Hasil untuk "Geography"

Teis Hansen, Lars Coenen, Lars Coenen

R. Baldwin, R. Forslid, Philippe Martin et al.

Economic Geography and Public Policy fills the gap by illustrating many new policy insights economic geography models can offer to the realm of theoretical policy analysis. Focusing primarily on trade policy, tax policy, and regional policy, Richard Baldwin and coauthors show how these models can be used to make sense of real-world situations. The book not only provides much fresh analysis but also synthesizes insights from the existing literature.The authors begin by presenting and analyzing the widest range of new economic geography models to date. From there they proceed to examine previously unaddressed welfare and policy issues including, in separate sections, trade policy (unilateral, reciprocal, and preferential), tax policy (agglomeration with taxes and public goods, tax competition and agglomeration), and regional policy (infrastructure policies and the political economy of regional subsidies). A well-organized, engaging narrative that progresses smoothly from fundamentals to more complex material, Economic Geography and Public Policy is essential reading for graduate students, researchers, and policymakers seeking new approaches to spatial policy issues.

Kate E. Jones, J. Bielby, M. Cardillo et al.

Mark W. Smith, J. Carrivick, D. Quincey

M. Gertler

D. Conradson

A. Hay, D. Harvey

D. F. Roberts, L. Cavalli-Sforza

S. Redding, A. Venables

B. Asheim, M. Gertler

Joshua D. Coval, Tobias J. Moskowitz

D. Dziuba

P. Balland, D. Rigby

abstract There is consensus among scholars and policy makers that knowledge is one of the key drivers of long-run economic growth. It is also clear from the literature that not all knowledge has the same value. However, too often in economic geography and cognate fields we have been obsessed with counting knowledge inputs and outputs rather than assessing the quality of knowledge produced. In this article we measure the complexity of knowledge, we map the distribution and the evolution of knowledge complexity in US cities, and we explore how the spatial diffusion of knowledge is linked to complexity. Our knowledge complexity index rests on the bimodal network models of Hidalgo and Hausmann. Analysis is based on more than two million patent records from the US Patent and Trademark Office that identify the technological structure of US metropolitan areas in terms of the patent classes in which they are most active between 1975 and 2010. We find that knowledge complexity is unevenly distributed across the United States and that cities with the most complex technological structures are not necessarily those with the highest rates of patenting. Citation data indicate that more complex patents are less likely to be cited than less complex patents when citing and cited patents are located in different metropolitan areas.

Defu Lian, Yongji Wu, Yong Ge et al.

Sequential location recommendation plays an important role in many applications such as mobility prediction, route planning and location-based advertisements. In spite of evolving from tensor factorization to RNN-based neural networks, existing methods did not make effective use of geographical information and suffered from the sparsity issue. To this end, we propose a Geography-aware sequential recommender based on the Self-Attention Network (GeoSAN for short) for location recommendation. On the one hand, we propose a new loss function based on importance sampling for optimization, to address the sparsity issue by emphasizing the use of informative negative samples. On the other hand, to make better use of geographical information, GeoSAN represents the hierarchical gridding of each GPS point with a self-attention based geography encoder. Moreover, we put forward geography-aware negative samplers to promote the informativeness of negative samples. We evaluate the proposed algorithm with three real-world LBSN datasets, and show that GeoSAN outperforms the state-of-the-art sequential location recommenders by 34.9%. The experimental results further verify significant effectiveness of the new loss function, geography encoder, and geography-aware negative samplers.

Helen F. Wilson

Pol Antràs, Alonso de Gortari

This paper develops a multi‐stage general‐equilibrium model of global value chains (GVCs) and studies the specialization of countries within GVCs in a world with barriers to international trade. With costly trade, the optimal location of production of a given stage in a GVC is not only a function of the marginal cost at which that stage can be produced in a given country, but is also shaped by the proximity of that location to the precedent and the subsequent desired locations of production. We show that, other things equal, it is optimal to locate relatively downstream stages of production in relatively central locations. We also develop and estimate a tractable, quantifiable version of our model that illustrates how changes in trade costs affect the extent to which various countries participate in domestic, regional, or global value chains, and traces the real income consequences of these changes.

D. Richardson, N. Castree, M. Goodchild et al.

Yixian Chen, Sofie De Geeter, Jean Poesen et al.

Gully formation is a significant driver of soil erosion and land degradation worldwide and often leads to important downstream impacts. Nonetheless, our understanding of the global patterns and the factors controlling this process remains limited. Here, we present the first global assessment of gully density's spatial patterns. Using mapped observations from over 17,000 representative study sites worldwide, we trained random forest models that simulate both the susceptibility to gullying at a 1 km2 resolution and the corresponding gully head density (GHD). Through an interpretable machine learning framework, we demonstrate that global GHD patterns result from a combination of environmental factors with non-linear interactions, leading to significant regional variations in the dominant factors controlling GHD. We distinguish between gully hotspots driven primarily by natural factors such as topography, geomorphology, tectonics, pedology or climate and those where land use and land cover play a dominant role. Based on these insights, we identified critical global areas of gully erosion, i.e., hotspots where gully occurrence is likely highly sensitive to anthropogenic drivers. These include the Chinese Loess Plateau, the Ethiopian Highlands, and large parts of the Mediterranean and Sahel regions. Also desert regions are often characterized by high GHDs. However, in these cases, their occurrence is mainly driven by natural factors. The insights we provide are valuable to inform land management and targeted erosion mitigation strategies.

Louis Boucherie

Complex systems are made up of many interacting components. Network science provides the tools to analyze and understand these interactions. Community detection is a key technique in network science for uncovering the structures that shape the behavior of these networks. This thesis introduces the Adaptive Cut, a novel method that improves clustering methods by employing multi-level cuts in hierarchical dendrograms. Overcoming the limitations of traditional single-level cuts-especially in unbalanced dendrograms-the Adaptive Cut provides a multi-level cut by optimizing a Markov chain Monte Carlo with simulated annealing. In addition, we propose the Balanceness score, an information-theoretic metric that quantifies dendrogram balance and predicts the benefits of multilevel cuts. Evaluations on over 200 real and synthetic networks show significant improvements in partition density and modularity. In the second part, our analysis shows that incorporating network geometry allows redefining administrative boundaries into non-contiguous regions that better reflect social and spatial dynamics. We also discuss the representation of hierarchical data in hyperbolic space through Poincare maps, which can represent tree-like structures in low dimension. In addition, we examine how geography constrains human mobility, an aspect often overlooked in scale-free characterizations of mobility. By incorporating geography via the pair distribution function from condensed matter physics, we separate geographic constraints from mobility choices. Analyzing datasets containing millions of individual movements, we identify a universal power law that spans five orders of magnitude, thereby bridging the divide between distance-based and opportunity-driven models of human mobility.

Lisa Dellmuth, Maria-Therese Gustafsson, Suanne Mistel Segovia-Tzompa

Abstract There is a lively debate about the legitimacy of the international climate regime, as represented by the United Nations Framework Convention on Climate Change, and the quality of non-state actor participation in the regime. This commentary examines perceptions of involved non-state actors from 2021–2022 regarding their participation and regime legitimacy. The findings reveal no legitimacy crisis for the adaptation and mitigation regimes, but the surveyed NSAs are divided in their legitimacy beliefs. NSAs also express significant disappointment about their opportunities for participation.