scholarly journals “One size does not fit all”: Spatial nonstationarity in the determinants of elderly residential isolation in historicalEurope

2019 ◽  
Vol 25 (6) ◽  
Author(s):  
Mikołaj Szołtysek ◽  
Bartosz Ogórek ◽  
Radosław Poniat ◽  
Siegfried Gruber
Keyword(s):  
Spatial Nonstationarity

scholarly journals Small area estimation under spatial nonstationarity

2012 ◽  
Vol 56 (10) ◽  
pp. 2875-2888 ◽  
Author(s):  
Hukum Chandra ◽  
Nicola Salvati ◽  
Ray Chambers ◽  
Nikos Tzavidis
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Spatial Nonstationarity

scholarly journals Spatial Nonstationarity and Autoregressive Models

1998 ◽  
Vol 30 (6) ◽  
pp. 957-973 ◽  
Author(s):  
C Brunsdon ◽  
A S Fotheringham ◽  
M Charlton
Keyword(s):  
Spatial Autocorrelation ◽  
Geographically Weighted Regression ◽  
Empirical Investigation ◽  
Spatial Association ◽  
Statistical Technique ◽  
Spatial Variations ◽  
Global Models ◽  
Spatial Autoregressive ◽  
Theoretical Foundations ◽  
Spatial Nonstationarity

Until relatively recently, the emphasis of spatial analysis was on the investigation of global models and global processes. Recent research, however, has tended to explore exceptions to general processes, and techniques have been developed which have as their focus the investigation of spatial variations in local relationships. One of these techniques, known as geographically weighted regression (GWR), developed by the authors is used here to investigate spatial variations in spatial association. The particular framework in which spatial association is examined here is the spatial autoregressive model of Ord, although the technique can easily be applied to any form of spatial autocorrelation measurement. The conceptual and theoretical foundations of GWR applied to the Ord model are followed by an empirical example which uses data on owner-occupation in the housing market of Tyne and Wear in northeast England where the problems of relying on global models of spatial association are demonstrated. This empirical investigation of spatial variations in spatial autocorrelation prompts a further discussion of several issues concerning the statistical technique.

scholarly journals Examining Scale Dependent Environmental Effects on American Lobster (Homarus americanus) Spatial Distribution in a Changing Gulf of Maine

2021 ◽  
Vol 8 ◽  
Author(s):  
Jamie Behan ◽  
Bai Li ◽  
Yong Chen
Keyword(s):  
Spatial Distribution ◽  
Species Distribution ◽  
Environmental Variables ◽  
Large Scale ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
American Lobster ◽  
Sediment Grain Size ◽  
Scale Models ◽  
Spatial Nonstationarity

The Gulf of Maine (GOM) is a highly complex environment and previous studies have suggested the need to account for spatial nonstationarity in species distribution models (SDMs) for the American lobster (Homarus americanus). To explore impacts of spatial nonstationarity on species distribution, we compared models with the following three assumptions : (1) large-scale and stationary relationships between species distributions and environmental variables; (2) meso-scale models where estimated relationships differ between eastern and western GOM, and (3) finer-scale models where estimated relationships vary across eastern, central, and western regions of the GOM. The spatial scales used in these models were largely determined by the GOM coastal currents. Lobster data were sourced from the Maine-New Hampshire Inshore Bottom Trawl Survey from years 2000–2019. We considered spatial and environmental variables including latitude and longitude, bottom temperature, bottom salinity, distance from shore, and sediment grain size in the study. We forecasted distributions for the period 2028–2055 using each of these models under the Representative Concentration Pathway (RCP) 8.5 “business as usual” climate warming scenario. We found that the model with the third assumption (i.e., finest scale) performed best. This suggests that accounting for spatial nonstationarity in the GOM leads to improved distribution estimates. Large-scale models revealed a tendency to estimate global relationships that better represented a specific location within the study area, rather than estimating relationships appropriate across all spatial areas. Forecasted distributions revealed that the largest scale models tended to comparatively overestimate most season × sex × size group lobster abundances in western GOM, underestimate in the western portion of central GOM, and overestimate in the eastern portion of central GOM, with slightly less consistent and patchy trends amongst groups in eastern GOM. The differences between model estimates were greatest between the largest and finest scale models, suggesting that fine-scale models may be useful for capturing effects of unique dependencies that may operate at localized scales. We demonstrate how estimates of season-, sex-, and size- specific American lobster spatial distribution would vary based on the spatial scale assumption of nonstationarity in the GOM. This information may help develop appropriate local adaptation measures in a region that is susceptible to climate change.

scholarly journals Geographically weighted regression: Method for analysing spatial nonstationarity of geographical phenomenon

Geografie ◽  
2008 ◽  
Vol 113 (2) ◽  
pp. 125-139 ◽  
Author(s):  
Pavlína Spurná
Keyword(s):  
Linear Regression ◽  
Geographically Weighted Regression ◽  
Quantitative Method ◽  
Regression Method ◽  
Weighted Regression ◽  
Parliamentary Election ◽  
Voting Behaviour ◽  
Significant Difference ◽  
Spatial Nonstationarity ◽  
Weighted Regression Method

The article deals with one of the new quantitative method used in geography, geographically weighted regression (GWR). This method is based on the premise that relationships between variables might not be constant across the study area and explores this phenomenon called spatial non-stationarity. Using the GWR technique to study voting behaviour in Czechia in the parliamentary election in 2002, it is evident that there is a significant difference between the linear regression and GWR models. The examples highlight the relevance and usefulness of GWR and show how it can improve geographical research and potentially also our understanding of geographical processes.

scholarly journals Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity

2010 ◽  
Vol 28 (4) ◽  
pp. 281-298 ◽  
Author(s):  
Chris Brunsdon ◽  
A. Stewart Fotheringham ◽  
Martin E. Charlton
Keyword(s):  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Spatial Nonstationarity
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