Component Selection for the Metro Visualisation of the Self-Organising Map

Self-Organising Maps have been used for a wide range of clustering applications. They are well-suited for various visualisation techniques to offer better insight into the clustered data sets. A particularly feasible visualisation is the plotting of single components of a data set and their distribution across the SOM. One central problem of the visualisation of Component Planes is that a single plot is needed for each component; this understandably leads to problems with higher-dimensional data. We therefore build on the Metro Visualisation for Self-Organising Maps which integrates the idea of Component Planes into one illustration. Higher-dimensional data sets still pose problems in terms of overloaded visualisations - component selection and aggregation techniques are highly desirable. We therefore propose and compare two methods, one for the aggregation of correlated components, one for the selection of the components most feasible for visualisation for a given clustering

Component Selection for the Metro Visualisation of the Self-Organising Map

Self-Organising Maps have been used for a wide range of clustering applications. They are well-suited for various visualisation techniques to offer better insight into the clustered data sets. A particularly feasible visualisation is the plotting of single components of a data set and their distribution across the SOM. One central problem of the visualisation of Component Planes is that a single plot is needed for each component; this understandably leads to problems with higher-dimensional data. We therefore build on the Metro Visualisation for Self-Organising Maps which integrates the idea of Component Planes into one illustration. Higher-dimensional data sets still pose problems in terms of overloaded visualisations - component selection and aggregation techniques are highly desirable. We therefore propose and compare two methods, one for the aggregation of correlated components, one for the selection of the components most feasible for visualisation for a given clustering

Alternative Analysemöglichkeiten geographischer Daten in der Kartographie mittels Self-Organizing Maps

Die Kartographie ist eine Wissenschaft, die in ihrem Charakter starke interdisziplinäre Züge aufweist. Sie zeigt sich in den verschiedensten Facetten und wird darum in den unterschiedlichsten Wissenschaften angewandt. Markantester Charakter ist, schon per Definition, die Modellierung von geowissenschaftlichen Ereignissen und Sachverhalten. „A unique facility for the creation and manipulation of visual or virtual representations of geospace – maps – to permit the exploration, analysis, understanding and communication of information about that space.“(ICA 2003) Aus dieser Definition wird die Charakteristik einer Kommunikationswissenschaft (Brassel) deutlich. Gerade seit dem Paradigmenwechsel der 1970er Jahre fließen zahlreiche weitere Aspekte wie Informatik, Semiotik und Psychologie in das Verständnis von Kartographie ein. Dadurch wird die Karte nicht mehr als reines graphisches Mittel verstanden, sondern als Träger und Übermittler von Informationen verstanden. Der Kartennutzer und dessen Verständnis von Karten rücken dabei immer weiter in den Vordergrund und werden „Ziel“ der kartographischen Verarbeitung. Aus diesem Verständnis heraus, möchte ich in der folgenden Arbeit einen relativ neuen Einfluss und Aspekt der Kartographie vorstellen. Es handelt sich um das Modell der Self-Organizing Maps (SOM), welches erstmalig Anfang der 1980er Jahre von Teuvo Kohonen vorgestellt wurde und deshalb auch, von einigen Autoren, als Kohonenmaps bezeichnet wird. Dem Typus nach, handelt es sich dabei um künstliche neuronale Netze, welche dem Nervensystem des menschlichen Gehirns nachempfunden sind und damit allgemein als eine Art selbständiger, maschineller Lernvorgang angesehen werden können. Im Speziellen sind Self-Organizing Maps ein unüberwachtes Lernverfahren, das in der Lage ist völlig unbekannte Eingabewerte zu erkennen und zu verarbeiten. Durch diese Eigenschaft eignen sie sich als optimales Werkzeug für Data Mining sowie zur Visualisierung von hochdimensionalen Daten. Eine Vielzahl von Wissenschaftlern hat diesen Vorteil bereits erkannt und das Modell in ihre Arbeit einbezogen oder auf dessen Verwendbarkeit analysiert. Deshalb möchte in dieser Arbeit, einige dieser Verwendungsmöglichkeiten und den daraus resultierenden Vorteil für die Kartographie aufzeigen.:1.) Einleitung ...........................................................................................2 2.) Aufbau und Funktionsweise von SOM ............................................ 5 2.1.) Was sind Self-Organizing Maps? ................................................5 2.2.) Funktionsweise ............................................................................7 2.3.) Visualisierung des trainierten Kohonen-Netz .......................... 11 2.4.) Software ..................................................................................... 12 3. Möglichkeiten für die Kartographie................................................ 14 3.1 Geowissenschaftliches Data Mining ........................................... 15 3.2 Visualisierung von Daten............................................................. 17 4. explorative Datenanalyse geographischer Daten .......................... 19 4.1 SOM als Geovisualisierung .......................................................... 19 4.1.1 U-Matrix-Darstellung .............................................................22 4.1.2 Projektionen (Netzdarstellungen) ........................................26 4.1.3 2D & 3D-Plots .........................................................................28 4.1.4 Komponentenebenen ...........................................................29 4.2 Geo-SOM & andere Möglichkeiten zur Verarbeitung von geowissenschaftlichen Daten ................................................... 32 4.2.1 Hierarchische SOMs ...............................................................33 4.2.2 Geo-enforced SOM ................................................................34 4.2.3 Geo-SOM ................................................................................35 4.3 SOM & GIS .................................................................................... 38 5. Datenverarbeitende Anwendungen ............................................... 40 5.1 Klassifizierung von Fernerkundungsdaten................................. 40 5.2 Kantendetektion in Satellitenbildern......................................... 43 5.3 Auswertung von Zeitreihen & Monitoring................................. 47 5.4 Klassifikation von SAR-Daten...................................................... 49 5.5 Generalisierung............................................................................ 50 5.6 Problem des Handlungsreisenden (Travelling Salesman Problem)..................................................................................... 52 6. SOM als Kartenmetapher zur Visualisierung nicht-geographischer Daten .............................................................................................. 54 7. Zusammenfassung............................................................................ 62 X. Quellenverzeichnis ........................................................................... 63 X.I Literaturnachweise ....................................................................... 63 X.II Lehrinhalte aus dem Internet ..................................................... 69 X.III Softwarelösungen ...................................................................... 6

