Hasil untuk "Geography"

H. Triandis

J. Davidson, C. Milligan

T. Cresswell

J. Murdoch

N. Coe, David C. Jordhus‐Lier

Mark Graham

ABSTRACT Platforms in the urban environment are fundamentally unaccountable. They present themselves as too big to control, too new to regulate, and too innovative to stifle, and remain un-democratic, and usually distant, organizations with no interest in promoting local voices or investing in local priorities. This paper argues that platforms control urban interactions whilst remaining unaccountable through a strategic deployment of ‘conjunctural geographies’ – a way of being simultaneously embedded and disembedded from the space-times they mediate. These conjunctural geographies, however, render platforms vulnerable. The ephemeral nature of platforms means we can avoid them, circumvent them and replicate them; their material nature suggests points of regulation and resistance. The paper closes by pointing to three broad strategies —regulate, replicate, and resist - which can be deployed to build alternate platform futures. Each of which is built on understanding the simultaneously embedded and disembedded ways in which platforms occupy their conjunctural geographies.

K. Birch, Callum Ward

An asset is both a resource and property, in that it generates income streams with its sale price based on the capitalization of those revenues. Although an asset's income streams can be financially sliced up, aggregated, and speculated upon across highly diverse geographies, there still has to be something underpinning these financial operations. Something has to generate the income that a political economic actor can lay claim to through a property or other right, entailing a process of enclosure, rent extraction, property formation, and capitalization. Geographers and other social scientists are producing a growing literature illustrating the range of new (and old) asset classes created by capitalists in their search for revenue streams, for which we argue assetization is a necessary concept to focus on the moment of enclosure and rent extraction. It is a pressing task for human geographers to unpack the diverse and contingent ‘asset geographies’ entailed in this assetization process. As a middle range concept and empirical problematic, we argue that assetization is an important focal point for wider debates in human geography by focusing attention on the moment of enclosure, rent extraction, and material remaking of society which the making of a financial asset implies.

Sławomir Dadas, Małgorzata Grębowiec, Michał Perełkiewicz et al.

Large language models (LLMs) are becoming increasingly proficient in processing and generating multilingual texts, which allows them to address real-world problems more effectively. However, language understanding is a far more complex issue that goes beyond simple text analysis. It requires familiarity with cultural context, including references to everyday life, historical events, traditions, folklore, literature, and pop culture. A lack of such knowledge can lead to misinterpretations and subtle, hard-to-detect errors. To examine language models' knowledge of the Polish cultural context, we introduce the Polish linguistic and cultural competency benchmark, consisting of 600 manually crafted questions. The benchmark is divided into six categories: history, geography, culture & tradition, art & entertainment, grammar, and vocabulary. As part of our study, we conduct an extensive evaluation involving over 30 open-weight and commercial LLMs. Our experiments provide a new perspective on Polish competencies in language models, moving past traditional natural language processing tasks and general knowledge assessment.

Finn Orson Koepke

Let $(X, \mathcal{F})$ be a hypergraph. The Maker-Breaker game on $(X, \mathcal{F})$ is a combinatorial game between two players, Maker and Breaker. Beginning with Maker, the players take turns claiming vertices from $X$ that have not yet been claimed. Maker wins if she manages to claim all vertices of some hyperedge $F \in \mathcal{F}$. Breaker wins if he claims at least one vertex in every hyperedge. M. L. Rahman and Thomas Watson proved in 2021 that, even when only Maker-Breaker games on 6-uniform hypergraphs are considered, the decision problem of determining which player has a winning strategy is PSPACE-complete. They also showed that the problem is NL-hard when considering hypergraphs of rank 5. In this paper, we improve the latter result by showing that deciding who wins Maker-Breaker games on 5-uniform hypergraphs is still a PSPACE-complete problem. We achieve this by polynomial transformation from the problem of solving the generalized geography game on bipartite digraphs with vertex degrees 3 or less, which is known to be PSPACE-complete.

R. Rose-Redwood, Rob Kitchin, Elia Apostolopoulou et al.

The spread of the novel coronavirus (SARS-CoV-2) has resulted in the most devastating global public health crisis in over a century. At present, over 10 million people from around the world have contracted the Coronavirus Disease 2019 (COVID-19), leading to more than 500,000 deaths globally. The global health crisis unleashed by the COVID-19 pandemic has been compounded by political, economic, and social crises that have exacerbated existing inequalities and disproportionately affected the most vulnerable segments of society. The global pandemic has had profoundly geographical consequences, and as the current crisis continues to unfold, there is a pressing need for geographers and other scholars to critically examine its fallout. This introductory article provides an overview of the current special issue on the geographies of the COVID-19 pandemic, which includes 42 commentaries written by contributors from across the globe. Collectively, the contributions in this special issue highlight the diverse theoretical perspectives, methodological approaches, and thematic foci that geographical scholarship can offer to better understand the uneven geographies of the Coronavirus/COVID-19.

Guonian Lü, M. Batty, J. Strobl et al.

ABSTRACT Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic perspective, certain challenges remain, including the modelling of geographic-featured environments with GIS data model, the enhancement of GIS’s analysis functions for comprehensive geographic analysis and achieving human-oriented geographic information presentation. Several basic theoretical and technical ideas that follow the workflow and processes of geographic information induction, geographic scenario modelling, geographic process analysis and geographic environment representation are proposed to fill the gaps between GIS and geography. We also call for designing methods for big geographic data-oriented analysis, making best use of videos and developing virtual geographic scenario-based GIS for further evolution.

