Topographic map, Manhattan, New York, N.Y., 1845 (Raster Image) (Image 1 of 3)

This layer is a georeferenced raster image of the historic paper map entitled: Topographical map of the city and county of New-York and the adjacent country : with views in the border of the principal buildings and interesting scenery of the island, engraved & printed by S. Stiles & Co. It was published by Sherman & Smith in 1845. Scale [ca. 1:16,000]. Covers Manhattan and adjacent portions of Brooklyn and New Jersey. This layer is image 1 of 3 total images of the three sheet source map, representing the southern portion of the map. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Universal Transverse Mercator (UTM) Zone 18N NAD83 projection. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as topography, ground cover, roads, drainage, selected public buildings, forts, city wards, squares, parks, and more. Relief is shown by hachures. Includes inset views: Broadway from the park -- Nieuw Amsterdam, 1659. This layer is part of a selection of digitally scanned and georeferenced historic maps from The Harvard Map Collection as part of the Imaging the Urban Environment project. Maps selected for this project represent major urban areas and cities of the world, at various time periods. These maps typically portray both natural and manmade features at a large scale. The selection represents a range of regions, originators, ground condition dates, scales, and purposes.

Topographic map, Manhattan, New York, N.Y., 1845 (Raster Image) (Image 3 of 3)

This layer is a georeferenced raster image of the historic paper map entitled: Topographical map of the city and county of New-York and the adjacent country : with views in the border of the principal buildings and interesting scenery of the island, engraved & printed by S. Stiles & Co. It was published by Sherman & Smith in 1845. Scale [ca. 1:16,000]. Covers Manhattan and adjacent portions of Brooklyn and New Jersey. This layer is image 3 of 3 total images of the three sheet source map, representing the northern portion of the map. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Universal Transverse Mercator (UTM) Zone 18N NAD83 projection. All map collar and inset information is also available as part of the raster image, including any inset maps, profiles, statistical tables, directories, text, illustrations, index maps, legends, or other information associated with the principal map. This map shows features such as topography, ground cover, roads, drainage, selected public buildings, forts, city wards, squares, parks, and more. Relief is shown by hachures. Includes inset views: Broadway from the park -- Nieuw Amsterdam, 1659. This layer is part of a selection of digitally scanned and georeferenced historic maps from The Harvard Map Collection as part of the Imaging the Urban Environment project. Maps selected for this project represent major urban areas and cities of the world, at various time periods. These maps typically portray both natural and manmade features at a large scale. The selection represents a range of regions, originators, ground condition dates, scales, and purposes.

Gazetteer to maps of Korea. AMS 2, September 1950.

"Supercedes the September 1944 edition."

Gazetteer to maps of Korea, map series AMS L551, 1:250,000 (First edition) and two sheets from AMS L401, 1:500,000.

Mode of access: Internet.

Gazetteer to maps of Northern Honshū, map series AMS L773, 1:50,000

"Glossary of Japanese generic terms and designations": p. v. vi. "Notes on Japanese place names": p. vii x.

Multispectral image maps from Landsat thematic mapper data /

"September 1991."

Predictive gene lists for breast cancer prognosis: a topographic visualisation study

Background: The controversy surrounding the non-uniqueness of predictive gene lists (PGL) of small selected subsets of genes from very large potential candidates as available in DNA microarray experiments is now widely acknowledged 1. Many of these studies have focused on constructing discriminative semi-parametric models and as such are also subject to the issue of random correlations of sparse model selection in high dimensional spaces. In this work we outline a different approach based around an unsupervised patient-specific nonlinear topographic projection in predictive gene lists. Methods: We construct nonlinear topographic projection maps based on inter-patient gene-list relative dissimilarities. The Neuroscale, the Stochastic Neighbor Embedding(SNE) and the Locally Linear Embedding(LLE) techniques have been used to construct two-dimensional projective visualisation plots of 70 dimensional PGLs per patient, classifiers are also constructed to identify the prognosis indicator of each patient using the resulting projections from those visualisation techniques and investigate whether a-posteriori two prognosis groups are separable on the evidence of the gene lists. A literature-proposed predictive gene list for breast cancer is benchmarked against a separate gene list using the above methods. Generalisation ability is investigated by using the mapping capability of Neuroscale to visualise the follow-up study, but based on the projections derived from the original dataset. Results: The results indicate that small subsets of patient-specific PGLs have insufficient prognostic dissimilarity to permit a distinction between two prognosis patients. Uncertainty and diversity across multiple gene expressions prevents unambiguous or even confident patient grouping. Comparative projections across different PGLs provide similar results. Conclusion: The random correlation effect to an arbitrary outcome induced by small subset selection from very high dimensional interrelated gene expression profiles leads to an outcome with associated uncertainty. This continuum and uncertainty precludes any attempts at constructing discriminative classifiers. However a patient's gene expression profile could possibly be used in treatment planning, based on knowledge of other patients' responses. We conclude that many of the patients involved in such medical studies are intrinsically unclassifiable on the basis of provided PGL evidence. This additional category of 'unclassifiable' should be accommodated within medical decision support systems if serious errors and unnecessary adjuvant therapy are to be avoided.

Iowa : index to topographic and other map coverage / published and distributed by United States Geological Survey, National Mapping Program, 1989

This index is designed to inform map users of the various series of maps produced and distributed by the U.S. Geological Survey, and to assist users in selecting and purchasing maps.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.