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Journal articles on the topic 'Spatial point patterns'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

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

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2

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

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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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

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12

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

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13

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

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14

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

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15

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

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16

Yang, Zhuoyi, Zeyi Li, Haitao Zhang, Wei Zhang, Yanwei Wang, and Yihang Huang. "GeoFAN: Point Pattern Recognition in Spatial Vector Data." ISPRS International Journal of Geo-Information 14, no. 6 (2025): 214. https://doi.org/10.3390/ijgi14060214.

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The recognition of point patterns in spatial vector data has important applications in geographic mapping and formation recognition. However, the application of traditional methods to spatial vector data faces two difficulties. Firstly, these data are low signal-to-noise ratio data in which the point patterns are mixed with a large number of normal point clusters; thus, it is difficult to recognize point patterns from these unstructured data using traditional clustering or machine learning methods. Secondly, the lack of edge connectivity relationships in spatial vector data directly hinders the application of graph models. Few studies have systematically solved the above difficulties. In this article, we propose a geometric feature attention scheme to overcome the above challenges. We also present an implementation of the scheme based on the graph method, termed GeoFAN, to extract and classify point patterns simultaneously in spatial vector data. Firstly, the raw data are transformed into a graph structure consisting of adjacency and attribute matrices. Secondly, a geometric feature attention module is proposed to enhance the feature representation of point patterns. Finally, the recognition results of all points are output via GeoFAN. The macro precision, recall, and F1 score of five simulated point pattern types with different attributes and point numbers are 92.8%, 90.3%, and 91.5%, respectively, and GeoFAN is trained with simulated data to recognize real location-based point patterns successfully. The proposed GeoFAN showed superior performance and generalization ability in point pattern recognition.

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17

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

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

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18

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 (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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19

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

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

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20

Zimeras, Stelios. "Spatial Pattern Simulation of Antenna Base Station Positions Using Point Process Techniques." Telecom 3, no. 3 (2022): 541–47. http://dx.doi.org/10.3390/telecom3030030.

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Spatial statistics is a powerful tool for analyzing data that are illustrated as points or positions in a regular or non-regular state space. Techniques that are proposed to investigate the spatial association between neighboring positions are based on the point process analysis. One of the main goals is to simulate real data positions (such as antenna base stations) using the type of point process that most closely matches the data. Spatial patterns could be detailed describing the observed positions and appropriate models were proposed to simulate these patterns. A common model to simulate spatial patterns is the Poisson point process. In this work analyses of the Poisson point process—as well as modified types such as inhibition point process and determinantal Poisson point process—are presented with simulated data close to the true data (i.e., antenna base station positions). Investigation of the spatial variation of the data led us to the spatial association between positions by applying Ripley’s K-functions and L-Function.

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21

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

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22

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 (2012): e36841. http://dx.doi.org/10.1371/journal.pone.0036841.

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23

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

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24

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

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25

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

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26

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 (2013): 151–58. http://dx.doi.org/10.3724/sp.j.1003.2012.08163.

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27

Huang, Xingchang, Tobias Ritschel, Hans-Peter Seidel, Pooran Memari, and Gurprit Singh. "Patternshop: Editing Point Patterns by Image Manipulation." ACM Transactions on Graphics 42, no. 4 (2023): 1–14. http://dx.doi.org/10.1145/3592418.

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Point patterns are characterized by their density and correlation. While spatial variation of density is well-understood, analysis and synthesis of spatially-varying correlation is an open challenge. No tools are available to intuitively edit such point patterns, primarily due to the lack of a compact representation for spatially varying correlation. We propose a low-dimensional perceptual embedding for point correlations. This embedding can map point patterns to common three-channel raster images, enabling manipulation with off-the-shelf image editing software. To synthesize back point patterns, we propose a novel edge-aware objective that carefully handles sharp variations in density and correlation. The resulting framework allows intuitive and backward-compatible manipulation of point patterns, such as recoloring, relighting to even texture synthesis that have not been available to 2D point pattern design before. Effectiveness of our approach is tested in several user experiments. Code is available at https://github.com/xchhuang/patternshop.

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28

Zimeras, Stelios. "Exploratory Point Pattern Analysis for Modeling Biological Data." International Journal of Systems Biology and Biomedical Technologies 2, no. 1 (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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29

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

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30

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

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

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31

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

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32

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

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33

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

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34

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

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35

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

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36

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

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37

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

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38

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

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39

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

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40

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

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41

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

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42

Che, B. "Modelling spatial point patterns of Asian Giant Hornets occurrences." Theoretical and Natural Science 31, no. 1 (2024): 223–42. http://dx.doi.org/10.54254/2753-8818/31/20241140.

