scholarly journals Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic

2009 ◽  
Vol 67 (1) ◽  
pp. 145-154 ◽  
Author(s):  
Matthew J. S. Windle ◽  
George A. Rose ◽  
Rodolphe Devillers ◽  
Marie-Josée Fortin
Keyword(s):  
Survey Data ◽  
Geographically Weighted Regression ◽  
Goodness Of Fit ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Weighted Regression ◽  
Snow Crab ◽  
Implicit Assumption ◽  
Northwest Atlantic ◽  
Goodness Of Fit Tests

Abstract Windle, M. J. S., Rose, G. A., Devillers, R., and Fortin, M-J. 2010. Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic. – ICES Journal of Marine Science, 67: 145–154. Analyses of fisheries data have traditionally been performed under the implicit assumption that ecological relationships do not vary within management areas (i.e. assuming spatially stationary processes). We question this assumption using a local modelling technique, geographically weighted regression (GWR), not previously used in fisheries analyses. Outputs of GWR are compared with those of global logistic regression and generalized additive models (GAMs) in predicting the distribution of northern cod off Newfoundland, Canada, based on environmental (temperature and distance from shore) and biological factors (snow crab and northern shrimp) from 2001. Results from the GWR models explained significantly more variability than the global logistic and GAM regressions, as shown by goodness-of-fit tests and a reduction in the spatial autocorrelation of model residuals. GWR results revealed spatial regions in the relationships between cod and explanatory variables and that the significance and direction of these relationships varied locally. A k-means cluster analysis based on GWR t-values was used to delineate distinct zones of species–environment relationships. The advantages and limitations of GWR are discussed in terms of potential application to fisheries ecology.

scholarly journals The effect of climate variables on the incidence of Crimean Congo Hemorrhagic Fever (CCHF) in Zahedan, Iran

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sairan Nili ◽  
Narges Khanjani ◽  
Yunes Jahani ◽  
Bahram Bakhtiari
Keyword(s):  
Goodness Of Fit ◽  
Moving Average ◽  
Seasonal Pattern ◽  
Generalized Additive Models ◽  
Hemorrhagic Fever ◽  
Additive Models ◽  
Meteorological Variables ◽  
Fatality Rate ◽  
Crimean Congo Hemorrhagic Fever ◽  
Sarima Models

Abstract Background The Crimean-Congo Hemorrhagic fever (CCHF) is endemic in Iran and has a high fatality rate. The aim of this study was to investigate the association between CCHF incidence and meteorological variables in Zahedan district, which has a high incidence of this disease. Methods Data about meteorological variables and CCHF incidence was inquired from 2010 to 2017 for Zahedan district. The analysis was performed using univariate and multivariate Seasonal Autoregressive Integrated Moving Average (SARIMA) models and Generalized Additive Models (GAM) using R software. AIC, BIC and residual tests were used to test the goodness of fit of SARIMA models, and R2 was used to select the best model in GAM/GAMM. Results During the years under study, 190 confirmed cases of CCHF were identified in Zahedan district. The fatality rate of the disease was 8.42%. The disease trend followed a seasonal pattern. The results of multivariate SARIMA showed the (0,1,1) (0,1,1)12 model with maximum monthly temperature lagged 5 months, forecasted the disease better than other models. In the GAM, monthly average temperature lagged 5 months, and the monthly minimum of relative humidity and total monthly rainfall without lag, had a nonlinear relation with the incidence of CCHF. Conclusions Meteorological variables can affect CCHF occurrence.

Spatial Analysis of Bering Sea Groundfish Survey Data Using Generalized Additive Models

1992 ◽  
Vol 49 (7) ◽  
pp. 1366-1378 ◽  
Author(s):  
Gordon Swartzman ◽  
Chisheng Huang ◽  
Stephen Kaluzny
Keyword(s):  
Survey Data ◽  
Bering Sea ◽  
Generalized Additive Models ◽  
Sampling Procedure ◽  
Additive Models ◽  
Trawl Survey ◽  
Flatfish Species ◽  
Eastern Bering Sea ◽  
Abundance Estimates ◽  
Average Abundance

