Relevant bibliographies by topics / Spatial point patterns

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'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Spatial point patterns"

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

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

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

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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 (1986): 263. http://dx.doi.org/10.2307/2288020.

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

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

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

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

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Dissertations / Theses on the topic "Spatial point patterns"

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Moradi, Mohammad Mehdi. "Spatial and spatio-temporal point patterns on linear networks." Doctoral thesis, Universitat Jaume I, 2018. http://hdl.handle.net/10803/664140.

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A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information Systems<br>The last decade witnessed an extraordinary increase in interest in the analysis of network related data and trajectories. This pervasive interest is partly caused by a strongly expanded availability of such datasets. In the spatial statistics field, there are numerous real examples such as the locations of traffic accidents and geo-coded locations of crimes in the streets of cities that need to restrict the support of the underlying process over such linear networks to set and define a more realistic scenario. Examples of trajectories are the path taken by moving objects such as taxis, human beings, animals, etc. Intensity estimation on a network of lines, such as a road network, seems to be a surprisingly complicated task. Several techniques published in the literature, in geography and computer science, have turned out to be erroneous. We propose several adaptive and non-adaptive intensity estimators, based on kernel smoothing and Voronoi tessellation. Theoretical properties such as bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation, and adaptive smoothing are discussed. Moreover, their statistical performance is studied through simulation studies and is compared with existing methods. Adding the temporal component, we also consider spatio-temporal point patterns with spatial locations restricted to a linear network. We present a nonparametric kernel-based intensity estimator and develop second-order characteristics of spatio-temporal point processes on linear networks such as K-function and pair correlation function to analyse the type of interaction between points. In terms of trajectories, we introduce the R package trajectories that contains different classes and methods to handle, summarise and analyse trajectory data. Simulation and model fitting, intensity estimation, distance analysis, movement smoothing, Chi maps and second-order summary statistics are discussed. Moreover, we analyse different real datasets such as a crime data from Chicago (US), anti-social behaviour in Castell´on (Spain), traffic accidents in Medell´ın (Colombia), traffic accidents in Western Australia, motor vehicle traffic accidents in an area of Houston (US), locations of pine saplings in a Finnish forest, traffic accidents in Eastbourne (UK) and one week taxi movements in Beijing (China).
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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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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.<br>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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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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Liu, Jie. "Exploring Ways of Identifying Outliers in Spatial Point Patterns." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2528.

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This work discusses alternative methods to detect outliers in spatial point patterns. Outliers are defined based on location only and also with respect to associated variables. Throughout the thesis we discuss five case studies, three of them come from experiments with spiders and bees, and the other two are data from earthquakes in a certain region. One of the main conclusions is that when detecting outliers from the point of view of location we need to take into consideration both the degree of clustering of the events and the context of the study. When detecting outliers from the point of view of an associated variable, outliers can be identified from a global or local perspective. For global outliers, one of the main questions addressed is whether the outliers tend to be clustered or randomly distributed in the region. All the work was done using the R programming language.
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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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Pranchai, Aor. "Spatial patterns and processes in a regenerating mangrove forest." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-171569.

