scholarly journals GWR-PM - Spatial variation relationship analysis with Geographically Weighted Regression (GWR) - An application at Peninsular Malaysia

2016 ◽  
Vol 37 ◽  
pp. 012032
Author(s):  
J Jamhuri ◽  
B M S Azhar ◽  
C L Puan ◽  
K Norizah
Keyword(s):  
Spatial Variation ◽  
Geographically Weighted Regression ◽  
Peninsular Malaysia ◽  
Weighted Regression ◽  
Relationship Analysis

Modeling the Spatial Variation in U.S. Airfares Utilizing Geographically Weighted Regression

2014 ◽  
Vol 5 (4) ◽  
pp. 54-71
Author(s):  
Hilton A. Cordoba ◽  
Russell L. Ivy
Keyword(s):  
Spatial Variation ◽  
Influencing Factors ◽  
Geographically Weighted Regression ◽  
Regression Models ◽  
Yield Management ◽  
Weighted Regression ◽  
Local Models ◽  
Multivariate Approach ◽  
Global Models ◽  
Economic Changes

Modeling airline fares is quite challenging due to the constantly changing fare structure of the airlines in response to competitors, yield management principles, and a variety of political and economic changes, and has become more complex since deregulation. This paper attempts to add to the literature by providing a more in-depth look at fare structure using a multivariate approach. A total 6,200 routes between 80 primary U.S. airports are analyzed using linear and geographically weighted regression models. The results from the global models reinforce some of the expectations mentioned in the literature, while the local models provide an opportunity to analyze the spatial variation of influencing factors and predictability.

scholarly journals Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1720
Author(s):  
Fiona H. Evans ◽  
Angela Recalde Salas ◽  
Suman Rakshit ◽  
Craig A. Scanlan ◽  
Simon E. Cook
Keyword(s):  
Spatial Variation ◽  
Geographically Weighted Regression ◽  
Large Scale ◽  
Treatment Effects ◽  
Management Practices ◽  
Response To Treatment ◽  
Weighted Regression ◽  
Yield Response ◽  
Practical Application ◽  
On Farm

On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment; show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects; and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.

scholarly journals Geographically Weighted Regression on dengue epidemic in Peninsular Malaysia

2021 ◽  
Vol 1988 (1) ◽  
pp. 012099
Author(s):  
Ayuna Sulekan ◽  
Jamaludin Suhaila ◽  
Nurmarni Athirah Abdul Wahid
Keyword(s):  
Geographically Weighted Regression ◽  
Peninsular Malaysia ◽  
Weighted Regression ◽  
Dengue Epidemic

scholarly journals Assessing spatial variation of PM2.5 and NO2 across Europe using Geographically Weighted Regression

2019 ◽  
Vol 3 ◽  
pp. 65
Author(s):  
Chen J ◽  
de Hoogh K ◽  
van Donkelaar A ◽  
Ketzel M ◽  
Hertel O ◽  
...  
Keyword(s):  
Spatial Variation ◽  
Geographically Weighted Regression ◽  
Weighted Regression

Geographic Heterogeneity in Otolaryngology Medicare New Patient Visits

2020 ◽  
Vol 162 (6) ◽  
pp. 860-866
Author(s):  
Kevin Hur ◽  
Joseph Gibbons ◽  
Brian Karl Finch
Keyword(s):  
Spatial Variation ◽  
Geographically Weighted Regression ◽  
Negative Binomial ◽  
Sociodemographic Factors ◽  
Weighted Regression ◽  
Cross Sectional ◽  
Commute Time ◽  
Medicare Patients ◽  
Work Commute ◽  
Sectional Analysis

Objective To analyze the spatial variation of sociodemographic factors associated with the geographic distribution of new patient visits to otolaryngologists. Study Design Retrospective cross-sectional analysis. Setting United States. Subject and Methods Medicare new patient visits pooled from 2012 to 2016 to otolaryngology providers were obtained from the Centers for Medicare and Medicaid Services, and county-level sociodemographic data were obtained from the 2012-2016 American Community Survey. The mean number of new patient visits per otolaryngology provider by county was calculated. The spatial variation was analyzed with negative binomial and geographically weighted regression. Predictors included various neighborhood characteristics. Results There were 7,199,129 Medicare new patient visits to otolaryngology providers from 2012 to 2016. A 41.7-fold difference in new patient evaluation rates was observed across US counties (range, 11-458.8 per otolaryngology provider). On multivariable regression analysis, median age, sex, work commute time, percentage insured, and the advantage index of a county were predictors for the rate of new patient visits to otolaryngology providers. However, geographically weighted regression demonstrated that the association of a county’s disadvantage index, advantage index, percentage insured, and work commute times with new patient visits per provider varied across space. Conclusions There are wide geographic differences in the number of new Medicare patients seen by otolaryngologists, and the influence of county sociodemographic factors varied regionally. Further research to analyze the variations in practice patterns of otolaryngologists is warranted to predict future public health needs.

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