South Sudan's infrastructure : a continental perspective

Newly independent South Sudan faces a challenge in making its own way in infrastructure development. Despite earning $6 billion in oil revenues since 2005, South Sudan's spending has not been proportional to its income, but rather has lagged behind North Sudan's development of infrastructure and social support. South Sudan benefitted from strong donor support during 2004-10, the interim period defined by the Comprehensive Peace Agreement. It focused on reestablishing regional transport links and access to seaports as well as rehabilitating its ports, airstrips, and single rail line. South Sudan also successfully liberalized the ICT sector. Nonetheless, the new country's infrastructure remains in such a dismal state that it is difficult to pinpoint a single most pressing challenge. The transport sector accounts for half of the country's spending needs, and water and sanitation account for a further quarter of the total. But so many improvements are needed that the nation cannot realistically catch up with its neighbors within 10 years, or even longer. South Sudan's annual infrastructure funding gap is$879 million per year. Given that the country's total needs are beyond its reach in the medium term, it must adopt firm priorities for its infrastructure spending. It also must attract international and private-sector investment and look to lower-cost technologies to begin to close its funding gap. Although South Sudan loses relatively little to inefficiencies, redressing those inefficiencies will be vital to creating solid institutions to attract new investors and get the most out of their investments.Transport Economics Policy&Planning,E-Business,Infrastructure Economics,Energy Production and Transportation,Roads&Highways

Dynamics and equilibria under incremental horizontal differentiation on the Salop circle

We study product differentiation on a Salop circle when firms relocate incrementally due to bounded rationality. We prove that, under common assumptions on demand, firms relocate only when two or more firms target the same niche. In any other case, there is no incentive for any firm to relocate incrementally. We prove that all distributions in which firms are sufficiently far apart in product space are unstable Nash equilibria. We prove, in particular, that the classical equidistant distribution is an unstable Nash equilibrium that cannot emerge from another distribution. However, we show that if each firm is engaged in head-on rivalry with one other competitor, the industry converges to a ’equidistantesque’ equilibrium of clusters of rivals

Continuous Spatial Query Processing:A Survey of Safe Region Based Techniques

In the past decade, positioning system-enabled devices such as smartphones have become most prevalent. This functionality brings the increasing popularity of location-based services in business as well as daily applications such as navigation, targeted advertising, and location-based social networking. Continuous spatial queries serve as a building block for location-based services. As an example, an Uber driver may want to be kept aware of the nearest customers or service stations. Continuous spatial queries require updates to the query result as the query or data objects are moving. This poses challenges to the query efficiency, which is crucial to the user experience of a service. A large number of approaches address this efficiency issue using the concept of safe region . A safe region is a region within which arbitrary movement of an object leaves the query result unchanged. Such a region helps reduce the frequency of query result update and hence improves query efficiency. As a result, safe region-based approaches have been popular for processing various types of continuous spatial queries. Safe regions have interesting theoretical properties and are worth in-depth analysis. We provide a comparative study of safe region-based approaches. We describe how safe regions are computed for different types of continuous spatial queries, showing how they improve query efficiency. We compare the different safe region-based approaches and discuss possible further improvements

Filtering data streams for entity-based continuous queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based. In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly. © 2006 IEEE.published_or_final_versio

Gasoline Taxes in Georgia

This report describes and compares Georgia's fuel tax with other states and evaluates it as a long-term dedicated revenue source for highway funding in the state. FRC Report 12

Do donors get what they paid for? micro evidence on the fungibility of development project aid

Recipient government responses to development project aid have typically been studied at high levels of aggregation, using cross-country comparisons and/or aggregate time series data. Yet increasingly the relevant decisions are being made at the local level, in response to specific community-level projects. The authors use local-level data to test for fungibility of World Bank financing of rural road rehabilitation targeted to specific geographic areas of Vietnam. A simple double difference estimate suggests that the project's net contribution to rehabilitated road increments is close to zero, suggesting complete displacement of funding. However, with better controls for the endogeneity of project placement the authors find much less evidence of fungibility, with displacement accounting for around one-third of the aid. The results point to the importance ofdealing with selection bias in assessing project aid fungibility.Housing&Human Habitats,Roads&Highways,ICT Policy and Strategies,Health Economics&Finance,Poverty Monitoring&Analysis

A comparison of strategies for missing values in data on machine learning classification algorithms

Abstract: Dealing with missing values in data is an important feature engineering task in data science to prevent negative impacts on machine learning classification models in terms of accurate prediction. However, it is often unclear what the underlying cause of the missing values in real-life data is or rather the missing data mechanism that is causing the missingness. Thus, it becomes necessary to evaluate several missing data approaches for a given dataset. In this paper, we perform a comparative study of several approaches for handling missing values in data, namely listwise deletion, mean, mode, k–nearest neighbors, expectation-maximization, and multiple imputations by chained equations. The comparison is performed on two real-world datasets, using the following evaluation metrics: Accuracy, root mean squared error, receiver operating characteristics, and the F1 score. Most classifiers performed well across the missing data strategies. However, based on the result obtained, the support vector classifier method overall performed marginally better for the numerical data and naïve Bayes classifier for the categorical data when compared to the other evaluated missing value methods