Patricia Noxolo

This first of three progress reports gives a brief overview of the new field of Black Geographies. It elucidates Black Geographies as a field that not only critiques the erasure of Blackness within the whiteness and coloniality of geographical thought, but also centres Black spatial thought and agency. Thus, Black Geographies is an im/possible undertaking. Nonetheless, Black Geographies speaks not only about the spatialities of Black people but overwhelmingly speaks from the voices of Black geographers: Geography will need to recruit and retain enough Black geographers to make such an undertaking truly possible.

Evan Dong, Aaron Schein, Yixin Wang et al.

Racial and other demographic imputation is necessary for many applications, especially in auditing disparities and outreach targeting in political campaigns. The canonical approach is to construct continuous predictions -- e.g., based on name and geography -- and then to $\textit{discretize}$ the predictions by selecting the most likely class (argmax). We study how this practice produces $\textit{discretization bias}$. In particular, we show that argmax labeling, as used by a prominent commercial voter file vendor to impute race/ethnicity, results in a substantial under-count of African-American voters, e.g., by 28.2% points in North Carolina. This bias can have substantial implications in downstream tasks that use such labels. We then introduce a $\textit{joint optimization}$ approach -- and a tractable $\textit{data-driven thresholding}$ heuristic -- that can eliminate this bias, with negligible individual-level accuracy loss. Finally, we theoretically analyze discretization bias, show that calibrated continuous models are insufficient to eliminate it, and that an approach such as ours is necessary. Broadly, we warn researchers and practitioners against discretizing continuous demographic predictions without considering downstream consequences.

Dillon Davis, Huiji Gao, Thomas Legrand et al.

The Airbnb search system grapples with many unique challenges as it continues to evolve. We oversee a marketplace that is nuanced by geography, diversity of homes, and guests with a variety of preferences. Crafting an efficient search system that can accommodate diverse guest needs, while showcasing relevant homes lies at the heart of Airbnb's success. Airbnb search has many challenges that parallel other recommendation and search systems but it has a unique information retrieval problem, upstream of ranking, called location retrieval. It requires defining a topological map area that is relevant to the searched query for homes listing retrieval. The purpose of this paper is to demonstrate the methodology, challenges, and impact of building a machine learning based location retrieval product from the ground up. Despite the lack of suitable, prevalent machine learning based approaches, we tackle cold start, generalization, differentiation and algorithmic bias. We detail the efficacy of heuristics, statistics, machine learning, and reinforcement learning approaches to solve these challenges, particularly for systems that are often unexplored by current literature.

Shawn Berry

The purpose of this article is to propose a novel model of the effects of changes in shelter and driving costs on car commuting distances in the overheated Toronto housing market from 2011 to 2016. The model borrows from theoretical concepts of microeconomics and urban geography to examine the Toronto housing market. Using 2011 and 2016 Census data for census metropolitan areas (CMAs) and census agglomerations (CAs) in Southern Ontario and computed driving costs, the model of car commuting distance is based on variables of allocation of monthly household income to monthly shelter costs and driving costs as a function of the car driving distance to Toronto. Using this model, we can predict the effect on car commuting distance due to changes in any of the variables. The model also offers an explanation for communities of Toronto car commuters beyond a driving radius that we might expect for daily commuting. The model confirms that increases in shelter costs in the Toronto housing market from 2011 to 2016 have forced the boundaries of feasible housing locations outward, and forced households to move farther away, thus increasing car commuting distance.

N. Limão, Anthony Venables, Anthony Venables

S. Radcliffe

S. Holloway, G. Valentine

Xuan Luo, Mingqing Huang, Rui Lv et al.

Sequential location recommendation plays a huge role in modern life, which can enhance user experience, bring more profit to businesses and assist in government administration. Although methods for location recommendation have evolved significantly thanks to the development of recommendation systems, there is still limited utilization of geographic information, along with the ongoing challenge of addressing data sparsity. In response, we introduce a Proximity-aware based region representation for Sequential Recommendation (PASR for short), built upon the Self-Attention Network architecture. We tackle the sparsity issue through a novel loss function employing importance sampling, which emphasizes informative negative samples during optimization. Moreover, PASR enhances the integration of geographic information by employing a self-attention-based geography encoder to the hierarchical grid and proximity grid at each GPS point. To further leverage geographic information, we utilize the proximity-aware negative samplers to enhance the quality of negative samples. We conducted evaluations using three real-world Location-Based Social Networking (LBSN) datasets, demonstrating that PASR surpasses state-of-the-art sequential location recommendation methods

Blair Attard-Frost, David Gray Widder

Researchers, practitioners, and policymakers with an interest in AI ethics need more integrative approaches for studying and intervening in AI systems across many contexts and scales of activity. This paper presents AI value chains as an integrative concept that satisfies that need. To more clearly theorize AI value chains and conceptually distinguish them from supply chains, we review theories of value chains and AI value chains from the strategic management, service science, economic geography, industry, government, and applied research literature. We then conduct an integrative review of a sample of 67 sources that cover the ethical concerns implicated in AI value chains. Building upon the findings of our integrative review, we recommend three future directions that researchers, practitioners, and policymakers can take to advance more ethical practices across AI value chains. We urge AI ethics researchers and practitioners to move toward value chain perspectives that situate actors in context, account for the many types of resources involved in co-creating AI systems, and integrate a wider range of ethical concerns across contexts and scales.