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The global effort to rehabilitate and restore destroyed mangrove forests is unable to keep up with the high mangrove deforestation rates which exceed the average pace of global deforestation by three to five times. Our knowledge of the underlying processes of mangrove forest regeneration is too limited in order to find suitable techniques for the restoration of degraded mangrove areas. The general objective of my dissertation was to improve mangrove restoration by understanding regeneration processes and local plant-plant interaction in a regenerating Avicennia germinans forest. The study was conducted in a high-shore mangrove forest area on the Ajuruteua peninsula, State of Para, Northern Brazil. The dwarf forest consisting of shrub-like trees is recovering from a stand-replacing event caused by a road construction in 1974 which interrupted the tidal inundation of the study area. Consequently, infrequent inundation and high porewater salinity limit tree growth and canopy closure. All trees and seedlings were stem-mapped in six 20 m x 20 m plots which were located along a tree density gradient. Moreover, height, crown extent, basal stem diameter of trees were measured. The area of herbaceous ground vegetation and wood debris were mapped as well. The mapped spatial distribution of trees, seedlings and covariates was studied using point pattern analysis and point process models, such as Gibbs and Thomas point process, in order to infer underlying ecological processes, such as seed dispersal, seedling establishment, tree recruitment and tree interaction. In the first study (chapter 2), I analyzed the influence of abiotic and biotic factors on the seedling establishment and tree recruitment of A. germinans during the recolonization of severely degraded mangrove sites using point process modeling. Most seedlings established adjacent to adult trees especially under their crown cover. Moreover, seedling density was higher within patches of the herbaceous salt-marsh plants Blutaparon portulacoides and Sesuvium portulacastrum than in uncovered areas. The higher density of recruited A. germinans trees in herb patches indicated that ground vegetation did not negatively influence tree development of A. germinans. In addition, tree recruitment occurred in clusters. Coarse wood debris had no apparent effect on either life stage. These results confirm that salt-marsh vegetation acts as the starting point for mangrove recolonization and indicate that the positive interaction among trees accelerates forest regeneration. In the second study (chapter 3), I analyzed how intraspecific interaction among A. germinans trees determines their growth and size under harsh environmental conditions. Interaction among a higher number of neighboring trees was positively related to the development of a focal tree. However, tree height, internode length and basal stem diameter were only positively associated in low-density forest stands (1.2 trees m-2) and not in forest stands of higher tree density (2.7 trees m-2). These results indicated a shift from facilitation, i.e. a positive effect of tree interaction, towards a balance between facilitation and competition. In the third study (chapter 4), I used point process modeling and the individual-based model mesoFON to disentangle the impact of regeneration and interaction processes on the spatial distribution of seedlings and trees. In this infrequently inundated area, propagules of A. germinans are only dispersed at a maximum distance of 3 m from their parent tree. Furthermore, there is no evidence that the following seedling establishment is influenced by trees. I was able to differentiate positive and negative tree interactions simulated by the mangrove model mesoFON regardless of dispersal processes based on static tree size information using the mark-correlation function. The results of this dissertation suggest that mangrove forest regeneration in degraded areas is a result of facilitative and not competitive interactions among mangrove trees, seedling and herbaceous vegetation. This has important implications for the restoration of degraded mangrove forest. Degraded mangrove areas are usually restored by planting a high number of evenly spaced seedlings. However, high costs constrain this approach to small areas. Assisting natural regeneration could be a less costly alternative. Herbaceous vegetation plays a crucial role in forest recolonization by entrapping propagules and possibly ameliorating harsh environmental conditions. So far only competition among mangrove trees has been considered during restoration. However, facilitative tree interactions could be utilized by planting seedling clusters in order to assist natural regeneration instead of planting seedlings evenly-spaced over large areas. This dissertation also showed that point pattern analysis and point process modeling can enable forest ecologists to describe the spatial distribution of trees as well as to infer underlying ecological processes.
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Packard, Kevin Carew. "Modeling spatial patterns of mixed-species Appalachian forests with Gibbs point processes." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/37385.

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Stochastic point processes and associated methodology provide a means for the statistical analysis and modeling of the spatial point pattern formed from forest tree stem locations. Stochastic Gibbs point processes were explored as models that could simulate short-range clustering arising from reproduction of trees by stump sprouting, and intermediate-range inhibition of trees that may result from competition for light and growing space. This study developed and compared three pairwise interaction processes with parametric models for 2nd-order potentials and three triplets processes with models for 2nd- and 3rd-order potentials applied to a mixed-species hardwood forest in the Southern Appalachian Mountains of western North Carolina. Although the 2nd-order potentials of both the pairwise interaction and triplets processes were allowed to be purely or partially attractive, the proposed Gibbs point process models were demonstrated to be locally stable. The proposed Gibbs point processes were simulated using Markov Chain Monte Carlo (MCMC) methods; in particular, a reversible-jump Metropolis-Hastings algorithm with birth, death, and shift proposals was utilized. Parameters for the models were estimated by a Bayesian inferential procedure that utilizes MCMC methods to draw samples from the Gibbs posterior density. Two Metropolis-Hastings algorithms that do this sampling were compared; one that estimated ratios of intractable normalizing constants of the Gibbs likelihood by importance sampling and another that introduced an auxiliary variable to cancel the normalizing constants with those in the auxiliary variableâ s proposal distribution. Results from this research indicated that attractive pairwise interaction models easily degenerate into excessively clustered patterns, whereas triplets processes with attractive 2nd-order and repulsive 3rd-order interactions are more robust against excessive clustering. Bayesian inference for the proposed triplets models was found to be very computationally expensive. Slow mixing of both algorithms used for the inference combined with the long iteration times limited the practicality of the Bayesian approach. However the results obtained here indicate that triplets processes can be used to draw inference for and simulate patterns of mixed-species Appalachian hardwood forests.<br>Ph. D.
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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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Books on the topic "Spatial point patterns"