Generalized additive models (GAM) are herein applied to trawl survey data in the eastern Bering Sea with an eye to (1) detecting trends in groundfish distributions and (2) improving abundance estimates by including the trend. GAM is a statistical method, analogous to regression, but without the assumptions of normality or linearity that relate a response variable (in this case, fish abundance) to location (latitude and longitude) and associated environmental variables (e.g. depth and bottom temperature). GAM provided reasonable (i.e. high r2) fits to the spatial distribution of five flatfish species and was able to define a spatial "signature" for each species, namely their preferred depth and temperature range. GAM also gave lower average abundance and abundance variability estimates for these five flatfish species than the stratified sampling procedure previously employed.

scholarly journals A Spatial Approach for Modeling Amphibian Road-Kills: Comparison of Regression Techniques

2021 ◽  
Vol 10 (5) ◽  
pp. 343
Author(s):  
Diana Sousa-Guedes ◽  
Marc Franch ◽  
Neftalí Sillero
Keyword(s):  
Geographically Weighted Regression ◽  
Environmental Variables ◽  
Linear Models ◽  
Model Performance ◽  
Regression Trees ◽  
Additive Models ◽  
Boosted Regression Trees ◽  
Weighted Regression ◽  
Road Kill ◽  
Regression Techniques

Road networks are the main source of mortality for many species. Amphibians, which are in global decline, are the most road-killed fauna group, due to their activity patterns and preferred habitats. Many different methodologies have been applied in modeling the relationship between environment and road-kills events, such as logistic regression. Here, we compared the performance of five regression techniques to relate amphibians’ road-kill frequency to environmental variables. For this, we surveyed three country roads in northern Portugal in search of road-killed amphibians. To explain the presence of road-kills, we selected a set of environmental variables important for the presence of amphibians and the occurrence of road-kills. We compared the performances of five modeling techniques: (i) generalized linear models, (ii) generalized additive models, (iii) random forest, (iv) boosted regression trees, and (v) geographically weighted regression. The boosted regression trees and geographically weighted regression techniques performed the best, with a percentage of deviance explained between 61.8% and 76.6% and between 55.3% and 66.7%, respectively. Moreover, the geographically weighted regression showed a great advantage over the other techniques, as it allows mapping local parameter coefficients as well as local model performance (pseudo-R2). The results suggest that geographically weighted regression is a useful tool for road-kill modeling, as well as to better visualize and map the spatial variability of the models.

Goodness‐of‐fit tests for distributions estimated from complex survey data

2019 ◽  
Vol 47 (3) ◽  
pp. 409-425
Author(s):  
Sharon L. Lohr ◽  
Minsun K. Riddles ◽  
J. Michael Brick
Keyword(s):  
Survey Data ◽  
Goodness Of Fit ◽  
Complex Survey ◽  
Goodness Of Fit Tests ◽  
Complex Survey Data

scholarly journals Selecting Prices Determinants and Including Spatial Effects in Peer-to-Peer Accommodation

2020 ◽  
Vol 9 (4) ◽  
pp. 259 ◽  
Author(s):  
Rafael Suárez-Vega ◽  
Juan M. Hernández
Keyword(s):  
Geographically Weighted Regression ◽  
Goodness Of Fit ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Peer To Peer ◽  
Weighted Regression ◽  
Spatial Effects ◽  
Digital Platforms ◽  
Starting Point ◽  
Wide Range

Peer-to-peer accommodation has grown significantly during the last decades, supported, in part, by digital platforms. These websites make available a wide range of information intended to help the customers’ decision. All these factors, in addition to the property location, may therefore influence rental price. This paper proposes different procedures for an efficient selection of a high number of price determinants in peer-to-peer accommodation when applying the perspective of the geographically weighted regression. As a case study, these procedures have been used to find the factors affecting the rental price of properties advertised on Airbnb in Gran Canaria (Spain). The results show that geographically weighted regression obtains better indicators of goodness of fit than the traditional ordinary least squares method, making it possible to identify those attributes influencing price and how their effect varies according to property locations. Moreover, the results also show that the selection procedures working directly on geographically weighted regression obtain better results than those that take good global solutions as their starting point.

scholarly journals Adaptive Non-Negative Geographically Weighted Regression for Population Density Estimation Based on Nighttime Light

