Journal articles: 'Spatial point patterns analysis'

1

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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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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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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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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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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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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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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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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9

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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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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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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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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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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14

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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15

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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16

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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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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Ben-Said, Mariem, Abdelilah Ghallab, Hajar Lamrhari, José Antonio Carreira, Juan Carlos Linares, and Lahcen Taïqui. "Characterizing spatial structure of Abies marocana forest through point pattern analysis." Forest Systems 29, no. 2 (August 25, 2020): e014. http://dx.doi.org/10.5424/fs/2020292-16754.

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Aim of study: Understanding small-scale patterns caused by stochastic factors or community interactions driving forest structure and diversity of Moroccan fir Abies marocana Trab.Area of study: Talassemtane fir forest, Talassemtane National Park, Rif Mountains, northern Morocco.Material and methods: Eight plots representative of the structural variability of A. marocana forests were selected, and all tree individuals with diameter at breast height (dbh) ≥2 cm were mapped and measured. We performed four types of spatial point pattern analyses: (1) Univariate analyses to explore the overall trees spatial pattern, (2) bivariate analyses to assess the spatial relationship between juveniles and A. marocana adults, (3) correlation between tree sizes (dbh) and distance between points pairs using the univariate mark correlation function, and (4) random labeling analyses between dominant and suppressed Moroccan fir individuals to assess competition patterns.Main results: We found a strong spatial aggregation of fir individuals and a positive intraspecific association between juveniles and adult trees. However, there were weak but significant distance-dependent effect on tree size and density-dependent effect on suppression pattern.Research highlights: Shade-tolerance, seed dispersal and/or microsite heterogeneity might play important roles in the observed fir patterns. Our results provide a basic knowledge on within-stand Moroccan fir spatial distribution, with implications for adaptive management of these relic forests, and prompting to further research to test advanced hypotheses.Additional key words: Moroccan fir; Talassemtane National Park; Rif forest; Spatial analysis; summary statistics; facilitation; aggregation.Abbreviations used: MF (Moroccan fir); Talassemtane National Park (TNP); dbh (diameter at breast height); Univariate pair-correlation function (g(r)); Heterogeneous Poisson (HP); Bivariate pair-correlation function (g12(r)); Univariate mark correlation function (kmm(r)), Mark connection function (p12(r)).

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DOMINGO, JUAN, GUILLERMO AYALA, and MARÍA ELENA DÍAZ. "MORPHOMETRIC ANALYSIS OF HUMAN CORNEAL ENDOTHELIUM BY MEANS OF SPATIAL POINT PATTERNS." International Journal of Pattern Recognition and Artificial Intelligence 16, no. 02 (March 2002): 127–43. http://dx.doi.org/10.1142/s0218001402001605.

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This paper presents a method for detecting abnormalities in spatial arrangements of cells within any tissue that can be described by different sets of relevant points. The method has been applied to the detection of subtle abnormalities in corneal endothelia. Images of this type of tissue can be characterized by two types of points: cell centroids and triple points associated with the apical intersections as it was proposed by Díaz.7 Both types of points jointly considered are modeled using a bivariate spatial point process; then a statistical analysis based on certain distributional descriptors proposed by Doguwa4,9 is carried out to discriminate severe and subtle abnormalities from controls. According to the results, the method constitutes a valid alternative to currently used techniques, which involve statistical descriptions of cell density, cell area and hexagonality, that fail in the detection of these subtle abnormalities.

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Tanemura, Masaharu. "NEARFST NEIGHBOUR DISTANCE APPROACH FOR THE ANALYSIS OF SPATIAL POINT PATTERNS." Japanese Journal of Biometrics 8, no. 1 (1987): 1_79–88. http://dx.doi.org/10.5691/jjb.8.1_79.

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21

Reader, Steven. "Using survival analysis to study spatial point patterns in geographical epidemiology." Social Science & Medicine 50, no. 7-8 (April 2000): 985–1000. http://dx.doi.org/10.1016/s0277-9536(99)00349-4.

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Diggle, Peter J., Nicholas Lange, and Francine M. Beneš. "Analysis of Variance for Replicated Spatial Point Patterns in Clinical Neuroanatomy." Journal of the American Statistical Association 86, no. 415 (September 1991): 618–25. http://dx.doi.org/10.1080/01621459.1991.10475087.

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23

MUGGLESTONE, MOIRA A., and ERIC RENSHAW. "THE EXPLORATORY ANALYSIS OF BIVARIATE SPATIAL POINT PATTERNS USING CROSS-SPECTRA." Environmetrics 7, no. 4 (July 1996): 361–77. http://dx.doi.org/10.1002/(sici)1099-095x(199607)7:4<361::aid-env217>3.0.co;2-u.

