Rumonge region, Burundi, 1994, Defense Mapping Agency (DMA) Series Z724, Sheet 4673-II (Raster Image)

This layer is a georeferenced raster image of the United States Defense Mapping Agency (DMA) Series Z724, Burundi, 1:50,000 Topographic Line Map (TLM) Series sheet map entitled: Rumonge. Printed in: 1994. Covers portions of Rumonge region, Burundi. Sheet: 4673-II. Edition statement: Ed. 1 - DMA. The image inside the map neatline is georeferenced to the surface of the earth and fit to World Geodetic System (1984) coordinates. All map collar information is also available as part of the raster image. Burundi 1:50:000 Series Z724 maps are in English and French (legends also include Rundi). Each source map in the series is printed in color at a scale of 1:50,000. Series source sheets were published in 1994-1995 by the United States Defense Mapping Agency, Hydrographic/Topographic Center. The source map was scanned and georeferenced for Harvard University's Center for Geographic Analysis' AfricaMap project by East View Cartographic. Individual TLM sheets covering Burundi (40 sheets in total) were selected from the TLM worldwide series. DMA Topographic Line Map series maps are typical topographic maps portraying both natural and manmade features. They show and name works of nature, such as mountains, valleys, lakes, rivers, vegetation, etc. They also identify the principal works of humans, such as roads, railroads, boundaries, transmission lines, major buildings, etc. Relief is shown with standard contour intervals of 20 meters, with some sheets having supplemental meter contours, form lines, hachures, shading, and/or spot heights. Depths shown by bathymetric isolines. Please pay close attention to map collar information on projections, spheroid, compilation dates, legend information, and keys to grid numbering and other numbers which appear inside the neatline.

Muramvya region, Burundi, 1994, Defense Mapping Agency (DMA) Series Z724, Sheet 4774-III (Raster Image)

This layer is a georeferenced raster image of the United States Defense Mapping Agency (DMA) Series Z724, Burundi, 1:50,000 Topographic Line Map (TLM) Series sheet map entitled: Muramvya. Printed in: 1994. Covers portions of Muramvya region, Burundi. Sheet: 4774-III. Edition statement: Ed. 1 - DMA. The image inside the map neatline is georeferenced to the surface of the earth and fit to World Geodetic System (1984) coordinates. All map collar information is also available as part of the raster image. Burundi 1:50:000 Series Z724 maps are in English and French (legends also include Rundi). Each source map in the series is printed in color at a scale of 1:50,000. Series source sheets were published in 1994-1995 by the United States Defense Mapping Agency, Hydrographic/Topographic Center. The source map was scanned and georeferenced for Harvard University's Center for Geographic Analysis' AfricaMap project by East View Cartographic. Individual TLM sheets covering Burundi (40 sheets in total) were selected from the TLM worldwide series. DMA Topographic Line Map series maps are typical topographic maps portraying both natural and manmade features. They show and name works of nature, such as mountains, valleys, lakes, rivers, vegetation, etc. They also identify the principal works of humans, such as roads, railroads, boundaries, transmission lines, major buildings, etc. Relief is shown with standard contour intervals of 20 meters, with some sheets having supplemental meter contours, form lines, hachures, shading, and/or spot heights. Depths shown by bathymetric isolines. Please pay close attention to map collar information on projections, spheroid, compilation dates, legend information, and keys to grid numbering and other numbers which appear inside the neatline.

