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This commentary explores the feasibility of blockchain technologies (and cryptocurrencies) in contesting the power of centralized, corporate platforms. While proponents of blockchain and cryptocurrencies regularly proclaim their power to decentralize and counter corporate power, I am much more constrained in my assessment and note the significant challenges facing open blockchain approaches in competing with platforms. From this, I highlight three key areas in which blockchains may complicate platform operations, albeit in indeterminate ways. These include (i) closed, state-based blockchain systems focused on making back-office processes more efficient, (ii) the use of cryptocurrencies for platform-based transactions and (iii) providing digital objects with an element of “uniqueness” that makes them tradable in new ways. In the end, blockchain and cryptocurrencies are technologies like any others, providing affordances for some kinds of action over others but ultimately their embeddedness in practice and space shapes how they impact the organization and geography of economies, societies and regions.

BackgroundPrurigo nodularis is a chronic skin disease characterised by intensely pruritic, hyperkeratotic nodules. Vixarelimab, a human monoclonal antibody, binds to the beta subunit of the oncostatin M receptor, inhibiting signalling of both interleukin 31 and oncostatin M, two cytokine pathways that contribute to pruritus and nodule formation in prurigo nodularis.MethodsThis double-blind, placebo-controlled, phase 2a trial was done at both private and academic dermatology outpatient research clinics across the United States and Canada (n=40). Patient eligibility criteria included age 18-75 years, physician-documented diagnosis of prurigo nodularis minimum 6 months duration of prurigo nodularis, presence of at least 10 pruritic nodules approximately 0.5-2cm in size on at least two different anatomical locations (excluding face and scalp) and involving the extremities, and presence of normal-appearing skin between nodules; atopic dermatitis as a comorbidity was exclusionary. Patients were required to have moderate-to-severe pruritus, defined as Worst Itch-Numeric Rating Scale (WI-NRS) score ≥7at screening and LS-mean weekly WI-NRS score ≥5 for each of the 2 consecutive weeks immediately before randomisation. Participants were randomly assigned (1:1) to receive weekly subcutaneous vixarelimab 360mg (720mg loading dose) or placebo using stratification factors (sex and presence of atopy) and block size 4 through the IWRS system. Stratification by atopy status was based on the reported high prevalence of atopy in this population and the potential impact of atopy in the immunopathologic process in prurigo nodularis. Patients, investigators, study sponsor, and site staff were masked to study treatment. The primary efficacy endpoint was least squares (LS)-mean percent change from baseline (PCFB) at Week 8 in weekly average Worst Itch-Numeric Rating Scale (WI-NRS) score. The primary efficacy endpoint was analysed with ANCOVA including treatment as fixed effect, with baseline WI-NRS, and randomisation stratification factor as covariates. All randomised patients who had at least 1 dose of study drug or placebo were included in the Safety Analysis Set. This trial is registered at ClinicalTrials.gov, NCT03816891.FindingsOf 50 patients randomised between March 11, 2019 and January 31, 2020, 23 vixarelimab recipients and 26 placebo recipients comprised the modified intent-to-treat analysis population (baseline LS-mean [SD] WI-NRS score, 8.3 [1.05]). Outcomes at Week 8 for vixarelimab versus placebo included LS-mean PCFB in WI-NRS score,-50.6% versus-29.4% (LS-mean difference [95% CI],-21.2% [-40.82,-1.60]; p=0.03); ≥4-point reduction in WI-NRS score, 52.2% (12/23) versus 30.8% (8/26) (p=0.11); PN-IGA score of 0 or 1, 30.4% (7/23) versus 7.7% (2/26) (p=0.03); LS-mean PCFB in pruritus VAS score,-54.4% versus-32.6% (p=0.03); and LS-mean PCFB sleep loss reduction (improvement),-56.3% versus-30.0% (p=0.02). No deaths, serious TEAEs, or TEAEs leading to dose interruption were reported. The percentage of vixarelimab recipients reporting any TEAE was 91.3% (21/23) versus 76.9% (20/26) of placebo recipients; drug-related TEAEs generally were similar between the two groups (vixarelimab, 43.5% [10/23]; placebo, 38.5% [10/26]).InterpretationVixarelimab demonstrated rapid reduction of pruritus and achievement of clear/almost clear skin in one-thirdof the patients by Week 8. Relief of itch and clearing of skin nodules represent two important potential therapeutic advances in the management of patients suffering from the debilitating disease Prurigo Nodularis.FundingKiniksa Pharmaceuticals, Ltd.