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24

Zhang, Shen, Jinjun Tang, Hua Wang, and Yinhai Wang. "Enhancing Traffic Incident Detection by Using Spatial Point Pattern Analysis on Social Media." Transportation Research Record: Journal of the Transportation Research Board 2528, no. 1 (January 2015): 69–77. http://dx.doi.org/10.3141/2528-08.

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Expedient incident detection and understanding are important in traffic management and control. Social media as important information venues have immense value for increasing an awareness of traffic incidents. In this paper, an attempt is made to assess the potential of using harvested social media for traffic incident detection. Twitter in Seattle, Washington, was chosen as a representative sample environment for this work. A hybrid mechanism based on latent Dirichlet allocation and document clustering was proposed to model incident-level semantic information, while spatial point pattern analysis was applied to explore the spatial patterns and to assess the spatial dependence between incident-topic tweets and traffic incidents. A global Monte Carlo K-test indicated that the incident-topic tweets were significantly clustered at different scales up to 600 m. The nearest neighbor clutter removal method was used to separate feature tweet points from clutter; then a density-based algorithm successfully detected the clusters of tweets posted spatially close to traffic incidents. In multivariate spatial point pattern analysis, K-cross functions were investigated with Monte Carlo simulation to characterize and model the spatial dependence, and a positive spatial correlation was inferred between incident-topic tweets and traffic incidents up to 800 m. Finally, the tweet intensity as a function of distance from the nearest traffic incident was estimated, and a log-linear model was summarized. The experiments supported the notion that social media feeds acted as sensors, which allowed enhancing awareness of traffic incidents and their potential disturbances.

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Kosfeld, Reinhold, Hans-Friedrich Eckey, and Jørgen Lauridsen. "Spatial point pattern analysis and industry concentration." Annals of Regional Science 47, no. 2 (April 30, 2010): 311–28. http://dx.doi.org/10.1007/s00168-010-0385-5.

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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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TSOUTSOS, Michail-Christos, and Yorgos Photis. "Spatial point pattern analysis of urban retail stores: the case of twelve large- and medium-sized Greek cities." European Journal of Geography 11, no. 4 (December 16, 2020): 36–63. http://dx.doi.org/10.48088/ejg.m.mar.11.4.36.63.

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The retailers’ profitability and the consumers’ satisfaction depend on finding the optimal location for a retail store. When considering the stores’ spatial distribution, business potential can be understood and a squandering planning of resources can be avoided. In this paper we identify the spatial patterns of retail stores located in the traditional commercial centers of twelve large -and medium-sized Greek cities, aiming to explain why such patterns exist. The type of retail activities was determined using the image of the ground-floor stores provided by the Google Street View (GSV) service and thus 7322 stores were recorded in a geodatabase as point features. The results reveal that the retail stores’ distribution has a clustered and random spatial pattern at least in one city, where the high population density and the increase in rental prices of premises for professional activities constitute the factors that form these spatial patterns respectively.

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Cocu, N., K. Conrad, R. Harrington, and M. D. A. Rounsevell. "Analysis of spatial patterns at a geographical scale over north-western Europe from point-referenced aphid count data." Bulletin of Entomological Research 95, no. 1 (February 2005): 47–56. http://dx.doi.org/10.1079/ber2004338.

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AbstractThe spatial analysis by distance indices (SADIE) technique was developed to evaluate the spatial pattern of point-referenced count data as well as the spatial association between two sets of data sharing the same point locations. This paper presents an analysis of spatial patterns in aphid count data and the association of these data with climate across north-west Europe. The paper tests the applicability of the technique to large geographical areas. Aggregation and cluster indices were calculated for the total annual abundance of the peach–potato aphid Myzus persicae (Sulzer) and for the annual mean rainfall and temperature at aphid monitoring sites. Association indices demonstrated the stability in time of aphid spatial structures and the correlation between aphid density and climate patterns. Groups of relatively large numbers of aphids, termed patches, and groups of relatively small numbers of aphids, termed gaps, were located and their mean size estimated. The aphid patterns were quite stable in time and the spatial patterns of temperature and rainfall were weakly associated with M. persicae annual abundance. Similarities were observed between the results of SADIE and those from the more widely used technique of spatial autocorrelation (SAC). However, the SADIE association index has the advantage of quantifying the possible associations between aphid data and the factors that determine population distribution. Thus, high temperature and low rainfall were identified as environmental factors that were positively associated with aphid abundance across north-west Europe.

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Mateu, Jorge, and Francisco Montes. "Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns." International Statistical Review / Revue Internationale de Statistique 69, no. 1 (April 2001): 81. http://dx.doi.org/10.2307/1403531.