Visual-Interactive Analysis With Self-Organizing Maps - Advances and Research Challenges

Based on the Self-Organizing Map (SOM) algorithm, development of effective solutions for visual analysis and retrieval in complex data is possible. Example application domains include retrieval in multimedia data bases, and analysis in financial, text, and general high-dimensional data sets. While early work defined basic concepts for data representation and visual mappings for SOM-based analysis, recent work contributed advanced visual representations of the output of the SOM algorithm, and explored innovative application concepts

Exploratory data analysis using self-organising maps defined in up to three dimensions

The SOM is an artificial neural network based on an unsupervised learning process that performs a nonlinear mapping of high dimensional input data onto an ordered and structured array of nodes, designated as the SOM output space. Being simultaneously a quantization algorithm and a projection algorithm, the SOM is able to summarize and map the data, allowing its visualization. Because using the most common visualization methods it is very difficult or even impossible to visualize the SOM defined with more than two dimensions, the SOM output space is generally a regular two dimensional grid of nodes. However, there are no theoretical problems in generating SOMs with higher dimensional output spaces. In this thesis we present evidence that the SOM output space defined in up to three dimensions can be used successfully for the exploratory analysis of spatial data, two-way data and three-way data. Although the differences between the methods that are proposed to visualize each group of data, the approach adopted is commonly based in the projection of colour codes, which are obtained from the output space of 3D SOMs, in some specific bi-dimensional surface, where data can be represented according to its own characteristics. This approach is, in some cases, also complemented with the simultaneous use of SOMs defined in one and two dimensions, so that patterns in data can be properly revealed. The results obtained by using this visualization strategy indicates not only the benefits of using the SOM defined in up to three dimensions but also shows the relevance of the combined and simultaneous use of different models of the SOM in exploratory data analysis

Location analysis of city sections: socio-demographic segmentation and restaurant potentiality estimation : a case study of Lisbon

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesOne of the objectives of this study is to perform classification of socio-demographic components for the level of city section in City of Lisbon. In order to accomplish suitable platform for the restaurant potentiality map, the socio-demographic components were selected to produce a map of spatial clusters in accordance to restaurant suitability. Consequently, the second objective is to obtain potentiality map in terms of underestimation and overestimation in number of restaurants. To the best of our knowledge there has not been found identical methodology for the estimation of restaurant potentiality. The results were achieved with combination of SOM (Self-Organized Map) which provides a segmentation map and GAM (Generalized Additive Model) with spatial component for restaurant potentiality. Final results indicate that the highest influence in restaurant potentiality is given to tourist sites, spatial autocorrelation in terms of neighboring restaurants (spatial component), and tax value, where lower importance is given to household with 1 or 2 members and employed population, respectively. In addition, an important conclusion is that the most attractive market sites have shown no change or moderate underestimation in terms of restaurants potentiality

Regular Grids: An Irregular Approach to the 3D Modelling Pipeline

The 3D modelling pipeline covers the process by which a physical object is scanned to create a set of points that lay on its surface. These data are then cleaned to remove outliers or noise, and the points are reconstructed into a digital representation of the original object. The aim of this thesis is to present novel grid-based methods and provide several case studies of areas in the 3D modelling pipeline in which they may be effectively put to use. The first is a demonstration of how using a grid can allow a significant reduction in memory required to perform the reconstruction. The second is the detection of surface features (ridges, peaks, troughs, etc.) during the surface reconstruction process. The third contribution is the alignment of two meshes with zero prior knowledge. This is particularly suited to aligning two related, but not identical, models. The final contribution is the comparison of two similar meshes with support for both qualitative and quantitative outputs

