Geographic Information Analysis

O’Sullivan, David; Unwin, David

doi:10.1002/9780470549094

Cited by 554 publications

(444 citation statements)

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“…Acceptance of Tobler's first law is an acknowledgment that CSR can never exist. Rejecting a null of CSR therefore reveals little about the process that is actually operating [14]. We get a sense of this when generating line-up tests with choropleth maps (Figure 1).…”

Section: B Ackground 21 Spatial Autocorrelation Structure In Geographymentioning

confidence: 99%

“…When testing for statistical significance, Moran's I can be compared to a theoretical distribution, but since the spatial structure of the map is also a parameter in the analysis [14] -the geometry of a region partly constrains the possible Moran's I that can be achieved -a more common procedure is to generate a sampling distribution of Moran's I empirically by permuting attribute values within a region any number of times and calculating Moran's I on each permutation. This is the same technique proposed in Wickham et al [20] for generating decoys in line-up tests of spatial independence.…”

Section: B Ackground 21 Spatial Autocorrelation Structure In Geographymentioning

confidence: 99%

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Map LineUps: Effects of spatial structure on graphical inference

Beecham

Dykes

Meulemans

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-Fundamental to the effective use of visualization as an analytic and descriptive tool is the assurance that presenting data visually provides the capability of making inferences from what we see. This paper explores two related approaches to quantifying the confidence we may have in making visual inferences from mapped geospatial data. We adapt Wickham et al.'s 'Visual Line-up' method as a direct analogy with Null Hypothesis Significance Testing (NHST) and propose a new approach for generating more credible spatial null hypotheses. Rather than using as a spatial null hypothesis the unrealistic assumption of complete spatial randomness, we propose spatially autocorrelated simulations as alternative nulls. We conduct a set of crowdsourced experiments (n = 361) to determine the just noticeable difference (JND) between pairs of choropleth maps of geographic units controlling for spatial autocorrelation (Moran's I statistic) and geometric configuration (variance in spatial unit area). Results indicate that people's abilities to perceive differences in spatial autocorrelation vary with baseline autocorrelation structure and the geometric configuration of geographic units. These results allow us, for the first time, to construct a visual equivalent of statistical power for geospatial data. Our JND results add to those provided in recent years by Klippel et al. (2011), Harrison et al. (2014 and Kay & Heer (2015) for correlation visualization. Importantly, they provide an empirical basis for an improved construction of visual line-ups for maps and the development of theory to inform geospatial tests of graphical inference.

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Section: B Ackground 21 Spatial Autocorrelation Structure In Geographymentioning

confidence: 99%

Section: B Ackground 21 Spatial Autocorrelation Structure In Geographymentioning

confidence: 99%

Map LineUps: Effects of spatial structure on graphical inference

Beecham

Dykes

Meulemans

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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“…that is a regular (ordered) pattern, a Poisson (random) pattern, and a clustered (aggregated) pattern [18,19]. In a regular pattern, points are distributed uniformly.…”

Section: Spatial Point Analysis Of Quantum Dot Nucleation Sites On Inmentioning

confidence: 99%

“…In this study, such invalid Voronoi cells were excluded from the study region. For more precise analysis, second-order properties of point patterns like nearest-neighbor distances are useful [19][20][21][22]. Let r denote the distance to the nearest point from a randomly selected location in the study region R. The F (d) function denotes the cumulative frequency distribution of r [19] and hence the probability that r occurs less than any particular distance d. Since the F function is practically identical to the probability of plotting a random point within any of circles C i (d), of each radius d, centered on each of the points, it is simply computed by…”

