This new edition of Ann Bowling's well-known and highly respected text has been thoroughly revised and updated to reflect key methodological developments in health research. It is a comprehensive, easy to read, guide to the range of methods used to study and evaluate health and health services. It describes the concepts and methods used by the main disciplines involved in health research, including: demography, epidemiology, health economics, psychology and sociology.The research methods described cover the assessment of health needs, morbidity and mortality trends and rates, costing health services, sampling for survey research, cross-sectional and longitudinal survey design, experimental methods and techniques of group assignment, questionnaire design, interviewing techniques, coding and analysis of quantitative data, methods and analysis of qualitative observational studies, and types of unstructured interviewing. With new material on topics such as cluster randomization, utility analyses, patients' preferences, and perception of risk, the text is aimed at students and researchers of health and health services. It has also been designed for health professionals and policy makers who have responsibility for applying research findings in practice, and who need to know how to judge the value of that research.

Research Methods in Health. Investigating health and health services

Review: A survey of active and passive indoor localisation systems

Methods and systems of continuously optimizing data in WiFi positioning systems. A location-based services system uses WiFi-enabled devices to monitor WiFi access points in a target area to indicate whether a WiFi access point has moved relative to its previously recorded location. A WiFi-enabled device communicates with WiFi access points within range of the WiFi-enabled device so that observed WiFi access points identify themselves; A reference database is accessed to obtain information specifying a recorded location for each observed WiFi access point in the target area. The recorded location information is used for each of the observed WiFi access points in conjunction with predefined rules to infer whether an observed WiFi access point has moved relative to its recorded location. The reference database is informed of the identity of any observed WiFi access point that is inferred to have moved.

Continuous data optimization of moved access points in positioning systems

In one embodiment, when it is determined that one or more Wi-Fi positioning system (WPS) tiles that are necessary to estimate a location of a mobile client device are not present in a WPS tile store on the mobile client device, or are present in the WPS tile store on the mobile client device but out of date, one or more new WPS tiles are requested from a remote server. Each WPS tile is a set of Wi-Fi access point data for Wi-Fi access points within a bounded geographical region. The one or more new WPS tiles are received from the remote server, and are cached in the WPS tile store on the mobile client device. Subsequent to the caching, one or more further locations of the mobile client device are estimated autonomously from the remote server, utilizing Wi-Fi access point data in the WPS tile store.

Method and system for selecting and providing a relevant subset of Wi-Fi location information to a mobile client device so the client device may estimate its position with efficient utilization of resources

The development of real-time locating systems (RTLS) has become an important add-on to many existing location aware systems. While GPS has solved most of the outdoor RTLS problems, it fails to repeat this success indoors. A number of technologies have been used to address the indoor tracking problem. The ability to accurately track the location of people indoors has many applications ranging from medical, military and logistical to entertainment. However, current systems cannot provide continuous real-time tracking of a moving target or lose capability when coverage is poor. The deployment of a real-time location determination system however is fraught with problems. To date there has been little research into comparing commercial systems on the market with regard to informing IT departments as to their performance in various aspects which are important to tracking devices and people in relatively confined areas. This article attempts to provide such a useful comparison by providing a review of the practicalities of installing certain location-sensing systems. We also comment on the accuracies achieved and problems encountered using the position-sensing systems.

An evaluation of indoor location determination technologies

Location awareness is becoming an important capability for mobile computing; however, it has not been possible until now to provide cheap pervasive positioning systems. Wide area coverage is most famously achieved by using global positioning systems (GPS). A constellation of low-orbit satellites cover the earth's surface. Unfortunately GPS does not work indoors and has limited success in big cities because of the 'urban canyon' effect. PlaceLab is a research project that attempts to solve the ubiquity issues surrounding 802.11-based location estimation. PlaceLab, like RADAR, uses a device's 802.11 interface; however, it does not require the area to be pre-calibrated. It predicts location via the known positions of the access points detected by the device. Commonly used systems have a number of drawbacks, including cost, accuracy and the ability to work indoors. PlaceLab is a piece of open source software developed by Intel Research that can pinpoint a user within a Wi-Fi network. We set out here to investigate whether PlaceLab can be used as a means of establishing a user's position. This type of investigation could, if successful, pave the way for the development of other location-based applications. This report documents the efforts to answer the above question. PlaceLab was found to work, but only in ideal locations where factors such as the number of floors and the lack of available APs did not affect its use. It was concluded that these factors prevent the system from being effective as a means of establishing a user's position in most locations on campus.

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Pinpointing users with location estimation techniques and Wi-Fi hotspot technology

This article introduces a microsimulation of urban traffic flows within a large-scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys that only partly cover shopping, leisure, or recreational trips. To account for the latter, the presented traffic modeling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals’ decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities, and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed microsimulation reproduces not only the journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy.

/pdf/using-digital-footprints-for-a-city-scale-traffic-simulation-3pwmrm0cxf.pdf

Using Digital Footprints for a City-Scale Traffic Simulation

In a world of ever-growing customer data, businesses are required to have a clear line of sight into what their customers think about the business, its products, people and how it treats them. Insight into these critical areas for a business will aid in the development of a robust customer experience strategy and in turn drive loyalty and recommendations to others by their customers. It is key for business to access and mine their customer data to drive a modern customer experience. This article investigates the use of a text mining approach to aid sentiment analysis in the pursuit of understanding what customers are saying about products, services and interactions with a business. This is commonly known as Voice of the Customer (VOC) data and it is key to unlocking customer sentiment. The authors analyse the relationship between unstructured customer sentiment in the form of verbatim feedback and structured data in the form of user review ratings or satisfaction ratings to explore the question of whether customers say what they really think when given the opportunity to provide free text feedback as opposed to how they rate a product on a scale of one to five. Using various Sentiment Analysis approaches, the authors assign a sentiment score to a piece of verbatim feedback and then categorise it as positive, negative, or neutral. Using this normalised sentiment score, they compare it to the corresponding rating score and investigate the potential business insights. The results obtained indicate that a business cannot rely solely on a standalone single metric as a source of truth regarding customer experience. There is a significant difference between the customer ratings score and the sentiment of their corresponding review of the product. The authors propose that it is imperative that a business supplements their customer feedback scores with a robust sentiment analysis strategy.

/pdf/the-application-of-sentiment-analysis-and-text-analytics-to-2wxv7gcecd.pdf

The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying

This paper introduces a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys which only partly cover shopping, leisure or recreational trips. To account for the latter, the presented traffic modelling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These datasets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities and a list of typical activity sequences of city dwellers collected within a national road survey, the developed micro-simulation reproduces not only the journey statistics but also the traffic volumes at main road segments with surprising accuracy.

/pdf/city-scale-traffic-simulation-from-digital-footprints-3kmvkcxa2j.pdf

Eoghan Furey

Papers

An evaluation of indoor location determination technologies

Pinpointing users with location estimation techniques and Wi-Fi hotspot technology

Using Digital Footprints for a City-Scale Traffic Simulation

The Application of Sentiment Analysis and Text Analytics to Customer Experience Reviews to Understand What Customers Are Really Saying

City-scale traffic simulation from digital footprints