Scaling SDI systems via query clustering and aggregation

Master'sMASTER OF SCIENC

Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom

The term "Geographic Information Systems" (GIS) has been added to MeSH in 2003, a step reflecting the importance and growing use of GIS in health and healthcare research and practices. GIS have much more to offer than the obvious digital cartography (map) functions. From a community health perspective, GIS could potentially act as powerful evidence-based practice tools for early problem detection and solving. When properly used, GIS can: inform and educate (professionals and the public); empower decision-making at all levels; help in planning and tweaking clinically and cost-effective actions, in predicting outcomes before making any financial commitments and ascribing priorities in a climate of finite resources; change practices; and continually monitor and analyse changes, as well as sentinel events. Yet despite all these potentials for GIS, they remain under-utilised in the UK National Health Service (NHS). This paper has the following objectives: (1) to illustrate with practical, real-world scenarios and examples from the literature the different GIS methods and uses to improve community health and healthcare practices, e.g., for improving hospital bed availability, in community health and bioterrorism surveillance services, and in the latest SARS outbreak; (2) to discuss challenges and problems currently hindering the wide-scale adoption of GIS across the NHS; and (3) to identify the most important requirements and ingredients for addressing these challenges, and realising GIS potential within the NHS, guided by related initiatives worldwide. The ultimate goal is to illuminate the road towards implementing a comprehensive national, multi-agency spatio-temporal health information infrastructure functioning proactively in real time. The concepts and principles presented in this paper can be also applied in other countries, and on regional (e.g., European Union) and global levels

The application of data mining techniques to interrogate Western Australian water catchment data sets

Current environmental challenges such as increasing dry land salinity, waterlogging, eutrophication and high nutrient runoff in south western regions of Western Australia may have both cultural and environmental implications in the near future. Advances in computer science disciplines, more specifically, data mining techniques and geographic information services provide the means to be able to conduct longitudinal climate studies to predict changes in the Water catchment areas of Western Australia. The research proposes to utilise existing spatial data mining techniques in conjunction of modern open-source geospatial tools to interpret trends in Western Australian water catchment land use. This will be achieved through the development of a innovative data mining interrogation tool that measures and validates the effectiveness of data mining methods on a sample water catchment data set from the Peel Harvey region of WA. In doing so, the current and future statistical evaluation on potential dry land salinity trends can be eluded. The interrogation tool will incorporate different modern geospatial data mining techniques to discover meaningful and useful patterns specific to current agricultural problem domain of dry land salinity. Large GIS data sets of the water catchments on Peel-Harvey region have been collected by the state government Shared Land Information Platform in conjunction with the LandGate agency. The proposed tool will provide an interface for data analysis of water catchment data sets by benchmarking measures using the chosen data mining techniques, such as: classical statistical methods, cluster analysis and principal component analysis.The outcome of research will be to establish an innovative data mining instrument tool for interrogating salinity issues in water catchment in Western Australia, which provides a user friendly interface for use by government agencies, such as Department of Agriculture and Food of Western Australia researchers and other agricultural industry stakeholders

Объектная модель системы избирательного распространения информации

The author examines the object model of the selective dissemination of information (SDI) that forms the basis for designing user information awareness system of the RAS Library for natural Sciences. The SDI system users are offered two types of services: the first is to provide awareness on the list of journals defined by the user, and the second is awareness building on thematic enquiries being formed with the keywords that the user supplies. The suggested SDI system differs from the traditional systems of the kind: designers do not limit the list of journals or the list of primary sources to be addressed within thematic search with the list of full-text resources available by the library’s subscription. This object model and the corresponding SDI system prototype have been operating since 2016. Its successful operation enables to suggest the described object model as a standard model for SDI system.Представлена объектная модель системы избирательного распространения информации (ИРИ), которая легла в основу разработки прототипа системы избирательного информирования пользователей в Библиотеке по естественным наукам РАН. Пользователям системы ИРИ предлагаются два вида обслуживания: оповещения по определяемому самим пользователем перечню научных журналов и оповещения по тематическим запросам, сформированным на основе сообщённых пользователем ключевых слов. Разработанная система избирательного информирования имеет важное отличие от традиционных для библиотечных учреждений систем подобного рода: мы не ограничиваем ни перечень журналов, сообщаемых пользователем, ни перечень первоисточников, обрабатываемых в рамках тематических запросов, теми ресурсами, к которым библиотека имеет полнотекстовый доступ по подписке. Подчёркнуто, что пользователи системы ИРИ получают персональные оповещения посредством рассылки по электронной почте, которые включают обычные поля библиографических описаний и ряд полей для расширения навигационных функций оповещения.Отмечено, что рассмотренная объектная модель и разработанный на её основе прототип системы ИРИ эксплуатировались в Библиотеке по естественным наукам РАН начиная с 2016 г. Успешная эксплуатация позволяет рекомендовать эту объектную модель как типовую для систем ИРИ

Policy and Place: A Spatial Data Science Framework for Research and Decision-Making

abstract: A major challenge in health-related policy and program evaluation research is attributing underlying causal relationships where complicated processes may exist in natural or quasi-experimental settings. Spatial interaction and heterogeneity between units at individual or group levels can violate both components of the Stable-Unit-Treatment-Value-Assumption (SUTVA) that are core to the counterfactual framework, making treatment effects difficult to assess. New approaches are needed in health studies to develop spatially dynamic causal modeling methods to both derive insights from data that are sensitive to spatial differences and dependencies, and also be able to rely on a more robust, dynamic technical infrastructure needed for decision-making. To address this gap with a focus on causal applications theoretically, methodologically and technologically, I (1) develop a theoretical spatial framework (within single-level panel econometric methodology) that extends existing theories and methods of causal inference, which tend to ignore spatial dynamics; (2) demonstrate how this spatial framework can be applied in empirical research; and (3) implement a new spatial infrastructure framework that integrates and manages the required data for health systems evaluation. The new spatially explicit counterfactual framework considers how spatial effects impact treatment choice, treatment variation, and treatment effects. To illustrate this new methodological framework, I first replicate a classic quasi-experimental study that evaluates the effect of drinking age policy on mortality in the United States from 1970 to 1984, and further extend it with a spatial perspective. In another example, I evaluate food access dynamics in Chicago from 2007 to 2014 by implementing advanced spatial analytics that better account for the complex patterns of food access, and quasi-experimental research design to distill the impact of the Great Recession on the foodscape. Inference interpretation is sensitive to both research design framing and underlying processes that drive geographically distributed relationships. Finally, I advance a new Spatial Data Science Infrastructure to integrate and manage data in dynamic, open environments for public health systems research and decision- making. I demonstrate an infrastructure prototype in a final case study, developed in collaboration with health department officials and community organizations.Dissertation/ThesisDoctoral Dissertation Geography 201

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management