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Journal articles on the topic 'Spatial point patterns'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Ayala, G., I. Epifanio, A. Simó, and V. Zapater. "Clustering of spatial point patterns." Computational Statistics & Data Analysis 50, no. 4 (February 2006): 1016–32. http://dx.doi.org/10.1016/j.csda.2004.10.013.

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2

Symanzik, Jürgen. "Statistical Analysis of Spatial Point Patterns." Technometrics 47, no. 4 (November 2005): 516–17. http://dx.doi.org/10.1198/tech.2005.s318.

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3

Solow, Andrew R. "Bootstrapping Sparsely Sampled Spatial Point Patterns." Ecology 70, no. 2 (April 1989): 379–82. http://dx.doi.org/10.2307/1937542.

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4

Katti, S. K., Peter J. Diggle, and Brian D. Ripley. "Statistical Analysis of Spatial Point Patterns." Journal of the American Statistical Association 81, no. 393 (March 1986): 263. http://dx.doi.org/10.2307/2288020.

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5

Pellegrini, Pasquale A., and Steven Reader. "Duration Modeling of Spatial Point Patterns." Geographical Analysis 28, no. 3 (September 3, 2010): 219–43. http://dx.doi.org/10.1111/j.1538-4632.1996.tb00932.x.

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6

Schilcher, Udo, Günther Brandner, and Christian Bettstetter. "Quantifying inhomogeneity of spatial point patterns." Computer Networks 115 (March 2017): 65–81. http://dx.doi.org/10.1016/j.comnet.2016.12.018.

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7

ARAÚJO, Edmary Silveira Barreto, João Domingos SCALON, and Lurimar Smera BATISTA. "EXPLORATORY SPECTRAL ANALYSIS IN THREE-DIMENSIONAL SPATIAL POINT PATTERNS." REVISTA BRASILEIRA DE BIOMETRIA 39, no. 1 (March 31, 2021): 177–93. http://dx.doi.org/10.28951/rbb.v39i1.524.

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A spatial point pattern is a collection of points irregularly located within a bounded area (2D) or space (3D) that have been generated by some form of stochastic mechanism. Examples of point patterns include locations of trees in a forest, of cases of a disease in a region, or of particles in a microscopic section of a composite material. Spatial Point pattern analysis is used mostly to determine the absence (completely spatial randomness) or presence (regularity and clustering) of spatial dependence structure of the locations. Methods based on the space domain are widely used for this purpose, while methods conducted in the frequency domain (spectral analysis) are still unknown to most researchers. Spectral analysis is a powerful tool to investigate spatial point patterns, since it does not assume any structural characteristics of the data (ex. isotropy), and uses only the autocovariance function, and its Fourier transform. There are some methods based on the spectral frameworks for analyzing 2D spatial point patterns. There is no such methods available for the 3D situation and, therefore, the aim of this work is to develop new methods based on spectral framework for the analysis of three-dimensional point patterns. The emphasis is on relating periodogram structure to the type of stochastic process which could have generated a 3D observed pattern. The results show that the methods based on spectral analysis developed in this work are able to identify patterns of three typical three-dimensional point processes, and can be used, concurrently, with analyzes in the space domain for a better characterization of spatial point patterns.

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8

OGATA, Yosihiko, and Masaharu TANEMURA. "THE LIKELIHOOD ANALYSIS FOR SPATIAL POINT PATTERNS." Japanese Journal of Biometrics 8, no. 1 (1987): 1_27–38. http://dx.doi.org/10.5691/jjb.8.1_27.

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9

HöUgmander, Harri, and Aila SäUrkkä. "Multitype Spatial Point Patterns with Hierarchical Interactions." Biometrics 55, no. 4 (December 1999): 1051–58. http://dx.doi.org/10.1111/j.0006-341x.1999.01051.x.

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10

Baddeley, Adrian, and Rolf Turner. "Practical maximum pseudolikelihood for spatial point patterns." Advances in Applied Probability 30, no. 2 (June 1998): 273. http://dx.doi.org/10.1017/s000186780004698x.

