Relevant bibliographies by topics / Spatial point patterns analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Spatial point patterns analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 February 2022

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Journal articles on the topic "Spatial point patterns analysis"

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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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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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Dissertations / Theses on the topic "Spatial point patterns analysis"

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Wilson, Helen Elizabeth. "Statistical analysis of replicated spatial point patterns." Thesis, Lancaster University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268009.

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The field of pathology provides us with many opportunities for collecting replicated spatial data. Using an ordinary microscope, for example, we can digitise cell positions within windows imposed on pieces of tissue. Suppose now that we have some such replicated spatial data from several groups of individuals, where each point in each window represents a cell position. We seek to determine whether the spatial arrangement of cells differs between the groups. We propose and develop a new method which allows us to answer such questions, and apply it to some spatial neuro-anatomical data. We introduce point process theory, and extend the existing second order methods to deal with replicated spatial data. We conclude the first part of the thesis by defining Sudden Infant Death Syndrome (S.LD.S.) and Intra-Uterine Growth Retardation (LU.G.R.), and stating why these conditions are neuro-anato,mically interesting. We develop and validate a method for comparing groups of spatial data, which is motivated by analysis of variance, and uses a Monte Carlo procedure to attach significance to between-group differences. Having carried out our initial investigative work looking exclusively at the one-way set up, we extend the new methods to cope with two and higher way set ups, and again carry out some validation. We turn our attention to practical issues which arise in the collection of spatial neuroanatomical data. How, for example, should we collect the data to ensure the unbiasedness of any inference we may draw from it? We introduce the field of stereology which facilitates the unbiased sampling of tissue. We note a recent proposal to assess spatial distribution of cells using a stereological approach, and compare it with an existing second order method. We also note the level of structural heterogeneity within the brain, and consider the best way to design a sampling protocol. We conclude with a spatial analysis of cell position data, collected using our specified design, from normal birth-weight non S.LD.S., normal birth-weight S.I.D.S and low birth-weight S.LD.S cases.
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Doguwa, S. I. "Statistical analysis of mapped spatial point patterns." Thesis, University of Essex, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383379.

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Pereira, Sandra M. C. "Analysis of spatial point patterns using hierarchical clustering algorithms." University of Western Australia. School of Mathematics and Statistics, 2003. http://theses.library.uwa.edu.au/adt-WU2004.0056.

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[Formulae and special characters can only be approximated here. Please see the pdf version of the abstract for an accurate reproduction.] This thesis is a new proposal for analysing spatial point patterns in spatial statistics using the outputs of popular techniques of (classical, non-spatial, multivariate) cluster analysis. The outputs of a chosen hierarchical algorithm, named fusion distances, are applied to investigate important spatial characteristics of a given point pattern. The fusion distances may be regarded as a missing link between the fields of spatial statistics and multivariate cluster analysis. Up to now, these two fields have remained rather separate because of fundamental differences in approach. It is shown that fusion distances are very good at discriminating different types of spatial point patterns. A detailed study on the power of the Monte Carlo test under the null hypothesis of Complete Spatial Randomness (the benchmark of spatial statistics) against chosen alternative models is also conducted. For instance, the test (based on the fusion distance) is very powerful for some arbitrary values of the parameters of the alternative. A new general approach is developed for analysing a given point pattern using several graphical techniques for exploratory data analysis and inference. The new strategy is applied to univariate and multivariate point patterns. A new extension of a popular strategy in spatial statistics, named the analysis of the local configuration, is also developed. This new extension uses the fusion distances, and analyses a localised neighbourhood of a given point of the point pattern. New spatial summary function and statistics, named the fusion distance function H(t), area statistic A, statistic S, and spatial Rg index, are introduced, and proven to be useful tools for identifying relevant features of spatial point patterns. In conclusion, the new methodology using the outputs of hierarchical clustering algorithms can be considered as an essential complement to the existing approaches in spatial statistics literature.
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Soale, Abdul-Nasah. "Spatio-Temporal Analysis of Point Patterns." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etd/3120.