Section: Spatial Point Analysis Of Quantum Dot Nucleation Sites On Inmentioning

confidence: 99%

Spatial point analysis of quantum dot nucleation sites on InAs wetting layer

Konishi

Tsukamoto

2011

Surface Science

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We perform spatial point analysis of InAs quantum dot nucleation sites and surface reconstruction domain pattern on an InAs wetting layer, giving insights for quantum dot nucleation mechanism. An InAs wetting layer grown to 1.5 monolayers in thickness on a GaAs(001) substrate has been observed at 300 • C by using in situ scanning tunneling microscopy. The surface exhibits (1 × 3)/(2 × 3) and (2 × 4) reconstruction domains. A nearest-neighbor analysis finds that point pattern of quantum dot precursors was more similar to that of (1 × 3)/(2 × 3) domains which are specific to Ga-rich region. This provides the evidence that InAs quantum dot nucleation is induced by Ga-rich fluctuation within an InAs wetting layer.PACS numbers: 68.37. Ef, 68.43.Hn, 68.47.Fg, 68.55.ag Quantum dots (QDs) are potentially used in semiconductor laser devices and single photon sources of quantum computation and quantum communication [1][2][3][4]. Although it has been pointed out that highly dense and uniform QD arrays are essential for the efficiency of the devices, little is known of the growth mechanism of QDs to control the nucleation sites on a Stranski-Krastanow (SK) grown wetting layer (WL). Some atomic-level theoretical studies on dynamics of surface atoms have been carried out to understand the growth mechanisms [5][6][7][8]. First principle calculations showed that the migration barrier energy of In adatom on GaAs(001) surface is higher than that on 1ML-InAs/GaAs(001) [6,7]. Using kinetic Monte Carlo (kMC) simulations [8], Tsukamoto et al. found that some migrating In adatoms were captured on Ga-rich fluctuation, within an In/Ga mixed layer, to become a nucleation site [9]. To the best of our knowledge, however, there has not been reported any direct evidence that alloy fluctuation becomes a QD nucleation site. It is still vital to investigate WL surface in an atomic scale, in particular the surface reconstruction, preceding QD nucleation.Since surface reconstruction changes microscopically and dynamically in the course of WL growth [10][11][12], in situ scanning tunneling microscopy (STM) during molecular beam epitaxy (MBE) growth at high temperatures, such as STMBE [13], is one of powerful tools to observe it. It is reported that fast Fourier transform analysis of atomic-scale in situ STM images of InAs WL on a GaAs(001) substrate, as well as reflectance highenergy electron diffraction (RHEED) measurements, has revealed that the surface reconstruction changes from c(4 × 4) to the mixed structure of (1 × 3)/(2 × 3) domains and (2 × 4) domains prior to QD formation [9].Structure models of (1 × 3)/(2 × 3) and (2 × 4) surface reconstructions have been investigated by many researchers using core-level photoemission spectroscopy [14], reflectance-difference spectroscopy [15], ab initio calculations in a local density approximation [6, * konishi@anan-nct.ac.jp [110] [110]In Ga As 15], and STM observations [16,17], which are, however, still under discussion. Figure 1 shows the schematic diagrams, reproduced from the literature, of...

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“…Similarly, these editors also change category tags to make them match the latest community agreements or ensure that taxonomies are not confused with partonomies. Such assistant tools and rule systems have been frequently described as the next step in understanding and making use of VGI [4,9,10]. To assist users by suggesting the most likely feature type tags, or notify them if a similar feature already exists in the vicinity, requires the understanding of the spatial as well as the semantic patterns in OSM.…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Spatial-Semantic Interaction of Points of Interest in Volunteered Geographic Information

Mülligann

Janowicz

et al. 2011

Spatial Information Theory

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Abstract. With the increasing success and commercial integration of Volunteered Geographic Information (VGI), the focus shifts away from coverage to data quality and homogeneity. Within the last years, several studies have been published analyzing the positional accuracy of features, completeness of specific attributes, or the topological consistency of line and polygon features. However, most of these studies do not take geographic feature types into account. This is for two reasons. First, and in contrast to street networks, choosing a reference set is difficult. Second, we lack the measures to quantify the degree of feature type miscategorization. In this work, we present a methodology to analyze the spatial-semantic interaction of point features in Volunteered Geographic Information. Feature types in VGI can be considered special in both, the way they are formed and the way they are applied. Given that they reflect community agreement more accurately than top-down approaches, we argue that they should be used as the primary basis for assessing spatial-semantic interaction. We present a case study on a spatial and semantic subset of OpenStreetMap, and introduce a novel semantic similarity measure based on the change history of OpenStreetMap elements. Our results set the stage for systems that assist VGI contributors in suggesting the types of new features, cleaning up existing data, and integrating data from different sources.

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Geographic Information Analysis

Cited by 554 publications

References 0 publications

Map LineUps: Effects of spatial structure on graphical inference

Map LineUps: Effects of spatial structure on graphical inference

Spatial point analysis of quantum dot nucleation sites on InAs wetting layer

Analyzing the Spatial-Semantic Interaction of Points of Interest in Volunteered Geographic Information

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