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11

Rajala, Tuomas A., Aila Särkkä, Claudia Redenbach, and Martina Sormani. "Estimating geometric anisotropy in spatial point patterns." Spatial Statistics 15 (February 2016): 100–114. http://dx.doi.org/10.1016/j.spasta.2015.12.005.

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12

Vestal, Brian E., Nichole E. Carlson, and Debashis Ghosh. "Filtering spatial point patterns using kernel densities." Spatial Statistics 41 (March 2021): 100487. http://dx.doi.org/10.1016/j.spasta.2020.100487.

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13

Baddeley, Adrian, and Rolf Turner. "Practical Maximum Pseudolikelihood for Spatial Point Patterns." Australian & New Zealand Journal of Statistics 42, no. 3 (September 2000): 283–322. http://dx.doi.org/10.1111/1467-842x.00128.

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14

Agterberg, Frederik P. "Fractals and spatial statistics of point patterns." Journal of Earth Science 24, no. 1 (February 2013): 1–11. http://dx.doi.org/10.1007/s12583-013-0305-6.

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15

Joyner, Michele L., Edith Seier, and Thomas C. Jones. "Distances to a point of reference in spatial point patterns." Spatial Statistics 10 (November 2014): 63–75. http://dx.doi.org/10.1016/j.spasta.2014.08.002.

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16

Ha, Olivia K., and Martin A. Andresen. "Spatial Patterns of Immigration and Property Crime in Vancouver: A Spatial Point Pattern Test." Canadian Journal of Criminology and Criminal Justice 62, no. 4 (October 1, 2020): 30–51. http://dx.doi.org/10.3138/cjccj.2020-0041.

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17

Marcon, Eric, Florence Puech, and Stéphane Traissac. "Characterizing the Relative Spatial Structure of Point Patterns." International Journal of Ecology 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/619281.

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We generalize Ripley’sKfunction to get a new function,M, to characterize the spatial structure of a point pattern relatively to another one. We show that this new approach is pertinent in ecology when space is not homogenous and the size of objects matters. We present how to use the function and test the data against the null hypothesis of independence between points. In a tropical tree data set we detect intraspecific aggregation and interspecific competition.

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18

Zimeras, Stelios. "Exploratory Point Pattern Analysis for Modeling Biological Data." International Journal of Systems Biology and Biomedical Technologies 2, no. 1 (January 2013): 1–13. http://dx.doi.org/10.4018/ijsbbt.2013010101.

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Data in the form of sets of points, irregular distributed in a region of space could be identified in varies biological applications for examples the cell nuclei in a microscope section of tissue. These kinds of data sets are defined as spatial point patterns and the presentation of the positions in the space are defined as points. The spatial pattern generated by a biological process, can be affected by the physical scale on which the process is observed. With these spatial maps, the biologists will usually want a detailed description of the observed patterns. One way to achieve this is by forming a parametric stochastic model and fitting it to the data. The estimated values of the parameters could be used to compare similar data sets providing statistical measures for fitting models. Also a fitted model can provide an explanation of the biological processes. Model fitting especially for large data sets is difficult. For that reason, statistical methods can apply with main purpose to formulate a hypothesis for the implementation of biological process. Spatial statistics could be implemented using advance statistical techniques that explicitly analyses and simulates point structures data sets. Typically spatial point patterns are data that explain the location of point events. The author’s interest is the investigation of the significance of these patterns. In this work, an investigation of biological spatial data is analyzed, using advance statistical modeling techniques like kriging.

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19

Joyner, Michele, Chelsea Ross, and Edith Seier. "Distance to the border in spatial point patterns." Spatial Statistics 6 (November 2013): 24–40. http://dx.doi.org/10.1016/j.spasta.2013.05.002.

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20

Weston, David J., Niall M. Adams, Richard A. Russell, David A. Stephens, and Paul S. Freemont. "Analysis of Spatial Point Patterns in Nuclear Biology." PLoS ONE 7, no. 5 (May 16, 2012): e36841. http://dx.doi.org/10.1371/journal.pone.0036841.