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In this thesis, the basic tools of spatial statistics and time series analysis are applied to the case study of the earthquakes in a certain geographical region and time frame. Then some of the existing methods for joint analysis of time and space are described and applied. Finally, additional research questions about the spatial-temporal distribution of the earthquakes are posed and explored using statistical plots and models. The focus in the last section is in the relationship between number of events per year and maximum magnitude and its effect on how clustered the spatial distribution is and the relationship between distances in time and space in between consecutive events as well as the distribution of the distances.
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González, Monsalve Jonatan A. "Statistical tests for comparisons of spatial and spatio-temporal point patterns." Doctoral thesis, Universitat Jaume I, 2018. http://hdl.handle.net/10803/462034.

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We mainly introduce a new set of tests to compare functional descriptors in point processes context. Firstly, since the study of spatio-temporal point processes has not been widely covered in the literature, a complete review is made. The review is a reference paper of the available techniques and approaches regarding the spatio-temporal context. Secondly, a studentized permutation test is developed in the spatio-temporal case. This test is motivated by locations of tornadoes in the U.S. in a period of 36 years. Some tools have been developed as a non-separable estimator of the first-order intensity, which allows a realistic analysis of the phenomenon through the new test. Finally, a factorial two-way design is considered, where the observations are spatial point patterns in presence of replication. This methodology is motivated by a minerals engineering experiment. We develop statistics to test the influence of the factors and the possible interaction effects.
Desarrollamos un nuevo conjunto de pruebas para comparar descriptores funcionales en el contexto de procesos puntuales. Puesto que el estudio de los procesos puntuales espacio-temporales no ha sido muy exhaustivo en la literatura, hemos hecho un artículo de resumen. Introducimos un test de permutación para grupos de patrones puntuales espacio-temporales motivado por las ubicaciones de ocurrencias de tornados en EE.UU. durante 36 años. Hemos desarrollado algunas técnicas como la estimación de la intensidad de primer-orden sin suponer separabilidad, lo que permite un tratamiento más realista del fenómeno climático en sí mismo a través del nuevo test.Finalmente, hemos desarrollado algunas técnicas para el análisis de la varianza de experimentos de dos factores en presencia de réplicas cuando las observaciones son patrones puntuales espaciales. Esta metodología está motivada por un experimento de ingeniería de minerales. Desarrollamos algunos estadísticos adecuados para probar la influencia de los factores y su posible interacción.
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Stanford, Derek C. "Fast automatic unsupervised image segmentation and curve detection in spatial point patterns /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/8976.

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Eckel, Stefanie. "Statistical analysis of spatial point patterns - applications to economical, biomedical and ecological data." [S.l. : s.n.], 2008. http://nbn-resolving.de/urn:nbn:de:bsz:289-vts-66022.

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Umande, Philip Pembe. "Spatial point pattern analysis with application to confocal microscopy data." Thesis, Imperial College London, 2008. http://hdl.handle.net/10044/1/8569.

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Wong, Ka Yiu. "Model-free tests for isotropy, equal distribution and random superposition in spatial point pattern analysis." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/202.