Kirundo region, Burundi, 1994, Defense Mapping Agency (DMA) Series Z724, Sheet 4875-IV (Raster Image)

This layer is a georeferenced raster image of the United States Defense Mapping Agency (DMA) Series Z724, Burundi, 1:50,000 Topographic Line Map (TLM) Series sheet map entitled: Kirundo. Printed in: 1994. Covers portions of Kirundo region, Burundi. Sheet: 4875-IV. Edition statement: Ed. 1 - DMA. The image inside the map neatline is georeferenced to the surface of the earth and fit to World Geodetic System (1984) coordinates. All map collar information is also available as part of the raster image. Burundi 1:50:000 Series Z724 maps are in English and French (legends also include Rundi). Each source map in the series is printed in color at a scale of 1:50,000. Series source sheets were published in 1994-1995 by the United States Defense Mapping Agency, Hydrographic/Topographic Center. The source map was scanned and georeferenced for Harvard University's Center for Geographic Analysis' AfricaMap project by East View Cartographic. Individual TLM sheets covering Burundi (40 sheets in total) were selected from the TLM worldwide series. DMA Topographic Line Map series maps are typical topographic maps portraying both natural and manmade features. They show and name works of nature, such as mountains, valleys, lakes, rivers, vegetation, etc. They also identify the principal works of humans, such as roads, railroads, boundaries, transmission lines, major buildings, etc. Relief is shown with standard contour intervals of 20 meters, with some sheets having supplemental meter contours, form lines, hachures, shading, and/or spot heights. Depths shown by bathymetric isolines. Please pay close attention to map collar information on projections, spheroid, compilation dates, legend information, and keys to grid numbering and other numbers which appear inside the neatline.

Burnt district of Boston, Massachusetts, 1873 (Raster Image)

This layer is a georeferenced raster image of the historic paper map entitled: Plan of burnt district (by fire of Nov. 9th and 10th, 1872) : showing street improvements as adopted by Board of Street Commissioners and City Council, 1873, Thos. W. Davis, city surveyor ; presented by the Boston Safe Deposit and Trust Co. It was published in 1876. Shows street improvements, property lots, lot ownership, and square footage of lots affected by the fire of 1872. Covers area bounded by Washington St. to Oliver St., State St. to Summer St. and Atlantic Ave., Boston, Massachusetts. The image inside the map neatline is georeferenced to the surface of the earth and fit to the Massachusetts State Plane Coordinate System, Mainland Zone (in Feet) (Fipszone 2001). 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, or other information associated with the principal map. This layer is part of a selection of digitally scanned and georeferenced historic maps of Massachusetts from the Harvard Map Collection. These maps typically portray both natural and manmade features. The selection represents a range of regions, originators, ground condition dates (1755-1922), scales, and purposes. The digitized selection includes maps of: the state, Massachusetts counties, town surveys, coastal features, real property, parks, cemeteries, railroads, roads, public works projects, etc.

Mapping Statistics on Loss of Nationality in the EU: A new online database. CEPS Liberty and Security in Europe No. 76/December 2014

Statistics can be useful when assessing the practical relevance of varying rules and practices on the involuntary loss of nationality across EU member states. Yet while much progress has been made within the EU in recent years with regard to the collection of comparable and reliable information on the acquisition of nationality, statistics on the loss of nationality are hard to find and, where available, difficult to interpret. In this comparative report, the authors explore the landscape of existing statistical data on loss of nationality in the European Union. They identify challenges to the existing methods of data collection and data interpretation and introduce an online statistical database, bringing together all existing statistical data on loss of nationality in the EU. These data are summarised in tables and graphs and discussed with reference to the relevant national and European sources. The authors conclude with recommendations to policy-makers on how to improve data collection in this area.

Loran-A table : Asiatic area.

"Rate 1L6."

Application of remote sensing and GIS in mineral resource mapping - An overview

Remote sensing, as a direct adjunct to field, lithologic and structural mapping, and more recently, GIS have played an important role in the study of mineralized areas. A review on the application of remote sensing in mineral resource mapping is attempted here. It involves understanding the application of remote sensing in lithologic, structural and alteration mapping. Remote sensing becomes an important tool for locating mineral deposits, in its own right, when the primary and secondary processes of mineralization result in the formation of spectral anomalies. Reconnaissance lithologic mapping is usually the first step of mineral resource mapping. This is complimented with structural mapping, as mineral deposits usually occur along or adjacent to geologic structures, and alteration mapping, as mineral deposits are commonly associated with hydrothermal alteration of the surrounding rocks. In addition to these, understanding the use of hyperspectral remote sensing is crucial as hyperspectral data can help identify and thematically map regions of exploration interest by using the distinct absorption features of most minerals. Finally coming to the exploration stage, GIS forms the perfect tool in integrating and analyzing various georeferenced geoscience data in selecting the best sites of mineral deposits or rather good candidates for further exploration.

