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

Implementasi Self Organizing Maps (SOM) Klasifikasi Penduduk untuk Menentukan Keputusan Pembangunan Daerah Prioritas Miskin (Studi Kasus Kota Makassar)

Indikator kemiskinan merupakan masalah yang terjadi di semua negara yang tidak pernah dapat di selesaikan secara tuntas. Dampak tidak langsung dari aktifitas pembangunan yang hanya beriorentasi kepada pertumbuhan telah mengakibatkan terjadinya kesenjangan social. Sulitnya merancang program pengentasan kemiskinan apabila didasarkan pada skala kecil studi kualitatif , yang hasilnya tidak dapat dengan mudah divalidasi untuk kelompok yang lebih besar. Self Oragnizing map (SOM) jaringan saraf, disebut juga neural network Kohonen merupakan metode yang efektif  untuk menganalisis data multidimensi. Jaringan ini dapat digunakan untuk analisis cluster sambil mempertahankan data  struktur (topologi), SOM merupakan model atau metode yang tingkat akurat cukup baik untuk mengklasifikasi sebuah tempat, daerah, wilayah, benda.  Hasil akhir dalam penelitian ini adalah sistem informasi klasifikasi tingkat kemiskinan yang menghasilkan informasi pemetaan penduduk miskin berdasarkan karekteristik demografi, pendidikan, ketenagakerjaan, perumahan dan membantu pengambilan kebijakan dalam pemerataan program kemiskinan sehingga dapat melakukan pembangunan daerah secara prioritas.

Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns

Spatiotemporal data pose serious challenges to analysts in geographic and other domains. Owing to the complexity of the geospatial and temporal components, this kind of data cannot be analyzed by fully automatic methods but require the involvement of the human analyst's expertise. For a comprehensive analysis, the data need to be considered from two complementary perspectives: (1) as spatial distributions (situations) changing over time and (2) as profiles of local temporal variation distributed over space. In order to support the visual analysis of spatiotemporal data, we suggest a framework based on the “Self-Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives. SOM can be considered as a combination of clustering and dimensionality reduction. In the first perspective, SOM is applied to the spatial situations at different time moments or intervals. In the other perspective, SOM is applied to the local temporal evolution profiles. The integrated visual analytics environment includes interactive coordinated displays enabling various transformations of spatiotemporal data and post-processing of SOM results. The SOM matrix display offers an overview of the groupings of data objects and their two-dimensional arrangement by similarity. This view is linked to a cartographic map display, a time series graph, and a periodic pattern view. The linkage of these views supports the analysis of SOM results in both the spatial and temporal contexts. The variable SOM grid coloring serves as an instrument for linking the SOM with the corresponding items in the other displays. The framework has been validated on a large dataset with real city traffic data, where expected spatiotemporal patterns have been successfully uncovered. We also describe the use of the framework for discovery of previously unknown patterns in 41-years time series of 7 crime rate attributes in the states of the USA

Adaptive Cooperative Learning Methodology for Oil Spillage Pattern Clustering and Prediction

The serious environmental, economic and social consequences of oil spillages could devastate any nation of the world. Notable aftermath of this effect include loss of (or serious threat to) lives, huge financial losses, and colossal damage to the ecosystem. Hence, understanding the pattern and  making precise predictions in real time is required (as opposed to existing rough and discrete prediction) to give decision makers a more realistic picture of environment. This paper seeks to address this problem by exploiting oil spillage features with sets of collected data of oil spillage scenarios. The proposed system integrates three state-of-the-art tools: self organizing maps, (SOM), ensembles of deep neural network (k-DNN) and adaptive neuro-fuzzy inference system (ANFIS). It begins with unsupervised learning using SOM, where four natural clusters were discovered and used in making the data suitable for classification and prediction (supervised learning) by ensembles of k-DNN and ANFIS. Results obtained showed the significant classification and prediction improvements, which is largely attributed to the hybrid learning approach, ensemble learning and cognitive reasoning capabilities. However, optimization of k-DNN structure and weights would be needed for speed enhancement. The system would provide a means of understanding the nature, type and severity of oil spillages thereby facilitating a rapid response to impending oils spillages. Keywords: SOM, ANFIS, Fuzzy Logic, Neural Network, Oil Spillage, Ensemble Learnin

A framework for using self-organising maps to analyse spatiotemporal patterns, exemplified by analysis of mobile phone usage

We suggest a visual analytics framework for the exploration and analysis of spatially and temporally referenced values of numeric attributes. The framework supports two complementary perspectives on spatio-temporal data: as a temporal sequence of spatial distributions of attribute values (called spatial situations) and as a set of spatially referenced time series of attribute values representing local temporal variations. To handle a large amount of data, we use the self-organising map (SOM) method, which groups objects and arranges them according to similarity of relevant data features. We apply the SOM approach to spatial situations and to local temporal variations and obtain two types of SOM outcomes, called space-in-time SOM and time-in-space SOM, respectively. The examination and interpretation of both types of SOM outcomes are supported by appropriate visualisation and interaction techniques. This article describes the use of the framework by an example scenario of data analysis. We also discuss how the framework can be extended from supporting explorative analysis to building predictive models of the spatio-temporal variation of attribute values. We apply our approach to phone call data showing its usefulness in real-world analytic scenarios

Design and development of a generic spatial decision support system, based on artificial intelligence and multicriteria decision analysis

A new integrated and generic Spatial Decision Support System (SDSS) is presented based on a combination of Artificial Intelligence and Multicriteria Decision Analysis techniques. The approach proposed is developed to address commonly faced spatial decision problems of site selection, site ranking, impact assessment and spatial knowledge discovery under one system. The site selection module utilises a theme-based Analytical Hierarchy Process. Two novel site ranking techniques are introduced. The first is based on a systematic neighbourhood comparison of sites with respect to key datasets (criterions). The second utilises multivariate ordering capability of one-dimensional Self-Organizing Maps. The site impact assessment module utilises a new spatially enabled Rapid Impact Assessment Matrix. A spatial variant of General Regression Neural Networks is developed for Geographically Weighted Regression (GWR) and prediction analysis. The developed system is proposed as a useful modern tool that facilitates quantitative and evidence based decision making in multicriteria decision environment. The intended users of the system are decision makers in government organisations, in particular those involved in planning and development when taking into account socio-economic, environmental and public health related issues

Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes

A framework for exploratory analysis of extreme weather events using geostatistical procedures and 3D self-organizing maps

publishersversionpublishe