Search Results for « GWR »

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a global one. Standard GWR assumes that the relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to inform the choice of whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises primary steps: a basic linear regression, a MS-GWR, and investigations of the results of these. The paper provides guidance for deciding whether to use a GWR approach, and if so for determining the appropriate GWR variant. It describes the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided, and further considerations are described in an extensive Appendix.

Geographically Weighted Regression (GWR) is a seminal technique with rich applications in geospatial data analysis. However, it has critical drawbacks in the age of big data in terms of expressiveness, i.e., predictive power, and scalability. This work proposes Augmented GWR (A-GWR) that alleviates these drawbacks. A-GWR adapts a novel technique, Stateless-MGWR or S-MGWR, that enriches the predictive power by allowing different training data features to influence at different spatial scales. S-MGWR uses a customized black-box optimization approach for discovering optimal parameters in a fast and efficient way. In addition, A-GWR modularly combines S-MGWR with versatile models such as random forest models. Moreover, A-GWR enables scalability by operating on partitioned data to adapt to tight computational budgets. Our extensive experiments on various real and synthetic datasets demonstrate the scalability and accuracy benefits of the proposed techniques over state-of-the-art competitors.

Local spatial models such as Geographically Weighted Regression (GWR) and Multiscale Geographically Weighted Regression (MGWR) serve as instrumental tools to capture intrinsic contextual effects through the estimates of the local intercepts and behavioral contextual effects through estimates of the local slope parameters. GWR and MGWR provide simple implementation yet powerful frameworks that could be extended to various disciplines that handle spatial data. This bibliography aims to serve as a comprehensive compilation of peer-reviewed papers that have utilized GWR or MGWR as a primary analytical method to conduct spatial analyses and acts as a useful guide to anyone searching the literature for previous examples of local statistical modeling in a wide variety of application fields.

This article contributes to advancing the knowledge on the phenomenon of the most popular short‐term rental platforms, Airbnb. By implementing a geographically weighted regression (GWR) and its multiscale form, MGWR, we examine the relationship between Airbnb locations and the core elements of urban tourism including hotels, food and beverages (F&B) venues, as well as access to public transport. This article’s contributions are twofold: methodological and empirical. First, the results show that incorporating localities improve overall model performance. It allows us to account for the nuance of each area of interest as the MGWR performs slightly better than the GWR in the case of spatially sparse data. Second, both models show that Airbnbs collocate with hotels supported by various amenities, but Airbnbs also go beyond traditional hotel zones. This analysis highlights and extends the latter where Airbnb concentrations are those for which there are strong associations with F&B establishments and access to public transports. This suggests that Airbnbs might benefit local businesses outside the reach of major tourist zones. However, there is further work to be done to understand whether the economic benefit to the local economy is worth the associated social costs raised by previous studies.

Although a number of studies have developed fast geographically weighted regression (GWR) algorithms for large samples, none of them has achieved linear-time estimation, which is considered a requisite for big data analysis in machine learning, geostatistics, and related domains. Against this backdrop, this study proposes a scalable GWR (ScaGWR) for large datasets. The key improvement is the calibration of the model through a pre-compression of the matrices and vectors whose size depends on the sample size, prior to the leave-one-out cross-validation, which is the heaviest computational step in conventional GWR. This pre-compression allows us to run the proposed GWR extension so that its computation time increases linearly with the sample size. With this improvement, the ScaGWR can be calibrated with one million observations without parallelization. Moreover, the ScaGWR estimator can be regarded as an empirical Bayesian estimator that is more stable than the conventional GWR estimator. We compare the ScaGWR with the conventional GWR in terms of estimation accuracy and computational efficiency using a Monte Carlo simulation. Then, we apply these methods to a US income analysis. The code for ScaGWR is available in the R package scgwr. The code is embedded into C++ code and implemented in another R package, GWmodel.