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This thesis introduces three new model-free tests for isotropy, equal distribution and random superposition in non-rectangular windows respectively. For isotropy, a bootstrap-type test is proposed. The corresponding test statistic assesses the discrepancy between the uniform distribution and the empirical normalised reduced second-order moment measure of a sector of fixed radius with increasing central angle. The null distribution of the discrepancy is then estimated by stochastic reconstruction, which generates bootstrap-type samples of point patterns that resemble the spatial structure of the given pattern. The new test is applicable for small sample sizes and is shown to have more robust powers to different choices of user-chosen parameter when compared with the asymptotic chi-squared test by Guan et al. (2006) in our simulation. For equal distribution, a model-free asymptotic test is introduced. The proposed test statistic compares the discrepancy between the empirical second-order product densities of the observed point patterns at some pre-chosen lag vectors. Under certain mild moment conditions and a weak dependence assumption, the limiting null distribution of the test statistic is the chi-squared distribution. Simulation results show that the new test is more powerful than the permutation test by Hahn (2012) for comparing point patterns with similar structures but different distributions. The new test for random superposition is a modification of the toroidal shift test by Lotwick and Silverman (1982). The idea is to extrapolate the pattern observed in a non-rectangular window to a larger rectangular region by the stochastic reconstruction so that the toroidal shift test can be applied. Simulation results show that the powers of the test applied to patterns with extrapolated points are remarkably higher than those of the test applied to the largest inscribed rectangular windows, with only slightly increased type I error rates. Real data sets are used to illustrate the advantages of the tests developed in this thesis over the existing tests in the literature.
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Catella, Samantha A. "Investigating herbaceous layer plant community patterns: when does abiotic complexity matter?" Case Western Reserve University School of Graduate Studies / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1559905264222712.

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Books on the topic "Spatial point patterns analysis"

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Boots, B. N. Point pattern analysis. Newbury Park, Calif: Sage Publications, 1988.

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Illian, Janine, Antti Penttinen, Helga Stoyan, and Dietrich Stoyan. Statistical Analysis and Modelling of Spatial Point Patterns. Chichester, UK: John Wiley & Sons, Ltd, 2007. http://dx.doi.org/10.1002/9780470725160.

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Rowlingson, B. S. SPLANCS: Spatial point pattern analysis code in S-Plus. Lancaster: NorthWest Regional Research Laboratory, 1991.

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Gill, Manmohan Singh. Spatial patterns: A socio-ecological analysis. New Delhi, India: National Book Organisation, 1991.

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Berry, Joseph K. Map analysis: Understanding spatial patterns and relationships. San Francisco, CA: GeoTec Media, 2007.

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Gelfand, Alan E., and Erin M. Schliep. Bayesian Inference and Computing for Spatial Point Patterns. Beachwood, Ohio; Alexandria, Virginia: Institute of Mathematical Statistics and American Statistical Association, 2018. http://dx.doi.org/10.1214/cbms/1530065028.

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Openshaw, Stan. User-centred intelligent spatial analysis of point data. Leeds: University of Leeds, School of Geography, 1995.

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Plenge, Waagepetersen Rasmus, ed. Statistical inference and simulation for spatial point processes. Boca Raton, Fla: Chapman & Hall/CRC, 2004.

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Markov point processes and their applications. London: Imperial College Press, 2000.

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Gatrell, Anthony C. On modelling spatial point patterns in epidemiology: Cancer of the larynx in Lancashire. Lancaster: NorthWest Regional Research Laboratory, 1990.

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Book chapters on the topic "Spatial point patterns analysis"

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Bivand, Roger S., Edzer Pebesma, and Virgilio Gómez-Rubio. "Spatial Point Pattern Analysis." In Applied Spatial Data Analysis with R, 173–211. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7618-4_7.

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Getis, Arthur, and Janet Franklin. "Second-Order Neighborhood Analysis of Mapped Point Patterns." In Perspectives on Spatial Data Analysis, 93–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-642-01976-0_7.

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Franklin, Janet. "Spatial Point Pattern Analysis of Plants." In Perspectives on Spatial Data Analysis, 113–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-642-01976-0_9.

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Ripley, B. D. "Spatial Point Pattern Analysis in Ecology." In Develoments in Numerical Ecology, 407–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-70880-0_11.

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Getis, Arthur. "Second-Order Analysis of Point Patterns: The Case of Chicago as a Multi-center Urban Region." In Perspectives on Spatial Data Analysis, 83–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-642-01976-0_6.

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Robertson, Colin, and Steven Roberts. "Bivariate Spatial Clustering Analysis of Point Patterns: A Graph-Based Approach." In Lecture Notes in Computer Science, 403–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39649-6_29.