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Exploring city spaces : an exploration into mapping practices and rule based design

This paper has been written in 3 sections. With some adjustments, the first section is largely the theory paper, the second my technology paper, and the third an exploration of my design. These three sections have been written as disparate parts. Ideas and themes are carried through the three papers, but they do not read as a coherent whole. This year has been a journey into the city of Cape Town; an exploration of its complexity, vibrant city spaces and everyday life. This is essentially what this paper is about, and is a theme that is explored in all three sections in different ways- with the third drawing and building on the first two. I started with ideas of Lefebvre and the work of CHORA as a methodology for exploring the 'everyday practices' in the city- and moved through this to an engagement with rule based design and algorithmic architecture. The design chapter loops back to the beginning of the paper, and draws from and is informed by both the initial research, a'1d rule- based methodology. All three of these sections have been exploratory processes engaging with this set of ideas around complexity within the city. I do not see them as providing an answer as to how to design or explore cities, but rather as an attempt to engage with these very real questions. They are a series of ideas that have enabled me to see parts of the 'hidden world' within Cape Town, and explore this through ideas of the unknown and unimaginable in architecture

Street-level desires, Discovering the city on foot:

In the spring of 2004, Delft University of Technology was approached by Norwich City Council with a request to participate in their project Spatial Metro. Spatial Metro was developed within the framework of Interreg IIIB. Interreg is a community initiative which aims to stimulate interregional cooperation within the EU, financed by the European Regional Development Fund (ERDF). The programme aspires to strengthen economic and social cohesion throughout the EU by fostering the balanced development of the continent through cross-border, transnational and interregional cooperation. The B strain of Interreg deals with transnational cooperation. As an Interreg IIIB project in the North-west European region, Spatial Metro brings together partners from the United Kingdom, France, Germany, The Netherlands and even Switzerland. The original Spatial Metro project proposal is straightforward. It claims that cities are chaotic places. It states that tourists, visiting business people, shoppers and even residents rarely have a clear or coherently expressed view of what a city has to offer geographically or thematically. The proposal assumes that people’s stay is shortened by their lack of overview of or information on what a town can offer them. As lead partner of the project, Norwich explains in quantitative terms what this means to the economy of a city: Visitors who plan a day trip to a city will stay in town for an average four to four-and-half hours and spend about £ 100. If the welcome they receive is inhospitable, the destination is confusing, and demands are not met, the same visitor will tend to leave after only two hours and spend less than £ 50. If their arrival is welcoming, the destination is safe, clean, relaxed and intelligible and if visitors can navigate their way around and their initial expectations are fulfilled or surpassed, they will stay for six to seven hours and spend more than £ 150. At first glance, these statements may seem somewhat narrow in scope. Not every city is chaotic and surely there is more to life than just money. However, placed in their proper context, these words make perfect sense. Five cities are participating in Spatial Metro: Norwich and Bristol (UK), Rouen (F), Koblenz (D) and Biel/Bienne (CH). Each of these cities is characterised by a historic city centre. Norwich itself is proud to have the most intact mediaeval street pattern of the United Kingdom. Mediaeval street patterns are the product of spontaneous urban growth and lack the sometimes rigid clarity of modern planned developments. Mediaeval street patterns are indeed difficult to navigate and pose a true challenge. Norwich also developed a successful and long-standing policy to prevent out of town shopping by strengthening the vitality of its original historic district. Such a policy requires a city to take a serious look at its economic performance. From this perspective, it is a sound approach to optimise conditions allowing people to discover a city on foot. As such, the Spatial Metro project prompted the Delft University of Technology to tap into a greater European experience that integrates aspects such as urban renaissance, built heritage, public space, pedestrian mobility, leisure economy and even sustainability. The partnership also included knowledge organisations. Each of these partners has supported the project in their own unique way. The University of East Anglia deployed its automated modelling software to visualise the original historic centres. The University Koblenz/Landau delivered a so-called Blue Box that provides on the spot information by means of Bluetooth technology. The Swiss Pedestrian Association made various contributions as a strategic and competent expert organisation on pedestrian mobility. The Delft University of Technology examined the question as to how to assess the effectiveness of the investments made in Norwich, Rouen and Koblenz. How can aspects like the accessibility and navigability of public spaces be measured? Much of the effectiveness hereof naturally depends on the way people use the public space. We used novel tools to analyse in detail the movement patterns of people visiting these three city centres. Finally, Delft decided to capture the essence of the Spatial Metro experience in a document reflecting the versatility of the transnational response to pedestrian mobility and the regeneration of the historic European city centre. The document became this book, ‘Street Level Desires’. The book aims to disseminate our experience and knowledge to further strengthen social and economic cohesion throughout Europe. &nbsp