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

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

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Janine, Illian, ed. Statistical analysis and modelling of spatial point patterns. John Wiley, 2008.

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Janine, Illian, ed. Statistical analysis and modelling of spatial point patterns. John Wiley, 2008.

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

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Boots, B. N. Point pattern analysis. Sage Publications, 1988.

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

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Baddeley, Adrian, Ege Rubak, and Rolf Turner. Spatial Point Patterns. Chapman and Hall/CRC, 2015. http://dx.doi.org/10.1201/b19708.

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STATISTICAL ANALYSIS OF SPATIAL POINT PATTERNS. 2nd ed. ARNOLD, 2003.

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Statistical Analysis Of Spatial And Spatiotemporal Point Patterns. Taylor & Francis Inc, 2013.

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Book chapters on the topic "Spatial point patterns"

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Young, Linda J., and Jerry H. Young. "Spatial Point Patterns." In Statistical Ecology. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2829-3_8.

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Moraga, Paula. "Spatial point patterns." In Spatial Statistics for Data Science. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781032641522-17.

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Kaluzny, Stephen P., Silvia C. Vega, Tamre P. Cardoso, and Alice A. Shelly. "Analyzing Spatial Point Patterns." In S+SpatialStats. Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4615-7826-0_6.

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Stoica, Radu S. "Marked point processes." In Random Patterns and Structures in Spatial Data. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003380382-2.

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Baddeley, Adrian. "Spatial Point Patterns: Models and Statistics." In Stochastic Geometry, Spatial Statistics and Random Fields. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33305-7_3.

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Ariza, Juan Medina, and Reka Solymosi. "Spatial point patterns of crime events." In Crime Mapping and Spatial Data Analysis using R. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003154914-7.

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Andreano, M. Simona, and Andrea Mazzitelli. "Exploring Spatial Point Patterns in Agriculture." In Spatial Econometric Methods in Agricultural Economics Using R. CRC Press, 2021. http://dx.doi.org/10.1201/9780429155628-7.

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

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Pawlasová, Kateřina, Iva Karafiátová, and Jiří Dvořák. "Supervised Classification via Neural Networks for Replicated Point Patterns." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_32.

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AbstractA spatial point pattern is a collection of points observed in a bounded region of $${\bf{\mathbb{R}}}^d$$ R d , $$d \ge 2$$ d ≥ 2 . Individual points represent, e.g., observed locations of cell nuclei in a tissue (d = 2) or centers of undesirable air bubbles in industrial materials (d = 3). The main goal of this paper is to show the possibility of solving the supervised classification task for point patterns via neural networks with general input space. To predict the class membership for a newly observed pattern, we compute an empirical estimate of a selected functional characteristic (e.g., the pair correlation function). Then, we consider this estimated function to be a functional variable that enters the input layer of the network. A short simulation example illustrates the performance of the proposed classifier in the situation where the observed patterns are generated from two models with different spatial interactions. In addition, the proposed classifier is compared with convolutional neural networks (with point patterns represented by binary images) and kernel regression. Kernel regression classifiers for point patterns have been studied in our previous work, and we consider them a benchmark in this setting.
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Knura, Martin, and Jochen Schiewe. "Improvement of Task-Oriented Visual Interpretation of VGI Point Data." In Volunteered Geographic Information. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_10.