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Palenichka, Roman M., and Marek B. Zaremba. "A Spatio-Temporal Isotropic Operator for the Attention-Point Extraction." In Computer Analysis of Images and Patterns, 318–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_39.

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Panayirci, Erdal, and Richard C. Dubes. "Spatial Point Processes and Clustering Tendency in Exploratory Data Analysis." In Pattern Recognition Theory and Applications, 81–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-83069-3_8.

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D’Angelo, Nicoletta, Mauro Ferrante, Antonino Abbruzzo, and Giada Adelfio. "Determinants of spatial intensity of stop locations on cruise passengers tracking data." In Proceedings e report, 159–64. Florence: Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.31.

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This paper aims at analyzing the spatial intensity in the distribution of stop locations of cruise passengers during their visit at the destination through a stochastic point process modelling approach on a linear network. Data collected through the integration of GPS tracking technology and questionnaire-based survey on cruise passengers visiting the city of Palermo are used, to identify the main determinants which characterize their stop locations pattern. The spatial intensity of stop locations is estimated through a Gibbs point process model, taking into account for both individual-related variables, contextual-level information, and for spatial interaction among stop points. The Berman-Turner device for maximum pseudolikelihood is considered, by using a quadrature scheme generated on the network. The approach used allows taking into account the linear network determined by the street configuration of the destination under analysis. The results show an influence of both socio-demographic and trip-related characteristics on the stop location patterns, as well as the relevance of distance from the main attractions, and potential interactions among cruise passengers in stop configuration. The proposed approach represents both improvements from the methodological perspective, related to the modelling of spatial point process on a linear network, and from the applied perspective, given that better knowledge of the determinants of spatial intensity of visitors’ stop locations in urban contexts may orient destination management policy.
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Han, Ling, Tingting Wu, Zhiheng Liu, and Qing Liu. "Cloud Detection in Landsat Imagery Using the Fractal Summation Method and Spatial Point-Pattern Analysis." In Geo-informatics in Sustainable Ecosystem and Society, 201–7. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7025-0_21.

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Conference papers on the topic "Spatial point patterns analysis"

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Zou, Yibo, Markus Kästner, and Eduard Reithmeier. "Characterization of porous surfaces with spatial point pattern analysis." In Photonics Prague 2014, edited by Pavel Tománek, Dagmar Senderáková, and Petr Páta. SPIE, 2015. http://dx.doi.org/10.1117/12.2070329.

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Dargahi, Mozhgan Momtaz, and David Lattanzi. "Spatial Statistical Methods for Complexity-Based Point Cloud Analysis." In ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/smasis2020-2294.

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Abstract Modern remote sensing technologies now provide the basis for flexible and highly accurate three-dimensional geometric modeling of structures in the form of point clouds. To date, most efforts are focused on how to use these point clouds to form a digital twin of an asset, but these models can also be used to augment and improve condition assessment and structural health monitoring (SHM). However, point cloud analytics require unique approaches given the complexity and scale of the data. To illustrate these capabilities, we propose a new SHM method that leverages 3D point cloud data and the evolution of this data over time. Taking inspiration from recent work on the use of complexity measures for sensor driven SHM, here we adapt the concept for spatial analysis of 3D digital twins. The fundamental assumption that underpins the approach presented here is that, as a structure degrades in integrity, the randomness of the data increases when compared against the null model of the homogeneous Poisson process, otherwise described as ‘complete spatial randomness’ (CSR). In spatial point analysis, points from a baseline model are generated and placed within a normalized Cartesian reference frame. The spatial randomness of this baseline is considered the null model of the homogeneous Poisson process. In subsequent 3D models of an asset, spatial complexity metrics are recomputed on a local neighborhood level, with increased complexity corresponding to potential damage or degradation of the asset. Another question of interest is to provide a suitable mathematical model for this underlying temporal evolution. Compared to more conventional analytical approaches that can only detect data anomalies via a single computation, this complexity-based approach enables us to further integrate multi-level information, in the form of first and second order moment metrics, to evaluate data anomalies in more depth. In this method we use the variation of the first and second moments of the average intensity of the points in space. A first order metric of a point pattern represents the density change across the study region such as Quadrat density or Kernel density. The second-order metric of the point pattern considers the distance between points, effectively quantifying how points are distributed relative to one another. Examples include Ripley’s K-function, the L-function or Baddeley’s J-function. This analytical approach was tested on a variety of laboratory scale specimens with varying levels of damage and degradation. The results show that this new technique provides rapid analytical capabilities for finding damage and quantifying both damage and evolution in point clouds. Ongoing work seeks to scale up these measures to full-scale specimens, and to explore methods of using the results for damage prognosis through statistical time-series modeling of the evolution of the complexity metrics.
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Boyacioglu, Kadir Faruk, and Dogu Arifler. "Second-Order Analysis of Formation of Holes in Spatial Point Patterns: Applications in Wireless Sensor Networks." In 2007 5th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks (WiOpt). IEEE, 2007. http://dx.doi.org/10.1109/wiopt.2007.4480074.