The emergence of new sources of so-called ‘big data’ is regularly described as revolutionizing the study of urban life. Of particular interest is gentrification, which has been measured and mapped in fairly standard ways – even as its place in the broader public consciousness has grown rapidly. We argue that big data offers a new approach to the persistent problem of defining and measuring gentrification. Moreover, using big data also allows us to rethink broader questions about theory and methodological approaches in urban geography and GIScience. Using geotagged Twitter data as an indicator of social activity and connections, we demonstrate how the changing geographies of users’ tweets are proxies for the evolving social and spatial contours of urban neighborhoods. We use the case of Lexington, Kentucky and its gentrifying Northside to analyze the evolving mobilities and relational connections of neighborhood residents and visitors as gentrification intensified over time. We argue that these kinds of big data allow for an analytical approach that focuses on the dynamic, relational connections between people and places, and provides a useful, additional avenue in understanding a process as complex and multifaceted as gentrification.

The new availability of big data sources provides an opportunity to revisit our ability to predict neighborhood change. This article explores how data on urban activity patterns, specifically, geotagged tweets, improve the understanding of one type of neighborhood change—gentrification—by identifying dynamic connections between neighborhoods and across scales. We first develop a typology of neighborhood change and risk of gentrification from 1990 to 2015 for the San Francisco Bay Area based on conventional demographic data from the Census. Then, we use multivariate regression to analyze geotagged tweets from 2012 to 2015, finding that outsiders are significantly more likely to visit neighborhoods currently undergoing gentrification. Using the factors that best predict gentrification, we identify a subset of neighborhoods that Twitter-based activity suggests are at risk for gentrification over the short term—but are not identified by analysis with traditional census data. The findings suggest that combining Census and social media data can provide new insights on gentrification such as augmenting our ability to identify that processes of change are underway. This blended approach, using Census and big data, can help policymakers implement and target policies that preserve housing affordability and protext tenants more effectively.

Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to infer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code the entire tweet histories of a subset of the accounts identified as migrants by these methods. Upon close inspection very few of the accounts that are classified as migrants appear to be migrants in any conventional sense or international students. Rather we find these approaches identify other highly mobile populations such as frequent business or leisure travellers, or people who might best be described as “transnationals”. For demographic research that draws on this kind of data to generate estimates of migration flows this high mis-classification rate implies that findings are likely sensitive to the adjustment model used. For most research trying to use these data to study migrant populations, the data will be of limited utility. We suspect that increasing the correct classification rate substantially will not be easy and may introduce other biases.

Currency, the specific instruments of money used as a medium of exchange, has come in many forms—grain, beads, cowry shells, peppercorns, cigarettes, cans of mackerel and potatoes. While digital money comes in many forms—bank account records, foreign exchange, commercial currencies run by platform companies, cryptocurrencies—they share the common characteristic of compressing time-space through technologies. Blockchain-based cryptocurrencies are fundamentally predicated on a logic of algorithmic proofs, rather than centralized human governance. To achieve workable decentralized systems, cryptocurrencies rely on three key technologies: distributed databases, for recording interactions; networks of computer nodes; and encryption protocols. Cryptocurrencies have the standard characteristics expected from currency—readily divisible into smaller parts, easily transportable—and offer some new protections against fraud, counterfeiting, or seizure by a centralized authority. The design of Bitcoin and subsequent cryptocurrencies emerges from, and is embedded in, a particular segment of society working to mainstream some rather fringe ideas about money and currency.

The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.

We propose the construction of a Digital Knowledge Economy Index, quantified by way of measuring content creation and participation through digital platforms, namely the code sharing platform GitHub, the crowdsourced encyclopaedia Wikipedia, and Internet domain registrations and estimating a fifth sub-index for the World Bank Knowledge Economy Index for year 2012. This approach complements conventional data sources such as national statistics and expert surveys and helps reflect the underlying digital content creation, capacities, and skills of the population. An index that combines traditional and novel data sources can provide a more revealing view of the status of the world’s digital knowledge economy and highlight where the (un)availability of digital resources may actually reinforce inequalities in the age of data.