Integrating JERS-1 imaging radar and elevation models for mapping tropical rainforest communities in far North Queensland, Australia

The Wet Tropics World Heritage Area in Far North Queens- land, Australia consists predominantly of tropical rainforest and wet sclerophyll forest in areas of variable relief. Previous maps of vegetation communities in the area were produced by a labor-intensive combination of field survey and air-photo interpretation. Thus,. the aim of this work was to develop a new vegetation mapping method based on imaging radar that incorporates topographical corrections, which could be repeated frequently, and which would reduce the need for detailed field assessments and associated costs. The method employed G topographic correction and mapping procedure that was developed to enable vegetation structural classes to be mapped from satellite imaging radar. Eight JERS-1 scenes covering the Wet Tropics area for 1996 were acquired from NASDA under the auspices of the Global Rainforest Mapping Project. JERS scenes were geometrically corrected for topographic distortion using an 80 m DEM and a combination of polynomial warping and radar viewing geometry modeling. An image mosaic was created to cover the Wet Tropics region, and a new technique for image smoothing was applied to the JERS texture bonds and DEM before a Maximum Likelihood classification was applied to identify major land-cover and vegetation communities. Despite these efforts, dominant vegetation community classes could only be classified to low levels of accuracy (57.5 percent) which were partly explained by the significantly larger pixel size of the DEM in comparison to the JERS image (12.5 m). In addition, the spatial and floristic detail contained in the classes of the original validation maps were much finer than the JERS classification product was able to distinguish. In comparison to field and aerial photo-based approaches for mapping the vegetation of the Wet Tropics, appropriately corrected SAR data provides a more regional scale, all-weather mapping technique for broader vegetation classes. Further work is required to establish an appropriate combination of imaging radar with elevation data and other environmental surrogates to accurately map vegetation communities across the entire Wet Tropics.

Mapping structural parameters and species composition of riparian vegetation using IKONOS and Landsat ETM+ data in Australian tropical savannahs

Government agencies responsible for riparian environments are assessing the utility of remote sensing for mapping and monitoring vegetation structural parameters. The objective of this work was to evaluate Ikonos and Landsat-7 ETM+ imagery for mapping structural parameters and species composition of riparian vegetation in Australian tropical savannahs for a section of Keelbottom Creek, Queensland, Australia. Vegetation indices and image texture from Ikonos data were used for estimating leaf area index (R-2 = 0.13) and canopy percentage foliage cover (R-2 = 0.86). Pan-sharpened Ikonos data were used to map riparian species composition (overall accuracy = 55 percent) and riparian zone width (accuracy within +/- 3 m). Tree crowns could not be automatically delineated due to the lack of contrast between canopies and adjacent grass cover. The ETM+ imagery was suited for mapping the extent of riparian zones. Results presented demonstrate the capabilities of high and moderate spatial resolution imagery for mapping properties of riparian zones.

