A demonstration of ERTS-1 analog and digital techniques applied to strip mining in Maryland and West Virginia

The largest contour strip mining operations in western Maryland and West Virginia are located within the Georges Creek and the Upper Potomac Basins. These two coal basins lie within the Georges Creek (Wellersburg) syncline. The disturbed strip mine areas were delineated with the surrounding geological and vegetation features using ERTS-1 data in both analog (imagery) and digital form. The two digital systems used were: (1) the ERTS-Analysis system, a point-by-point digital analysis of spectral signatures based on known spectral values, and (2) the LARS Automatic Data Processing System. The digital techniques being developed will later be incorporated into a data base for land use planning. These two systems aided in efforts to determine the extent and state of strip mining in this region. Aircraft data, ground verification information, and geological field studies also aided in the application of ERTS-1 imagery to perform an integrated analysis that assessed the adverse effects of strip mining. The results indicated that ERTS can both monitor and map the extent of strip mining to determine immediately the acreage affected and indicate where future reclamation and revegetation may be necessary

Mining a medieval social network by kernel SOM and related methods

This paper briefly presents several ways to understand the organization of a large social network (several hundreds of persons). We compare approaches coming from data mining for clustering the vertices of a graph (spectral clustering, self-organizing algorithms. . .) and provide methods for representing the graph from these analysis. All these methods are illustrated on a medieval social network and the way they can help to understand its organization is underlined

Similarity-Aware Spectral Sparsification by Edge Filtering

In recent years, spectral graph sparsification techniques that can compute ultra-sparse graph proxies have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this paper proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to VLSI CAD, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications

Specmine: an R package for metabolomics and spectral data analysis and mining

Book of Abstracts of CEB Annual Meeting 2017info:eu-repo/semantics/publishedVersio

Finding Young Stellar Populations in Elliptical Galaxies from Independent Components of Optical Spectra

Elliptical galaxies are believed to consist of a single population of old stars formed together at an early epoch in the Universe, yet recent analyses of galaxy spectra seem to indicate the presence of significant younger populations of stars in them. The detailed physical modelling of such populations is computationally expensive, inhibiting the detailed analysis of the several million galaxy spectra becoming available over the next few years. Here we present a data mining application aimed at decomposing the spectra of elliptical galaxies into several coeval stellar populations, without the use of detailed physical models. This is achieved by performing a linear independent basis transformation that essentially decouples the initial problem of joint processing of a set of correlated spectral measurements into that of the independent processing of a small set of prototypical spectra. Two methods are investigated: (1) A fast projection approach is derived by exploiting the correlation structure of neighboring wavelength bins within the spectral data. (2) A factorisation method that takes advantage of the positivity of the spectra is also investigated. The preliminary results show that typical features observed in stellar population spectra of different evolutionary histories can be convincingly disentangled by these methods, despite the absence of input physics. The success of this basis transformation analysis in recovering physically interpretable representations indicates that this technique is a potentially powerful tool for astronomical data mining.Comment: 12 Pages, 7 figures; accepted in SIAM 2005 International Conference on Data Mining, Newport Beach, CA, April 200

Geosocial Graph-Based Community Detection

We apply spectral clustering and multislice modularity optimization to a Los Angeles Police Department field interview card data set. To detect communities (i.e., cohesive groups of vertices), we use both geographic and social information about stops involving street gang members in the LAPD district of Hollenbeck. We then compare the algorithmically detected communities with known gang identifications and argue that discrepancies are due to sparsity of social connections in the data as well as complex underlying sociological factors that blur distinctions between communities.Comment: 5 pages, 4 figures Workshop paper for the IEEE International Conference on Data Mining 2012: Workshop on Social Media Analysis and Minin

Utilizing Skylab data in on-going resources management programs in the state of Ohio

The author has identified the following significant results. The use of Skylab imagery for total area woodland surveys was found to be more accurate and cheaper than conventional surveys using aerial photo-plot techniques. Machine-aided (primarily density slicing) analyses of Skylab 190A and 190B color and infrared color photography demonstrated the feasibility of using such data for differentiating major timber classes including pines, hardwoods, mixed, cut, and brushland providing such analyses are made at scales of 1:24,000 and larger. Manual and machine-assisted image analysis indicated that spectral and spatial capabilities of Skylab EREP photography are adequate to distinguish most parameters of current, coal surface mining concern associated with: (1) active mining, (2) orphan lands, (3) reclaimed lands, and (4) active reclamation. Excellent results were achieved when comparing Skylab and aerial photographic interpretations of detailed surface mining features. Skylab photographs when combined with other data bases (e.g., census, agricultural land productivity, and transportation networks), provide a comprehensive, meaningful, and integrated view of major elements involved in the urbanization/encroachment process

PEPSI deep spectra. II. Gaia benchmark stars and other M-K standards

We provide a homogeneous library of high-resolution, high-S/N spectra for 48 bright AFGKM stars, some of them approaching the quality of solar-flux spectra. Our sample includes the northern Gaia benchmark stars, some solar analogs, and some other bright Morgan-Keenan (M-K) spectral standards. Well-exposed deep spectra were created by average-combining individual exposures. The data-reduction process relies on adaptive selection of parameters by using statistical inference and robust estimators.We employed spectrum synthesis techniques and statistics tools in order to characterize the spectra and give a first quick look at some of the science cases possible. With an average spectral resolution of R=220,000 (1.36 km/s), a continuous wavelength coverage from 383 nm to 912 nm, and S/N of between 70:1 for the faintest star in the extreme blue and 6,000:1 for the brightest star in the red, these spectra are now made public for further data mining and analysis. Preliminary results include new stellar parameters for 70 Vir and alpha Tau, the detection of the rare-earth element dysprosium and the heavy elements uranium, thorium and neodymium in several RGB stars, and the use of the 12C to 13C isotope ratio for age-related determinations. We also found Arcturus to exhibit few-percent CaII H&K and H-alpha residual profile changes with respect to the KPNO atlas taken in 1999.Comment: in press, 15 pages, 7 figures, data available from pepsi.aip.d