IntroductionIn Wales, the Children in Need (CIN) dataset includes information relating to needs of children and social care support. Before the Social Services and Well-being (Wales) Act 2014 came into force in April 2016, this data collection was named the Children in Need census, changing to Children Receiving Care and Support (CRCS) after this date to reflect better the children eligible for inclusion. This paper describes these datasets, their potential for research and their limitations. We describe data that researchers can access via the Secure Anonymised Information Linkage (SAIL) Databank and exploratory linkages made to health records. MethodsCIN and CRCS data were transferred to the SAIL Databank using a standardised approach to provide de-identified data with Anonymised Linking Fields (ALF) for successfully matched records. The linkage method relies on the use of Unique Pupil Numbers (UPN). As such, no records are currently available for children without a UPN, which includes most under age three. ALFs enabled linkage to individual-level health data within SAIL. Health service use was compared to non-CIN/CRCS populations. ResultsCRCS data held within the SAIL Databank comprises 25,972 records, 81% of the total number of records reported by the Welsh Government. The CIN data contains 108,449 records, 79% of the Welsh Government's records for this data collection. Health service use of children in need, and children receiving care and support, was roughly equal to that of the non-CIN/CRCS population, except GP visits, where children in need had fewer consultations, and children receiving care and support had more consultations than the comparison population. ConclusionResearchers can access Welsh CIN and CRCS datasets through the SAIL Databank, enabling research opportunities. Work is ongoing to improve records and to understand better the health and health service use among children captured by CIN and CRCS censuses.

Any dataset containing information about relationships between entities can be modelled as a network. This network can be static, where the entities/relationships do not change over time, or dynamic, where the entities/relationships change over time. Network data that changes over time, dynamic network data, is a powerful resource when studying many important phenomena, across wide-ranging fields from travel networks to epidemiology.However, it is very difficult to analyse this data, especially if it covers a long period of time (e.g, one month) with respect to its temporal resolution (e.g. seconds). In this thesis, we address the problem of visualising long in time dynamic networks: networks that may not be particularly large in terms of the number of entities or relationships, but are long in terms of the length of time they cover when compared to their temporal resolution.We first introduce Dynamic Network Plaid, a system for the visualisation and analysis of long in time dynamic networks. We design and build for an 84" touch-screen vertically-mounted display as existing work reports positive results for the use of these in a visualisation context, and that they are useful for collaboration. The Plaid integrates multiple views and we prioritise the visualisation of interaction provenance. In this system we also introduce a novel method of time exploration called ‘interactive timeslicing’. This allows the selection and comparison of points that are far apart in time, a feature not offered by existing visualisation systems. The Plaid is validated through an expert user evaluation with three public health researchers.To confirm observations of the expert user evaluation, we then carry out a formal laboratory study with a large touch-screen display to verify our novel method of time navigation against existing animation and small multiples approaches. From this study, we find that interactive timeslicing outperforms animation and small multiples for complex tasks requiring a compari-son between multiple points that are far apart in time. We also find that small multiples is best suited to comparisons of multiple sequential points in time across a time interval.To generalise the results of this experiment, we later run a second formal laboratory study in the same format as the first, but this time using standard-sized displays with indirect mouse input. The second study reaffirms the results of the first, showing that our novel method of time navigation can facilitate the visual comparison of points that are distant in time in a way that existing approaches, small multiples and animation, cannot. The study demonstrates that our previous results generalise across display size and interaction type (touch vs mouse).In this thesis we introduce novel representations and time interaction techniques to improve the visualisation of long in time dynamic networks, and experimentally show that our novel method of time interaction outperforms other popular methods for some task types.