Spatial Regression Models

2017 ◽  
pp. 299-404
Author(s):  
Oliver Schabenberger ◽  
Carol A. Gotway
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Spatial Regression Models

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2012 ◽  
pp. 308-320
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Hedonic Pricing ◽  
Spatial Lag Model ◽  
Hedonic Pricing Method ◽  
Spatial Weights Matrix ◽  
Spatial Regression Models ◽  
Gis Environment ◽  
Value Of Irrigation Water

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2010 ◽  
Vol 1 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Software Tool ◽  
Hedonic Pricing ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

scholarly journals Scalable Model Selection for Spatial Additive Mixed Modeling: Application to Crime Analysis

2020 ◽  
Vol 9 (10) ◽  
pp. 577
Author(s):  
Daisuke Murakami ◽  
Mami Kajita ◽  
Seiji Kajita
Keyword(s):  
Big Data ◽  
Model Selection ◽  
Regression Models ◽  
Large Scale ◽  
Spatial Regression ◽  
Varying Coefficients ◽  
Model Selection Approach ◽  
Selection Approach ◽  
Spatial Regression Models ◽  
Spatially Varying

A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction, dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects a model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran.

Testing spatial regression models under nonregular conditions

2018 ◽  
Vol 55 (1) ◽  
pp. 85-111
Author(s):  
Sheena Yu-Hsien Kao ◽  
Anil K. Bera
Keyword(s):  
Regression Models ◽  
Spatial Regression ◽  
Spatial Regression Models

Spatial Regression Models for Large-Cohort Studies Linking Community Air Pollution and Health

2003 ◽  
Vol 66 (16-19) ◽  
pp. 1811-1824 ◽  
Author(s):  
Sabit Cakmak ◽  
Richard Burnett ◽  
Michael Jerrett ◽  
Mark Goldberg ◽  
C. Arden Pope ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cohort Studies ◽  
Regression Models ◽  
Spatial Regression ◽  
Large Cohort ◽  
Spatial Regression Models
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