Robust automatic mapping algorithms in a network monitoring scenario

Automatically generating maps of a measured variable of interest can be problematic. In this work we focus on the monitoring network context where observations are collected and reported by a network of sensors, and are then transformed into interpolated maps for use in decision making. Using traditional geostatistical methods, estimating the covariance structure of data collected in an emergency situation can be difficult. Variogram determination, whether by method-of-moment estimators or by maximum likelihood, is very sensitive to extreme values. Even when a monitoring network is in a routine mode of operation, sensors can sporadically malfunction and report extreme values. If this extreme data destabilises the model, causing the covariance structure of the observed data to be incorrectly estimated, the generated maps will be of little value, and the uncertainty estimates in particular will be misleading. Marchant and Lark [2007] propose a REML estimator for the covariance, which is shown to work on small data sets with a manual selection of the damping parameter in the robust likelihood. We show how this can be extended to allow treatment of large data sets together with an automated approach to all parameter estimation. The projected process kriging framework of Ingram et al. [2007] is extended to allow the use of robust likelihood functions, including the two component Gaussian and the Huber function. We show how our algorithm is further refined to reduce the computational complexity while at the same time minimising any loss of information. To show the benefits of this method, we use data collected from radiation monitoring networks across Europe. We compare our results to those obtained from traditional kriging methodologies and include comparisons with Box-Cox transformations of the data. We discuss the issue of whether to treat or ignore extreme values, making the distinction between the robust methods which ignore outliers and transformation methods which treat them as part of the (transformed) process. Using a case study, based on an extreme radiological events over a large area, we show how radiation data collected from monitoring networks can be analysed automatically and then used to generate reliable maps to inform decision making. We show the limitations of the methods and discuss potential extensions to remedy these.

Topographic mapping and source localization of the pattern onset visual evoked magnetic response

The topography of the visual evoked magnetic response (VEMR) to a pattern onset stimulus was investigated using 4 check sizes and 3 contrast levels. The pattern onset response consists of three early components within the first 200ms, CIm, CIIm and CIIIm. The CIIm is usually of high amplitude and is very consistent in latency within a subject. Half field (HF) stimuli produce their strongest response over the contralateral hemisphere; the RHF stimulus exhibiting a lower positivity (outgoing field) and an upper negativity (ingoing field), rotated towards the midline. LHF stimulation produced the opposite response, a lower negative and an upper positive. Larger check sizes produce a single area of ingoing and outgoing field while smaller checks produce on area of ingoing and outgoing field over each hemisphere. Latency did not appear to vary with change in contrast but amplitudes increased with increasing contrast. A more detailed topographic study incorporating source localisation procedures suggested a source for CIIm - 4cm below the scalp, close to the midline with current flowing towards the lateral surface. Similar depth and position estimates but with opposite polarity were obtained for the pattern shift P100m previously. Hence, the P100m and the CIIm may originate in similar areas of visual cortex but reveal different aspects of visual processing. © 1992 Human Sciences Press, Inc.

Automated mapping of environmental variables from a SEIS or SISE perspective

The INTAMAP FP6 project has developed an interoperable framework for real-time automatic mapping of critical environmental variables by extending spatial statistical methods and employing open, web-based, data exchange protocols and visualisation tools. This paper will give an overview of the underlying problem, of the project, and discuss which problems it has solved and which open problems seem to be most relevant to deal with next. The interpolation problem that INTAMAP solves is the generic problem of spatial interpolation of environmental variables without user interaction, based on measurements of e.g. PM10, rainfall or gamma dose rate, at arbitrary locations or over a regular grid covering the area of interest. It deals with problems of varying spatial resolution of measurements, the interpolation of averages over larger areas, and with providing information on the interpolation error to the end-user. In addition, monitoring network optimisation is addressed in a non-automatic context.

Lithological mapping of Northwest Argentina with remote sensing data using tonal, textural and contextual features

