Suchergebnisse

There is significant national interest in tackling issues surrounding the needs of vulnerable children and adults. At the same time, UK cities are under significant financial strain, as local government financial settlements (the distribution of central government resources) decrease in real terms and yet urban populations, which draw on local government services, continue to grow. This study focusses on the city of Birmingham, the UK's largest and most populous city outside of London. In a data-led study, using data derived from personal social care records, we analyse the management and delivery of social care services by Birmingham City Council, which itself is the largest local authority in Europe. This research employs state-of-the-art data analytic techniques to analyse six years of Birmingham City Council social care data, to identify: (i) Service cost profiles over time; (ii) Geographic dimensions to service demand and delivery; (iii) Patterns in the provision of services, which may assist with future service planning and provision, and (iv) The extent to which data value and data protection interact. In response to recent fiscal challenges, Birmingham City Council is expected to make savings of £815 million over the 9-year period 2011/12 to 2019/20. Delivering savings of this scale, whilst protecting and safeguarding the most vulnerable citizens within a growing urban population, is one of the biggest challenges facing the UK's second largest city.

Trophic coherence, a measure of a graph's hierarchical organisation, has been shown to be linked to a graph's structural and dynamical aspects such as cyclicity, stability and normality. Trophic levels of vertices can reveal their functional properties, partition and rank the vertices accordingly. Trophic levels and hence trophic coherence can only be defined on graphs with basal vertices, i.e. vertices with zero in-degree. Consequently, trophic analysis of graphs had been restricted until now. In this paper we introduce a hierarchical framework which can be defined on any simple graph. Within this general framework, we develop several metrics: hierarchical levels, a generalisation of the notion of trophic levels, influence centrality, a measure of a vertex's ability to influence dynamics, and democracy coefficient, a measure of overall feedback in the system. We discuss how our generalisation relates to previous attempts and what new insights are illuminated on the topological and dynamical aspects of graphs. Finally, we show how the hierarchical structure of a network relates to the incidence rate in a SIS epidemic model and the economic insights we can gain through it.

Trophic coherence, a measure of a graph's hierarchical organisation, has been shown to be linked to a graph's structural and dynamical aspects such as cyclicity, stability and normality. Trophic levels of vertices can reveal their functional properties, partition and rank the vertices accordingly. Trophic levels and hence trophic coherence can only be defined on graphs with basal vertices, i.e. vertices with zero in-degree. Consequently, trophic analysis of graphs had been restricted until now. In this paper we introduce a hierarchical framework which can be defined on any simple graph. Within this general framework, we develop several metrics: hierarchical levels, a generalisation of the notion of trophic levels, influence centrality, a measure of a vertex's ability to influence dynamics, and democracy coefficient, a measure of overall feedback in the system. We discuss how our generalisation relates to previous attempts and what new insights are illuminated on the topological and dynamical aspects of graphs. Finally, we show how the hierarchical structure of a network relates to the incidence rate in a SIS epidemic model and the economic insights we can gain through it.

Exposure to nature is thought to benefit mental health and wellbeing. However, many studies consider greenspace as a single entity, which overlooks the potential significance of the various forms of greenspace, and natural greenspaces in particular. This study was designed to investigate the association between different types of greenspace and mental wellbeing. Drawing wellbeing and socioeconomic data from the Annual Population Survey (2012–2015), and shapefiles from the Greenspace Information for Greater London group, the amount of greenspace accessible within a 300 m walk of individual's postcodes was calculated, and categorised according to type. Spatial Error Models were used to account for spatial patterns in the data. Natural greenspace was significantly associated with improved life satisfaction ( B =  0.028, p <  0.001) and happiness ( B =  0.023, p =  0.019) scores. The spatial autoregressive parameter ([Formula: see text]) was small but significant ( p <  0.001), implying slight second-order spatial variation in the model. These results imply that natural areas may be more important for hedonic mental wellbeing than other greenspaces. Future research is needed on exploring causal relationships between exposure to greenspace and mental wellbeing outcomes.

While local-area greenspace is associated with reduced symptoms of mental distress and greater life satisfaction, most previous research has measured the amount of local-area greenspace within administrative boundaries, and found mixed results for associations between greenspace and multidimensional mental wellbeing. The study was designed to examine whether the amount of greenspace within a radius of individuals' homes was associated with mental wellbeing, testing the government guideline that greenspace should be available within 300 m of homes. Individual and Household-level data were drawn from the Annual Population Survey at postcode level (APS, Pooled Dataset 2012–2015), which includes 3 mental wellbeing measures, covering aspects of life satisfaction, sense of worth, and happiness, as well as a range of socio-demographic variables. Greenspace data were obtained Greenspace Information for Greater London Group (GiGL), and was used to calculated the amount of greenspace within a 300 m radius of individuals. Linear regression models revealed positive and statistically significant associations between the amount of greenspace and indicators of life satisfaction and worth. Moran's I, an indicator of spatial autocorrelation, revealed statistically significant clustering of the residuals of these models, so Geographically Weighted Regression (GWR) models were calculated, in order to adjust for underlying spatial processes within the data and investigate the geographic variation in the association between local greenspace and mental wellbeing. The global GWR model revealed that an increase in 1 ha of greenspace within 300 m of residents was associated with a statistically significant 0.803 increase in life satisfaction, 0.740 and 0.521 for worth and happiness, respectively. This therefore provides some support for the inclusion of greenspace within 300 m of homes. Local GWR coefficients revealed slight variation in the strength of these associations across the study space. Therefore, further analyses are required to investigate whether the walking (network distance), absolute size, or type of each greenspace are able to explain this spatial variation.