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There are many challenges in recruiting and engaging participants when conducting research, especially with HIV-positive individuals. Some of these challenges include geographical barriers, insufficient time and financial resources, and perceived HIV-related stigma. This paper describes the methodology of a recruitment approach that capitalized on existing online social media venues and other Internet resources in an attempt to overcome some of these barriers to research recruitment and retention. From May through August 2013, a campaign approach using a combination of online social media, non-financial incentives, and Web-based survey software was implemented to advertise, recruit, and retain participants, and collect data for a survey study with a limited budget. Approximately US $5,000 was spent with a research staff designated at 20% of full-time effort, yielding 2034 survey clicks, 1404 of which met the inclusion criteria and initiated the survey, for an average cost of US $3.56 per survey initiation. A total of 1221 individuals completed the survey, yielding 86.97% retention. These data indicate that online recruitment is a feasible and efficient tool that can be further enhanced by sophisticated online data collection software and the addition of non-financial incentives.

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods. While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning. It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

Modification of pulse radiolysis (PR) setup and construction of a new laser flash photolysis (LFP) setup at the Institute of Nuclear Chemistry and Technology (INCT) is described. Both techniques are dedicated to studying fast reactions in real time by direct observation of transients. Time resolution of the PR setup at INCT was ~11 ns, limited by the duration of the electron pulse. Implementation of a new spectrophotometric detection system resulted in a significant broadening of experimental spectral range with respect to the previous setup. Noticeable reduction of the noise-to-signal ratio was also achieved. The LFP system was built from scratch. Its time resolution was ~6 ns, limited by the duration of a laser pulse. LFP and PR were purposely designed to share the same hardware and software solutions. Therefore, components of the detection systems can be transferred between both setups, significantly lowering the costs and shortening the construction/upgrading time. Opened architecture and improved experimental flexibility of both techniques were accomplished by implementation of Ethernet transmission control protocol/Internet protocol (TCP/IP) communication core and newly designed software. This is one of the most important enhancements. As a result, new experimental modes are available for both techniques, improving the quality and reducing the time of data collections. In addition, both systems are characterized by relatively high redundancy. Currently, implementation of new equipment into the systems hardly ever requires programming. In contrast to the previous setup, daily adaptations of hardware to experimental requirements are possible and relatively easy to perform.

Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend.

Geographic analysis has long supported transport plans that are appropriate to local contexts. Many incumbent ‘tools of the trade’ are proprietary and were developed to support growth in motor traffic, limiting their utility for transport planners who have been tasked with twenty-first century objectives such as enabling citizen participation, reducing pollution, and increasing levels of physical activity by getting more people walking and cycling. Geographic techniques—such as route analysis, network editing, localised impact assessment and interactive map visualisation—have great potential to support modern transport planning priorities. The aim of this paper is to explore emerging open source tools for geographic analysis in transport planning, with reference to the literature and a review of open source tools that are already being used. A key finding is that a growing number of options exist, challenging the current landscape of proprietary tools. These can be classified as command-line interface, graphical user interface or web-based user interface tools and by the framework in which they were implemented, with numerous tools released as R, Python and JavaScript packages, and QGIS plugins. The review found a diverse and rapidly evolving ‘ecosystem’ tools, with 25 tools that were designed for geographic analysis to support transport planning outlined in terms of their popularity and functionality based on online documentation. They ranged in size from single-purpose tools such as the QGIS plugin AwaP to sophisticated stand-alone multi-modal traffic simulation software such as MATSim, SUMO and Veins. Building on their ability to re-use the most effective components from other open source projects, developers of open source transport planning tools can avoid ‘reinventing the wheel’ and focus on innovation, the ‘gamified’ A/B Street https://github.com/dabreegster/abstreet/#abstreet simulation software, based on OpenStreetMap, a case in point. The paper, the source code of which can be found at https://github.com/robinlovelace/open-gat, concludes that, although many of the tools reviewed are still evolving and further research is needed to understand their relative strengths and barriers to uptake, open source tools for geographic analysis in transport planning already hold great potential to help generate the strategic visions of change and evidence that is needed by transport planners in the twenty-first century.

