Dynamic Graphics for Exploring Spatial Data with Application to Locating Global and Local Anomalies

Haslett, John; Bradley, Ronan; Craig, P.; Unwin, Antony; Wills, Graham

doi:10.1080/00031305.1991.10475810

Cited by 109 publications

(49 citation statements)

References 13 publications

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“…In the case of a simple histogram, the selection of some bars of this histogram will show the corresponding zones on the map, which is just a more elaborate variant of the previous tool as in Haslett et al (1991). In the other direction, a selection of a subregion of the map produces the subhistogram of the distribution of the variable in this subregion.…”

Section: Descriptive Functionsmentioning

confidence: 99%

“…It is a simple scatterplot of the half square of the difference between the value of the variable at two locations against the distance between these points. As in Haslett et al (1991), outliers may be mapped by highlighting those points on this graph which have a high value of the second coordinate. An option allows the user to overlay an empirical variogram or a smooth of this scatterplot thus estimating the variogram function (with the possibility of a robust alternative Cressie 1993).…”

Section: Geostatistical Functionsmentioning

confidence: 99%

“…The coupling is the fact that the selection of a zone on the map results in the automatic highlighting of the corresponding points on the statistical graph or conversely the selection of a portion of the graph results in the automatic highlighting of the corresponding points on the map. Haslett, Bradley, Craig, Unwin, and Wills (1991) link histograms, double histograms, scatterplot matrices, and varioclouds (see Section 4) with the maps using the Pascal language. Anselin and Bao (1997) implement the methods advocated in Anselin (1994) linking ArcView and SpaceStat.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

GeoXp: AnRPackage for Exploratory Spatial Data Analysis

Laurent¹,

Ruiz‐Gazen²,

Thomas‐Agnan³

2012

J. Stat. Soft.

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We present GeoXp, an R package implementing interactive graphics for exploratory spatial data analysis. We use a data set concerning public schools of the French MidiPyrénées region to illustrate the use of these exploratory techniques based on the coupling between a statistical graph and a map. Besides elementary plots like boxplots, histograms or simple scatterplots, GeoXp also couples maps with Moran scatterplots, variogram clouds, Lorenz curves and other graphical tools. In order to make the most of the multidimensionality of the data, GeoXp includes dimension reduction techniques such as principal components analysis and cluster analysis whose results are also linked to the map.

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Section: Descriptive Functionsmentioning

confidence: 99%

Section: Geostatistical Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

GeoXp: AnRPackage for Exploratory Spatial Data Analysis

Laurent¹,

Ruiz‐Gazen²,

Thomas‐Agnan³

2012

J. Stat. Soft.

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“…They also explored the concepts of multiple brushes, composite brushes (formed by logical combinations of brushes), and fuzzy brushes, that allow points to be partially contained within a brush. Haslett et al [18] introduced the ability to show the average value of the points that are currently selected by the brush.…”

Section: Brush Basicsmentioning

confidence: 99%

Scalable Visual Hierarchy Exploration

Stroe

Rundensteiner

Ward

2000

Lecture Notes in Computer Science

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More and more modern computer applications, from business decision support to scientific data analysis, utilize visualization techniques to support exploratory activities.Most visualization tools do not scale well with regard to the size of the dataset upon which they operate. Specifically, the level of cluttering on the screen is typically unacceptable and the performance is poor. To solve the problem of cluttering at the interface level, visualization tools have recently been extended to support hierarchical views of the data, with support for focusing and drilling-down using interactive brushes. To solve the scalability problem, this thesis investigates how best to couple such a visualization tool with a database management system without losing the real-time characteristic required in any interactive application.This integration must be done carefully, since visual user interactions implemented as main memory operations do not map directly into efficient database operations. In our context, the main efficiency issue is to avoid the recursive processing required for hierarchical data retrieval. For this problem, we have developed a tree labeling method, called MinMax tree, that allows the movement of the on-line recursive processing into an off-line precomputation step. Thus, at run time, the recursive processing operations translate into linear cost range queries. Secondly, we employ a main memory access strategy to support incremental loading of data into the main memory. To further reduce the response time in the system, we have designed a speculative non-pure prefetcher that brings data into memory when the system is idle. The techniques have been incorporated into XmdvTool, a multidimensional visual exploration tool, in order to achieve scalability.The tool efficiently scales up now to datasets of the order 10 5 10 7 records. Lastly, we report experimental results that illustrate the impact of the proposed techniques on the system's overall performance. AcknowledgementsMany thanks to my professors and to my friends for their support.

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“…At an exploratory stage of analysis one is usually interested in the detection of nonstationary pockets, influential observations, and (clusters of) outliers. For these purposes simple graphical tools such as pocket plots and variogram clouds are now commonly available (see, e.g., Haslett et al 1991;Panatier 1996). After the model-fitting procedure, formal deletion diagnostics can also be computed (Martin 1992;Christensen, Johnson, and Pearson 1992;Haslett and Hayes 1998).…”

Section: Introductionmentioning

confidence: 99%

The Ordering of Spatial Data and the Detection of Multiple Outliers

Cerioli¹,

Riani²

1999

Journal of Computational and Graphical Statistics

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In this article we suggest a unified approach to the exploratory analysis of spatial data. Our technique is based on a forward search algorithm that orders the observations from those most in agreement with a specified autocorrelation model to those least in agreement with it. This leads to the identification of spatial outliers-that is, extreme observations with respect to their neighboring values-and of nonstationary pockets. In particular, the focus of our analysis is on spatial prediction models. We show that standard deletion diagnostics for prediction are affected by masking and swamping problems when multiple outliers are present. The effectiveness of the suggested method in detecting masked multiple outliers, and more generally in ordering spatial data, is shown by means of a number of simulated datasets. These examples clearly reveal the power of our method in getting inside the data in a way which is more simple and powerful than it would be using standard diagnostic procedures. Furthermore, the behavior of our algorithm under the null hypothesis of no outliers is investigated through a Monte Carlo experiment. Such simulations are also used to build envelopes for the forward search.

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Dynamic Graphics for Exploring Spatial Data with Application to Locating Global and Local Anomalies

Cited by 109 publications

References 13 publications

GeoXp: AnRPackage for Exploratory Spatial Data Analysis

GeoXp: AnRPackage for Exploratory Spatial Data Analysis

Scalable Visual Hierarchy Exploration

The Ordering of Spatial Data and the Detection of Multiple Outliers

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