Analytical and practical examples of estimating the average nearest-neighbor distance in a rain gauge network

2006 ◽  
Vol 15 (5) ◽  
pp. 565-573 ◽  
Author(s):  
Gregor Skok
Keyword(s):  
Nearest Neighbor ◽  
Rain Gauge ◽  
Neighbor Distance ◽  
Nearest Neighbor Distance ◽  
Rain Gauge Network

scholarly journals Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing Hu ◽  
Shiqiang Hu ◽  
Xiaoyu Zhang ◽  
Huanlong Zhang ◽  
Lingkun Luo
Keyword(s):  
Anomaly Detection ◽  
Nearest Neighbor ◽  
Computation Time ◽  
Compact Representation ◽  
Feature Descriptor ◽  
Neighbor Distance ◽  
Low Level ◽  
Feature Descriptors ◽  
Nearest Neighbor Distance ◽  
Crowded Scenes

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance.

The spatial pattern of a Juniperus communis var. depressa population on a continental dune in subarctic Québec, Canada

1999 ◽  
Vol 29 (4) ◽  
pp. 446-450 ◽  
Author(s):  
Gilles Houle ◽  
Mario Duchesne
Keyword(s):  
Sex Ratio ◽  
Nearest Neighbor ◽  
Dispersion Pattern ◽  
Neighbor Distance ◽  
Juniperus Communis ◽  
Biased Sex Ratio ◽  
Males And Females ◽  
Nearest Neighbor Distance ◽  
Population Dispersion ◽  
Crown Size

We performed a nearest-neighbor analysis to determine the population dispersion pattern and the association between males and females in a Juniperus communis L. var. depressa Pursh population occupying a continental dune in subarctic Quebec, Canada. The overall dispersion pattern was contagious, and males (or females) were proportionately as likely to have a male as they were to have a female nearest neighbor. Crown size was positively related to nearest-neighbor distance for the male-male comparison only, suggesting a somewhat stronger intrasex competition between males. Nearest-neighbor distance increased with crown size (significantly related to age) suggesting a change in the intensity of aggregation with age possibly related to self-thinning. Higher mortality as a result of stronger male-male competition could explain the female-biased sex ratio and the absence of spatial segregation between sexes. The overall contagious dispersion pattern in the population may be related to the fact that most seed cones fall directly underneath the mother plant. Birds can eat the cones of J. communis and thus disperse seeds. However, these seeds are deposited in clumps, a process that may also explain contagion within the population.

scholarly journals Photoluminescence Enhancement and Change in the Second Nearest Neighbor Distance of Sm‐Doped TiO 2 Thin Films

2019 ◽  
Vol 256 (6) ◽  
pp. 1800522 ◽  
Author(s):  
Mariko Murayama ◽  
Kensaku Yoda ◽  
Keita Shiraishi ◽  
Iain F. Crowe ◽  
Shuji Komuro ◽  
...  
Keyword(s):  
Thin Films ◽  
Nearest Neighbor ◽  
Neighbor Distance ◽  
Photoluminescence Enhancement ◽  
Nearest Neighbor Distance

The Statistical Analysis of a Distribution of Activity Points in Relation to Surface-Like Elements

1988 ◽  
Vol 20 (5) ◽  
pp. 609-620 ◽  
Author(s):  
A Okabe ◽  
T Yoshikawa ◽  
A Fujii ◽  
K Oikawa
Keyword(s):  
Statistical Analysis ◽  
Probability Density Function ◽  
Nearest Neighbor ◽  
Distance Measure ◽  
Computational Method ◽  
Spatial Dependency ◽  
Neighbor Distance ◽  
High Class ◽  
Apartment Buildings ◽  
Nearest Neighbor Distance

The objective of this paper is to formulate a statistical method of testing the hypothesis that the distribution of activity points (such as retail stores) is independent of location of ‘surface-like’ infrastructural elements (such as parks). In order to do this, first, the probability density function of a distance from a random point to the nearest surface-like element is derived. Second, through the use of this function, a measure, R, of spatial dependency on the surface-like elements is defined as the ratio of the average nearest-neighbor distance to the expected average nearest-neighbor distance. This measure is an extension of the ordinary nearest-neighbor distance measure frequently referred to in geography and ecology. Third, the statistical use of measure R is shown. Fourth, as this measure is difficult to compute geometrically, the computational method of calculating the value of R is developed. Last, by use of this method, a test is conducted to decide whether or not the distribution of high-class apartment buildings in Setagaya, Tokyo, is affected by the location of big parks.

Nearest-neighbor distance at a single mobile trap

1998 ◽  
Vol 58 (5) ◽  
pp. 6821-6823 ◽  
Author(s):  
Zbigniew Koza ◽  
Tomer Yanir ◽  
Haim Taitelbaum
Keyword(s):  
Nearest Neighbor ◽  
Neighbor Distance ◽  
Nearest Neighbor Distance

LEED AND STM STUDY OF Cs ON Cu(211)

1999 ◽  
Vol 06 (05) ◽  
pp. 865-870 ◽  
Author(s):  
M. CARAGIU ◽  
TL. SEYLLER ◽  
R. D. DIEHL ◽  
A. G. NORRIS ◽  
R. McGRATH ◽  
...  
Keyword(s):  
Low Temperature ◽  
Nearest Neighbor ◽  
Variable Density ◽  
Neighbor Distance ◽  
Nearest Neighbor Distance ◽  
Step Edges

A low-temperature (25 K) STM study of Cs adsorption on Cu(211) indicates that Cs forms variable-density structures which align along the step edges of the Cu(211) surface. The density of the overlayer increases with Cs coverage, forming a quasihexagonal c(2×2) structure at a coverage of 0.17. A dynamical LEED study of that structure at 130 K indicates that the Cs atoms are adsorbed on top of the Cu atoms in the center of the terraces, with a Cs–Cu nearest-neighbor distance of 3.56± 0.04 Å. This structure is accompanied by a significant rumpling of the Cu(211) surface.

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