GWR based on a flow-distance composite weight matrix (FDWM GWR)OverviewThis repository contains simulation data and codes for the FDWM GWR method in "Reconceptualizing Proximity: A Flow-Distance Augmented GWR for Analyzing Urban Vitality".RequirementsMake sure to install the following dependencies:Python 3.8 or above.Required Python libraries: pip install numpy scipy pandas.Code StructureFDWM_GWR.py: The model and a usage example. Validity has been verified on Python 3.8 and 3.10.test_od.txt: A simulated OD matrix file.test_data.csv: A simulated data.Usage Instructions1. Prepare DataThe data should include a table file (e.g., 'test_data.csv') and an OD matrix (e.g., 'test_od.txt'). The table should contain the sample ID, coordinates, and variables.2. Run the ModelRunning the model should include the following steps:Load data.Calculate Euclidean distance and normalize it.Calculate flow space proximity and composite Euclidean distance.Run the model. You can select either Fixed or Adaptive bandwidth type, search for the optimal bandwidth using the golden section method, and fit the model.Save the results.The 'FDWM_GWR.py' file contains a complete usage example. You can modify it as needed or run it directly, and the results will be saved as a CSV file. After fitting the model, you can directly obtain its bandwidth, R2, and AICc.In the gravity model, the alpha and beta parameters can be directly modified in the 'FDWM_GWR.py' file. By using different parameter combinations, the impact of these parameters on the model can be further explored.3. Results AnalysisUse ArcGIS or other software for further visualization and analysis of the generated results.Charts and the Calculation ProcessThe calculation process for the charts and figures presented in Section 4.3 and Chapter 5 are listed in ‘The Process of Calculating Figures and Tables.docx’.

This paper evaluates the relationship between the food industry and the local variations of temporary accommodation (TAs, including hotels and short-term rentals). The aim is to capture the variance of the local statistic and pinpoint areas where food and beverages (F&B) presence is highly related to TAs in London. We explain the phenomena using OLS and compare the result with the local model - Geographically Weighted Regression (GWR) and multi-scale GWR (Fotheringham et al., 2017) allowing the use of different optimal bandwidths instead of assuming that relationship varies at the same spatial scale. The comparison is presented and the result shows that the GWR model shows significant improvement over Ordinary Least Square (OLS), increasing the R-squared from 0.28 to 0.75. MGWR further improves the model estimate, increasing the R-squared to 0.77, indicating the relationship happens in different spatial scales. Lastly, as an estimate for F&B, hotels appear to perform better in a high concentration of commercial and transport links functions, whilst Airbnb seems to perform better in highly residential areas proximate to the mainstream tourist attractions. Overall, this paper describes the use of the MGWR method in cases where localities is an important aspect of the spatial analysis process.

Display processing results using the GWR (Geographically Weighted Regression) method, display maps, and show the results of the Mixed GWR (Mixed Geographically Weighted Regression) model which automatically selects global variables based on variability between regions. This function refers to Yasin, & Purhadi. (2012). "Mixed Geographically Weighted Regression Model (Case Study the Percentage of Poor Households in Mojokerto 2008)". European Journal of Scientific Research, 188-196. .

This paper examines the spatial distribution of income in Ireland. Median gross household disposable income data from the CSO, available at the Electoral Division (ED) level, is used to explore the spatial variability in income. Geary's C highlights the spatial dependence of income, highlighting that the distribution of income is not random across space and is influenced by location. Given the presence of spatial autocorrelation, utilising a global OLS regression will lead to biased results. Geographically Weighted Regression (GWR) is used to examine the spatial heterogeneity of income and the impact of local demographic drivers on income. GWR results show the demographic drivers have varying levels of influence on income across locations. Lone parent has a stronger negative impact in the Cork commuter belt than it does in the Dublin commuter belt. The relationship between household income and the demographic context of the area is a complicated one. This paper attempts to examine these relationships acknowledging the impact of space.