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Stoica, R. S., P. Gregori, and J. Mateu. "Simulated annealing and object point processes: Tools for analysis of spatial patterns." Stochastic Processes and their Applications 115, no. 11 (November 2005): 1860–82. http://dx.doi.org/10.1016/j.spa.2005.06.007.

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Lisitsin, Vladimir. "Spatial data analysis of mineral deposit point patterns: Applications to exploration targeting." Ore Geology Reviews 71 (December 2015): 861–81. http://dx.doi.org/10.1016/j.oregeorev.2015.05.019.

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Cressie, Noel, and Linda Brant Collins. "Analysis of spatial point patterns using bundles of product density LISA functions." Journal of Agricultural, Biological, and Environmental Statistics 6, no. 1 (March 2001): 118–35. http://dx.doi.org/10.1198/108571101300325292.

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Barbini, Paolo, Gabriele Cevenini, and Maria Rita Massai. "Nearest-Neighbor Analysis of Spatial Point Patterns: Application to Biomedical Image Interpretation." Computers and Biomedical Research 29, no. 6 (December 1996): 482–93. http://dx.doi.org/10.1006/cbmr.1996.0035.

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Landau, Sabine, Sophia Rabe-Hesketh, and Ian P. Everall. "Nonparametric One-way Analysis of Variance of Replicated Bivariate Spatial Point Patterns." Biometrical Journal 46, no. 1 (February 2004): 19–34. http://dx.doi.org/10.1002/bimj.200310010.

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Stoyan, Dietrich. "Book Review: Statistical Analysis of Spatial Point Patterns. By Peter J. Diggle." Biometrical Journal 47, no. 3 (June 2005): 402. http://dx.doi.org/10.1002/bimj.200410132.

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Mateu, Jorge, and Francisco Montes. "Likelihood Inference for Gibbs Processes in the Analysis of Spatial Point Patterns." International Statistical Review 69, no. 1 (April 2001): 81–104. http://dx.doi.org/10.1111/j.1751-5823.2001.tb00481.x.

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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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Rozas, V., and J. J. Camarero. "Spatial analysis techniques applied in forest ecology: point pattern analyses." Investigación Agraria: Sistemas y Recursos Forestales 14, no. 1 (April 1, 2005): 79. http://dx.doi.org/10.5424/srf/2005141-00875.

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Ramsay, Paul M. "Handbook of spatial point-pattern analysis in ecology." Journal of Applied Statistics 42, no. 4 (December 9, 2014): 916–17. http://dx.doi.org/10.1080/02664763.2014.989467.

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Rosenberg, Michael S. "Handbook of spatial point-pattern analysis in ecology." International Journal of Geographical Information Science 29, no. 9 (June 22, 2015): 1718–19. http://dx.doi.org/10.1080/13658816.2015.1059433.

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Lu, Wenxing, Jinhua Cheng, Wei Wang, Hongjiang Zhang, and Hongwen Zhou. "Application of the method of spatial point pattern analysis to the horizontal spatial distribution of preferential flow paths." Forestry Chronicle 91, no. 04 (August 2015): 384–94. http://dx.doi.org/10.5558/tfc2015-068.

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Preferential flow significantly influences hydrological processes in forests. The occurrence and development of this flow is directly affected by its spatial distribution. To determine whether or not point pattern analysis method can be used to examine the horizontal spatial distribution of preferential flow paths, experiments were conducted with dye tracer permeation to observe flow processes. Results indicated that an increase in penetration water volume exerted only a specific effect on preferential flow paths of large class in the topmost soil. Moreover, such paths showed distinct clumped patterns at the 25-cm scale under both high permeation water volume and low permeation water volume treatments. Nonetheless, the distribution pattern became uniform as scale range increased. The significance of the correlation between the spatial distribution of preferential flow paths and plant roots decreased from the top soil layer to the bottom. These findings suggest that soil depth and water permeation volume are important to the horizontal spatial distribution of preferential flow paths. Moreover, point pattern analysis method is suitable for investigating the horizontal spatial distribution of preferential flow paths and determining the correlation between the spatial distribution of preferential flow paths and plant roots.

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Ghodousi, Mostafa, Abolghasem Sadeghi-Niaraki, Farzaneh Rabiee, and Soo-Mi Choi. "Spatial-Temporal Analysis of Point Distribution Pattern of Schools Using Spatial Autocorrelation Indices in Bojnourd City." Sustainability 12, no. 18 (September 19, 2020): 7755. http://dx.doi.org/10.3390/su12187755.

