Spatial Scale Problems and Geostatistical Solutions: A Review

Atkinson, Peter M.; Tate, Nicholas

doi:10.1111/0033-0124.00250

Cited by 228 publications

(148 citation statements)

References 53 publications

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“…The anisotropic behavior of the indices is not very significant since the ratio of the major and minor axes varies from 0.7 to 1.0 ( [5], and Atkinson and Tate [66] discovered, that in case of uncertain spatial point configuration, deterministic methods should be the preferable option for spatial interpolation, two deterministic methods-thin plate spline (TPS) and inverse distance weighting (IDW)-have been applied for interpolation of the indices. These methods have mostly been applied in climate modeling by the geostatisticians.…”

Section: Justifying the Choice Of Spatial Interpolation Methodsmentioning

confidence: 99%

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

Bhowmik

2012

ISRN Meteorology

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This study analyses the degree and margin of differences among the surfaces of annual total precipitation in wet days (PRCPTOT) and the yearly maximum value of the daily maximum temperature (TXx) of Bangladesh, produced by thin plate spline (TPS), inverse distance weighting (IDW), ordinary kriging (OK), and universal kriging (UK) methods of spatial interpolation. From the surface differences, the maximum and minimum differences are observed between the surfaces produced by TPS and IDW, and OK and UK, respectively. The residual plots from cross-validation depict that IDW and OK methods mostly under predict and TPS and UK methods mostly overpredict the observed climate indices' values. Both the tendency of methods' over and underprediction and the surface-differences decrease with the increase in the number of available spatial point observations. Finally, two performance measures-the index of agreement (d) and the coefficient of variation of prediction (ρ f )-imply that there is a little difference in the prediction ability of the four different methods. The performance of the spatial interpolation improves with the increase in the number of available spatial points, and eventually the predicted climate surfaces get similar. However, UK shows better interpolation performance in most of the years.

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Section: Justifying the Choice Of Spatial Interpolation Methodsmentioning

confidence: 99%

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

Bhowmik

2012

ISRN Meteorology

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“…The issues associated with thematic, spatial, and temporal scales have been widely discussed in the ecological (e.g., Levin, 1992;Schneider, 2001;Hobbs, 2003) and geospatial literatures (e.g., Stone, 1972;Atkinson and Tate, 2000;Goodchild, 2011), including a recent review by on scale in marine habitat mapping. Here, I focus on how scale impacts mapping outcomes, and the implications of scale for decision-making.…”

Section: Scalementioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.

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“…This approach was because it has been shown to represent spatial dependence within the data set [Atkinson and Tate, 2000]. Semivariance numerically describes the dissimilarity between spatially discrete data points at a given separation (lag) distance, and is inversely related to correlation [Goovaerts, 1999].…”

Section: Semivariogram Analysismentioning

confidence: 99%

“…[16] The range (a) provides information on the maximum scale of spatial variation [Atkinson and Tate, 2000] and is the point after which no further increase in distance results in an increase in semivariance. The range denotes clustering in the data set, indicating the length scale of spatial data patterns.…”

Section: Semivariogram Analysismentioning

confidence: 99%

Modeling fine‐scale soil surface structure using geostatistics

Croft

Anderson

Brazier

et al. 2013

Water Resources Research

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[1] There is widespread recognition that spatially distributed information on soil surface roughness (SSR) is required for hydrological and geomorphological applications. Such information is necessary to describe variability in soil structure, which is highly heterogeneous in time and space, to parameterize hydrology and erosion models and to understand the temporal evolution of the soil surface in response to rainfall. This paper demonstrates how results from semivariogram analysis can quantify key elements of SSR for such applications. Three soil types (silt, silt loam, and silty clay) were used to show how different types of structural variance in SSR evolve during simulated rainfall events. All three soil types were progressively degraded using artificial rainfall to produce a series of roughness states. A calibrated laser profiling instrument was used to measure SSR over a 10 cm Â 10 cm spatial extent, at a 2 mm resolution. These data were geostatistically analyzed in the context of aggregate breakdown and soil crusting. The results show that such processes are represented by a quantifiable decrease in sill variance, from 7.81 (control) to 0.94 (after 60 min of rainfall). Soil surface features such as soil cracks, tillage lines and erosional areas were quantified by local maxima in semivariance at a given length scale. This research demonstrates that semivariogram analysis can retrieve spatiotemporal variations in soil surface condition; in order to provide information on hydrological pathways. Consequently, geostatistically derived SSR shows strong potential for inclusion as spatial information in hydrology and erosion models to represent complex surface processes at different soil structural scales.Citation: Croft, H., K. Anderson, R. E. Brazier, and N. J. Kuhn (2013), Modeling fine-scale soil surface structure using geostatistic,

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Spatial Scale Problems and Geostatistical Solutions: A Review

Cited by 228 publications

References 53 publications

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Modeling fine‐scale soil surface structure using geostatistics

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