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This study explores statistical methods for modelling the spatial patterns of Asian giant hornets using sighting records in the Republic of Korea from 2008 to 2013. The focus is on simulating the spatial distribution of hornets, with a key aspect being the statistical inference of their intensity. Gaussian kernel-smoothing estimation is utilized to model the hornets intensity based on sighting records, which also transforms the occurrences of honeybees, a primary prey of the hornets, into a covariate. Results show a significant dependency of hornets intensity on honeybees intensity, with a calculated probability that a hornet sighting location has higher honeybee intensity than a random location. By this finding, the parametric modelling of the hornets spatial intensity is applied with the covariate of honeybees in each year, with the basic inhomogeneous Poisson point process along with the log-linear model. The model is refined for each year using backward stepwise selection based on the Akaike Information Criteria. Model validation confirms the Poisson process assumption and shows promising results with raw residuals against honeybee intensity. The analysis demonstrates that the spatial pattern modelling method employed is both sensible and valid.

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43

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

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44

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

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45

Ukrainsky, P. A., Z. A. Buryak, and E. A. Terekhin. "Mapping the structure of spatial point patterns on a regional scale." Geodesy and Cartography 989, no. 11 (2022): 50–63. http://dx.doi.org/10.22389/0016-7126-2022-989-11-50-63.

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To identify geographical regularities in changing the structure of spatial point patterns, we proposed the use of cartographic visualization. Approaches to gathering initial data, mechanisms for calculating the characteristics of the spatial structure and ways of displaying them on the map are described. The process of mapping combines elements of geoinformatics, spatial statistics and cartography. The elements of the methodology were integrated using the R programming language and the spatstat package. A script was written in the R that enables automating the calculation of the spatial structure’s quantitative characteristics. Thirteen indicators were identified that can be used for cartographic visualization of the spatial point images structure features. The described approach was tested on the example of sparse woody vegetation areas found in the ravine-gully network of the Central Chernozem region.

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46

Leong, Kelvin, Junco Li, Stephen Chan, and Vincent Ng. "An Application of the Dynamic Pattern Analysis Framework to the Analysis of Spatial-Temporal Crime Relationships." JUCS - Journal of Universal Computer Science 15, no. (9) (2009): 1852–70. https://doi.org/10.3217/jucs-015-09-1852.

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Dynamic pattern analysis refers to analyzing the relationship of spatial patterns at different time points. Traditional spatial pattern analysis such as data clustering can find the spatial patterns extant at a geographical location at a particular time point but failing to identify spatial dynamics, or changes that occur over time in a particular place. In this paper, we present a dynamic pattern analysis framework, the DPA framework. This framework allows user to identify three types of dynamic patterns in spatial-temporal data: 1) similar spatial patterns at different time points, 2) interactive relationship between two geographical locations as a result of a specific reason and 3) frequent association rules related to particular types of events, geographical locations, and time points. To evaluate the proposed framework, we used it to analyze a set of reported crime data for a district of Hong Kong and compared the identified patterns with some expectations of field experts and prior empirical studies for this kind of data and patterns. In line with expert predictions, we found strong correlations between school holidays and crime clusters. On the contrary, in our data set, we could not find obvious seasonal dependency. These findings are corroborated by related empirical crime studies.

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47

Verma, Archit, Siddhartha G. Jena, Danielle R. Isakov, Kazuhiro Aoki, Jared E. Toettcher, and Barbara E. Engelhardt. "A self-exciting point process to study multicellular spatial signaling patterns." Proceedings of the National Academy of Sciences 118, no. 32 (2021): e2026123118. http://dx.doi.org/10.1073/pnas.2026123118.

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Multicellular organisms rely on spatial signaling among cells to drive their organization, development, and response to stimuli. Several models have been proposed to capture the behavior of spatial signaling in multicellular systems, but existing approaches fail to capture both the autonomous behavior of single cells and the interactions of a cell with its neighbors simultaneously. We propose a spatiotemporal model of dynamic cell signaling based on Hawkes processes—self-exciting point processes—that model the signaling processes within a cell and spatial couplings between cells. With this cellular point process (CPP), we capture both the single-cell pathway activation rate and the magnitude and duration of signaling between cells relative to their spatial location. Furthermore, our model captures tissues composed of heterogeneous cell types with different bursting rates and signaling behaviors across multiple signaling proteins. We apply our model to epithelial cell systems that exhibit a range of autonomous and spatial signaling behaviors basally and under pharmacological exposure. Our model identifies known drug-induced signaling deficits, characterizes signaling changes across a wound front, and generalizes to multichannel observations.

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48

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

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

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49

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

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50

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

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