Smart technology – in its many facets – is often critiqued within Geography in ways that parallel the critiques of quantitative geography in the 1960s and GIScience in the 1990s. In this way, both the development on ‘smart’ technology itself and its criticisms are the latest chapter in a long-standing disciplinary debate around quantification and technology. We re-evaluate this history and argue that quantitative methodology and its theoretical critiques are not as incompatible as often claimed.To illustrate how we might address this apparent tension between theory and quantitative methods we review how both approaches conceptualize one of Geography’s core concepts: space, and highlight opportunities for symbiosis. Although smart technologies can further orthodox positivist approaches, we argue that the actual practice is more nuanced and not necessarily absolute or totalizing. For example, recent computational work builds upon critical geographic theories to analyze and visualize topological and relational spaces, relevant to topics such as gentrification and segregation. The result is not a Geography in which smart technology and algorithms remove the need for human input but rather a rejoinder in line with the recent resurgence of a critical quantitative geography. In short, a Geography where social theory and the human intellect play a key role in guiding computational approaches to analyze the largest, most versatile and relevant datasets on social space that we have ever had.

While exciting, Big Data (particularly geotagged social media data) has proven difficult for many urbanists and social science researchers to use. As a partial solution, we propose a strategy that enables the fast extracting of only relevant data from large sets of geosocial data. While contrary to many Big Data approaches—in which analysis is done on the entire dataset—much productive social science work can use smaller datasets—around the same size as census or survey data—within standard methodological frameworks. The approach we outline in this paper—including the example of a fully operating system—offers a solution for urban researchers interested in these types of data but reluctant to personally build data science skills.

Contemporary practices of sex and intimacy are increasingly digitally mediated. In this paper, we identify two distinctly spatial effects of these mediations. First, the digital extends the spaces of sex/uality beyond the immediately proximate, simultaneously expanding the potential for non-human object choice in intimate encounters. Second, the digital intensifies the experiential fidelity of intimate encounters by folding the remote into the spatially immediate, such that non-proximate intimate relations with human subjects as well as non-human objects may feel more proximate. We articulate these effects by building on and contributing to developments in the geographies of encounter, which allows us to bring together theories and conceptual framings of intimacy, digitality and sexuality in a uniquely spatial register. These effects of extension and intensification resonate in a selection of empirical examples of digitally mediated sex/uality that we place along continuums of more-and-less human and more-and-less proximate. These continuums comprise the conceptual axes of a heuristic framework that we advance to both (i) capture particular points at which configurations of spaces, practices and subject/object choices of sex crystallize given conditions of pervasive digital mediation, and (ii) provoke further interrogations of the multiple ways in which sex, sexuality and intimacy are recast by the digital.

In this article we use big data methods to analyze the attention paid to the fashion industry on social media. The article argues that for the fashion industry, like many industries, the core product is a form of knowledge that is dependent on gaining and holding people’s attention. To understand this attentional economy, social media offers a unique window because it is increasingly a central space within which fashion knowledge is created and shared. Using long-term, geotagged big data from Twitter, we analyze the hitherto difficult-to-explore spaces and places of the global fashion industry. The article suggests that the data confirm the ideas that there are a series of global fashion capitals that are especially important to the industry and that attention paid to fashion is highly uneven and varied across industry functions, national origins, and companies. Evidence is presented that attention to fashion is a global phenomenon that does not always directly link to where fashion products are sold. Attention to fashion is both a market-making mechanism for the industry as well as an indicator of wider social and cultural processes of tastemaking and identity formation within which fashion is entwined. The article concludes by suggesting that such data offer geographers new ways of looking at and linking economic, social, and cultural spaces and geographies and that social media analysis can help bridge boundaries that divide geographers.

Many of our quotidian digital practices are thoroughly mediated by digital location, including the ways in which we make our ways across cities; how we consume products, services, and content; and how we participate on social media. Simultaneously, it has been estimated that up to 80 percent of big data productions are spatial—i.e., they include a locational component (see Leszczynski & Crampton 2016). Both of these phenomena are underwritten by the ways in which digital location—or simply geolocation—has become an established fixture of digital devices, platforms, and economies: GPS chips are native components of smartphones; digital platforms routinely extend geotagging capabilities to users; mobile apps organize content and user experiences around map-based interfaces; and ‘disruptive’ market actors such as Uber and Airbnb rely on geolocation affordances as a core component of their services. To date, much of the public and academic conversation about geolocation has converged around privacy concerns arising from the ways in which individuals’ highly sensitive personal location data is continuously harvested, captured, generated, disclosed, hacked, and repurposed within a networked ecosystem of connected devices, sensor-seeded environments, third-party applications, and a burgeoning data brokerage industry organized around the purchase and resale of personal location data trails, often without users’ knowledge or consent (e.g., Kitchin,2015; Leszczynski,2017; Light et al.,2018; Mims,2018; Mitchell & Highfield,2017; Valentineo-DeVries et al., 2018). Yet the narrow focus on these geoprivacy issues has obscured the key role that geolocation, as a central component of data and digital media, has in securing and eroding trust in digital platforms and economies, both materially and discursively. This panel starts from the premise that geolocation is both a ‘technology of trust’ (Withers 2018, p. 490)—or a sociotechnical agent by which digital systems present and build themselves as trustworthy—and a socialrelation of trustworthiness.This panel brings together papers that engage with geolocation and (mis)trust in both of these senses of sociotechnical agents and social relations of trust in digital systems, with an emphasis on platforms and economies.

