Diffusion et Réception de la Dynamique La Correspondance Entre Leibniz et Wolff

By analysing the correspondence between Leibniz and Wolff, this article aims to present the expository modalities used by Leibniz to transmit his dynamics and the metaphysical system underpinning it. The point is here to study the ambivalence of the vocabulary of action, both as object of the conservation principle and as the essence of substance. By examining the way Wolff receives the dynamics of action, it is possible to highlight, as early as 1705, the uniqueness of his metaphysical framework. While publicizing the dynamics, he propounds a meaning of substance that is unequivocally different.     

Big Code

Big data, the “new oil” of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These “big code” practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy.     

A Visual Analytics System for Space–Time Dynamics of Regional Income Distributions Utilizing Animated Flow Maps and Rank-based Markov Chains

Regional income convergence and divergence has been an active field of research for more than 20 years, and research papers in this field are still being produced at a prodigious rate. Despite their importance for the study of dynamics of income distribution, interactive visualization tools revealing spatiotemporal dimensions of the income data have been sparsely developed. This study introduces a visual analytics system for the space–time analysis of income dynamics. We use state-level US income data from 1929 to 2009 to demonstrate the visual analytics system and its utility for exploring similar data. The system consists of two modules, visualization and analytics. The visualization module, a Web-based front-end called Rank-Path Visualizer (RPV), draws inspiration from the cartographic technique of flow mapping, originally developed by Tobler and embodied in his canonical Flow Mapper application.