Victor Lobo

Self-organizing maps as substitutes for k-means clustering

One of the most widely used clustering techniques used in GISc problems is the k-means algorithm. One of the most important issues in the correct use of k-means is the initialization procedure that ultimately determines which part of the solution space will be searched. In this paper we briefly review different initialization procedures, and propose Kohonen’s Self-Organizing Maps as the most convenient method, given the proper training parameters. Furthermore, we show that in the final stages of its training procedure the Self-Organizing Map algorithms is rigorously the same as the k-means algorithm. Thus we propose the use of Self-Organizing Maps as possible substitutes for the more classical k-means clustering algorithms.

The self-organizing map, the Geo-SOM, and relevant variants for geosciences

In this paper we explore the advantages of using Self-Organized Maps (SOMs) when dealing with geo-referenced data. The standard SOM algorithm is presented, together with variants which are relevant in the context of the analysis of geo-referenced data. We present a new SOM architecture, the Geo-SOM, which was especially designed to take into account spatial dependency. The strengths and weaknesses of the different variants proposed are shown through a set of tests based on artificial data. A real world application of these techniques is given through the analysis of geodemographic data from Lisbons metropolitan area.

Applying genetic algorithms to zone design

Genetic algorithms (GA) have been found to provide global near optimal solutions in a wide range of complex problems. In this paper genetic algorithms have been used to deal with the complex problem of zone design. The zone design problem comprises a large number of geographical tasks, from which electoral districting is probably the most well known. The electoral districting problem is described and formalized mathematically. Different problem encodings, suited to GA optimization, are presented, together with different objective functions. A practical real world example is given and tests performed in order to evaluate the effectiveness of the GA approach.

Geo-Self-Organizing Map (Geo-SOM) for Building and Exploring Homogeneous Regions

Regionalization and uniform/homogeneous region building constitutes one of the most longstanding concerns of geographers. In this paper we explore the Geo-Self-Organizing Map (Geo-SOM) as a tool to develop homogeneous regions and perform geographic pattern detection. The Geo-SOM presents several advantages over other available methods. The possibility of “what-if” analysis, coupled with powerful visualization tools and the accommodation of spatial constraints, constitute some of the most relevant features of the Geo-SOM. In this paper we show the opportunities made available by this tool and explore different features which allow rich exploratory spatial data analysis.

Carto-SOM: cartogram creation using self-organizing maps

The basic idea of a cartogram is to distort a geographical map by substituting the geographic area of a region by some other variable of interest. The objective is to rescale each region according to the value of the variable of interest while keeping the map, as much as possible, recognizable. There are several algorithms for building cartograms. None of these methods has proved to be universally better than any other, since the trade‐offs made to get the correct distortion vary. In this paper we present a new method for building cartograms, based on self‐organizing neural networks (Kohonens self‐organizing maps or SOM). The proposed method is widely available and is easy to carry out, and yet has several appealing properties, such as easy parallelization, making up a good tool for geographic data presentation and analysis. We present a series of tests on different problems, comparing the new algorithm with existing ones. We conclude that it is competitive and, in some circumstances, can perform better then existing algorithms.

Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps

The Self-Organizing Map (SOM) is an artificial neural network that performs simultaneously vector quantization and vector projection. Due to this characteristic, the SOM can be visualized through the output space, i.e. considering the vector projection perspective, and through the input data space, emphasizing the vector quantization process. Among all the strategies for visualizing the SOM, we will focus in those that allow dealing with spatial dependency, generally present in geo-referenced data. In this paper a method is presented for spatial clustering that integrates the visualization of both perspectives of a SOM: linking its output space, defined in up to three dimensions (3D), to the cartographic representation through a ordered set of colors; and exploring the use of border lines among geo-referenced elements, computed according to the distances in the input data space between their Best Matching Units. The promising results presented in this paper, focused on ecological modeling, urban modeling and climate analysis, show that the proposed method is a valuable tool for addressing a wide range of problems within the geosciences, especially when it is necessary to visualize high dimensional geo-referenced data.

Exploratory geospatial data analysis using the GeoSOM suite

Abstract Clustering constitutes one of the most popular and important tasks in data analysis. This is true for any type of data, and geographic data is no exception. In fact, in geographic knowledge discovery the aim is, more often than not, to explore and let spatial patterns surface rather than develop predictive models. The size and dimensionality of the existing and future databases stress the need for efficient and robust clustering algorithms. This need has been successfully addressed in the context of general-purpose knowledge discovery. Geographic knowledge discovery, nonetheless can still benefit from better tools, especially if these tools are able to integrate geographic information and aspatial variables in order to assist the geographic analyst’s objectives and needs. Typically, the objectives are related with finding spatial patterns based on the interaction between location and aspatial variables. When performing cluster-based analysis of geographic data, user interaction is essential to understand and explore the emerging patterns, and the lack of appropriate tools for this task hinders a lot of otherwise very good work. In this paper, we present the GeoSOM suite as a tool designed to bridge the gap between clustering and the typical geographic information science objectives and needs. The GeoSOM suite implements the GeoSOM algorithm, which changes the traditional Self-Organizing Map algorithm to explicitly take into account geographic information. We present a case study, based on census data from Lisbon, exploring the GeoSOM suite features and exemplifying its use in the context of exploratory data analysis.

Location model for CCA-treated wood waste remediation units using GIS and clustering methods

In the next decades, a significant increase is expected in the amounts of CCA-treated wood waste that annually need to be properly disposed. This waste should be recycled only after its remediation, so planning and optimisation of the remediation units location is of major importance. A location model for CCA-treated wood waste was implemented using Geographic Information Systems (ArcGIS 8.2), with geographic information, namely land use information and the results of a questionnaire sent to Portuguese wood preservation industries. Two different clustering methods (Self-Organizing Maps and K-means) were tested in different conditions to solve the multisource Weber problem using SOMToolbox for MATLAB. The solutions obtained with the data and with both clustering methods could be used to decide on the location of these plants. SOM provided more robust and reproducible results than K-means, with the disadvantage of longer computing times. The main advantage of K-means, compared to SOM, is the reduced computing time (considering an average of all the runs, the K-means computing time is half the SOM computing time) together with the fact that it allows to obtain the best solutions in the majority of the cases, in spite of bigger variances and more geographical dispersion.

A cooperative multi-robot team for the surveillance of shipwreck survivors at sea

The sea as a very extensive area, renders difficult a pre-emptive and long-lasting search for shipwreck survivors. The operational cost for deploying manned teams with such proactive strategy is high and, thus, these teams are only reactively deployed when a disaster like a shipwreck has been communicated. To reduce the involved financial costs, unmanned robotic systems could be used instead as background surveillance teams patrolling the seas. In this sense, a robotic team for search and rescue (SAR) operations at sea is presented in this work. Composed of an Unmanned Surface Vehicle (USV) piggybacking a watertight Unmanned Aerial Vehicle (UAV) with vertical take-off and landing capabilities, the proposed cooperative system is capable of search, track and provide basic life support while reporting the position of human survivors to better prepared manned rescue teams. The USV provides long-range transportation of the UAV and basic survival kits for victims. The UAV assures an augmented perception of the environment due to its high vantage point.

Application of Self-Organizing Maps to the Maritime Environment

Self-Organizing Maps (SOMs), or Kohonen networks, are widely used neural network architecture. This paper starts with a brief overview of how SOMs can be used in different types of prob- lems. A simple and intuitive explanation of how a SOM is trained is provided, together with a formal explanation of the algorithm, and some of the more important parameters are discussed. Finally, an overview of different applications of SOMs in maritime problems is presented.