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Cao, Zhidong, Pengfei Zhao, Jiayue Liu, and Wei Zhong. "A Spatial Point Pattern Analysis of the 2003 SARS Epidemic in Beijing." In SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3152465.3152466.

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Chan, J. C. W., A. C. Alegria, M. G. Veratelli, M. Folegani, and H. Sahli. "Combined spatial point pattern analysis and remote sensing for assessing landmine affected areas." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352394.

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Wang, Hui, Saumuy Suriano, Liang Zhou, and S. Jack Hu. "High-Definition Metrology Based Spatial Variation Pattern Analysis for Machining Process Monitoring and Diagnosis." In ASME 2009 International Manufacturing Science and Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/msec2009-84154.

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Non-contact high-definition measurement technology, such as laser holographic interferometry, makes it feasible to quickly inspect dimensional variation at micron level, providing up to 2 million data points over a surface area of up to 300×300 mm2. Such high-definition metrology (HDM) data contain rich spatial variation information but it is challenging to utilize this information for process monitoring and control. The spatial distribution of the data is in high-dimensional form and may show nonlinear patterns. Conventional statistical process monitoring and diagnostic schemes based on simple test statistics and linear statistical process models are incapable of capturing the complex surface characteristics as reflected by large amounts of spatial data. This paper develops a framework for efficient monitoring of spatial variation in HDM data using principal curves and quality control charts. Since large scale surface variation patterns (caused by fixturing and part bending) may camouflage those in the smaller scale (generally associated with tooling conditions), it is essential to separate the patterns in these scales and monitor them individually. At each scale, process monitoring is implemented in a sequential manner by monitoring the overall spatial features followed by localized variation identification if an out-of-control condition is detected. To examine the overall spatial characteristics, a principal-component-analysis (PCA) filtered principal curve regression is proposed in conjunction with multivariate control charts whereby nonlinear patterns of spatial data are extracted and monitored. When the overall monitoring indicates a problem, the identification of a surface variation change can be achieved through localized monitoring over each surface region based on variogram pattern analysis and control charts. The location of surface region change provides clues for variation source diagnosis. The proposed method is illustrated using simulated HDM data.
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Kusumaningrum, Choriah Margareta, Nur Iriawan, and Wiwiek Setya Winahju. "Pattern analysis of community health center location in Surabaya using spatial Poisson point process." In PROCEEDINGS OF THE 13TH IMT-GT INTERNATIONAL CONFERENCE ON MATHEMATICS, STATISTICS AND THEIR APPLICATIONS (ICMSA2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5012204.

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Ma, Junjie, Shenglong Yang, Jianxin You, and Maolin Zhang. "Spatial Pattern Detection and BP Neural Network Analysis of Bank Mesh Point in Urban Area." In 2009 Fifth International Conference on Natural Computation. IEEE, 2009. http://dx.doi.org/10.1109/icnc.2009.473.

