Educational Performance and Spatial Convergence in Peru

While an enormous and growing literature exists on the topic of regional income convergence, other aspects of socioeconomic well-being and development have attracted much less attention. Social indicators are a valuable complement to economic indicators when analyzing spatial patterns in a given geographic region, and can often yield a more comprehensive view about regional socioeconomic behavior. In poorer nations dominated by many low income areas that exhibit similar economic performance, social indicators may reveal further insight into the differences among regions. This paper explores the issue of educational convergence in Peru over the period 1993 to 2005. Using both exploratory spatial data analysis and spatial econometrics, the study is conducted at province level in order to uncover potential spatial patterns that help explain variation in educational performance over time, among regions, and across different terrain.

EDUCATIONAL PERFORMANCE AND SPATIAL CONVERGENCE IN PERU

While an enormous and growing literature exists on the topic of re-gional income convergence, other aspects of socioeconomic well-being and development have attracted much less attention. Social indicators are a valuable complement to economic indicators when analyzing spatial patterns in a given geographic region, and can often yield a more comprehensive view about re-gional socioeconomic behavior. In poorer nations dominated by many low in-come areas that exhibit similar economic performance, social indicators may reveal further insight into the differences among regions. This paper explores the issue of educational convergence in Peru over the period 1993 to 2005. Using both exploratory spatial data analysis and spatial econometrics, the study is conducted at province level in order to uncover potential spatial patterns that help explain variation in educational performance over time, among regions, and across different terrain.SPATIAL CONVERGENCE, EDUCATION, PERU

ON THE RELATIONSHIPS BETWEEN SPATIAL CLUSTERING, INEQUALITY, AND ECONOMIC GROWTH IN THE UNITED STATES : 1969-2000

The literature on economic development has been divided as to the nature of the relationship between inequality and growth. Recent exploratory work in the field has provided evidence that the dynamic and spatial relationships between the two may be far more complicated than previously thought. This paper provides an spatial econometric specification for the analysis of economic growth, that allows for simultaneity as it relates to inequality. Furthermore, attention is given to the possible impacts of local clustering on the performance of individual economies in a global setting. The new methodology is applied to the US states from 1969–2000, where the counties are used for the local inequality and clustering estimates.ECONOMIC GROWTH, INEQUALITY, SIMULTANEITY, SPATIAL CLUSTERING

Spatial Clustering, Inequality and Income Convergence

This paper examines the relationship between spatial clustering and inequality at the county scale with overall state per capita income in the U.S. over the period 1969-2000. For each of the 48 coterminous states we examine measures of inequality and spatial clustering and explore how a state's overall income level may be influenced by, or influence, these measures. Our exploratory analysis utilizes the open- source package Space-Time Analysis of Regional Systems (STARS) to illustrate some new techniques for analyzing regional income dynamics. The results provide insight into the possible relationships between inequality, clustering and relative income levels, and generates a number of interesting avenues for future research.spatial clustering; spatial dependence; inequality; convergence; geocomputation

Dynamic Manipulation of Spatial Weights Using Web Services

Spatial analytical tools are mostly provided in a desktop environment, which tends to restrict user access to the tools. This project intends to exploit up-to-date web technologies to extend user accessibility to spatial analytic tools. The first step is to develop web services for widely used spatial analysis such as spatial weights manipulation and provide easy-to-use web-based user interface to the services. Users can create, transform, and convert spatial weights for their data sets on web browsers without installing any specialized software.

Effects of Irregular Topology in Spherical Self-Organizing Maps

We explore the effect of different topologies on properties of self-organizing maps (SOM). We suggest several diagnostics for measuring topology-induced errors in SOM and use these in a comparison of four different topologies. The results show that SOM is less sensitive to localized irregularities in the network structure than the literature may otherwise suggest. Further, the results support the use of spherical topologies as a solution to the boundary problem in traditional SOM.

Uncertainty in Integrated Regional Models

This paper examines the nature of uncertainty in integrated econometric+input-output (ECIO) regional models. We focus on three sources of uncertainty: [a] econometric model parameter uncertainty; [b] econometric disturbance term uncertainty; and [c] input-output coefficient uncertainty. Through a series of Monte Carlo simulations we analyze the relative importance of each component as well as the question of how their interaction may propagate through the integrated model to affect the distributions of the endogenous variables. Our results suggest that there is no simple answer to the question of which source of uncertainty is most important in an integrated model. Instead, that answer is conditioned upon the focus of the analysis and whether the industry specific or macro level variables are of central concerns.regional econometric model, input-output, integrated, uncertainty

Measuring Spatial Dynamics in Metropolitan Areas

This paper introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods†a priori and then studying how resident attributes change over time, our approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both as- pects of a neighborhood transform from one period to the next. Our approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. We also develop indicators of spatial change at both the macro (city) level as well as local (neighborhood) scale. We illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the US for the period 1990-2000.

The open source dynamics in geospatial research and education

Peer reviewing is one of the core processes of science. While the typical blind system helps to improve original submissions, there are opportunities for academic publishing to learn from open source practices (commits, bug reports, feature requests, documentation, etc.), which are entirely open and done in public view. But beyond, with greater significance, peer reviewing offers a good opportunity to illustrate how the characteristics of the open source model can favor simultaneously the acknowledgment of programming efforts, a high quality evaluation standard, but also reproducibility and transparency. Code has to be considered as a full research object in geospatial development. It should be made available in full and examined as part of any contribution, like the article going with it. A good example is the Journal of Statistical Software. Created in 1996, it publishes articles and code on statistics and algorithms. The contents are freely available on-line, code snippets and source code being published along with the paper. An advantage of this approach is to prevent the propagation of “black boxes”. The approach clearly also adds value to code by acknowledging programming efforts as scientific contributions. But publishing code along with a paper also results in the ability of subsequent research projects to build on this basis. This ability is even becoming a requirement for publicly funded research projects. In addition, we may notice that code malleability provides the researcher with the opportunity to adapt the software to the scientific questions, instead of being constrained by the limiting functionalities of the software. As regards education, there are two key freedoms inherent to open source software and practices that offer potential pedagogical wins for geospatial education. First, “free as in beer” allows students to indefinitely install software on computers without license limitations. A consequence of this unconstrained context is a greater degree of exploration and discovery by the students working by themselves and at their own pace. But there is still a long way to go before all the benefits are fully realized. Indeed, current demands and offerings are focusing on “buttonology”, which consists of learning how to use tools constrained by software licenses carefully negotiated over the years by universities. It raises then some important questions regarding the role of geospatial education. Is it not to train students to equip them with the skill sets and knowledge so that they are ready for, and can create, the future geospatial labor market? Therein, we can consider the second freedom, “free as in speech,” as able to empower the students by revealing the logic of particular algorithms and computational concepts. Open source code—as text—is available for reading, manipulating, and understanding. The expected advantage is that students’ engagement with fundamental concepts is deepened in a way that is per se not possible with closed source software. In other words, students come to see geospatial methods not only as tools they can use in their own research, but as possible subjects for research

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

International audienceThis study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. In this study, we pro-pose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments. The main design and implementation decisions for both alternatives are described and validated through experiments, demonstrating that our approaches provide high reliability when executing on pervasive environments. The analysis of the experiments also leads to several insights on the requirements and constraints from dynamic and volatile systems, reinforcing the importance of context-aware information and advanced fault-tolerance features to provide efficient and reliable MapReduce services on pervasive grids