Sub-Saharan Africa at a crossroads: a quantitative analysis of regional development

This repository item contains a single issue of The Pardee Papers, a series papers that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. The Pardee Papers series features working papers by Pardee Center Fellows and other invited authors. Papers in this series explore current and future challenges by anticipating the pathways to human progress, human development, and human well-being. This series includes papers on a wide range of topics, with a special emphasis on interdisciplinary perspectives and a development orientation.Sub-Saharan Africa is at a crossroads of development. Despite a quarter of a century of economic reforms propagated by national policies and international financial agencies and institutions, sub-Saharan Africa is still lagging in development. In this paper, the authors adopt two techniques using both qualitative (e.g. governance) and quantitative factors (e.g., GDP) to examine regional patterns of development in sub-Saharan Africa. More specifically, they examine and analyze similarities and differences among the countries in this region using a multivariate statistical technique, Principal Component Analysis (PCA), and a unsupervised neural network called Kohonen’s Self-Organizing Map (SOM) to cluster levels of development. PCA serves as a tool for determining regional patterns while SOM is more useful for determining continental patterns in development. Both PCA and SOM results show a “developed” cluster in Southern Africa (South Africa, Namibia, Botswana, and Gabon). SOM exhibits a cluster of least developed countries in southern Western Africa and western Central Africa. The results demonstrate that the applied techniques are highly effective to compress multidimensional qualitative and quantitative data sets to extract relevant information about development from a policy perspective. Our analysis indicates the significance of governance variables in some clusters while a combination of variables explains other regional clusters. Zachary Tyler works for a consulting firm in Massachusetts that conducts program evaluations for energy efficiency programs, and he continues to work on statistical and geospatial analyses of human development issues. In 2010, he will receive a master’s degree in energy and environmental analysis from Boston University. Sucharita Gopal is Professor and Director of Graduate Studies in the Department of Geography and Environment and a member of the Cognitive & Neural Systems (CNS) Technology Lab at Boston University. She teaches and conducts research in geographical information systems (GIS), spatial analysis and modeling, and remote sensing for environmental and public health applications. Her recent research includes the development of a marin integrated decision analysis system (MIDAS) for Belize, Panama, and Massachusetts, and a post-disaster geospatial risk model for Haiti. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications, and symposia exploring African futures in various aspects related to development on continental and regional scales. For more information, visit www-staging.bu.edu/pardee/research/

Analysis of WRF extreme daily precipitation over Alaska using self-organizing maps

We analyze daily precipitation extremes from simulations of a polar-optimized version of the Weather Research and Forecasting (WRF) model. Simulations cover 19 years and use the Regional Arctic System Model (RASM) domain. We focus on Alaska because of its proximity to the Pacific and Arctic oceans; both provide large moisture fetch inland. Alaska\u27s topography also has important impacts on orographically forced precipitation. We use self-organizing maps (SOMs) to understand circulation characteristics conducive for extreme precipitation events. The SOM algorithm employs an artificial neural network that uses an unsupervised training process, which results in finding general patterns of circulation behavior. The SOM is trained with mean sea level pressure (MSLP) anomalies. Widespread extreme events, defined as at least 25 grid points experiencing 99th percentile precipitation, are examined using SOMs. Widespread extreme days are mapped onto the SOM of MSLP anomalies, indicating circulation patterns. SOMs aid in determining high-frequency nodes, and hence, circulations are conducive to extremes. Multiple circulation patterns are responsible for extreme days, which are differentiated by where extreme events occur in Alaska. Additionally, several meteorological fields are composited for nodes accessed by extreme and nonextreme events to determine specific conditions necessary for a widespread extreme event. Individual and adjacent node composites produce more physically reasonable circulations as opposed to composites of all extremes, which include multiple synoptic regimes. Temporal evolution of extreme events is also traced through SOM space. Thus, this analysis lays the groundwork for diagnosing differences in atmospheric circulations and their associated widespread, extreme precipitation events

A self-organizing map approach to characterize hydrogeology of the fractured Serra-Geral transboundary aquifer