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21

Mateu, Jorge, and Orietta Nicolis. "Multiresolution analysis of linearly oriented spatial point patterns." Journal of Statistical Computation and Simulation 85, no. 3 (September 19, 2013): 621–37. http://dx.doi.org/10.1080/00949655.2013.838565.

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22

Chakraborty, Avishek, and Alan E. Gelfand. "Analyzing spatial point patterns subject to measurement error." Bayesian Analysis 5, no. 1 (March 2010): 97–122. http://dx.doi.org/10.1214/10-ba504.

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23

Bognar, Matthew A. "Bayesian modeling of continuously marked spatial point patterns." Computational Statistics 23, no. 3 (July 25, 2007): 361–79. http://dx.doi.org/10.1007/s00180-007-0073-9.

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24

Xinting, Wang, Hou Yali, Liang Cunzhu, Wang Wei, and Liu Fang. "Point pattern analysis based on different null models for detecting spatial patterns." Biodiversity Science 20, no. 2 (January 8, 2013): 151–58. http://dx.doi.org/10.3724/sp.j.1003.2012.08163.

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25

Wheeler, Andrew Palmer, Wouter Steenbeek, and Martin A. Andresen. "Testing for similarity in area-based spatial patterns: Alternative methods to Andresen's spatial point pattern test." Transactions in GIS 22, no. 3 (June 2018): 760–74. http://dx.doi.org/10.1111/tgis.12341.

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26

Mateu, Jorge, Jordi Artés, and José A. López. "Computational issues for perfect simulation in spatial point patterns." Communications in Nonlinear Science and Numerical Simulation 9, no. 2 (April 2004): 229–40. http://dx.doi.org/10.1016/s1007-5704(03)00114-x.

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27

Scott, B. T. "Summary fucntions in the analysis of spatial point patterns." Bulletin of the Australian Mathematical Society 65, no. 3 (June 2002): 527–28. http://dx.doi.org/10.1017/s000497270002058x.

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28

Pereira, Sandra M. C. "Analysis of spatial point patterns using hierarchical clustering algorithms." Bulletin of the Australian Mathematical Society 71, no. 1 (February 2005): 175. http://dx.doi.org/10.1017/s0004972700038120.

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29

Rajala, T., C. Redenbach, A. Särkkä, and M. Sormani. "A review on anisotropy analysis of spatial point patterns." Spatial Statistics 28 (December 2018): 141–68. http://dx.doi.org/10.1016/j.spasta.2018.04.005.

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30

Yang, Chen, Feng Han, Leigh Shutter, and Hangbin Wu. "Capturing spatial patterns of rural landscapes with point cloud." Geographical Research 58, no. 1 (October 30, 2019): 77–93. http://dx.doi.org/10.1111/1745-5871.12381.

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31

Lieshout, M. N. M., and A. J. Baddeley. "A nonparametric measure of spatial interaction in point patterns." Statistica Neerlandica 50, no. 3 (November 1996): 344–61. http://dx.doi.org/10.1111/j.1467-9574.1996.tb01501.x.

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32

Møller, Jesper, and Rasmus Waagepetersen. "Some Recent Developments in Statistics for Spatial Point Patterns." Annual Review of Statistics and Its Application 4, no. 1 (March 7, 2017): 317–42. http://dx.doi.org/10.1146/annurev-statistics-060116-054055.

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33

Ogata, Yosihiko, and Koichi Katsura. "Likelihood analysis of spatial inhomogeneity for marked point patterns." Annals of the Institute of Statistical Mathematics 40, no. 1 (March 1988): 29–39. http://dx.doi.org/10.1007/bf00053953.

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34

Wallet, F., and C. Dussert. "Comparison of spatial point patterns and processes characterization methods." Europhysics Letters (EPL) 42, no. 5 (June 1, 1998): 493–98. http://dx.doi.org/10.1209/epl/i1998-00279-7.

