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The Southeast Asian region is experiencing some of the world’s highest rates of deforestation and forest degradation, the principle drivers of which are agricultural expansion and wood extraction in combination with an increased incidence of fire. Recent changes in fire regimes in Southeast Asia are indicative of increased human-causd forest disturbance, but El Niño–Southern Oscillation (ENSO) events also play a role in exacerbating fire occurrence and severity. Fires are now occurring on a much more extensive scale - in part because forest margins are at greater risk of fire as a result of disturbance through logging activities, but also as a result of rapid, large-scale forest clearance for the establish-ment of plantations. Millions of hectares have been deforested and drained to make way for oil palm and pulpwood trees, and many plantation companies, particularly in Indonesia, have employed fire as a cheap land clearance tool; uncontrolled fires have entered adjacent forests or plantation estates, and burnt both the forest biomass and, in peatland areas, underlying peat. Forest fires cause changes to forest structure, biodiversity, soil and hydrology. Repeated fires over successive or every few years lead to a progressive decline in the number of primary forest species. Fire leads to reduction in both aboveground and below ground organic carbon stocks and also changes carbon cycling patterns. In non-peatland areas, losses of carbon from fire affected forest vegetation exceed greatly soil carbon losses, but on carbon-rich substrates, e.g. peat, combustion losses can be considerable. Peatland fires make a major contribution to atmospheric emissions of greenhouse gases, fine particular matter and aerosols and thus contribute to climate change as well as presenting a problem for human health. The scale of emissions is unlikely to reduce in coming decades, since climate modelling studies have predicted that parts of this region will experience lower rainfall in future and greater seasonality. Protecting the rainforests of this region from further fire disasters should be at the top of the global environmental agenda, with highest priority given to peatland areas.

Involving the collaboration of researchers from Classics, Geography and Archaeological Computing, and supported by funding from the AHRC, HESTIA (the Herodotus Encoded Space-Text-Imaging Archive) aims to enrich contemporary discussions of space by developing an innovative methodology for the study of an ancient narrative, Herodotus’s Histories. Using the latest digital technology in combination with close textual study, we investigate the geographical concepts through which Herodotus describes the conflict between Greeks and Persians. Our findings nuance the customary topographical vision of an east versus west polarity by drawing attention to the topological network culture that criss-crosses the two, and develop the means of bringing that world to a mass audience via the internet. In this paper we discuss three main aspects to the project: the data capture of place-names in Herodotus; their visualization and dissemination using the web-mapping technologies of GIS, Google Earth and Timemap; and the interrogation of the relationships that Herodotus draws between different geographical concepts using the digital resources at our disposal. Our concern will be to set out in some detail the digital basis to our methodology and the technologies that we have been exploiting, as well as the problems that we have encountered, in the hope of contributing not only to a more complex picture of space in Herodotus but also to a basis for future digital projects across the Humanities that spatially visualize large text-based corpora. With this in mind we end with a brief discussion of some of the ways in which this study is being developed, with assistance from research grants from the Google Digital Humanities Awards Program and JISC.

The potential of open data as a resource for driving citizen-led urban innovation relies not only on a suitable technical infrastructure but also on the skills and knowledge of the citizens themselves. In this chapter, we describe how a smart city project in Milton Keynes, UK, is supporting multiple stages of citizen innovation, from ideation to citizen-led smart city projects. The Our MK initiative provides support and funding to help citizens develop their ideas about making their communities more sustainable into reality. This approach encounters challenges when engaging with citizens in identifying and implementing data-driven solutions to urban problems. The majority of citizens have little practical experience with the types of data sets that might be available or possess the appropriate skills for their analysis and utilisation for addressing urban issues or finding novel ways to hack their city. We go on to describe the Urban Data School, which aims to offer a long-term solution to this problem by providing teaching resources around urban data sets aimed at raising the standard of data literacy amongst future generations. Lesson resources that form part of the Urban Data School have been piloted in one primary and three secondary schools in Milton Keynes. This work has demonstrated that with the appropriate support, even young children can begin to develop the skills necessary to work with large complex data sets. Through our two approaches, we illustrate some of the barriers to citizen participation in urban innovation and detail our solutions to overcoming those barriers. Post-print / Final draft

Multilevel principal components analysis (mPCA) has previously been shown to provide a simple and straightforward method of forming point distribution models that can be used in (active) shape models. Here we extend the mPCA approach to model image texture as well as shape. As a test case, we consider a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Shape (in terms of landmark points) and image texture are considered separately in this initial analysis. Three-level models are constructed that contain levels for biological sex, “within-subject” variation (i.e., facial expression), and “between-subject” variation (i.e., all other sources of variation). By considering eigenvalues, we find that the order of importance as sources of variation for facial shape is: facial expression (47.5%), between-subject variations (45.1%), and then biological sex (7.4%). By contrast, the order for image texture is: between-subject variations (55.5%), facial expression (37.1%), and then biological sex (7.4%). The major modes for the facial expression level of the mPCA models clearly reflect changes in increased mouth size and increased prominence of cheeks during smiling for both shape and texture. Even subtle effects such as changes to eyes and nose shape during smile are seen clearly. The major mode for the biological sex level of the mPCA models similarly relates clearly to changes between male and female. Model fits yield “scores” for each principal component that show strong clustering for both shape and texture by biological sex and facial expression at appropriate levels of the model. We conclude that mPCA correctly decomposes sources of variation due to biological sex and facial expression (etc.) and that it provides a reliable method of forming models of both shape and image texture.

Smartphones, as highly portable networked computing devices with embedded sensors including GPS receivers, are ideal platforms to support context-aware language learning. They can enable learning when the user is en-gaged in everyday activities while out and about, complementing formal language classes. A significant challenge, however, has been the practical implementation of services that can accurately identify and make use of context, particularly location, to offer meaningful language learning recommendations to users. In this paper we review a range of approaches to identifying context to support mobile language learning. We consider how dynamically changing aspects of context may influence the quality of recommendations presented to a user. We introduce the MASELTOV project’s use of context awareness combined with a rules-based recommendation engine to present suitable learning content to recent immigrants in urban areas; a group that may benefit from contextual support and can use the city as a learning environment.