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AbstractVGI is often generated as point data representing points of interest (POIs) and semantic qualities (such as accident locations) or quantities (such as noise levels), which can lead to geometric and thematic clutter in visual presentations of regions with numerous VGI contributions. As a solution, cartography provides several point generalization operations that reduce the total number of points and therefore increase the readability of a map. However, these operations are applied rather general and could remove specific spatial pattern, possibly leading to false interpretations in tasks where these spatial patterns are of interest. In this chapter, we want to tackle this problem by defining task-oriented sets of map generalization constraints that help to maintain spatial pattern characteristics during the generalization process. Therefore, we conduct a study to analyze the user behavior while solving interpretation tasks and use the findings as constraints in the following point generalization process, which is implemented through agent-based modeling.
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Conference papers on the topic "Spatial point patterns"

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Zhang, LeLe, Xin Wu, Kailun Liu, and Chenfeng Xiong. "Evaluating the Spatial-Temporal Impact of Urban Flooding on Mobility Patterns and Point of Interest." In 2024 IEEE Global Humanitarian Technology Conference (GHTC). IEEE, 2024. https://doi.org/10.1109/ghtc62424.2024.10771563.

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Zulfana, Nadiya Firma, Nur Iriawan, and Christiono Utomo. "On the Spatial Point Pattern on Customer Affect Retail Fuel Stations Businesses." In 2024 IEEE International Symposium on Consumer Technology (ISCT). IEEE, 2024. https://doi.org/10.1109/isct62336.2024.10791245.

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"The impact of spatial scales on discretised spatial point patterns." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.i4.kang.

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Martynova, Evgenia, and Johannes Textor. "A Uniformly Bounded Correlation Function for Spatial Point Patterns." In KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2024. http://dx.doi.org/10.1145/3637528.3671891.

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Burguet, Jasmine, Philippe Mailly, and Philippe Andrey. "Statistical groupwise comparison of spatial point patterns in biological imaging." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556510.

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Skolnick, Michael M., and Peter Marineau. "Pebble_Pond: a morphological wave propagation algorithm for representing spatial point patterns." In San Diego '92, edited by Paul D. Gader, Edward R. Dougherty, and Jean C. Serra. SPIE, 1992. http://dx.doi.org/10.1117/12.60654.

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Maćešić, Stevan, Ana Ivanović-Šašić, and Željko Čupić. "NUMERICAL SIMULATIONS OF SPATIO-TEMPORAL PHENOMENA IN BELOUSOV-ZHABOTINSKY OSCILLATORY REACTION." In 17th International Conference on Fundamental and Applied Aspects of Physical Chemistry. Society of Physical Chemists of Serbia, 2024. https://doi.org/10.46793/phys.chem24i.143m.

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This study investigates the Belousov-Zhabotinsky reaction’s spatio-temporal patterns using a realistic numerical model. Our simulations show that when spatially uniform system is unstable conditions, traveling waves emerge from a central point and move inwards when spatial inhomogeneities are introduced. Stable conditions, however, lead to Turing patterns, expanding outwards to form stationary zones of high and low concentrations. These findings emphasize diffusion’s key role in shaping the BZ reaction’s dynamics, offering insights for understanding nonequilibrium systems and potential applications in reaction engineering.
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Kirchner, Thomas R., Donna Vallone, Jennifer Cantrell, et al. "Individual mobility patterns and real-time geo-spatial exposure to point-of-sale tobacco marketing." In the conference. ACM Press, 2012. http://dx.doi.org/10.1145/2448096.2448104.

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Kanfar, Rayan, Charles Breithaupt, and Tapan Mukerji. "Spatial statistical analysis and geomodelling of banana holes using point patterns and generative adversarial networks." In International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists and American Association of Petroleum Geologists, 2023. http://dx.doi.org/10.1190/image2023-3905116.1.

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Peng, Tao, and Satyandra K. Gupta. "Algorithms for Generating Adaptive Projection Patterns for 3-D Shape Measurement." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35452.

