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Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error
16 references·0 citing·27 second hop
Regionalization of Multiscale Spatial Processes by Using a Criterion for Spatial Aggregation Error (2016)Regionalization of Multiscale Spatial Pro…Hierarchical Modeling and Analysis for Spatial Data (2014)Hierarchical Modeling and Ana…A Solution to the Ecological Inference Problem (2013)A Solution to the Ecological …Statistics for Spatio‐Temporal Data (2012)Statistics for Spatio‐Tempora…THE MAX‐P‐REGIONS PROBLEM* (2011)THE MAX‐P‐REGIONS PROBLEM*Statistics for Spatio-Temporal Data (2011)Statistics for Spatio-Tempora…Improving the performance of predictive process modeling for large datasets (2008)Improving the performance of …Fixed Rank Kriging for Very Large Spatial Data Sets (2008)Fixed Rank Kriging for Very L…Applied Spatial Statistics for Public Health Data (2004)Applied Spatial Statistics fo…Spatiotemporal Hierarchical Bayesian Modeling Tropical Ocean Surface Winds (2001)Spatiotemporal Hierarchical B…A dimension-reduced approach to space-time Kalman filtering (1999)A dimension-reduced approach …Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data (1997)Solution to the Ecological In…Statistics for Spatial Data (1993)Statistics for Spatial DataStatistical Applications in the Spatial Sciences. (1981)Statistical Applications in t…A million or so correlation coefficients : three experiments on the modifiable areal unit problem (1979)A million or so correlation c…A Geographical Solution to Scale and Aggregation Problems in Region-Building, Partitioning and Spatial Modelling (1977)A Geographical Solution to Sc…Ecological Correlations and the Behavior of Individuals (1950)Ecological Correlations and t…Statistics for Spatial Data, Revised Edition. (1994)Model-Based Geostatistics (1998)Gaussian Predictive Process Models for Large Spatial Data Sets (2008)Bayesian Spatial Modelling withR-INLA (2015)From the Watching of Shadows: The Origins of Radiological Tomography (1990)Scaling Down: The Subnational Comparative Method (2001)Under the hood Issues in the specification and interpretation of spatial regression models (2002)Faculty do Matter: The Role of College Faculty in Student Learning and Engagement (2005)A Three-Stage Iterative Procedure for Space-Time Modeling (1980)STATISTICS FOR SPATIAL DATA (1992)Nonlinear modeling and forecasting (1993)Probabilistic Forecasting (2014)An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach (2011)Geospatial big data handling theory and methods: A review and research challenges (2015)Spatio-Temporal Interpolation using gstat (2016)Spatial Panel Econometrics (2008)Measuring Organizational Cultures: A Qualitative and Quantitative Study Across Twenty Cases (1990)Modernization, cultural change, and democracy the human development sequence (2005)The Control-Value Theory of Achievement Emotions: Assumptions, Corollaries, and Implications for Educational Research and Practice (2006)Multivariable geostatistics in S: the gstat package (2004)Methods to account for spatial autocorrelation in the analysis of species distributional data: a review (2007)Institutions and Ethnic Politics in Africa (2005)Forecasting: theory and practice (2022)Thirty years of spatial econometrics (2010)3. Measures of Spatial Segregation (2004)Ethnic Diversity and Economic Performance (2005)Modifiable Areal Unit Problem (2019)
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References

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  • 1950Ecological Correlations and the Behavior of Individuals4,547 cited
  • 2014Hierarchical Modeling and Analysis for Spatial Data2,522 cited
  • 2012Statistics for Spatio‐Temporal Data1,645 cited
  • 2004Applied Spatial Statistics for Public Health Data1,630 cited
  • 2011Statistics for Spatio-Temporal Data1,318 cited
  • 2008Fixed Rank Kriging for Very Large Spatial Data Sets998 cited
  • 1979A million or so correlation coefficients : three experiments on the modifiable areal unit problem746 cited
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  • 2011THE MAX‐P‐REGIONS PROBLEM*234 cited

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Improving the performance of predictive process modeling for large datasets
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