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Tanada, E. L. M., and A. C. Blanco. "Using spatial point pattern analysis as supplement for object-based image classification of tree clusters." In GEOBIA 2016 : Solutions and Synergies. University of Twente Faculty of Geo-Information and Earth Observation (ITC), 2016. http://dx.doi.org/10.3990/2.413.

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Tian, Limao, Xina Cheng, Masaaki Honda, and Takeshi Ikenaga. "3D pose reconstruction with multi-perspective and spatial confidence point group for jump analysis in figure skating." In Fifth International Workshop on Pattern Recognition, edited by Xudong Jiang, Chuan Zhang, and Yinglei Song. SPIE, 2020. http://dx.doi.org/10.1117/12.2574598.

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Reports on the topic "Spatial point patterns analysis"

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Harrell, Krystle. Homelessness in Portland, Oregon: An Analysis of Homeless Campsite Spatial Patterns and Spatial Relationships. Portland State University Library, January 2000. http://dx.doi.org/10.15760/geogmaster.24.

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Collins, Clarence O., and Tyler J. Hesser. altWIZ : A System for Satellite Radar Altimeter Evaluation of Modeled Wave Heights. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39699.

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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the design and implementation of a wave model evaluation system, altWIZ, which uses wave height observations from operational satellite radar altimeters. The altWIZ system utilizes two recently released altimeter databases: Ribal and Young (2019) and European Space Agency Sea State Climate Change Initiative v.1.1 level 2 (Dodet et al. 2020). The system facilitates model evaluation against 1 Hz1 altimeter data or a product created by averaging altimeter data in space and time around model grid points. The system allows, for the first time, quantitative analysis of spatial model errors within the U.S. Army Corps of Engineers (USACE) Wave Information Study (WIS) 30+ year hindcast for coastal United States. The system is demonstrated on the WIS 2017 Atlantic hindcast, using a 1/2° basin scale grid and a 1/4° regional grid of the East Coast. Consistent spatial patterns of increased bias and root-mean-square-error are exposed. Seasonal strengthening and weakening of these spatial patterns are found, related to the seasonal variation of wave energy. Some model errors correspond to areas known for high currents, and thus wave-current interaction. In conjunction with the model comparison, additional functions for pairing altimeter measurements with buoy data and storm tracks have been built. Appendices give information on the code access (Appendix I), organization and files (Appendix II), example usage (Appendix III), and demonstrating options (Appendix IV).
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Junge, Justin. GIS Spatial Analysis of Arctic Settlement Patterns: A Case Study in Northwest Alaska. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.5766.

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M.L. Jones, G.M. O'Brien, and M.L. Jones. The Hydrograph Analyst, an Arcview GIS Extension That Integrates Point, Spatial, and Temporal Data Provides A Graphical User Interface for Hydrograph Analysis. Office of Scientific and Technical Information (OSTI), September 2000. http://dx.doi.org/10.2172/840692.

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Aromi, J. Daniel, María Paula Bonel, Julián Cristia, Martín Llada, and Luis Palomino. Socioeconomic Status and Mobility during the COVID-19 Pandemic: An Analysis of Eight Large Latin American Cities. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003315.

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This study analyzes mobility patterns during the COVID-19 pandemic for eight large Latin American cities. Indicators of mobility by socioeconomic status (SES) are generated by combining georeferenced mobile phone information with granular census data. Before the pandemic, a strong positive association between SES and mobility is documented. With the arrival of the pandemic, in most cases, a negative association between mobility and SES emerges. This new pattern is explained by a notably stronger reduction in mobility by high-SES individuals. A comparison of mobility for SES decile 1 vs decile 10 shows that, on average, the reduction is 75% larger in the case of decile 10. According to estimated lasso models, an indicator of government restrictions provides a parsimonious description of these heterogeneous responses. These estimations point to noticeable similarities in the patterns observed across cities. We also explore how the median distance traveled changed for individuals that travel at least 1 km (the intensive margin). We find that the reduction in mobility in this indicator was larger for high-SES individuals compared to low-SES individuals in six out of eight cities analyzed. The evidence is consistent with asymmetries in the feasibility of working from home and in the ability to smooth consumption under temporary income shocks.
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Patel, Reena. Complex network analysis for early detection of failure mechanisms in resilient bio-structures. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41042.

