Part I	176
	8.2.1 State-of-the-Art Practice	177
	8.