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Point cloud acquisition using digital fringe projection (PCCDFP) is a non-contact technique for acquiring dense point clouds to represent the 3-D shapes of objects. Most existing PCCDFP systems use projection patterns consisting of straight fringes with fixed fringe pitches. In certain situations, such patterns do not give the best results. In our earlier work, we have shown that in some situations, patterns that use curved fringes with spatial pitch variation can significantly improve the process of constructing point clouds. This paper describes algorithms for automatically generating adaptive projection patterns that use curved fringes with spatial pitch variation to provide improved results for an object being measured. In addition, we also describe the supporting algorithms that are needed for utilizing adaptive projection patterns. Both simulation and physical experiments show that, adaptive patterns are able to achieve improved performance, in terms of measurement accuracy and coverage, than fixed-pitch straight fringe patterns.
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Reports on the topic "Spatial point patterns"

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Peterson, Brian, J. Beeco, Sharolyn Anderson, and Damon Joyce. Exploring spatial patterns of overflights at Mount Rushmore National Memorial. National Park Service, 2022. http://dx.doi.org/10.36967/nrr-2293663.

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The purpose of this study was to explore the spatial patterns of overflights at Mount Rushmore National Memorial (Mount Rushmore). Overflights were analyzed from August 15th, 2020 to May 15th, 2021 using Automatic Dependent Surveillance-Broadcast (ADS-B) and radar data from L3Harris. The first phase of analysis focused on all overflights and found a high concentration of overflights fly above Mount Rushmore. The second phase of analysis focused on low-level overflights that fly below 9,000ft mean sea level (MSL), which showed that many low-level overflights fly in a ring-shaped pattern around Mount Rushmore. The third phase of analysis removed all overflights known to not be air tours. Point density analysis was conducted using waypoints segmented into 500ft above ground level (AGL) altitude intervals, which showed that the density of overflights varied by altitude interval and the 1,000-1,500ft AGL range had the highest density of overflights around and above Mount Rushmore. This altitude interval also had the highest number of waypoints. It was also determined that the aircraft owners who fly the most helicopter routes fly at mean and median altitudes less than 1,600ft AGL. This information can be used for planning and management purposes and this study serves as a resource for future research that intends to use more advanced analytics.
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Dasberg, Shmuel, Jan W. Hopmans, Larry J. Schwankl, and Dani Or. Drip Irrigation Management by TDR Monitoring of Soil Water and Solute Distribution. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7568095.bard.

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Drip irrigation has the potential of high water use efficiency, but actual water measurement is difficult because of the limited wetted volume. Two long-term experiments in orchards in Israel and in California and several field crop studies supported by this project have demonstrated the feasibility of precise monitoring of soil water distribution for drip irrigation in spite of the limited soil wetting. Time Domain Reflectometry (TDR) enables in situ measurement of soil water content of well defined small volumes. Several approaches were tried in monitoring the soil water balance in the field during drip irrigation. These also facilitated the estimation of water uptake: 1. The use of multilevel moisture probe TDR system. This approach proved to be of limited value because of the extremely small diameter of measurement. 2. The placement of 20 cm long TDR probes at predetermined distances from the drippers in citrus orchards. 3. Heavy instrumentation with neutron scattering access tubes and tensiometers of a single drip irrigated almond tree. 4. High resolution spatial and temporal measurements (0.1m x 0.1m grid) of water content by TDR in corn irrigated by surface and subsurface drip. The latter approach was accompanied by parametric modelling of water uptake intensity patterns by corn roots and superimposed with analytical solutions for water flow from point and line sources. All this lead to general and physically based suggestions for the placement of soil water sensors for scheduling drip irrigation.
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Stjernberg, Mats, Anna Vasilevskaya, and Oskar Penje. Towards a grid-based Nordic territorial typology - A new tool for analysis across the urban-rural continuum. Nordregio, 2024. http://dx.doi.org/10.6027/r2024:91403-2503.

