Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development

The proliferation of large, complex data spatial data sets presents challenges to the way that regional science --- and geography more widely -- is researched and taught. Increasingly, it is not 'just' quantitative skills that are needed, but computational ones. However, the majority of undergraduate programmes have yet to offer much more than a one-off ‘GIS programming’ class since such courses are seen as challenging not only for students to take, but for staff to deliver. Using evaluation criterion of minimal complexity, maximal flexibility, interactivity, utility, and maintainability, we show how the technical features of Jupyter notebooks -- particularly when combined with the popularity of Anaconda Python and Docker -- enabled us to develop and deliver a suite of three 'geocomputation' modules to Geography undergraduates, with some progressing to data science and analytics roles

Face-to-Face and Central Place: Covid and the Prospects for Cities

This contribution looks at how great-city working life and business are increasingly oriented towards the activities in high-added-value trades and 'opaque' markets, where face-to-face interactions are still a vital part of what they offer. It argues that whilst the pandemic has undoubtedly hit hard, its longer-term impacts should not be over-stressed: the world cities look set for continued dominance, centrality and scale will still be vital for the smaller conurbations, and the prospects for more peripheral locations may not be as positive as proponents of ex-urban flight might anticipate

Geographical Python Teaching Resources: GeoPyTeR

GeoPyTeR, an acronym of Geographical Python Teaching Resources, provides a hub for the distribution of ‘best practice’ in computational and spatial analytic instruction, enabling instructors to quickly and flexibly remix contributed content to suit their needs and delivery framework and encouraging contributors from around the world to ‘give back’ whether in terms of how to teach individual concepts or deliver whole courses. As such, GeoPyTeR is positioned at the confluence of two powerful streams of thought in software and education: the free and open-source software movement in which contributors help to build better software, usually on an unpaid basis, in return for having access to better tools and the recognition of their peers); and the rise of Massive Open Online Courses, which seek to radically expand access to education by moving course content online and providing access to students anywhere in the world at little or no cost. This paper sets out in greater detail the origins and inspiration for GeoPyTeR, the design of the system and, through examples, the types of innovative workflows that it enables for teachers. We believe that tools like GeoPyTeR, which build on open teaching practices and promote the development of a shared understanding of what it is to be a computational geographer represent an opportunity to expand the impact of this second wave of innovation in instruction while reducing the demands placed on those actively teaching in this area

Mind the gap: implications of overseas investment for regional house price divergence in Britain

The UK has had a long-standing regional house price gap with prices in London much higher than the rest of the UK. Using price data from 1969 to 2016 we track price differentials through several cycles of boom and bust, and note the growing divergence of London, particularly central London, from the rest of Britain. In explaining this divergence, we highlight the growing importance of international investment since the global financial crisis. We conclude that, although ‘Brexit’ may have brought the latest long London boom to a close, there is nothing to suggest that the regional house price gap will close. Given the ongoing importance of global financial inflows to major world cities, this has significant implications for how governments approach affordability and housing policy

Geography and computers: Past, present, and future

The discipline of Geography has long been intertwined with the use of computers. This close interaction is likely to increase with the embeddedness of computers and concomitant growth of spatially referenced data. To better understand the current situation, and to be able to better speculate about the future, this article provides two parallel perspectives: first, we offer an historical perspective on the relationship between Geography and computers; second, we document developments—in particular the nascent field of data science—that are currently taking place outside of Geography and to which we argue the discipline should be paying close attention. Combining both perspectives, we identify the benefits of tighter integration between Geography and Data Science and argue for the establishment of a new space—that we term Geographic Data Science—in which cross‐pollination could occur to the benefit of both Geography and the larger data community

Understanding urban gentrification through machine learning

Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and urban change, this paper shows it is possible to analyse existing patterns and processes of neighbourhood change to identify areas likely to experience change in the future. This is evidenced through an analysis of socio-economic transition in London neighbourhoods (based on 2001 and 2011 Census variables) which is used to predict those areas most likely to demonstrate ‘uplift’ or ‘decline’ by 2021. The paper concludes with a discussion of the implications of such modelling for the understanding of gentrification processes, noting that if qualitative work on gentrification and neighbourhood change is to offer more than a rigorous post-mortem then intensive, qualitative case studies must be confronted with – and complemented by – predictions stemming from other, more extensive approaches. As a demonstration of the capabilities of machine learning, this paper underlines the continuing value of quantitative approaches in understanding complex urban processes such as gentrification

Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities

The geography of taste: analyzing cell-phone mobility and social events

This paper deals with the analysis of crowd mobility during special events. We analyze nearly 1 million cell-phone traces and associate their destinations with social events. We show that the origins of people attending an event are strongly correlated to the type of event, with implications in city management, since the knowledge of additive flows can be a critical information on which to take decisions about events management and congestion mitigation