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In recent years, attention has been given to the construction and development of new educational centers, but their spatial distribution across the cities has received less attention. In this study, the Average Nearest Neighbor (ANN) and the optimized hot spot analysis methods have been used to determine the general spatial distribution of the schools. Also, in order to investigate the spatial distribution of the schools based on the substructure variables, which include the school building area, the results of the general and local Moran and Getis Ord analyses have been investigated. A differential Moran index was also used to study the spatial-temporal variations of the schools’ distribution patterns based on the net per capita variable, which is the amount of school building area per student. The results of the Average Nearest Neighbor (ANN) analysis indicated that the general spatial patterns of the primary schools, the first high schools, and the secondary high schools in the years 2011, 2016, 2018, and 2021 are clustered. Applying the optimized hot spot analysis method also identified the southern areas and the suburbs as cold polygons with less-density. Also, the results of the differential Moran analysis showed the positive trend of the net per capita changes for the primary schools and first high schools. However, the result is different for the secondary high schools.

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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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Wilson, Helen, Peter Diggle, and C. Vyvyan Howard. "Methods for the analysis of replicated spatial point patterns in clinical neuro-anatomy." Advances in Applied Probability 30, no. 2 (June 1998): 293–94. http://dx.doi.org/10.1017/s0001867800047273.

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Dutilleul, Pierre, Timothy W. Haltigin, and Wayne H. Pollard. "Analysis of polygonal terrain landforms on Earth and Mars through spatial point patterns." Environmetrics 20, no. 2 (March 2009): 206–20. http://dx.doi.org/10.1002/env.924.

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46

Ogata, Yosihiko. "Cluster analysis of spatial point patterns: posterior distribution of parents inferred from offspring." Japanese Journal of Statistics and Data Science 3, no. 1 (December 11, 2019): 367–90. http://dx.doi.org/10.1007/s42081-019-00065-9.

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47

Peters, Ruby, Dylan Owen, and Juliette Griffie. "Analysis of Fibrous Spatial Point Patterns from Single-Molecule Super-Resolution Microscopy Data." Biophysical Journal 112, no. 3 (February 2017): 142a. http://dx.doi.org/10.1016/j.bpj.2016.11.784.

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Zheng, Qiang, and Jian Sun. "Effective Point Cloud Analysis Using Multi-Scale Features." Sensors 21, no. 16 (August 19, 2021): 5574. http://dx.doi.org/10.3390/s21165574.

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Fully exploring the correlation of local features and their spatial distribution in point clouds is essential for feature modeling. This paper, inspired by convolutional neural networks (CNNs), explores the relationship between local patterns and point coordinates from a novel perspective and proposes a lightweight structure based on multi-scale features and a two-step fusion strategy. Specifically, local features of multi-scales and their spatial distribution can be regarded as independent features corresponding to different levels of geometric significance, which are extracted by multiple parallel branches and then merged on multiple levels. In this way, the proposed model generates a shape-level representation that contains rich local characteristics and the spatial relationship between them. Moreover, with the shared multi-layer perceptrons (MLPs) as basic operators, the proposed structure is so concise that it converges rapidly, and so we introduce the snapshot ensemble to improve performance further. The model is evaluated on classification and part segmentation tasks. The experiments prove that our model achieves on-par or better performance than previous state-of-the-art (SOTA) methods.

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Zöller, G., and S. Hainzl. "Detecting premonitory seismicity patterns based on critical point dynamics." Natural Hazards and Earth System Sciences 1, no. 1/2 (June 30, 2001): 93–98. http://dx.doi.org/10.5194/nhess-1-93-2001.

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Abstract. We test the hypothesis that critical point dynamics precedes strong earthquakes in a region surrounding the future hypocenter. Therefore, we search systematically for regions obeying critical point dynamics in terms of a growing spatial correlation length (GCL). The question of whether or not these spatial patterns are correlated with future seismicity is crucial for the problem of predictability. The analysis is conducted for earthquakes with M > 6.5 in California. As a result, we observe that GCL patterns are correlated with the distribution of future seismicity. In particular, there are clear correlations in some cases, e.g. the 1989 Loma Prieta earthquake and the 1999 Hector Mine earthquake. We claim that the critical point concept can improve the seismic hazard assessment.

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Wilschut, Liesbeth I., Anne Laudisoit, Nelika K. Hughes, Elisabeth A. Addink, Steven M. de Jong, Hans A. P. Heesterbeek, Jonas Reijniers, Sally Eagle, Vladimir M. Dubyanskiy, and Mike Begon. "Spatial distribution patterns of plague hosts: point pattern analysis of the burrows of great gerbils in Kazakhstan." Journal of Biogeography 42, no. 7 (May 19, 2015): 1281–92. http://dx.doi.org/10.1111/jbi.12534.

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Journal articles on the topic 'Spatial point patterns analysis'

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