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This report presents the grid-based Nordic urban–rural typology, which was developed as a new analytical tool for studying different types of spatial phenomena across Nordic territories. In this study this meant developing a typology that classifies all Nordic territories into seven different typology classes based on different degrees of urbanity and rurality. A key starting point for this work was the need for a territorial typology that would help enrich and provide new understanding of different types of urban and rural areas across the Nordic countries and shed light on how they are developing. This report first presents how the typology was created, including the rationale behind the typology, key considerations at different stages of the work, and the main operational steps taken. The main purpose was to create a new territorial typology, to which different types of data could be combined, thus helping to provide a more nuanced and fine-grained understanding of territorial differences across the Nordic countries. Several key principles were specified early in the work. These include that the typology should be created at grid-level (1 x 1 km) as this allows identifying the characteristics of different types of areas at a very detailed territorial level. Another key decision was to create the typology mainly using open-source data and following a replicable method, to make any possible future updates to the typology easier and less costly. For the development of the Nordic typology, the Finnish grid-based urban–rural classification (Kaupunki-maaseutuluokitus) was the main source of inspiration. This Nordic typology and population data at grid level (linked to the typology) is then used as an analytical lens for studying territorial differences, settlement pattens and demographic change dynamics in the five Nordic countries. According to the typology, the Nordic countries are predominantly rural when considering how their land areas are classified. However, an examination of settlement patterns according to the Nordic typology shows that the settlements are rather unevenly distributed in all the Nordic countries, and the majority of the population live relatively concentrated in areas that are classified as urban. In general, the population is largely concentrated in coastal areas and along waterways, where the major urban regions are found, reflecting historical patterns and features of physical geography. The Nordic typology is also used to examine what types of population change dynamics occurred in the Nordic countries during the period 2008–2022. The analysis shows that urbanisation has been a general trend during the past couple of decades, with the largest population growth occurring in the typology classes inner urban and outer urban. A relatively noticeable increase in population is also evident in peri-urban areas, suggesting suburbanisation and that intermediate areas located on the urban fringes have increasingly attracted new residents. In rural areas, the general trend shows that depopulation has occurred in many rural localities, but different types of rural areas have developed quite differently. Based on the analysis, rural areas that are in the vicinity of cities and towns appear to have become more attractive places for people to settle, while sparsely populated rural areas seem to be less favourably placed and have generally witnessed population decrease. This report shows how this typology and more fine-grained data can help reveal territorial differences that cannot be observed with more general statistics and data. The grid-based Nordic typology shows that many municipalities are at the same time both urban, intermediate, and rural, and in many cases these different categories seem to be undergoing quite different types of development. While the Nordic urban–rural typology is used in this study to examine settlement patterns and population change dynamics, it should be stressed that the typology is also well-suited to be used in combination with other types of data and as an analytical framework for studying also other types of spatial phenomena across the urban–rural continuum.
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Bell, Matthew A., and Marcel P. Huijser. Patterns of Domestic Animal-Vehicle Collisions on Tribal Lands in Montana, U.S. Western Transportation Institute, 2024. http://dx.doi.org/10.15788/1727735166.

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Animal-vehicle collisions (AVCs) are a significant concern for motorist safety and pose a risk to both wildlife and domestic animals. This report analyzes spatial patterns of wildlife-vehicle collisions (WVCs) and domestic animal-vehicle collisions (DAVCs) on Montana’s tribal lands to identify high-risk areas and inform mitigation strategies. Data from the Montana Department of Transportation (MDT) for large mammal carcasses (2008–2022) and reported crashes (2008–2020) were used to perform Kernel Density Estimation (KDE) and Getis-Ord Gi* (GOG) hotspot analyses for three tribal reservations with sufficient data: Blackfeet, Crow, and Flathead. The KDE results show distinct spatial patterns for DAVCs and WVCs on each reservation, with DAVC hotspots concentrated near agricultural and grazing areas, while WVC hotspots were associated with natural habitats and wildlife corridors. The GOG analysis further revealed that DAVC hotspots tend to be more temporally stable, suggesting that collisions with domestic animals are influenced by consistent factors such as livestock access points and grazing practices. In contrast, WVC hotspots were more variable, likely driven by changes in wildlife movement patterns and seasonal behavior. Overall, the findings indicate that the elevated rates of DAVCs on tribal lands, compared to non-tribal lands, are likely due to unique factors such as open range grazing practices and road infrastructure adjacent to grazing lands. This report emphasizes the need for targeted mitigation strategies on tribal roads, such as enhanced livestock fencing, road signage, and livestock underpasses in high-risk areas, to reduce collisions and improve safety for both motorists and animals. Understanding the distinct spatial and temporal patterns of DAVCs and WVCs is crucial for developing comprehensive mitigation approaches that enhance safety and connectivity on Montana’s tribal lands.
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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.), 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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Tercek, Michael. Climate monitoring in the Mediterranean Coast Network 2020: Santa Monica Mountains National Recreation Area. National Park Service, 2022. http://dx.doi.org/10.36967/2294435.