Tonal, textural and contextual properties are used in manual photointerpretation of remotely sensed data. This study has used these three attributes to produce a lithological map of semi arid northwest Argentina by semi automatic computer classification procedures of remotely sensed data. Three different types of satellite data were investigated, these were LANDSAT MSS, TM and SIR-A imagery. Supervised classification procedures using tonal features only produced poor classification results. LANDSAT MSS produced classification accuracies in the range of 40 to 60%, while accuracies of 50 to 70% were achieved using LANDSAT TM data. The addition of SIR-A data produced increases in the classification accuracy. The increased classification accuracy of TM over the MSS is because of the better discrimination of geological materials afforded by the middle infra red bands of the TM sensor. The maximum likelihood classifier consistently produced classification accuracies 10 to 15% higher than either the minimum distance to means or decision tree classifier, this improved accuracy was obtained at the cost of greatly increased processing time. A new type of classifier the spectral shape classifier, which is computationally as fast as a minimum distance to means classifier is described. However, the results for this classifier were disappointing, being lower in most cases than the minimum distance or decision tree procedures. The classification results using only tonal features were felt to be unacceptably poor, therefore textural attributes were investigated. Texture is an important attribute used by photogeologists to discriminate lithology. In the case of TM data, texture measures were found to increase the classification accuracy by up to 15%. However, in the case of the LANDSAT MSS data the use of texture measures did not provide any significant increase in the accuracy of classification. For TM data, it was found that second order texture, especially the SGLDM based measures, produced highest classification accuracy. Contextual post processing was found to increase classification accuracy and improve the visual appearance of classified output by removing isolated misclassified pixels which tend to clutter classified images. Simple contextual features, such as mode filters were found to out perform more complex features such as gravitational filter or minimal area replacement methods. Generally the larger the size of the filter, the greater the increase in the accuracy. Production rules were used to build a knowledge based system which used tonal and textural features to identify sedimentary lithologies in each of the two test sites. The knowledge based system was able to identify six out of ten lithologies correctly.

The use of spot simulation imagery for forestry mapping and management

SPOT simulation imagery was acquired for a test site in the Forest of Dean in Gloucestershire, U.K. This data was qualitatively and quantitatively evaluated for its potential application in forest resource mapping and management. A variety of techniques are described for enhancing the image with the aim of providing species level discrimination within the forest. Visual interpretation of the imagery was more successful than automated classification. The heterogeneity within the forest classes, and in particular between the forest and urban class, resulted in poor discrimination using traditional `per-pixel' automated methods of classification. Different means of assessing classification accuracy are proposed. Two techniques for measuring textural variation were investigated in an attempt to improve classification accuracy. The first of these, a sequential segmentation method, was found to be beneficial. The second, a parallel segmentation method, resulted in little improvement though this may be related to a combination of resolution in size of the texture extraction area. The effect on classification accuracy of combining the SPOT simulation imagery with other data types is investigated. A grid cell encoding technique was selected as most appropriate for storing digitised topographic (elevation, slope) and ground truth data. Topographic data were shown to improve species-level classification, though with sixteen classes overall accuracies were consistently below 50%. Neither sub-division into age groups or the incorporation of principal components and a band ratio significantly improved classification accuracy. It is concluded that SPOT imagery will not permit species level classification within forested areas as diverse as the Forest of Dean. The imagery will be most useful as part of a multi-stage sampling scheme. The use of texture analysis is highly recommended for extracting maximum information content from the data. Incorporation of the imagery into a GIS will both aid discrimination and provide a useful management tool.

Mapping the yeast host cell response to recombinant membrane protein production:relieving the biological bottlenecks

Understanding the structures and functions of membrane proteins is an active area of research within bioscience. Membrane proteins are key players in essential cellular processes such as the uptake of nutrients, the export of waste products, and the way in which cells communicate with their environment. It is therefore not surprising that membrane proteins are targeted by over half of all prescription drugs. Since most membrane proteins are not abundant in their native membranes, it is necessary to produce them in recombinant host cells to enable further structural and functional studies. Unfortunately, achieving the required yields of functional recombinant membrane proteins is still a bottleneck in contemporary bioscience. This has highlighted the need for defined and rational optimization strategies based upon experimental observation rather than relying on trial and error. We have published a transcriptome and subsequent genetic analysis that has identified genes implicated in high-yielding yeast cells. These results have highlighted a role for alterations to a cell's protein synthetic capacity in the production of high yields of recombinant membrane protein: paradoxically, reduced protein synthesis favors higher yields. These results highlight a potential bottleneck at the protein folding or translocation stage of protein production.