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This paper explores collaborative practices as robust strategies for dealing with the grand challenge of producing ICT statistics in developing countries. We present the case of CETIC.br, responsible for measuring the information society in Brazil, as an instrumental case to illustrate how collaborative practices deal with the complex, uncertain and evaluative process of producing ICT statistics. This process is taken as a grand challenge, since it involves a diversified number of actors, it is in constant evolution and it involves different perceptions of value. We aim to identify how CETIC.br brings heterogeneous actors to interact, produce collaborative advantage and promote evolutionary learning. From a theoretical perspective, we contribute with the literature of grand challenges with theories coming from the field of collaborative practices. To the practie, we intend to contribute to agencies producing statistics in developing economies, that can learn with these collaborative practices for dealing with their grand challenges. [ABSTRACT FROM AUTHOR]

The paper is developed in the framework of CFD to study the performance of Vertical Axis Turbines (VAT). One direct application is to establish design trends regarding the solidity. The model is validated with benchmarks from the literature for fluvial turbine. As for turbulence models, transitional SST version of k-w is used. The model includes two additional conservation equations for intermittency and critical Reynolds that establishes the transition from laminar to turbulent. Flow pattern are analyzed at intermediate positions along the revolution. A reduction in solidity increases the operation conditions. [ABSTRACT FROM AUTHOR]

The present study looks at geocaching, a popular location-based mobile game, where the goal is to use a Global Navigation Satellite System (GNSS), usually the Global Positioning System (GPS) to hide and seek containers placed anywhere in the field. People who engage in this activity, the geocachers, constitute a geographically distributed community that makes use of mobile and Web 2.0 technologies to coordinate and document their activities. Consequently, this treasure-hunting game, besides being a ludic activity, associated with a strong social networking element, also promotes new ways of exploring, interacting and communicating experiences and perceptions of the geographical environment where the activity occurs. The majority of existing literature analyzes geocaching from a social point of view, and little reference is made to the geographical context of this activity. The aim of this study is to fill that gap and thus characterize the phenomenon in terms of its temporal and spatial distribution. Observation instruments are proposed based on motorization indexes built from available data attributes. Such attributes reveal behaviors and patterns of geocachers (individuals) and geocaches (objects). The methodology is based on spatial data analysis; this can play an important role in exploring social phenomena that have a strong geographic component. Through the analysis of the freely available dataset that is voluntarily maintained by people engaged in the geocaching activity, a new dimension is explored: the spatial dimension. When, where and why this activity occurs was used as the framework for the analysis in this paper. The final output is an overall picture of the geocaching activity in mainland Portugal in this decade. In a later stage, environmental characteristics are used as possible explanations for observed patterns. It is shown, using spatial model specifications, that a small number of regressors are able to highlight important characteristics in the data. A final discussion underlines the potential of geocaching to encourage social interaction, and promote cultural and natural heritage; in short, it has some paramount attributes of an economically sound and sustainable sector. [ABSTRACT FROM AUTHOR]

Knowing the spatiotemporal distribution of population at the local level is fundamental for many applications, including risk management, health and environmental studies, territorial planning and management, and GeoMarketing. Census figures register where people reside and usually sleep, and are frequently the only data source available for such analyses. Currently, the analysis of service areas and population served is mostly made considering only census data as source of population distribution, while some businesses clearly serve mostly a daytime population. However, population density is not constant within census enumeration areas. Also, due to human activities, population counts and their distribution vary widely from nighttime to daytime, especially in metropolitan areas, and this variation is not captured by census data. Raster dasymetric mapping within geographic modeling allows transforming raw population counts to population density limited to specific areas where the variable is present, in more detailed temporal periods, by using ancillary data sets and zonal interpolation. In GeoMarketing, this information is especially useful for retail sales, banking, insurance, lodging, real estate, and franchising. These refined distributions can be used to improve such analyses as site selection, service area and population served, assessment of potential markets, routing activities, location-allocation, and gravity models. This study uses such a dasymetric mapping approach for detailed modeling and mapping of the spatiotemporal distribution of population in the daily cycle. These data sets are used to assess the location and the varying population contained in the service areas of existing and prospective commercial facilities in the daily cycle, for different types of businesses. Applications in GeoMarketing using spatial analysis are illustrated for three different scenarios involving private sector services where maximizing coverage of target population is paramount for success. The case studies show that when the spatiotemporal distribution of population is considered, the obtained set of solutions differs from the one produced by using census-based data alone. The results demonstrate that enhancing population distribution data through geographical modeling can greatly benefit spatial analysis in GeoMarketing, resulting in the production of better information that ultimately allows improved decision-making. [ABSTRACT FROM AUTHOR]