Geographical information science: GeoComputation and nonstationarity

Atkinson, Peter M.

doi:10.1177/030913330102500106

Cited by 10 publications

(8 citation statements)

References 32 publications

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“…Generally, an advantage of segmentation to classification of pixels is that the resulting division of space tends to involve fewer and more compact subregions. Alternatively, classification-based approaches within geostatistical frameworks exist (Atkinson, 2001). The multiscale segmentation based approach is designed to utilize information in the scales inherent in our spatial (image) data sets in addition to a range of auxiliary data sets, including for airborne and satellite data, but also to the scales of information inherent in single images.…”

Section: A Multiscale Object-based Gis Frameworkmentioning

confidence: 99%

The role of the spatial dimension within the framework of sustainable landscapes and natural capital

Blaschke

2006

Landscape and Urban Planning

154

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Section: A Multiscale Object-based Gis Frameworkmentioning

confidence: 99%

The role of the spatial dimension within the framework of sustainable landscapes and natural capital

Blaschke

2006

Landscape and Urban Planning

154

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“…Two important considerations in any analysis of spatial data are the presence of spatial autocorrelation among observations (e.g., Anselin , ; LeSage and Pace ) and spatial heterogeneity regarding the relationships between the dependent and independent variables (e.g., Fotheringham, Brunsdon, and Charlton ; McMillen ). The former is drawn from the “First Law of Geography” (Tobler ), whereas the latter is a form of nonstationarity (Atkinson ; Lloyd ; Harris ). Spatial nonstationarity refers to the instability of regression parameters over space for a model, not for the data (Myers ).…”

Section: Introductionmentioning

confidence: 99%

Exploring Heterogeneities with Geographically Weighted Quantile Regression: An Enhancement Based on the Bootstrap Approach

Chen

Yang

Matthews

2020

Geographical Analysis

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Geographically weighted quantile regression (GWQR) has been proposed as a spatial analytical technique to simultaneously explore two heterogeneities, one of spatial heterogeneity with respect to data relationships over space and one of response heterogeneity across different locations of the outcome distribution. However, one limitation of GWQR framework is that the existing inference procedures are established based on asymptotic approximation, which may suffer computation difficulties or yield incorrect estimates with finite samples. In this article, we suggest a bootstrap approach to address this limitation. Our bootstrap enhancement is first validated by a simulation experiment and then illustrated with an empirical U.S. mortality data. The results show that the bootstrap approach provides a practical alternative for inference in GWQR and enhances the utilization of GWQR.

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“…Common forms of spatial heterogeneity are ‘spatial regimes’ (patchy areas of varying intensity, abrupt changes) and ‘trends’ (smooth transitions between means) [ 22 ]. Regimes are common in urban areas and typically resemble the local-scale variability of such regions [ 23 ], while trends are more important to the physical sciences [ 24 ]. Dealing with these kinds of spatial heterogeneity is a widely discussed topic in spatial research.…”

Section: Introductionmentioning

confidence: 99%

Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis

et al. 2016

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Twitter and related social media feeds have become valuable data sources to many fields of research. Numerous researchers have thereby used social media posts for spatial analysis, since many of them contain explicit geographic locations. However, despite its widespread use within applied research, a thorough understanding of the underlying spatial characteristics of these data is still lacking. In this paper, we investigate how topological outliers influence the outcomes of spatial analyses of social media data. These outliers appear when different users contribute heterogeneous information about different phenomena simultaneously from similar locations. As a consequence, various messages representing different spatial phenomena are captured closely to each other, and are at risk to be falsely related in a spatial analysis. Our results reveal indications for corresponding spurious effects when analyzing Twitter data. Further, we show how the outliers distort the range of outcomes of spatial analysis methods. This has significant influence on the power of spatial inferential techniques, and, more generally, on the validity and interpretability of spatial analysis results. We further investigate how the issues caused by topological outliers are composed in detail. We unveil that multiple disturbing effects are acting simultaneously and that these are related to the geographic scales of the involved overlapping patterns. Our results show that at some scale configurations, the disturbances added through overlap are more severe than at others. Further, their behavior turns into a volatile and almost chaotic fluctuation when the scales of the involved patterns become too different. Overall, our results highlight the critical importance of thoroughly considering the specific characteristics of social media data when analyzing them spatially.

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Geographical information science: GeoComputation and nonstationarity

Cited by 10 publications

References 32 publications

The role of the spatial dimension within the framework of sustainable landscapes and natural capital

The role of the spatial dimension within the framework of sustainable landscapes and natural capital

Exploring Heterogeneities with Geographically Weighted Quantile Regression: An Enhancement Based on the Bootstrap Approach

Abundant Topological Outliers in Social Media Data and Their Effect on Spatial Analysis

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