The Impact of Postures and Moving Directions in Fire Evacuation in a Low-Visibility Environment.

Walking speed is a significant aspect of evacuation efficiency, and this speed varies during fire emergencies due to individual physical abilities. However, in evacuations, it is not always possible to keep an upright posture, hence atypical postures, such as stoop walking or crawling, may be required for survival. In this study, a novel 3D passive vision-aided inertial system (3D PVINS) for indoor positioning was used to track the movement of 20 volunteers during an evacuation in a low visibility environment. Participants' walking speeds using trunk flexion, trunk-knee flexion, and upright postures were measured. The investigations were carried out under emergency and non-emergency scenarios in vertical and horizontal directions, respectively. Results show that different moving directions led to a roughly 43.90% speed reduction, while posture accounted for over 17%. Gender, one of the key categories in evacuation models, accounted for less than 10% of the differences in speed. The speeds of participants under emergency scenarios when compared to non-emergency scenarios was also found to increase by 53.92-60% when moving in the horizontal direction, and by about 48.28-50% when moving in the vertical direction and descending downstairs. Our results also support the social force theory of the warming-up period, as well as the effect of panic on the facilitating occupants' moving speed.

Missing data as data.

Our "digified" lives have provided researchers with an unprecedented opportunity to study society at a much higher frequency and granularity. Such data can have a large sample size but can be sparse, biased, and exclusively contributed by the users of the technologies. We look at the increasing importance of missing data and under-representation and propose a new perspective that considers missing data as useful data to understand the underlying reasons for missingness and that provides a realistic view of the sample size of large but under-represented data.

Scaling the Peaks Research Protocol: understanding the barriers and drivers to providing and using dementia-friendly community services in rural areas-a mixed methods study.

INTRODUCTION: Scaling the Peaks is a cross-disciplinary research study that draws on medical ethnography, human geography and Geospatial Information Science (GIS) to address the issues surrounding the design and delivery of dementia-friendly services in rural communities. The research question seeks to understand the barriers and drivers to the development of relevant, robust, reliable and accessible services that make a difference among older rural families affected by dementia. METHODS AND ANALYSIS: This mixed methods study recruits both families affected by dementia who reside within the Peak District National Park, Derbyshire, and their service providers. The study explores the expectations and experiences of rural dementia by adopting a three-part approach 1 : longitudinal ethnographic enquiry with up to 32 families affected by dementia (aged 70 years plus) who identify themselves as rural residents 2 ; ethnographic semistructured interviews and systematic observations of a range of statutory, third sector, private and local community initiatives that seek to support older people living with dementia 3 ; and geospatial visual mapping of the qualitative and quantitative data. The ethnographic data will be used to explore the ideas of belonging in a community, perceptions of place and identity to determine the factors that influence everyday decisions about living well with dementia and, for the providers, working in a rural community. The geospatial component of the study seeks to incorporate quantitative and qualitative data, such as types, locations and allocation of services to produce an interactive web-based map for local communities to determine the future design and delivery of services when considering dementia-friendly services. ETHICS AND DISSEMINATION: The study is approved by the Leeds and Humberside Health Research Authority 16/YH/0163. The study is also approved by other participating organisations as required by their own governance procedures. The study includes people with dementia and as such adheres to the ethical considerations when including people with dementia. A publicly available interactive visual map of the findings will be produced in relation to current services related to location and, by default, identify gaps in provision. Formal reports and dissemination activities will be undertaken in collaboration with the study advisory group members. STUDY PROGRESS: The recruitment began in September 2016. The data analysis commenced June 2017, using 59 provider interviews and 27 family participants. Data collection will be completed June 2018. NOTE ON TERMINOLOGY: Please note that the term 'families affected by dementia' is the preferred term of usage by the family members of the Scaling the Peaks Study Advisory Group. The group wish to emphasise that they consider this term to be more representative of their lives than the term living with dementia. TRIAL REGISTRATION NUMBER: NIHR IRAS 188103; Pre-results.

Using Crowdsourced Trajectories for Automated OSM Data Entry Approach.

The concept of crowdsourcing is nowadays extensively used to refer to the collection of data and the generation of information by large groups of users/contributors. OpenStreetMap (OSM) is a very successful example of a crowd-sourced geospatial data project. Unfortunately, it is often the case that OSM contributor inputs (including geometry and attribute data inserts, deletions and updates) have been found to be inaccurate, incomplete, inconsistent or vague. This is due to several reasons which include: (1) many contributors with little experience or training in mapping and Geographic Information Systems (GIS); (2) not enough contributors familiar with the areas being mapped; (3) contributors having different interpretations of the attributes (tags) for specific features; (4) different levels of enthusiasm between mappers resulting in different number of tags for similar features and (5) the user-friendliness of the online user-interface where the underlying map can be viewed and edited. This paper suggests an automatic mechanism, which uses raw spatial data (trajectories of movements contributed by contributors to OSM) to minimise the uncertainty and impact of the above-mentioned issues. This approach takes the raw trajectory datasets as input and analyses them using data mining techniques. In addition, we extract some patterns and rules about the geometry and attributes of the recognised features for the purpose of insertion or editing of features in the OSM database. The underlying idea is that certain characteristics of user trajectories are directly linked to the geometry and the attributes of geographic features. Using these rules successfully results in the generation of new features with higher spatial quality which are subsequently automatically inserted into the OSM database.