As more and more aspects of contemporary urban society are tracked and quantified, the emerging cloud of so-called ‘big data’ is widely considered to represent a fundamental change in the way we interact with and understand cities. For some proponents of big data, like Anderson (2008), big data means the ‘end of theory’ and the ability to let “the numbers speak for themselves”. These emerging data-driven understandings of cities often run counter to a more theoretical and heterodox approach to urban geography and it is worth noting that these trends also pre-date the emergence of what we now call ‘big data’. In order to illustrate the potential of big data for urban geographic research, we explore how these data sources and methods might be usefully applied to the persistent question of gentrification. We first review how gentrification has been defined and measured in the existing literature, and how these definitions and metrics have shaped our understandings of the process. Next, we outline nascent attempts to use big data, especially social media data, to understand gentrification. We pay attention to more ‘naive’ approaches that draw upon big data but in ways that do not fully engage with its messy and complicated nature, or which fail to connect with longer standing approaches within urban geography. We then contrast these perspectives with a range of more constructive possibilities for using big data to study gentrification that build from existing scholarship and recognize both the advantages and disadvantages of big data over other more conventional forms of data used in previous research. In short, we argue that big data is unlikely to be a panacea for empirical studies of gentrification, or for any particular urban issue of interest, and the “multidimensionality of gentrification” still means that “the use of a single variable to identify it is almost certain to fail” (Bostic and Martin 2003: 2431). We do argue, however, that big data can supplement existing data sources and provide a richer understanding of the multiple social and spatial processes that characterize the process of gentrification, its constituent parts, causes and effects.

Automated high-frequency trading has grown tremendously in the past 20 years and is responsible for about half of all trading activities at stock exchanges worldwide. Geography is central to the rise of high-frequency trading due to a market design of “continuous trading” that allows traders to engage in arbitrage based upon informational advantages built into the socio-technical assemblages that make up current capital markets. Enormous investments have been made in creating transmission technologies and optimizing computer architectures, all in an effort to shave milliseconds of order travel time (or latency) within and between markets. We show that as a result of the built spatial configuration of capital markets, “public” is no longer synonymous with “equal” information. High-frequency trading increases information inequalities between market participants.

We propose the construction of a Digital Knowledge Economy Index, quantified by way of measuring content creation and participation through digital platforms, namely the code sharing platform GitHub, the crowdsourced encyclopaedia Wikipedia, and Internet domain registrations and estimating a fifth sub-index for the World Bank Knowledge Economy Index for year 2012. This approach complements conventional data sources such as national statistics and expert surveys and helps reflect the underlying digital content creation, capacities, and skills of the population. An index that combines traditional and novel data sources can provide a more revealing view of the status of the world’s digital knowledge economy and highlight where the (un)availability of digital resources may actually reinforce inequalities in the age of data.

Information-technologies are essential for global capitalism to function at speed across scale, space and complexity. The importance of software and algorithms in the governance of these systems is reflected in the attention of scholars to the ways digital code and materiality (re)combine to create hybrid digital/material spaces of economic activity, movement and everyday life. This paper extends this work in two key ways: first by emphasising the relational aspect of these code/ spaces, and second by showing how the digital algorithms of code/spaces are hackable rather than hegemonic. Using the case study of frequent flyer programmes we demonstrate how networked knowledge-sharing reshapes code/spaces to provide unintended opportunities such as low-cost travel and access to spaces normally only frequented by global elites. Although this case highlights vulnerabilities in the code of global capitalism, it is primarily only those with significant lifestyle privilege who are able to fully participate in these subversions. Moreover, while much of the value captured by “airline hackers” comes from airlines’ profit margins, the relationality of code/spaces can impact citizens and consumers not directly connected to or interested in airline travel. Ultimately, this paper demonstrates that in contrast to characterisations of omnipresence and hegemony, the information networks and algorithms of global capitalism contain moments of uneven porousness and selective hackability.

