Exchange matrices represent spatial weights as symmetric probability distributions on pairs of regions, whose margins yield regional weights, generally well-specified and known in most contexts. This contribution proposes a mechanism for constructing exchange matrices, derived from quite general symmetric proximity matrices, in such a way that the margin of the exchange matrix coincides with th...

Two statistics are proposed for summarizing spatial patterns of DNA diversity. These autocorrelation indices for DNA analysis, or AIDAs, can be applied to RFLP and sequence data; the resulting set of autocorrelation coefficients, or correlogram, measures whether, and to what extent, individual DNA sequences or haplotypes resemble the haplotypes sampled at arbitrarily chosen spatial distances. A...

Introduction: The Problem • Many signals are not stationary of the second order (their Autocorrelation Function (ACF) is time-varying) • The ACF is used to develop filters and predictors (e.g. for signal compression) • This suggests that we need filters and predictors that are time-varying • A common approach has been to assume local stationarity: We can estimate filters for the signal by using...

A new algorithm, Neighborhood MinMax Projections (NMMP), is proposed for supervised dimensionality reduction in this paper. The algorithm aims at learning a linear transformation, and focuses only on the pairwise points where the two points are neighbors of each other. After the transformation, the considered pairwise points within the same class are as close as possible, while those between di...

Our algorithm is based on autocorrelation. What distinguishes it from other autocorrelation approaches is that we computes the distribution of lag autocorrelation energy as a function of phase. This results in a lag-by-phase matrix that compactly represents the repetitive structure of a musical example. The model is designed to allow meter analysis of audio files. However it can also be used to...

A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of ...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatia...

Physical activity (PA) promotes healthy life and contributes to sustainable development. In this paper, we rely on the Utah Household Travel Survey data and analyze the determinants of PA in terms of neighborhood land use, accessibility to transportation, and socio-demographic status in Salt Lake County, Utah, United States using four-component walkability indices at various geographic scales. ...

In this paper, a method of analyzing the pattern of error when classification was done from remotely sensed data by using spatial autocorrelation analysis will be introduced. Various sites were picked (water, tree, grass, sand, and urban region) and corresponding reference data were supplied for comparison after classification. Classified images were compared to the reference data to assign whi...