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35

Burguet, Jasmine, and Philippe Andrey. "Statistical Comparison of Spatial Point Patterns in Biological Imaging." PLoS ONE 9, no. 2 (February 5, 2014): e87759. http://dx.doi.org/10.1371/journal.pone.0087759.

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36

Tscheschel, André, and Sung Nok Chiu. "Quasi-plus sampling edge correction for spatial point patterns." Computational Statistics & Data Analysis 52, no. 12 (August 2008): 5287–95. http://dx.doi.org/10.1016/j.csda.2008.05.012.

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37

Alba-Fernández, M., and Francisco Ariza-López. "A Homogeneity Test for Comparing Gridded-Spatial-Point Patterns of Human Caused Fires." Forests 9, no. 8 (July 27, 2018): 454. http://dx.doi.org/10.3390/f9080454.

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The statistical evaluation of the spatial similarity of human caused fire patterns is an important issue for wildland fire analysis. This paper proposes a method based on observed data and on a statistical tool (homogeneity test) that is based on non-explicit spatial distribution hypothesis for the human caused fire events. If a tessellation coming from a space filling curve is superimposed on the spatial point patterns, and a linearization mechanism applied, the statistical problem of testing the similarity between the spatial point patterns is equivalent to the one of testing the homogeneity between the two multinomial distributions obtained by modeling the proportions of cases on each cell of the tessellation. This way of comparing spatial point patterns is free of any hypothesis on any spatial point process. Because data are spatially over-dispersed, the existence of many cells of the grid without any count is a problem for classical statistical homogeneity tests. Our work overcomes this problem by applying specific test statistics based on the square Hellinger distance. Simulations and actual data are used in order to tune the process and to demonstrate the capabilities of the proposal. Results indicate that a new and robust method for comparing spatial point patterns of human caused fires is available.

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38

Myllymäki, Mari, Aila Särkkä, and Aki Vehtari. "Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates." Spatial Statistics 8 (May 2014): 104–21. http://dx.doi.org/10.1016/j.spasta.2013.07.006.

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39

Zhu, Jie, Jing Yang, Shaoning Di, Jiazhu Zheng, and Leying Zhang. "A novel dual-domain clustering algorithm for inhomogeneous spatial point event." Data Technologies and Applications 54, no. 5 (October 28, 2020): 603–23. http://dx.doi.org/10.1108/dta-08-2019-0142.

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PurposeThe spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.Design/methodology/approachIn this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.FindingsThe effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.Originality/valueTraditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.

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40

Luca, Stijn E., Marco A. F. Pimentel, Peter J. Watkinson, and David A. Clifton. "Point process models for novelty detection on spatial point patterns and their extremes." Computational Statistics & Data Analysis 125 (September 2018): 86–103. http://dx.doi.org/10.1016/j.csda.2018.03.019.

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41

Andresen, Martin A., and Nicolas Malleson. "Testing the Stability of Crime Patterns: Implications for Theory and Policy." Journal of Research in Crime and Delinquency 48, no. 1 (December 5, 2010): 58–82. http://dx.doi.org/10.1177/0022427810384136.

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Recent research in the ‘‘crime at places’’ literature is concerned with smaller units of analysis than conventional spatial criminology. An important issue is whether the spatial patterns observed in conventional spatial criminology focused on neighborhoods remain when the analysis shifts to street segments. In this article, the authors use a new spatial point pattern test that identifies the similarity in spatial point patterns. This test is local in nature such that the output can be mapped showing where differences are present. Using this test, the authors investigate the stability of crime patterns moving from census tracts to dissemination areas to street segments. The authors find that general crime patterns are somewhat similar at all spatial scales, but finer scales of analysis reveal significant variations within larger units. This result demonstrates the importance of analyzing crime patterns at small scales and has important implications for further theoretical development and policy implementation.

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42

Magnussen, Steen, Denis Allard, and Michael A. Wulder. "Poisson Voronoï tiling for finding clusters in spatial point patterns." Scandinavian Journal of Forest Research 21, no. 3 (June 2006): 239–48. http://dx.doi.org/10.1080/02827580600688178.