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This report details the climate (temperature, precipitation, drought, and streamflow) of Santa Monica Mountains National Recreation Area (NRA) during 2020. Gridded GIS maps are used to present broad-brush conditions across the region for each month. This regional presentation is compared to detailed data from Remote Automated Weather Stations (RAWS), weather stations in Global Historical Climatology Network (GHCN), and data from USGS stream gages. Prior to analysis, a set of data quality control checks were performed by the authors in accordance with an established National Park Service protocol (Rocky Mountain Climate Working Group 2010). Key points summarizing calendar year 2020 are presented as bullets in the Conclusion section at the end of the report. There was significant spatial and month to month variability in the data that provided exceptions to these generalizations, so an examination of the regional maps is key to understanding the climatic patterns for 2020. Readers can download the data used in this report and make custom climate graphs and tables of their own by visiting www.ClimateAnalyzer.org.
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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Capitol Reef National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, 2023. http://dx.doi.org/10.36967/2297289.

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Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects, such as impacts to forage, habitat, fire potential, and erosion, as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Capitol Reef National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.
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Thoma, David. Landscape phenology, vegetation condition, and relations with climate at Canyonlands National Park, 2000–2019. Edited by Alice Wondrak Biel. National Park Service, 2023. http://dx.doi.org/10.36967/2299619.

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Quantitatively linking satellite observations of vegetation condition and climate data over time provides insight to climate influences on primary production, phenology (timing of growth), and sensitivity of vegetation to weather and longer-term patterns of weather referred to as climate. This in turn provides a basis for understanding potential climate impacts to vegetation—and the potential to anticipate cascading ecological effects—such as impacts to forage, habitat, fire potential, and erosion—as climate changes in the future. This report provides baseline information about vegetation production and condition over time at Canyonlands National Park (NP), as derived from satellite remote sensing. Its objective is to demonstrate methods of analysis, share findings, and document historic climate exposure and sensitivity of vegetation to weather and climate as a driver of vegetation change. This report represents a quantitative foundation of vegetation–climate relationships on an annual timestep. The methods can be modified to finer temporal resolution and other spatial scales if further analyses are needed to inform park planning and management. The knowledge provided in this report can inform vulnerability assessments for Climate Smart Conservation planning by park managers. Patterns of pivot points and responses can serve as a guide to anticipate what, where, when, and why vegetation change may occur. For this analysis, vegetation alliance groups were derived from vegetation-map polygons (Von Loh et al. 2007) by lumping vegetation types expected to respond similarly to climate. Relationships between vegetation production and phenology were evaluated for each alliance map unit larger than a satellite pixel (~300 × 300 m). We used a water-balance model to characterize the climate experienced by plants. Water balance translates temperature and precipitation into more biophysically relevant climate metrics, such as soil moisture and drought stress, that are often more strongly correlated with vegetation condition than temperature or precipitation are. By accounting for the interactions between temperature, precipitation, and site characteristics, water balance helps make regional climate assessments relevant to local scales. The results provide a foundation for interpreting weather and climate as a driver of changes in primary production over a 20-year period at the polygon and alliance-group scale. Additionally, they demonstrate how vegetation type and site characteristics, such as soil properties, slope, and aspect, interact with climate at local scales to determine trends in vegetation condition. This report quantitatively defines critical water needs of vegetation and identifies which alliance types, in which locations, may be most susceptible to climate-change impacts in the future. Finally, this report explains how findings can be used in the Climate Smart Conservation framework, with scenario planning, to help manage park resources through transitions imposed by climate change.
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