1,295 research outputs found
A Statistical Toolbox For Mining And Modeling Spatial Data
Most data mining projects in spatial economics start with an evaluation of a set of attribute variables on a sample of spatial entities, looking for the existence and strength of spatial autocorrelation, based on the Moran’s and the Geary’s coefficients, the adequacy of which is rarely challenged, despite the fact that when reporting on their properties, many users seem likely to make mistakes and to foster confusion. My paper begins by a critical appraisal of the classical definition and rational of these indices. I argue that while intuitively founded, they are plagued by an inconsistency in their conception. Then, I propose a principled small change leading to corrected spatial autocorrelation coefficients, which strongly simplifies their relationship, and opens the way to an augmented toolbox of statistical methods of dimension reduction and data visualization, also useful for modeling purposes. A second section presents a formal framework, adapted from recent work in statistical learning, which gives theoretical support to our definition of corrected spatial autocorrelation coefficients. More specifically, the multivariate data mining methods presented here, are easily implementable on the existing (free) software, yield methods useful to exploit the proposed corrections in spatial data analysis practice, and, from a mathematical point of view, whose asymptotic behavior, already studied in a series of papers by Belkin & Niyogi, suggests that they own qualities of robustness and a limited sensitivity to the Modifiable Areal Unit Problem (MAUP), valuable in exploratory spatial data analysis
Parabolic theory of the discrete p-Laplace operator
We study the discrete version of the -Laplacian. Based on its variational
properties we discuss some features of the associated parabolic problem. Our
approach allows us in turn to obtain interesting information about positivity
and comparison principles as well as compatibility with the symmetries of the
graph. We conclude briefly discussing the variational properties of a handful
of nonlinear generalized Laplacians appearing in different parabolic equations.Comment: 35 pages several corrections and enhancements in comparison to the v
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Continuum limit of total variation on point clouds
We consider point clouds obtained as random samples of a measure on a
Euclidean domain. A graph representing the point cloud is obtained by assigning
weights to edges based on the distance between the points they connect. Our
goal is to develop mathematical tools needed to study the consistency, as the
number of available data points increases, of graph-based machine learning
algorithms for tasks such as clustering. In particular, we study when is the
cut capacity, and more generally total variation, on these graphs a good
approximation of the perimeter (total variation) in the continuum setting. We
address this question in the setting of -convergence. We obtain almost
optimal conditions on the scaling, as number of points increases, of the size
of the neighborhood over which the points are connected by an edge for the
-convergence to hold. Taking the limit is enabled by a transportation
based metric which allows to suitably compare functionals defined on different
point clouds
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