Abstract The aim of this work is to understand the exchange of water between the Serra Geral aquifer system (SGAS) and Guarani aquifer system (GAS). The objectives are two-fold. First, introduce the capability of the modified self-organizing maps (MSOM) as an unbiased nonlinear approach to estimate missing values of hydrochemistry and hydraulic transmissivity associated with the SGAS, a transboundary groundwater system spanning parts of four South American countries. Second, identify areas with potential connectivity of the SGAS with the GAS based on analysis of the spatial variability of key elements and comparison with current conceptual models of hydraulic connectivity. The MSOM is employed to calculate correlations (trends) between 27 variables from 1,132 wells. Hydraulic transmissivity is calculated from specific capacity values from well-pump tests in 157 locations. Hydrochemical facies estimates appear unbiased and consistent with current conceptual-connectivity models indicating that vertical fluxes from GAS are influenced by geological structure. The MSOM provides additional spatial estimates revealing new areas with likely connections between the two aquifer systems

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

Large Occupational Accidents Data Analysis with a Coupled Unsupervised Algorithm: The S.O.M. K-Means Method. An Application to the Wood Industry

Data on occupational accidents are usually stored in large databases by worker compensation authorities, and by the safety and prevention teams of companies. An analysis of these databases can play an important role in the prevention of accidents and the reduction of risks, but it can be a complex procedure because of the dimensions and complexity of such databases. The SKM (SOM K-Means) method, a two-level clustering system, made up of SOM (Self Organizing Map) and K-Means clustering, has obtained positive results in identifying the dynamics of critical accidents by referring to a database of 1200 occupational accidents that had occurred in the wood industry. The present research has been conducted to validate the recently presented SKM methodology through the analysis of a larger data set of more than 4000 occupational accidents that occurred in Piedmont (Italy), between 2006 and 2013. This work has partitioned the accidents into groups of different accident dynamics families and has quantified the severity and frequency of occurrence of these accidents. The obtained information may be of help to Company Managers and National Authorities to better address preventive measures and policies concerning the clusters that have been identified as being the most critical within a risk-based decision-making framework

THE USE OF KOHONEN FEATURE MAPS IN THE KINEMATIC ANALYSIS OF ROWING PERFORMANCE

This study used the self organising map (SOM) as a processing step in reducing the complexity of human movement dynamics obtained from execution of a rowing stroke. The SOM is an artificial neural network (ANN), adapted in an unsupervised manner using a self organising learning process. Three-dimensional joint angles, produced by the rowers, were projected onto a 2 dimensional topological neural map, thereby identifying rowing movement patterns. Unsupervised clustering allowed the time series rowing strokes to be positioned on the map in relation to each other and this enabled movement patterns to be compared. The larger kinematic variation of the novice rowers was observed. The weight vector associated with each SOM cluster illustrated underlying task related changes in the rowing stroke patterns between elite and novice

A Comprehensive Review of Techniques for Processing and Analyzing Fetal Heart Rate Signals

The availability of standardized guidelines regarding the use of electronic fetal monitoring (EFM) in clinical practice has not effectively helped to solve the main drawbacks of fetal heart rate (FHR) surveillance methodology, which still presents inter- and intra-observer variability as well as uncertainty in the classification of unreassuring or risky FHR recordings. Given the clinical relevance of the interpretation of FHR traces as well as the role of FHR as a marker of fetal wellbeing autonomous nervous system development, many different approaches for computerized processing and analysis of FHR patterns have been proposed in the literature. The objective of this review is to describe the techniques, methodologies, and algorithms proposed in this field so far, reporting their main achievements and discussing the value they brought to the scientific and clinical community. The review explores the following two main approaches to the processing and analysis of FHR signals: traditional (or linear) methodologies, namely, time and frequency domain analysis, and less conventional (or nonlinear) techniques. In this scenario, the emerging role and the opportunities offered by Artificial Intelligence tools, representing the future direction of EFM, are also discussed with a specific focus on the use of Artificial Neural Networks, whose application to the analysis of accelerations in FHR signals is also examined in a case study conducted by the authors

A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study