Non-standard spatial statistics and spatial econometrics:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Berlin [u.a.]
Springer
2011
|
| Schriftenreihe: | Advances in geographic information science
|
| Schlagworte: | |
| Online-Zugang: | Inhaltstext Inhaltsverzeichnis |
| Beschreibung: | XXXV, 262 S. Ill., graph. Darst., Kt. |
| ISBN: | 9783642266690 9783642160424 9783642160431 |
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| 245 | 1 | 0 | |a Non-standard spatial statistics and spatial econometrics |c Daniel A. Griffith ; Jean H. P. Paelinck |
| 264 | 1 | |a Berlin [u.a.] |b Springer |c 2011 | |
| 300 | |a XXXV, 262 S. |b Ill., graph. Darst., Kt. | ||
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Datensatz im Suchindex
| _version_ | 1805095107233316864 |
|---|---|
| adam_text |
Contents
Part I Non-standard Spatial Statistics
1
Introduction: Spatial Statistics
. 3
2
Individual Versus Ecological Analyses
. 5
2.1
Introduction
. 5
2.2
Spatial Autocorrelation Effects
. 5
2.3
Aggregation Impacts
. 6
2.3.1
The Syracuse Data
. 8
2.3.2
Previous Findings for Syracuse
. 10
2.4
Spatial Autocorrelation in the Syracuse Data
. 11
2.4.1
Spatial Autocorrelation in the Syracuse Data:
LN(BLL+
1)
Values
.
II
2.4.2
Spatial Autocorrelation in the Syracuse Data:
Appraised House Value
. 13
2.5
Spatial Autocorrelation in the Syracuse Data: Other Sources
. 16
2.6
Bayesian Analysis Using Gibbs Sampling (BUGS) and
Model Prediction Experiments
. 17
2.6.1
Results for the
2000
Census Tracts
. 20
2.7
Discussion and Implications
. 22
3
Statistical Models for Spatial Data: Some Linkages
and Communalities
. 25
3.1
Introduction
. 25
3.2
Background: Quantifying Spatial Autocorrelation
. 26
3.2.1
The
Moran
Scatterplot
. 27
3.2.2
The Semivariogram Plot
. 28
3.3
Specifications of Spatial
Autoregressive
and
Geostatistical Models
. 28
3.3.1
Spatial
Autoregressive
Models
. 29
3.4
Geostatistical Models
. 31
3.5
Linkages Between Spatial
Autoregression
and Geostatistics
. . 32
xii Contents
3.6
A
Major
Commonality of Spatial
Autoregression
and
Geostatistics
. 33
3.7
Implications for Quantitative Human Geography
. 35
4
Frequency Distributions for Simulated Spatially
Autocorrelated Random Variables
. 37
4.1
Introduction
. 37
4.2
The Normal Probability Model
. 38
4.2.1
Simulating Spatially Autocorrelated Normal RVs
. 39
4.2.2
Simulation Results for an Ideal Regular
Hexagonal Surface Partitioning
. 41
4.2.3
Simulation Results for the China County
Geographic Configuration
. 45
4.2.4
Implications
. 48
4.3
The
Poisson
Probability Model
. 50
4.3.1
Simulating Spatially Autocorrelated
Poisson
RVs
. 52
4.3.2
Simulation Results for an Ideal Regular
Hexagonal Surface Partitioning
. 55
4.3.3
Simulation Results for the China County
Geographic Configuration
. 56
4.3.4
Implications
. 60
4.4
The Binomial Probability Model,
N > 1 . 62
4.4.1
Simulating Spatially Autocorrelated Binomial RVs
. . 63
4.4.2
Simulation Results for an Ideal Regular
Hexagonal Surface Partitioning
. 65
4.4.3
Simulation Results for the China County
Geographic Configuration
. 68
4.4.4
Implications
. 70
4.5
Discussion
. 71
5
Understanding Correlations Among Spatial Processes
. 75
5.1
Introduction
. 75
5.2
Two Illustrative Examples
. 75
5.3
Geostatistical Semivariogram Model Implications
. 77
5.4
Spatial
Autoregressive
Model Implications
. 82
5.4.1
Variance and Covariance Inflation Attributable
to Spatial Autocorrelation
. 85
5.4.2
Effective Sample Size as a Function of
ρχ
and
ργ
. . 87
5.5
Spatial Filtering Model Implications
. 89
5.5.1
Correlation Coefficient Decomposition
. 90
5.5.2
Variance Inflation
. 93
5.6
Discussion
. 93
6
Spatially Structured Random Effects: A Comparison
of Three Popular Specifications
. 97
6.1
Introduction
. 97
6.2
Modeling Spatial Structure
. 97
Contents
6.3
Linear Mixed Models
. 99
6.4
Generalized Linear Mixed Models
. 105
6.5
Degrees of Freedom for GLMM Random Effects
. 110
6.6
Extensions to Space-Time Data Sets
.
