Geology of the Hanover-York district, Pennsylvania /

Folded map in pocket.

Prospectus of the Buffalo and Washington railway company : with maps of its route and connections with other railroads in Pennsylvania, New Jersey and Maryland. May 1, 1865.

Mode of access: Internet.

Report of a survey and exploration of the coal and ore lands belonging to the Allegheny Coal Company : in Somerset County, Pennsylvania ; accomppanied by maps, profiles and sections /

Mode of access: Internet.

An address, intended to promote a geological and mineralogical survey of Pennsylvania, the publication of a series of geological maps, and the formation of state and county geological and mineralogical collections /

"To which are prefixed the resolutions of the meeting before which it was read."

The geology of Pennsylvania; a government survey: with a general view of the geology of the United States, essays on coal-formation and its fossils, and a description of the coalfields of North America and Great Britain,

Vol. II, pt. I-II, paged continuously.

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developedto explore patterns in high-dimensional multivariate data. The conventional versionof the algorithm involves the use of Euclidean metric in the process of adaptation ofthe model vectors, thus rendering in theory a whole methodology incompatible withnon-Euclidean geometries.In this contribution we explore the two main aspects of the problem:1. Whether the conventional approach using Euclidean metric can shed valid resultswith compositional data.2. If a modification of the conventional approach replacing vectorial sum and scalarmultiplication by the canonical operators in the simplex (i.e. perturbation andpowering) can converge to an adequate solution.Preliminary tests showed that both methodologies can be used on compositional data.However, the modified version of the algorithm performs poorer than the conventionalversion, in particular, when the data is pathological. Moreover, the conventional ap-proach converges faster to a solution, when data is \well-behaved".Key words: Self Organizing Map; Artificial Neural networks; Compositional data

Weighted Logratio Biplots, Correspondence Analysis and Spectral Maps

Starting with logratio biplots for compositional data, which are based on the principle of subcompositional coherence, and then adding weights, as in correspondence analysis, we rediscover Lewi's spectral map and many connections to analyses of two-way tables of non-negative data. Thanks to the weighting, the method also achieves the property of distributional equivalence

Geology and ground-water resources of Cerro Gordo County, Iowa, 1970

The purposes of this report are to delineate and describe aquifers in Cerro Gordo County, evaluate the availability and quality of water in the aquifers, supply data on ground-water utilization, and determine the rate of growth and the magnitude of the cone of drawdown in the Mason City area. It includes photos and fold-out maps

The geologic and geomorphologic maps of Santa Coloma de Farners (Girona, Spain)

Notice about the geologic and geomorphologic maps of Santa Coloma de Farners, at scale 1:10,000 published by Unitat de Geologia of the Universitat de Girona

Geologic maps published by university de Girona's unity of geodynamics

In 1978 University de Girona's Unity of Geodynamics started publishing geological maps, in several themes and scales, of various areas of the Province of Girona (fig. 1). So far, up to 38 maps, gathered in several collections, have been published

Weighted Logratio Biplots, Correspondence Analysis and Spectral Maps

Starting with logratio biplots for compositional data, which are based on the principle of subcompositional coherence, and then adding weights, as in correspondence analysis, we rediscover Lewi's spectral map and many connections to analyses of two-way tables of non-negative data. Thanks to the weighting, the method also achieves the property of distributional equivalence

Using self organizing maps on compositional data

Self-organizing maps (Kohonen 1997) is a type of artificial neural network developed to explore patterns in high-dimensional multivariate data. The conventional version of the algorithm involves the use of Euclidean metric in the process of adaptation of the model vectors, thus rendering in theory a whole methodology incompatible with non-Euclidean geometries. In this contribution we explore the two main aspects of the problem: 1. Whether the conventional approach using Euclidean metric can shed valid results with compositional data. 2. If a modification of the conventional approach replacing vectorial sum and scalar multiplication by the canonical operators in the simplex (i.e. perturbation and powering) can converge to an adequate solution. Preliminary tests showed that both methodologies can be used on compositional data. However, the modified version of the algorithm performs poorer than the conventional version, in particular, when the data is pathological. Moreover, the conventional ap- proach converges faster to a solution, when data is \well-behaved". Key words: Self Organizing Map; Artificial Neural networks; Compositional data