Roberto Henriques

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.

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.

A geometric semantic genetic programming system for the electoral redistricting problem

Redistricting consists in dividing a geographic space or region of spatial units into smaller subregions or districts. In this paper, a Genetic Programming framework that addresses the electoral redistricting problem is proposed. The method uses new genetic operators, called geometric semantic genetic operators, that employ semantic information directly in the evolutionary search process with the objective of improving its optimization ability. The system is compared to several different redistricting techniques, including evolutionary and non-evolutionary methods. The simulations were made on ten real data-sets and, even though the studied problem does not belong to the classes of problems for which geometric semantic operators induce a unimodal fitness landscape, the results we present demonstrate the effectiveness of the proposed technique.

Multi-objective genetic algorithm with variable neighbourhood search for the electoral redistricting problem

Abstract In a political redistricting problem, the aim is to partition a territory into electoral districts or clusters, subject to some constraints. The most common of these constraints include contiguity, population equality, and compactness. We propose an algorithm to address this problem based on multi-objective optimization. The hybrid algorithm we propose combines the use of the well-known Pareto-based NSGA-II technique with a novel variable neighbourhood search strategy. A new ad-hoc initialization method is also proposed. Finally, new specific genetic operators that ensure the compliance of the contiguity constraint are introduced. The experimental results we present, which are performed considering five US states, clearly show the appropriateness of the proposed hybrid algorithm for the redistricting problem. We give evidence of the fact that our method produces better and more reliable solutions with respect to those returned by the state-of-the-art methods.

Modelling innovation performance of European regions using multi-output neural networks

Regional innovation performance is an important indicator for decision-making regarding the implementation of policies intended to support innovation. However, patterns in regional innovation structures are becoming increasingly diverse, complex and nonlinear. To address these issues, this study aims to develop a model based on a multi-output neural network. Both intra- and inter-regional determinants of innovation performance are empirically investigated using data from the 4th and 5th Community Innovation Surveys of NUTS 2 (Nomenclature of Territorial Units for Statistics) regions. The results suggest that specific innovation strategies must be developed based on the current state of input attributes in the region. Thus, it is possible to develop appropriate strategies and targeted interventions to improve regional innovation performance. We demonstrate that support of entrepreneurship is an effective instrument of innovation policy. We also provide empirical support that both business and government R&D activity have a sigmoidal effect, implying that the most effective R&D support should be directed to regions with below-average and average R&D activity. We further show that the multi-output neural network outperforms traditional statistical and machine learning regression models. In general, therefore, it seems that the proposed model can effectively reflect both the multiple-output nature of innovation performance and the interdependency of the output attributes.

Mining corporate annual reports for intelligent detection of financial statement fraud A comparative study of machine learning methods

We combine features derived from financial information and managerial comments.We employ feature selection and classification using a wide range of machine learning methods.Analysts forecasts of revenues and earnings are necessary to detect fraudulent firms.Misclassification cost ratio of 1:2 is based on the loss attributable to financial statement fraud and audit fees.Interpretable Nave Bayes-based models outperform remaining methods in terms of misclassification costs. Financial statement fraud has been serious concern for investors, audit firms, government regulators, and other capital market stakeholders. Intelligent financial statement fraud detection systems have therefore been developed to support decision-making of the stakeholders. Fraudulent misrepresentation of financial statements in managerial comments has been noticed in recent studies. As such, the purpose of this study was to examine whether an improved financial fraud detection system could be developed by combining specific features derived from financial information and managerial comments in corporate annual reports. To develop this system, we employed both intelligent feature selection and classification using a wide range of machine learning methods. We found that ensemble methods outperformed the remaining methods in terms of true positive rate (fraudulent firms correctly classified as fraudulent). In contrast, Bayesian belief networks (BBN) performed best on non-fraudulent firms (true negative rate). This finding is important because interpretable ``green flag values (for which fraud is likely absent) could be derived, providing potential decision support to auditors during client selection or audit planning. We also observe that both financial statements and text in annual reports can be utilised to detect non-fraudulent firms. However, non-annual report data (analysts forecasts of revenues and earnings) are necessary to detect fraudulent firms. This finding has important implications for selecting variables when developing early warning systems of financial statement fraud.

Spatial Clustering Using Hierarchical SOM

The amount of available geospatial data increases every day, placing additional pressure on existing analysis tools. Most of these tools were developed for a data poor environment and thus rarely address concerns of efficiency, high-dimensionality and automatic exploration [1]. Recent technological innovations have dramatically increased the availability of data on location and spatial characterization, fostering the proliferation of huge geospatial databas‐ es. To make the most of this wealth of data we need powerful knowledge discovery tools, but we also need to consider the particular nature of geospatial data. This context has raised new research challenges and difficulties on the analysis of multidimensional geo-referenced data. The availability of methods able to perform “intelligent” data reduction on vast amounts of high dimensional data is a central issue in Geographic Information Science (GISc) current research agenda.

UAV Path Planning Based on Event Density Detection

The method proposed in this paper supports the UAV network path definition in an autonomously way, taking into consideration the density of the detected events at each moment, in each place. We use the self-organizing maps to detect event patterns in the field of view of the sensors, allowing unmanned aerial vehicles(UAV) path definition based on events. The goal of this paper is to maximize the detection of ships using a UAV network.

GeoSOM Suite: A Tool for Spatial Clustering

The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, which is based on Self-Organizing Maps. This paper describes this tool, and shows that it is adequate for exploring spatial data.

A spatial decision support system for the Portuguese public transportation sector

SIGGESC is a spatial decision support system (SDSS), based on a Geographic Information System (GIS), directed towards the public transportation sector. This SDSS contributes to a paradigm shift at the Portuguese Transportation Authority (IMTT) in terms of the process of registering and granting concessions to the bus companies, and also increases IMTTs ability in other supervision tasks. It allows a better coordination and planning of bus lines, and contributes to the dematerialization of the licensing processes. This project not only brought an added value to IMTT, but also to the Portuguese passenger transportation companies; by setting up an integrated information system that offers an opportunity to automate work processes and routines, greater efficiency in inspection and licensing processes, and the organization of a database on the public passenger road transport service. Such a database allows the compilation of useful references, indicators and parameters for the regulatory process, leading to faster and better decisions in terms of planning.