Ill
6.7
Discussion and Implications
. 114
7
Spatial Filter Versus Conventional Spatial Model
Specifications: Some Comparisons
. 117
7.1
Introduction
. 117
7.1.1
Background
. 117
7.2
Variation and Covariation Considerations for
Poisson
Random Variables
. 120
7.2.1
Heterogeneity in Counts Data
. 121
7.2.2
Spatial Autocorrelation in
Poisson
Random Variables
124
7.2.3
Spatial Autocorrelation-induced Correlation Inflation
126
7.3
Principal Spatial Statistical Model Specifications
. 130
7.3.1
The Log-normal Approximation
. 130
7.3.2
A Winsorized Auto-Poisson Model
. 131
7.3.3
A Proper CAR Model Specification via GeoBUGS
. 134
7.4
Spatial Filter Model Specifications
. 136
7.4.1
The Log-normal Approximation Spatial Filter Model
. 136
7.4.2
A Poisson
Spatial Filter Model
. 137
7.4.3
A Spatial Filter Model Specification via BUGS
. . 139
7.5
Discussion
. 140
7.5.1
Cross-validation Results for the
Poisson
Spatial Filter Model
. 141
7.5.2
A Simulation Experiment Based Upon the
Poisson
Spatial Filter Model
. 141
7.5.3
Impacts of Incorporating Additional Information
. . . 143
7.5.4
Implications for Data Mapping
. 144
7.6
Concluding Comments
. 147
8
The Role of Spatial Autocorrelation in Prioritizing Sites
Within a Geographic Landscape
. 151
8.1
Introduction: The Problem
. 151
8.2
The Murray Superfund Site: Part I
. 152
8.2.1
State-of-the-Art Practice
. 153
8.2.2
A Spatial Methodology: Stage
1,
Spatial
Sampling Data Collection and Preprocessing
. 154
8.3
The Murray Superfund Site: Part
Π
. 156
8.3.1
A Spatial Methodology: Stage
2,
Spatial
Statistics for Calculating UCLs
. 159
8.4
The Murray Superfund Site: Part
Ш
. 161
8.4.1
A Spatial Methodology: Stage
3,
Prioritizing
Subregions for Remediation
. 163
8.5
The Murray Superfund Site: Part IV
. 163
xxxiv Contents
8.5.1
A Spatial Methodology: Stage
4,
Covariation
of Contaminants and Joint Pollutant Analyses
. 164
8.6
The Murray Superfund Site: Part V
. 168
8.7
Implications
. 170
9
General Conclusions: Spatial Statistics
. 171
Part II Non-standard Spatial Econometrics
10
Introduction: Spatial Econometrics
. 177
11
A Mixed Linear-Logarithmic Specification for
Lotka-
Volteira
Models with Endogenously Generated
SDLS-Variables
. 179
11.1
Lotka-Volterra Models
. 179
11.1.1
A General Specification
. 179
11.1.2
Applications
. 180
11.1.3
Simultaneous Dynamic Least Squares (SDLS)
Estimation
. 181
11.2
Mixed Specification
. 182
11.2.1
Equations
. 182
11.2.2
Stability
. 183
11.3
Application
. 183
11.4
Conclusion
. 186
12
Selecting Spatial Regimes by Threshold Analysis
. 189
12.1
Method
. 189
12.2
Spatial Income Generating Model
. 190
12.3
A Spatial Activity Complex Model
. 193
12.4
Conclusion
. 197
12.5
Appendix
. 197
13
Finite Automata
. 199
13.1
A Finite Automaton Bi-regional Dynamic Model
. 199
13.2
An Empirical Application
. 203
13.3
Conclusion
. 205
14
Learning from Residuals
. 207
14.1
Residuals
. 207
14.2
Multiple Regimes
. 209
14.3
Spatial Interpolation
. 212
14.4
Composite Parameters
. 213
14.5
Conclusion
. 215
15
Verhuist
and
Poisson
Distributions
. 217
15.1
Robust Estimation in the Binary Case: A Linear
Logistic Estimator (LLE)
. 217
15.2
A Logistic Dynamic Share Model
. 219
Contents xxxv
15.3
A
Linear
Poisson
Distribution
Estimator
. 221
15.4
Conclusion
. 225
16
Qualireg, A Qualitative Regression Method
. 227
16.1
Qualiflex
. 227
16.2
Qualireg
. 230
16.3
Spatial Setting
. 231
16.4
Conclusion
. 233
17
Filtering Complexity for Observational Errors and Spatial Bias
. 235
17.1
Complexity, Estimation and Testing
. 235
17.2
Filtering for Observational Errors
. 237
17.3
Further Filtering for Spatial Bias
. 240
17.4
Conclusions
. 241
18
General Spatial Econometric Conclusions
. 243
Epilogue
. 245
References
. 247
Author Index
. 257
Subject Index
. 259 |
| any_adam_object | 1 |
| author | Griffith, Daniel A. 1948- Paelinck, Jean H. P. 1930- |
| author_GND | (DE-588)124843697 (DE-588)12193523X |