2019 ◽  
Vol 8 (1) ◽  
pp. 26 ◽  
Author(s):  
Hone-Jay Chu ◽  
Chen-Han Yang ◽  
Chelsea Chou
Keyword(s):  
Population Density ◽  
Geographically Weighted Regression ◽  
Rural Population ◽  
Goodness Of Fit ◽  
Strong Relationship ◽  
Weighted Regression ◽  
Western Boundary ◽  
Nighttime Lights ◽  
Nighttime Light ◽  
Nighttime Light Imagery

Nighttime light imagery provides a perspective for studying urbanization and socioeconomic changes. Traditional global regression models have been applied to explore the nonspatial relationship between nighttime lights and population density. In this study, geographically weighted regression (GWR) identifies the spatially varying relationships between population density and nighttime lights in mainland China. However, the rural population does not have a strong relationship with remote-sensing spectral features. The rural population estimation using nighttime light data alone easily identifies meaningless negative population density in the rural area. This study proposes an adaptive non-negative GWR (ANNGWR) to explore the spatial pattern of population density by using nonnegative constraints with an adaptive bandwidth of kernel. The ANNGWR solves the negative value of population density and serious overestimation of the western boundary. The result shows that the ANNGWR provides the best goodness-of-fit compared with linear regression and original GWR. This study applies Moran’s I index to prove that the ANNGWR substantially decreases the spatial autocorrelation of the model residual. The model offers a robust and effective approach for estimating the spatial patterns of regional population density solely on the basis of nighttime light imagery.

Fitting Pareto Tails to Wealth Survey Data: A Practioners’ Guide

2021 ◽  
Author(s):  
Rafael Wildauer ◽  
Jakob Kapeller
Keyword(s):  
Survey Data ◽  
Goodness Of Fit ◽  
Household Wealth ◽  
Type I ◽  
Short Article ◽  
Complex Survey ◽  
Goodness Of Fit Tests ◽  
Complex Survey Data ◽  
Pareto Tails ◽  
Von Mises

Taking survey data of household wealth as our major example, this short article discusses some of the issues applied researchers are facing when fitting (Type I) Pareto distributions to complex survey data. The contribution of this article is threefold. First, we show how the ordering of the data vector is related to alternative definitions of the empirical CCDF. Second, we provide an intuitive reinterpretation of the bias-corrected estimator developed by Gabaix and Ibragimov (2011), in terms of the alternative definitions of the empirical CCDF, which allows us to generalize their result to the case of complex survey data. Third, we provide computational formulas for standard Kolmogorov-Smirnov (KS) and Cramer-von Mises (CvM) goodness- of-fit tests for complex survey data. Taken together the article provides a concise and hopefully useful presentation of the fundamentals of Pareto tail- fitting with complex survey data.

scholarly journals Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Chuqiao Chen ◽  
Simon Hu ◽  
Washington Y. Ochieng ◽  
Na Xie ◽  
Xiqun (Michael) Chen
Keyword(s):  
Land Use ◽  
Geographically Weighted Regression ◽  
Transportation Planning ◽  
Goodness Of Fit ◽  
Urban Land ◽  
Residential Area ◽  
Urban Land Use ◽  
Weighted Regression ◽  
Bus Station ◽  
The Impact

The emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regression (GWR) model to analyze the OD-based ride-sourcing travel flow. It makes a comparison with the existing ordinary least square (OLS) model and spatial autocorrelation model (SAM). We have collected ride-sourcing order data in Hangzhou, China, to provide an accurate source for acquiring ride-sourcing travel flow. We investigate the effects of the residential area, points of interest (POIs), and transit stations on ride-sourcing travel flow among traffic analysis zones (TAZs). The results show the following: (a) GWR has better goodness-of-fit than SAM and OLS. (b) Residential area, enterprise, and bus stations have positive correlations with ride-sourcing OD flows, but education and subway stations have negative correlations. We have further investigated the issue and found that it is not a causal relationship between the bus station and OD flow, due to collinearity between the two variables. The bus station builds on locations with high demand, but its capacity is not large enough to reduce the ride-sourcing flow to a low level, which results in a positive coefficient. (c) Based on the estimated coefficients, the prediction of ride-sourcing flows is feasible, supporting the impact analysis for urban land use and transportation planning. This paper contributes to understanding OD-based ride-sourcing travel flow distributions and provides a framework of long-term OD flow prediction for urban land use and transportation planning.

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