OpenStreetMap (OSM) has been demonstrated to be a valuable source of spatial data in the context of many applications. However concerns still exist regarding the quality of such data and this has limited the proliferation of its use. Consequently much research has been invested in the development of methods for assessing and/or improving the quality of OSM data. However most of these methods require ground-truth data, which, in many cases, may not be available. In this paper we present a novel solution for OSM data quality assessment that does not require ground-truth data. We consider the semantic accuracy of OSM street network data, and in particular, the associated semantic class (road class) information. A machine learning model is proposed that learns the geometrical and topological characteristics of different semantic classes of streets. This model is subsequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.
Abstract-Pandemics or high impact epidemics are one of the biggest threats facing humanity today. While a complete elimination of the occurrence of such threats is improbable, it is possible to contain their impact by efficient management which in turn depends on effective decision-making. In the event of a pandemic the data flows are enormous and pose severe cognitive overload to the public health decision-makers. In this context, this paper presents PandemCap, an innovative decision support tool that can be used by the public health officials for making better and well informed decisions in the event of pandemics or high impact epidemics. PandemCap provides an interactive, flexible platform to public health decision-makers by making extensive use of techniques from the domains of visual analytics and epidemic modeling. In addition, the tool also allows for the study of the impact of various interventions or control measures such as the use of vaccines, anti-virals, hospital beds, and ventilators.
Since blood centers in most countries typically rely on volunteer donors to meet the hospitals' needs, donor retention is critical for blood banks. Identifying regular donors is critical for the advance planning of blood banks to guarantee a stable blood supply. In this research, donors' data was collected from a Saudi blood bank from 2017 to 2018. Machine learning algorithms such as logistic regression (LG), random forest (RF) and support vector classifier (SVC) were applied to develop and evaluate models for classifying blood donors as return and nonreturn donors. The natural imbalance of the donors' distribution required extra attention and considerations to produce classifiers with good performance. Thus, over-SMOTE sampling was tested. Experiments of different classifiers showed very similar performance results. In addition to the donors return classification, a time series analysis on the donors dataset was also considered to find any seasonal variations that could be captured and delivered to blood banks for better planning and decision making. After aggregating the donation count by month, results showed that the number of donations each year was stable except for two discovered drops in June and September, which for the two observed years coincided with two religious periods: Fasting and Performing Hajj.
Purpose: Foodborne outbreaks are ubiquitous around the world. Outbreaks are commonly detected through laboratory genetic testing and investigators analyse food history in search of commonality between cases to find the source of the outbreak. Geospatial information has the potential to direct investigators to the source of an outbreak. To inform the design of surveillance countermeasures, we collected data to approximate how cases would disperse following a foodborne outbreak in a food establishment in malls located in the city centre or at transport hubs within satellite townships or in smaller food establishments within suburban regions in Singapore.Methods & Materials: Socio-demographics, residential and workplace address and time of survey from a cross-sectional survey at randomly selected malls (10 city area, 20 satellite townships) were analysed. Distances between residence and workplace of subjects, site of survey, and pair-wise distances between addresses were used as indicators of potential geographical dispersion. We assessed if subject characteristics, survey timing and site location was associated with distance from survey site using randomintercept multilevel linear regression. We then calculated the centre of minimum distance using all residential address alone, all work address alone and both addresses together.Results: We surveyed 5012 individuals across 30 sites in Singapore (10 city area, 20 satellite townships of which 10 were beside transport hubs and 10 at least 500m away from a transport hub). Multilevel linear regression confirmed that city centre subjects were more dispersed geographically (=0.979; p<0.001) than for satellite township subjects far away from a transport hub; also, working subjects were significantly more dispersed (=0.049; p=0.004), while those who ate food at the site were less dispersed (=-0.057; p<0.001) than those who did not. Residential addresses were able to pinpoint the source of the outbreak to under 500m for sites in satellite townships, while work addresses were able to do the same for city centre sites. Using both addresses together improved overall detection in all sites to about 500m from the simulated source except one site.Conclusion: When all cases are clearly known, residential and work addresses can help to narrow down the likely source of the outbreak.
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