| author_facet | Griffith, Daniel A. 1948- Paelinck, Jean H. P. 1930- |
| author_role | aut aut |
| author_sort | Griffith, Daniel A. 1948- |
| author_variant | d a g da dag j h p p jhp jhpp |
| building | Verbundindex |
| bvnumber | BV036867501 |
| classification_rvk | QC 150 QH 234 QH 300 QH 320 RB 10104 |
| ctrlnum | (OCoLC)707151802 (DE-599)DNB1006508384 |
| dewey-full | 519.5 330.015195 |
| dewey-hundreds | 500 - Natural sciences and mathematics 300 - Social sciences |
| dewey-ones | 519 - Probabilities and applied mathematics 330 - Economics |
| dewey-raw | 519.5 330.015195 |
| dewey-search | 519.5 330.015195 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics 330 - Economics |
| discipline | Geologie / Paläontologie Mathematik Wirtschaftswissenschaften Geographie |
| format | Book |
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| id | DE-604.BV036867501 |
| illustrated | Illustrated |
| indexdate | 2024-07-20T10:54:00Z |
| institution | BVB |
| isbn | 9783642266690 9783642160424 9783642160431 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-020783130 |
| oclc_num | 707151802 |
| open_access_boolean | |
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| owner_facet | DE-355 DE-BY-UBR DE-11 DE-19 DE-BY-UBM DE-N2 |
| physical | XXXV, 262 S. Ill., graph. Darst., Kt. |
| publishDate | 2011 |
| publishDateSearch | 2011 |
| publishDateSort | 2011 |
| publisher | Springer |
| record_format | marc |
| series2 | Advances in geographic information science |
| spelling | Griffith, Daniel A. 1948- Verfasser (DE-588)124843697 aut Non-standard spatial statistics and spatial econometrics Daniel A. Griffith ; Jean H. P. Paelinck Berlin [u.a.] Springer 2011 XXXV, 262 S. Ill., graph. Darst., Kt. txt rdacontent n rdamedia nc rdacarrier Advances in geographic information science Räumliche Statistik (DE-588)4386767-4 gnd rswk-swf Ökonometrisches Modell (DE-588)4043212-9 gnd rswk-swf Raumwirtschaftstheorie (DE-588)4121557-6 gnd rswk-swf Räumliche Statistik (DE-588)4386767-4 s Raumwirtschaftstheorie (DE-588)4121557-6 s Ökonometrisches Modell (DE-588)4043212-9 s 1\p DE-604 DE-604 Paelinck, Jean H. P. 1930- Verfasser (DE-588)12193523X aut X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=3532079&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020783130&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Griffith, Daniel A. 1948- Paelinck, Jean H. P. 1930- Non-standard spatial statistics and spatial econometrics Räumliche Statistik (DE-588)4386767-4 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Raumwirtschaftstheorie (DE-588)4121557-6 gnd |
| subject_GND | (DE-588)4386767-4 (DE-588)4043212-9 (DE-588)4121557-6 |
| title | Non-standard spatial statistics and spatial econometrics |
| title_auth | Non-standard spatial statistics and spatial econometrics |
| title_exact_search | Non-standard spatial statistics and spatial econometrics |
| title_full | Non-standard spatial statistics and spatial econometrics Daniel A. Griffith ; Jean H. P. Paelinck |
| title_fullStr | Non-standard spatial statistics and spatial econometrics Daniel A. Griffith ; Jean H. P. Paelinck |
| title_full_unstemmed | Non-standard spatial statistics and spatial econometrics Daniel A. Griffith ; Jean H. P. Paelinck |
| title_short | Non-standard spatial statistics and spatial econometrics |
| title_sort | non standard spatial statistics and spatial econometrics |
| topic | Räumliche Statistik (DE-588)4386767-4 gnd Ökonometrisches Modell (DE-588)4043212-9 gnd Raumwirtschaftstheorie (DE-588)4121557-6 gnd |
| topic_facet | Räumliche Statistik Ökonometrisches Modell Raumwirtschaftstheorie |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=3532079&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020783130&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT griffithdaniela nonstandardspatialstatisticsandspatialeconometrics AT paelinckjeanhp nonstandardspatialstatisticsandspatialeconometrics |