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Bio-structures owe their remarkable mechanical properties to their hierarchical geometrical arrangement as well as heterogeneous material properties. This dissertation presents an integrated, interdisciplinary approach that employs computational mechanics combined with flow network analysis to gain fundamental insights into the failure mechanisms of high performance, light-weight, structured composites by examining the stress flow patterns formed in the nascent stages of loading for the rostrum of the paddlefish. The data required for the flow network analysis was generated from the finite element analysis of the rostrum. The flow network was weighted based on the parameter of interest, which is stress in the current study. The changing kinematics of the structural system was provided as input to the algorithm that computes the minimum-cut of the flow network. The proposed approach was verified using two classical problems three- and four-point bending of a simply-supported concrete beam. The current study also addresses the methodology used to prepare data in an appropriate format for a seamless transition from finite element binary database files to the abstract mathematical domain needed for the network flow analysis. A robust, platform-independent procedure was developed that efficiently handles the large datasets produced by the finite element simulations. Results from computational mechanics using Abaqus and complex network analysis are presented.
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Komppula, Birgitta, Tomi Karppinen, Henrik Virta, Anu-Maija Sundström, Iolanda Ialongo, Kaisa Korpi, Pia Anttila, Jatta Salmi, Johanna Tamminen, and Katja Lovén. Air quality in Finland according to air quality measurements and satellite observations. Finnish Meteorological Institute, September 2021. http://dx.doi.org/10.35614/isbn.9789523361409.

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In this report the current air quality in Finland has been assessed with air quality measurement data and satellite observations. The assessment of ambient air concentrations included following air impurities: NO2, NOx, PM10, PM2,5, SO2, CO, O3, benzo(a)pyrene, benzene, Pb, As, Cd ja Ni. For these pollutants air quality assessment thresholds are given in air quality legislation (2008/50/EY, 2004/107/EY). Assessment has been performed for air quality zones. The main data set included air quality measurements performed in Finland during 2015–2019. Satellite observations were used as an objective assessment tool in analysis of the spatial variation of NO2 and CO concentrations. Air quality measurements show that air quality has improved in Finland in many respects. Especially the need to monitor NO2 and PM10 with continuous measurements has decreased. Growing understanding of national benzo(a)pyrene concentrations has increased the monitoring needs. Efforts to decrease ozone levels still requires international actions. SO2, CO, benzene and heavy metal concentrations are on a low level in Finland outside industrial areas and other assessment methods than continuous monitoring can be used, and the number of continuous monitoring sites has already decreased. Satellite-based concentrations of nitrogen dioxide and carbon monoxide as well as their spatial variation in Finland were analyzed using observations from the TROPOsperic Monitoring Instrument (TROPOMI). The analysis of CO over Finland was carried out for the first time in this project. Results show that overall annual CO concentrations over Finland are low and spatial variability is small. Also, NO2 concentrations over Finland are rather low, but spatial patterns are more clearly visible. The highest NO2 concentrations are observed over the largest cities. By establishing a relationship between ground-based and satellite total column concentrations, surface concentrations of NO2 and CO were estimated from the satellite data for the zones. The satellite-based estimate for annual NO2 surface concentration over Helsinki metropolitan area is 28 μg/m3, and for the rest of Finland mostly between 10–15 μg/m3. For CO the differences between monitoring areas are small, with estimates varying between 160–164 μg/m3 or in other words about 0,16 mg/m3.
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