Digital Data-Centric Geography: Implications for Geography's Frontier
TL;DR: The debate regarding geographic information systems (GIS) as tool, toolbox, or science still lingers in geography departments and among geographers as mentioned in this paper, and analysis of geographic information is a vital co...
Abstract: The debate regarding geographic information systems (GIS) as tool, toolbox, or science still lingers in geography departments and among geographers. Analysis of geographic information is a vital co...
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TL;DR: There are a number of ways in which increased adoption of open source practices in spatial analysis can enhance the development of the next generation of tools and the wider practice of scientific research and education.
Abstract: This paper considers the intersection of academic spatial analysis with the open source revolution. Its basic premise is that the potential for cross-fertilization between the two is rich, yet some misperceptions about these two communities pose challenges to realizing these opportunities. The paper provides a primer on the open source movement for academicians with an eye towards correcting these misperceptions. It identifies a number of ways in which increased adoption of open source practices in spatial analysis can enhance the development of the next generation of tools and the wider practice of scientific research and education.
77 citations
TL;DR: This paper introduces a data science paradigm with the aim of advancing research on movement and proposes a new approach to visualize, model, and analyze movement as a multidimensional process that involves space, time, and context.
Abstract: Author(s): Dodge, S | Abstract: Movement is the driving force behind the form and function of many ecological and human systems. Identification and analysis of movement patterns that may relate to thenbehavior of individuals and their interactions is a fundamental first step in understanding these systems. With advances in IoT and the ubiquity of smart connected sensors to collect movement and contextual data, we now have access to a wealth of geo-enriched high-resolution tracking data. These data promise new forms of knowledge and insight into movement of humans, animals, and goods, and hence can increase our understanding of complex spatiotemporal processes such as disease outbreak, urban mobility, migration, and human-species interaction. To take advantage of the evolution in our data, we need a revolution in how we visualize, model, and analyze movement as a multidimensional process that involves space, time, and context. This paper introduces a data science paradigm with the aim of advancing research on movement.
28 citations
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TL;DR: Using evaluation criterion of minimal complexity, maximal flexibility, interactivity, utility, and maintainability, it is shown how the technical features of Jupyter notebooks enabled a suite of three 'geocomputation' modules to Geography undergraduates, with some progressing to data science and analytics roles.
Abstract: 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.
9 citations
TL;DR: 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.
Abstract: 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.
6 citations
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...2017; Bowlick and Wright 2018)....
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References
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TL;DR: The era of Big Data has begun as discussed by the authors, where diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people.
Abstract: The era of Big Data has begun. Computer scientists, physicists, economists, mathematicians, political scientists, bio-informaticists, sociologists, and other scholars are clamoring for access to the massive quantities of information produced by and about people, things, and their interactions. Diverse groups argue about the potential benefits and costs of analyzing genetic sequences, social media interactions, health records, phone logs, government records, and other digital traces left by people. Significant questions emerge. Will large-scale search data help us create better tools, services, and public goods? Or will it usher in a new wave of privacy incursions and invasive marketing? Will data analytics help us understand online communities and political movements? Or will it be used to track protesters and suppress speech? Will it transform how we study human communication and culture, or narrow the palette of research options and alter what ‘research’ means? Given the rise of Big Data as a socio-tech...
3,955 citations
"Digital Data-Centric Geography: Imp..." refers background in this paper
...Big data analytics balance the technological capabilities and information available in this computing paradigm with the unique moral and ethical concerns that arise from access to such data (Boyd and Crawford 2012)....
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TL;DR: Large errors in flu prediction were largely avoidable, which offers lessons for the use of big data.
Abstract: In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google executives or the creators of the flu tracking system would have hoped. Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness (ILI) than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States ( 1 , 2 ). This happened despite the fact that GFT was built to predict CDC reports. Given that GFT is often held up as an exemplary use of big data ( 3 , 4 ), what lessons can we draw from this error?
2,062 citations
"Digital Data-Centric Geography: Imp..." refers background in this paper
...Big data and analytical products from such data are not panaceas and clear solutions on their own, however; instead, they are avenues toward decision making, not clear decisions themselves (Lazer et al. 2014)....
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TL;DR: The internal organization of the Research University consists of a series of research groups that have firm-like qualities, especially under conditions in which research funding is awarded on a competitive basis as mentioned in this paper.
1,347 citations
"Digital Data-Centric Geography: Imp..." refers background in this paper
...This aligns with trends in the modern research university (Etzkowitz 2003), and researchers certainly innovate and take risks of uncertainty as do entrepreneurs (Veciana 2007)....
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13 Feb 2013
TL;DR: It is argued that there are good reasons why it has been hard to pin down exactly what is data science, and that to serve business effectively, it is important to understand its relationships to other important related concepts, and to begin to identify the fundamental principles underlying data science.
Abstract: Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data-science programs, and publications are touting data science as a hot-even "sexy"-career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner's field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.
1,023 citations
"Digital Data-Centric Geography: Imp..." refers background in this paper
...…inquiry—an advantage when consolidating large or inefficiently accessed data sets that might be otherwise too large or burdensome to consolidate but a disadvantage when such answers are not guided by relevant theoretical questions (Wayman 2005; Provost and Fawcett 2013; Miller and Goodchild 2014)....
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TL;DR: The current state of research in a series of key areas is reviewed and why progress has been so uneven is speculated on and new areas of significant potential in this area of research are looked to.
Abstract: . Research papers at conferences such as EGIS and the International Symposia on Spatial Data Handling address a set of intellectual and scientific questions which go well beyond the limited technical capabilities of current technology in geographical information systems. This paper reviews the topics which might be included in a science of geographical information. Research on these fundamental issues is a better prospect for long-term survival and acceptance in the academy than the development of technical capabilities. This paper reviews the current state of research in a series of key areas and speculates on why progress has been so uneven. The final section of the paper looks to the future and to new areas of significant potential in this area of research.
856 citations
"Digital Data-Centric Geography: Imp..." refers background in this paper
...As noted by Goodchild (1992) outlining the fundamentals of the newly reconceived GIScience, “Few people have had the time to write the textbooks or to identify the intellectual core, or to publish the good examples” (43)....
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