Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Leibovici, D.G.; Bastin, L.; Jackson, M. (2011)
Languages: English
Types: Article
Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Baddeley, A., M ller, J., Waagepetersen, R., 2000. Non- and semi-parametric estimation of interaction in inhomogeneous point patterns. Statistica Neerlandica 54 (3), 329{350.
    • Baddeley, A., Turner, R., 2005. spatstat: An R package for analyzing spatial point patterns. Journal of Statistical Software 12 (6), 1{42.
    • Baddeley, A., Gregori, P., Mateu, J., Stoica, R., and Stoyan, D., 2006. Case Studies in Spatial Point Pattern Modelling. Springer-Verlag, New-York Inc, 306pp.
    • Bastin, L., Fisher, P., Bacon, M., Arnot, C., Hughes, M., 2007a. Reliability of vegetation community information derived using DECORANA ordination and fuzzy c-means clustering. In: Kokhan, A. M. . S. (Ed.), Geographic Uncertainty in Environmental Security. Springer, pp. 53{74.
    • Bastin, L., Rollason, J., Hilton, A., Pillay, D., Corcoran, C., Elgy, J., Lambert, P., De, P., Worthington, T., Burrows, K., 2007b. Spatial aspects of MRSA epidemiology: a case study using stochastic simulation, kernel estimation and SaTScan. International Journal of Geographical Information Science 21 (7), 811{836.
    • Bel, L., Allard, D., Laurent, J., Cheddadi, R., andBar Hen, A., 2009. CART algorithm for spatial data: Application to environmental and ecological data. Computational Statistics & Data Analysis 53, 3082{3093.
    • Bivand, R., 2008. Applied Spatial Data Analysis with R, 1st Edition. SpringerVerlag, New York Inc, 374pp.
    • Breiman, L., Friedman, J., Olshen, R., Stone, C., 1984. Classi cation and regression trees. Wadsworth statistics/probability series, Wadsworth International Group, Belmont, CA, 358pp.
    • Diggle, P., 2003. Statistical Analysis of Spatial Point Patterns, 2nd Edition. Hodder Arnold, London, 159pp.
    • Diggle, P., Gomez-Rubio, V. Brown, P., Chetwynd, A., Gooding, S., 2007. Second-order analysis of inhomogeneous spatial point processes using casecontrol data. Biometrics 63, 550{557.
    • Leibovici, D., 2010. Spatio-temporal multiway decomposition using principal tensor analysis on k-modes: the R package PTAk. Journal of Statistical Software 34 (10), 1{34.
    • Leibovici, D., 2009. De ning spatial entropy from multivariate distributions of co-occurrences. Spatial Information Theory 2009, Published in: Lecture Notes in Computer Science, vol. 5756/2009, 392{404.
    • Leibovici, D., Bastin, L., Jackson, M., 2008. Discovering spatially multiway collocations. In: GISRUK Conference 2008, Manchester, UK, 2-4 April, 2008. pp. 66{71.
    • Leibovici, D., Jackson, M., 2008. Multiscale integration for Spatio-Temporal ecoclimatic ecoregioning delineation. In: Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International. Vol. 3. pp. III { 996{III { 999.
    • Leibovici, D., Quillevere, G., Desconnets, J.-C., 2007. A Method to Classify Ecoclimatic Arid and Semi-Arid Zones in Circum-Saharan Africa Using Monthly Dynamics of Multiple Indicators. IEEE Transactions on Geoscience and Remote Sensing 45 (12), 4000{4007.
    • Li, X., Claramunt, C., 2006. A spatial entropy-based decision tree for classi cation of geographical information. Transactions in GIS 10 (3), 451{467.
    • Lotwick, H., Silverman, B. W., 1982. Methods for analysing spatial processes of several types of points. Journal of Royal Statistical Society B (44), 406{413.
    • O'Neill, R., Krummel, J., Gardner, R., Sugihara, G., Jackson, B., DeAngelis, D., Milne, B., Turner, M., Zygmunt, B., Christensen, S., Dale, V., Graham, R., 1988. Indices of landscape pattern. Landscape Ecology 1 (3), 153{162.
    • Phipps, M., 1981. Entropy and community pattern analysis. Journal of Theoretical Biology 93 (1), 253{273.
    • Quinlan, J., 1986. Induction on decision trees. Machine Learning 1, 81{106.
    • R Development Core Team, 2007. R: A Language and Environment for Statistical Computing. Vienna, Austria, ISBN 3-900051-07-0 Edition. http://www.R-project.org
    • Reza, Fazlollah M., 1994. An introduction to information theory. Dover, New York, 496pp.
    • Schabenberger, O., Gotway, C., 2004. Statistical Methods for Spatial Data Analysis, 1st Edition. Chapman & Hall/CRC, 488pp.
    • Schlather, M. Riberio, P., Diggle, P., 2004. Detecting dependence between marks and locations of marked point process. Journal of Royal Statistical Society B (66), 79{93.
    • van Lieshout, M., Baddeley, A., 1999. Indices of dependence between types in multivariate point patterns. Scandinavian Journal of Statistics 26, 511{532.
    • Wagner, H., Fortin, M.-J., 2005. Spatial analysis of landscapes: Concepts and statistics. Ecology 86 (8), 1975{1987.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article