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Spatial analysis

About: Spatial analysis is a research topic. Over the lifetime, 14835 publications have been published within this topic receiving 438256 citations. The topic is also known as: spatial statistics.


Papers
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Book
29 Jun 1995
TL;DR: A: Introduction 1. Spatial data analysis 2. Computers and Spatial Data Analysis B: The Analysis of Data Associated with Points 3. Methods Relating to Point Patterns 4. Methodsrelating to Marked Point Patterns 5. MethodsRelating to a Continuously Varying Attribute Sampled at Points.
Abstract: A: Introduction 1. Spatial Data Analysis 2. Computers and Spatial Data Analysis B: The Analysis of Data Associated with Points 3. Methods Relating to Point Patterns 4. Methods Relating to Marked Point Patterns 5. Methods Relating to a Continuously Varying Attribute Sampled at Points C: The Analysis of Data Associated with Areas 6. Univariate Analysis of Area Data 7. Analysis of Relationships Between Attributes of Areas 8. Multivariate Methods of Area Data D: The Analysis of Data Associated with Lines 9. Network Analysis 10. Spatial Interaction Models

2,168 citations

Journal ArticleDOI
TL;DR: In this article, the spatial heterogeneity of populations and communities plays a central role in many ecological theories, such as succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on.
Abstract: The spatial heterogeneity of populations and communities plays a central role in many ecological theories, for instance the theories of succession, adaptation, maintenance of species diversity, community stability, competition, predator-prey interactions, parasitism, epidemics and other natural catastrophes, ergoclines, and so on. This paper will review how the spatial structure of biological populations and communities can be studied. We first demonstrate that many of the basic statistical methods used in ecological studies are impaired by autocorrelated data. Most if not all environmental data fall in this category. We will look briefly at ways of performing valid statistical tests in the presence of spatial autocorrelation. Methods now available for analysing the spatial structure of biological populations are described, and illustrated by vegetation data. These include various methods to test for the presence of spatial autocorrelation in the data: univariate methods (all-directional and two-dimensional spatial correlograms, and two-dimensional spectral analysis), and the multivariate Mantel test and Mantel correlogram; other descriptive methods of spatial structure: the univariate variogram, and the multivariate methods of clustering with spatial contiguity constraint; the partial Mantel test, presented here as a way of studying causal models that include space as an explanatory variable; and finally, various methods for mapping ecological variables and producing either univariate maps (interpolation, trend surface analysis, kriging) or maps of truly multivariate data (produced by constrained clustering). A table shows the methods classified in terms of the ecological questions they allow to resolve. Reference is made to available computer programs.

2,166 citations

Book
21 Oct 2008
TL;DR: Hello, world: handling spatial data in R.
Abstract: Hello, world: handling spatial data in R.- Classes for spatial data in R.- Visualizing spatial data.- Spatial data import and export.- Further methods for handling spatial data.- Customising spatial data classes and methods.- Spatial point pattern analysis.- Interpolation and geostatistics.- Areal data and spatial autocorrelation.- Modelling areal data.- Disease mapping.- Afterword.- References.

2,105 citations

Book
01 Jul 2010
TL;DR: This chapter discusses the history and Ecological Basis of Species' Distribution Modeling, and the design and implementation of species' distribution models.
Abstract: Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.

1,944 citations

Journal ArticleDOI
TL;DR: In this article, an ecological model concerning the ecological theory to be used or tested, a data model concerning collection and measurement of the data, and a statistical model concerning statistical theory and methods used.

1,774 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023511
20221,255
2021736
2020736
2019774
2018673