The recent advances in location tracking technologies and the widespread use of location-aware applications have resulted in big datasets of moving object trajectories. While there exists a couple of research prototypes for moving object databases, there is a lack of systems that can process big spatiotemporal data. This work proposes HadoopTrajectory, a Hadoop extension for spatiotemporal data processing. The extension adds spatiotemporal types and operators to the Hadoop core. These types and operators can be directly used in MapReduce programs, which gives the Hadoop user the possibility to write spatiotemporal data analytics programs. The storage layer of Hadoop, the HDFS, is extended by types to represent trajectory data and their corresponding input and output functions. It is also extended by file splitters and record readers. This enables Hadoop to read big files of moving object trajectories such as vehicle GPS tracks and split them over worker nodes for distributed processing. The storage layer is also extended by spatiotemporal indexes that help filtering the data before splitting it over the worker nodes. Several data access functions are provided so that the MapReduce layer can deal with this data. The MapReduce layer is extended with trajectory processing operators, to compute for instance the length of a trajectory in meters. This paper describes the extension and evaluates it using a synthetic dataset and a real dataset. Comparisons with non-Hadoop systems and with standard Hadoop are given. The extension accounts for about 11,601 lines of Java code.

Association rule mining is one of the most popular data mining methods. However, mining association rules often results in a very large number of found rules, leaving the analyst with the task to go through all the rules and discover interesting ones. Sifting manually through large sets of rules is time consuming and strenuous. Although visualization has a long history of making large amounts of data better accessible using techniques like selecting and zooming, most association rule visualization techniques are still falling short when it comes to large numbers of rules. In this paper we introduce a new interactive visualization method, the grouped matrix representation, which allows to intuitively explore and interpret highly complex scenarios. We demonstrate how the method can be used to analyze large sets of association rules using the R software for statistical computing, and provide examples from the implementation in the R-package arulesViz .

For sequential analysis hypothesis testing, various alpha spending functions have been proposed. Given a prespecified overall alpha level and power, we derive the optimal alpha spending function that minimizes the expected time to signal for continuous as well as group sequential analysis. If there is also a restriction on the maximum sample size or on the expected sample size, we do the same. Alternatively, for fixed overall alpha, power and expected time to signal, we derive the optimal alpha spending function that minimizes the expected sample size. The method constructs alpha spending functions that are uniformly better than any other method, such as the classical Wald, Pocock or O’Brien–Fleming methods.The results are based on exact calculations using linear programming. All numerical examples were run by using the R Sequential package.

The rgeoprofile R package was developed to implement functions for the analysis of serial crime incidents. Geographic profiling is an investigative technique that utilizes the spatial relationship of a connected series of crime incidents to determine or predict the most probable area of offender residence or anchor point. If successfully used as a decision support system, criminal geography can be used to help law enforcement agencies strategically target certain areas for inquiry or prioritize suspects through a narrowed search window. As an open-source platform, the rgeoprofile package contains several rapid reproducible models of spatial analysis using either centrographic or distance decay functions to predict the offender’s home base. An open-source approach results in transparent analyses with no-cost availability for agencies. Additionally, since both mathematical models and investigator heuristics have been shown to provide viable options for criminal geographic profiling, a software package, which integrates different solutions to the geographic profiling problem was needed. Finally, the article demonstrates the various geographic profiling methods in a case study of the Boston Strangler to illustrate the advantages of each approach.

geopyter, an acronym of Geographical Python Teaching Resources, provides a hub for the distribution of ‘best practice’ in computational and spatial analytic instruction, enabling instructors to quickly and flexibly remix contributed content to suit their needs and delivery framework and encouraging contributors from around the world to ‘give back’ whether in terms of how to teach individual concepts or deliver whole courses. As such, geopyter is positioned at the confluence of two powerful streams of thought in software and education: the free and open-source software movement in which contributors help to build better software, usually on an unpaid basis, in return for having access to better tools and the recognition of their peers); and the rise of Massive Open Online Courses, which seek to radically expand access to education by moving course content online and providing access to students anywhere in the world at little or no cost. This paper sets out in greater detail the origins and inspiration for geopyter, the design of the system and, through examples, the types of innovative workflows that it enables for teachers. We believe that tools like geopyter, which build on open teaching practices and promote the development of a shared understanding of what it is to be a computational geographer represent an opportunity to expand the impact of this second wave of innovation in instruction while reducing the demands placed on those actively teaching in this area.