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43

Van Lieshout, M. N. M., and A. J. Baddeley. "A non-parametric measure of spatial interaction in point patterns." Advances in Applied Probability 28, no. 2 (June 1996): 337. http://dx.doi.org/10.1017/s0001867800048345.

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The strength and range of interpoint interactions in a spatial point process can be quantified by the function J = (1 - G)/(1 - F), where G is the nearest-neighbour distance distribution function and F the empty space function of the process. J(r) is identically equal to 1 for a Poisson process; values of J(r) smaller or larger than 1 indicate clustering or regularity, respectively. We show that, for a very large class of point processes, J(r) is constant for distances r greater than the range of spatial interaction. Hence both the range and type of interpoint interaction may be inferred from J without parametric model assumptions. We evaluate J(r) explicitly for a variety of point processes. The J function of the superposition of independent point processes is a weighted mean of the J functions of the individual processes.

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44

Lang, Gabriel, and Eric Marcon. "Testing randomness of spatial point patterns with the Ripley statistic." ESAIM: Probability and Statistics 17 (2013): 767–88. http://dx.doi.org/10.1051/ps/2012027.

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45

Barendregt, L. G., and M. J. Rottschäfer. "A Statistical analysis of spatial point patterns A case study." Statistica Neerlandica 45, no. 4 (December 1991): 345–63. http://dx.doi.org/10.1111/j.1467-9574.1991.tb01315.x.

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46

Stoica, R. S., E. Tempel, L. J. Liivamägi, G. Castellan, and E. Saar. "Spatial Patterns Analysis in Cosmology based on Marked Point Processes." EAS Publications Series 66 (2014): 197–226. http://dx.doi.org/10.1051/eas/1466013.

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47

Lucio, Paulo Sérgio, and Nilson Luiz Castelucio de Brito. "Detecting Randomness in Spatial Point Patterns: A “Stat-Geometrical” Alternative." Mathematical Geology 36, no. 1 (January 2004): 79–99. http://dx.doi.org/10.1023/b:matg.0000016231.05785.e4.

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48

Yamada, Ikuho, and Jean-Claude Thill. "Local Indicators of Network-Constrained Clusters in Spatial Point Patterns." Geographical Analysis 39, no. 3 (July 2007): 268–92. http://dx.doi.org/10.1111/j.1538-4632.2007.00704.x.

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49

Bell, M. L., and G. K. Grunwald. "Mixed models for the analysis of replicated spatial point patterns." Biostatistics 5, no. 4 (October 1, 2004): 633–48. http://dx.doi.org/10.1093/biostatistics/kxh014.

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50

Duda, Peter D., and Jeffrey O. Adams. "Hemispheric Asymmetries for Complex Visual Patterns." Perceptual and Motor Skills 64, no. 2 (April 1987): 463–68. http://dx.doi.org/10.2466/pms.1987.64.2.463.

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Three tachistoscopic studies examined the laterality of spatial-form perception in normal adults using randomly generated eight-point and 12-point patterns (Vanderplas & Garvin, 1959) as the lateralized stimuli. In the first study of recognition accuracy, 36 subjects were tested in a partial replication of Fontenot. No laterality effects were found, and over-all recognition was better for the more complex 12-point patterns. In a second similar study with 20 subjects, the lateralized stimulus was followed by a central masking pattern. A left-hemisphere superiority for recognition and better over-all recognition for more complex patterns was obtained. These data do not support Fontenot's report of right-hemisphere superiority in complex visuospatial processing. Given these diverse findings, a reaction time study using mental rotation was conducted using the same patterns to determine whether latency would reflect accuracy of recognition. Twenty-six subjects judged whether a rotated lateralized test pattern was the same or different from a central target pattern. Measures of both latency and accuracy were separately assessed. No main effect of visual field was obtained on either measure. These studies suggest that the nature of hemispheric involvement in spatial form perception is far from resolved.

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