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Objective: To conduct a time-cost analysis of formatting in scientific publishing. Design: International, cross-sectional study (one-time survey). Setting: Internet-based self-report survey, live between September 2018 and January 2019. Participants: Anyone working in research, science, or academia and who submitted at least one peer-reviewed manuscript for consideration for publication in 2017. Completed surveys were available for 372 participants from 41 countries (60% of respondents were from Canada). Main outcome measure: Time (hours) and cost (wage per hour x time) associated with formatting a research paper for publication in a peer-reviewed academic journal. Results: The median annual income category was US$61,000–80,999, and the median number of publications formatted per year was four. Manuscripts required a median of two attempts before they were accepted for publication. The median formatting time was 14 hours per manuscript, or 52 hours per person, per year. This resulted in a median calculated cost of US$477 per manuscript or US$1,908 per person, per year. Conclusions: To our knowledge, this is the first study to analyze the cost of manuscript formatting in scientific publishing. Our results suggest that scientific formatting represents a loss of 52 hours, costing the equivalent of US$1,908 per researcher per year. These results identify the hidden and pernicious price associated with scientific publishing and provide evidence to advocate for the elimination of strict formatting guidelines, at least prior to acceptance. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

Residential segregation into spatial neighborhoods and boroughs is a well-known spatial dynamic process that characterise complex urban environments. Existing models of segregation, including the pioneering Schelling ones, often do not consider all the factors that can contribute to this process. Segregation as well as aggregation emerges from local interactions among individuals, and is rooted in the complexity of social, economic and environmental interactions. The main objective of this study is to develop and implement a geospatial agent-based model to simulate the decision-making process of location of new household for incoming immigrant populations. Particularly this study aims to simulate and analyse the dynamics of the new immigrant populations arriving in the bilingual cities and boroughs of the island of Montreal. The model was implemented in NetLogo software, using real geospatial datasets. The obtained simulation results indicate realistic spatial patterns of spatial composition of the ethnographic fabric on the island of Montreal. The proposed model has the potential to be used as part of the city planning purposes. [ABSTRACT FROM AUTHOR]

Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: A key issue in the analysis of many spatial processes is the choice of an appropriate scale for the analysis. Smaller geographical units are generally preferable for the study of human phenomena because they are less likely to cause heterogeneous groups to be conflated. However, it can be harder to obtain data for small units and small-number problems can frustrate quantitative analysis. This research presents a new approach that can be used to estimate the most appropriate scale at which to aggregate point data to areas. Data and methods: The proposed method works by creating a number of regular grids with iteratively smaller cell sizes (increasing grid resolution) and estimating the similarity between two realisations of the point pattern at each resolution. The method is applied first to simulated point patterns and then to real publicly available crime data from the city of Vancouver, Canada. The crime types tested are residential burglary, commercial burglary, theft from vehicle and theft of bike. Findings: The results provide evidence for the size of spatial unit that is the most appropriate for the different types of crime studied. Importantly, the results are dependent on both the number of events in the data and the degree of spatial clustering, so a single ‘appropriate’ scale is not identified. The method is nevertheless useful as a means of better estimating what spatial scale might be appropriate for a particular piece of analysis. [ABSTRACT FROM AUTHOR]

“Vaccine hesitancy” is a concept now frequently used in vaccination discourse. The increased popularity of this concept in both academic and public health circles is challenging previously held perspectives that individual vaccination attitudes and behaviours are a simple dichotomy of accept or reject. A consultation study was designed to assess the opinions of experts and health professionals concerning the definition, scope, and causes of vaccine hesitancy in Canada. We sent online surveys to two panels (1- vaccination experts and 2- front-line vaccine providers). Two questionnaires were completed by each panel, with data from the first questionnaire informing the development of questions for the second. Our participants defined vaccine hesitancy as an attitude (doubts, concerns) as well as a behaviour (refusing some / many vaccines, delaying vaccination). Our findings also indicate that both vaccine experts and front-line vaccine providers have the perception that vaccine rates have been declining and consider vaccine hesitancy an important issue to address in Canada. Diffusion of negative information online and lack of knowledge about vaccines were identified as the key causes of vaccine hesitancy by the participants. A common understanding of vaccine hesitancy among researchers, public health experts, policymakers and health care providers will better guide interventions that can more effectively address vaccine hesitancy within Canada. [ABSTRACT FROM AUTHOR]

Objective: To conduct a systematic review of studies of social media platforms used by young people to discuss and view deliberate self-harm. Study Design: 11 electronic databases were searched from January 2000 to January 2012 for primary research; in June 2014 an updated search of Medline was conducted. Grey literature sources were also searched. Search results were screened by two reviewers. Data were extracted by one reviewer and verified by another. Methodological quality was assessed using the Mixed Methods Appraisal Tool. Results: Due to heterogeneity in study objectives and outcomes, results were not pooled; a narrative analysis is presented. 26 studies were included. Most were conducted in Canada or the UK (30.8% each), used qualitative designs (42.3%), and evaluated discussion forums (73.1%). Participants were most often aged 19–21 years (69.2%), female (mean 68.6%), and 19.2% had a documented history of depression. The social media platforms evaluated were commonly supportive and provided a sense of community among users. Support included suggestions for formal treatment, advice on stopping self-harming behavior, and encouragement. Harms included normalizing and accepting self-harming behavior; discussion of motivation or triggers, concealment, suicidal ideation or plans; and live depictions of self-harm acts. Conclusions: Although this evidence is limited by its descriptive nature, studies identify beneficial and detrimental effects for young people using social media to discuss and view deliberate self-harm. The connections users make online may be valuable to explore for therapeutic benefit. Prospective, longitudinal investigations are needed to identify short- and long-term potential harms associated with use. [ABSTRACT FROM AUTHOR]

With the growth of the low altitude remote sensing (LARS) industry in recent years, their practical application in precision agriculture seems all the more possible. However, only a few scientists have reported using LARS to monitor crop conditions. Moreover, there have been concerns regarding the feasibility of such systems for producers given the issues related to the post-processing of images, technical expertise, and timely delivery of information. The purpose of this study is to showcase actual requests by farmers to monitor crop conditions in their fields using an unmanned aerial vehicle (UAV). Working in collaboration with farmers in northeastern Ontario, we use optical and near-infrared imagery to monitor fertilizer trials, conduct crop scouting and map field tile drainage. We demonstrate that LARS imagery has many practical applications. However, several obstacles remain, including the costs associated with both the LARS system and the image processing software, the extent of professional training required to operate the LARS and to process the imagery, and the influence from local weather conditions (e.g. clouds, wind) on image acquisition all need to be considered. Consequently, at present a feasible solution for producers might be the use of LARS service provided by private consultants or in collaboration with LARS scientific research teams. [ABSTRACT FROM AUTHOR]