Self organizing maps for outlier detection

In this paper we address the problem of multivariate outlier detection using the (unsupervised) self-organizing map (SOM) algorithm introduced by Kohonen. We examine a number of techniques, based on summary statistics and graphics derived from the trained SOM, and conclude that they work well in cooperation with each other. Useful tools include the median interneuron distance matrix and the projection ofthe trained map (via Sammon's projection). SOM quantization errors provide an important complementary source of information for certain type of outlying behavior. Empirical results are reported on both artificial and real data

The Construction of Edmond Halley\u27s 1701 Map of Magnetic Declination

Using the navigational instruments of his time, Edmond Halley collected data during sea voyages of the HMS Paramore. Following these voyages, in 1701 he published a map showing lines of equal magnetic declination. Magnetic declination or variation is the angular difference between magnetic north and geographical or true north for any point on the earth’s surface. The map has been held up by many as an early, and good, example of statistical graphics. Halley did not reveal the data analytic techniques that he used in his map construction and they remain unknown to this day. Using some mathematical tools of his day, namely arithmetical averages and Newton’s divided difference method to fit a line to data, a plausible method for the map’s construction is given

GIS and Big Data Visualization

Geographic information system (GIS) has expanded its area of applications and services into various fields, from geo-positioning service to three dimensional demonstration and virtual reality. Big data analysis and its visualization tools boosters the capacity of GIS, especially in graphics and visual demonstration. In this chapter, I describe major traits of big data and its spatial analysis with visualization. And then I will find a linkage between big data and GIS. There are several GIS-based software and geo-web that deal with big data or similar scaled databases, such as ArcGIS, Google Earth, Google Map, Tableau, and InstantAtlas. For these software and websites are developed based on geography or location, they still have some limits in visualizing big data or persuading people with maps or graphics. I will search a way out of this limitation of GIS-based tools and show an alternative way to visualize big data and demonstrate thematic maps. This chapter will be a useful guide to lead GIS people into a new horizon of big data visualization

Integration of Virtual Map Rooms as a support tool in historical and social research

At the present time almost all map libraries on the Internet are image collections generated by the digitization of early maps. This type of graphics files provides researchers with the possibility of accessing and visualizing historical cartographic information keeping in mind that this information has a degree of quality that depends upon elements such as the accuracy of the digitization process and proprietary constraints (e.g. visualization, resolution downloading options, copyright, use constraints). In most cases, access to these map libraries is useful only as a first approach and it is not possible to use those maps for scientific work due to the sparse tools available to measure, match, analyze and/or combine those resources with different kinds of cartography. This paper presents a method to enrich virtual map rooms and provide historians and other professional with a tool that let them to make the most of libraries in the digital era