Highlights • Phase 3 vaccine efficacy trial design during outbreaks and emergencies is challenging. • InterVax-Tool (vaxeval.com) is a structured decision-support tool for trial design. • Optimal design must include epidemiological, statistical, ethical, and logistical difficulties. • Navigating these issues in real-time requires tools to assist in decision-making. • Dynamic guidance, note taking, and tailored choices are key to good user engagement., Background Licensed vaccines are urgently needed for emerging infectious diseases, but the nature of these epidemics causes challenges for the design of phase III trials to evaluate vaccine efficacy. Designing and executing rigorous, fast, and ethical, vaccine efficacy trials is difficult, and the decisions and limitations in the design of these trials encompass epidemiological, logistical, regulatory, statistical, and ethical dimensions. Results Trial design decisions are complex and interrelated, but current guidance documents do not lend themselves to efficient decision-making. We created InterVax-Tool (http://vaxeval.com), an online, interactive decision-support tool, to help diverse stakeholders navigate the decisions in the design of phase III vaccine trials. InterVax-Tool offers high-level visual and interactive assistance through a set of four decision trees, guiding users through selection of the: (1) Primary Endpoint, (2) Target Population, (3) Randomization Scheme, and, (4) Comparator. We provide guidance on how key considerations – grouped as Epidemiological, Vaccine-related, Infrastructural, or Sociocultural – inform each decision in the trial design process. Conclusions InterVax-Tool facilitates structured, transparent, and collaborative discussion of trial design, while recording the decision-making process. Users can save and share their decisions, which is useful both for comparing proposed trial designs, and for justifying particular design choices. Here, we describe the goals and features of InterVax-Tool as well as its application to the design of a Zika vaccine efficacy trial.

Big data is increasingly seen as a way of providing a more 'scientific' approach to the understanding and management of cities. But most geographic analyses of geotagged social media data have failed to mobilize a sufficiently complex understanding of socio-spatial relations. By combining the conceptual approach of relational socio-spatial theory with the methods of critical GIScience, this paper explores the spatial imaginaries and processes of segregation and mobility at play in the notion of the '9th Street Divide' in Louisville, Kentucky. Through a more context-sensitive analysis of this data, this paper argues against this popular spatial imaginary and the notion that the Louisville's West End is somehow separate and apart from the rest of the city. By analyzing the everyday activity spaces of different groups of Louisvillians through geotagged Twitter data, we instead argue for an understanding of these neighborhoods as fluid, porous and actively produced, rather than as rigid, static or fixed. Ultimately, this paper is meant to provide a conceptual and methodological framework for the analysis of social media data that is more attentive to the multiplicity of socio-spatial relations embodied in such data.

Information has always had geography. It is from somewhere; about somewhere; it evolves and is transformed somewhere; it is mediated by networks, infrastructures, and technologies: all of which exist in physical, material places. These geographies of information about places matter because they shape how we are able to find and understand different parts of the world. Places invisible or discounted in representations are invisible in practice to many people. In other words, geographic augmentations are much more than just representations of places: they are part of the place itself; they shape it rather than simply reflect it. This fusing of the spatial and informational augmentations that are immutable means that annotations of place emerge as sites of political contestation: with different groups of people trying to impose different narratives on informational augmentations. This paper therefore explores how information geographies have their own geographic distributions: geographies of access, of participation, and of representation. The paper offers a deliberately broad survey of a range of key platforms that mediate, host, and deliver different types of geographic information. It does so using a combination of existing statistics and bespoke data not previously mapped or analysed. Through this effort, the paper demonstrates that in addition to the geographies of uneven access to contemporary modes of communication, uneven geographies of participation and representation are also evident and in some cases are being amplified rather than alleviated. In other words, the paper comprehensively shows one important facet of contemporary information geographies: that geographic information itself is characterised by a host of uneven geographies. The paper concludes that there are few signs that global informational peripheries are achieving comparable levels of participation or representation with traditional information cores, despite the hopes that the fast-paced spread of the internet to three billion people might change this pattern.

Information has long played an important role in the economy and over the past decades its prominence has increased, particularly within the financial sector. Digital flows of information are central to building advantage within capital exchanges, the creation of synthetic worlds, and the functioning of dentralized currencies and shared recordkeeping. These new practices, spaces and geographies—manifesting within the architecture of computers and absolutely dependent upon information flows—are powerful influences on the financial industry and the entire global economy. The power to channel information flows makes it absolutely fundamental to analyse the advantages and disadvantages of these configurations. The economic geographies emerging from the current structure of information flows reflect the ideologies with which they were created and the goals of their designers.