Motif: an open-source R tool for pattern-based spatial analysis
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SHORT COMMUNICATION
Motif: an open-source R tool for pattern-based spatial analysis Jakub Nowosad
Received: 26 July 2020 / Accepted: 29 September 2020 Ó The Author(s) 2020
Abstract Context Pattern-based spatial analysis provides methods to describe and quantitatively compare spatial patterns for categorical raster datasets. It allows for spatial search, change detection, and clustering of areas with similar patterns. Objectives We developed an R package motif as a set of open-source tools for pattern-based spatial analysis. Methods This package provides most of the functionality of existing software (except spatial segmentation), but also extends the existing ideas through support for multi-layer raster datasets. It accepts larger-than-RAM datasets and works across all of the major operating systems. Results In this study, we describe the software design of the tool, its capabilities, and present four case studies. They include calculation of spatial signatures based on land cover data for regular and irregular areas, search for regions with similar patterns of geomorphons, detection of changes in land cover patterns, and clustering of areas with similar spatial patterns of land cover and landforms. Conclusions The methods implemented in motif should be useful in a wide range of applications,
J. Nowosad (&) Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Krygowskiego 10, 61-680 Poznan, Poland e-mail: [email protected]
including land management, sustainable development, environmental protection, forest cover change and urban growth monitoring, and agriculture expansion studies. The motif package homepage is https:// nowosad.github.io/motif. Keywords Spatial patterns Multi-layer similarity Query-by-example Similarity search Patterns comparison Patterns clustering
Introduction Discovering and describing spatial patterns is an important element of many environmental studies, as spatial patterns on different scales are related to ecological processes. With the ascent of computational methods, identification and characterization of spatial patterns started to be answered with numbers rather than by means of qualitative depiction. Most methods of spatial analysis for remotely sensed data treat single cells as basic units of analysis, and while this standard approach is sufficient for analysis of local areas, it is not well suited for analysis on regional, continental, or global scales. This is because cell-scale information is, in itself, irrelevant for broad-scale analysis. For example, mapping land cover change using cell-based transitions in a large area results in an unsatisfactory salt-and-pepper output due to
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Landscape Ecol
classification errors, or simply because cell size is smaller than objects whose change we want to detect. A ‘‘local landscape’’ is a more appropriate unit of analysis for broad-scale studies. For categorical raster data is represented by a block of cells containing a local pattern of a cell-based
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