Spatial Regression Models
By S. Srinivasan. ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems, 2008
339 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/1de75c3425a6f5108a57a14bed07b5e6c1e2fbbd
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Gianfranco Piras
- J. Paul Elhorst
- Roger S. Bivand
- Nils B. Weidmann
- Doreen Kuse
- Saharon Rosset
- Halvard Buhaug
- Håvard Strand
In Environmental Engineering
- Roger S. Bivand
- Saharon Rosset
- J. Paul Elhorst
- Nils B. Weidmann
- Gianfranco Piras
- Halvard Buhaug
- Kristian Skrede Gleditsch
- Doreen Kuse
In Global and Planetary Change
- Halvard Buhaug
- Håvard Strand
- A. Tøllefsen
- Roger S. Bivand
- Saharon Rosset
- J. Paul Elhorst
- Nils B. Weidmann
- Gianfranco Piras
Citations in the corpus
- Spatial data analysis (2024, neutral): “Ward & Gleditsch (2008) offers an intuitive introduction to spatial regression models, while Elhorst (2012), Halleck Vega & Elhorst (2015), LeSage (2014), and Rüttenauer (2022) present article-length introductions to spatial econometrics.”
- Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, methodology): “Before concluding, we compare the provision of functions for calculating emanating effects (Kelejian, Tavlas, and Hondroyiannis 2006), also known as impacts (LeSage and Fischer 2008; LeSage and Pace 2009), simultaneous spatial reaction function/reduced form (Anselin and Lozano-Gracia 2008), equilibr”
- The Geography of the International System: The CShapes Dataset (2010, methodology): “We refer to Ward and Gleditsch (2008) for further details on spatial dependence, constructing spatial variables, and model estimation, and focus here only on an example of how measures of spatial connectivities can be derived from CShapes.”
- PRIO-GRID: A unified spatial data structure (2012, background): “By decomposing the error term into a spatial part and a unique part, the unique component should be i.i.d. (Ward & Gleditsch, 2008: 39).”
- Linear Spatial Dependence Models for Cross-Section Data (2014, background): “…(1990), Hepple (1995), Bell and Bockstael (2000), Bordignon et al. (2003), Lacombe (2004), Allers and Elhorst (2005), McMillen et al. (2007), Ward and Gleditsch (2008); Dall’Erba et al. (2008), Elhorst and Fréret (2009), Lee and Liu (2010), Badinger and Egger (2011), and Elhorst et al.…”
- Cross-Validation for Correlated Data (2019, background): “…the training and prediction sets is common in other distributional settings in various applications, including applications involving spatial datasets (Ward and Gleditsch 2018), longitudinal datasets (Hand 2017), and datasets with hierarchical clustering structure (Raudenbush and Bryk 2002).”