At the European Colloquium of Theoretical and Quantitative Geography 2023 (ECTQG, 2023), Jorge Salgado – researcher at Citadyne – presented the progress of his research entitled: “Cities in the face of green technologies, skills and preferences transitions: a multilevel complex approach”. His agent-based modelling approach allows the simulation of changes in firms technologies and consumer preferences as a result of the green transition. The research has been well received because it simultaneously integrates key elements of the economic system, enabling bottom-up interactions to understand the reconfiguration of urban systems around the world.
If you are interested in this research you can contact Jorge Salgado:
Dr Jingyan Yu, – postdoctoral researcher and member of Citadyne – presented the first results of her research “A model-based spatio-temporal classification of global urban” at the European Colloquium of Theoretical and Quantitative Geography 2023 (ECTQG, 2023). She has developed static and dynamic measures to simulate urban expansion in Functional Urban Areas (FUA) around the world. She has found a global trend of physical spatial dispersion, producing discontinuous, dispersed built-up areas.
If you are interested in this research you can contact Dr Yu:
Charlie Wood of Quantamagazine published in 2022 an article that highlighted the 2017 research work of Roger Guimerà and Marta Sales-Pardo, who discovered a cause of cell division – the process that drives the growth of life – using an unpublished and novel tool, a digital assistant they called a “machine scientist”. The method quickly gained acceptance, and Sales-Pardo & Guimerà are among a handful of researchers developing the latest generation of tools, known as symbolic regression. A description of the key elements of the tool can be found in Guimera et al. (2020).
In general, symbolic regression is a type of machine learning that can identify mathematical relationships between variables in data sets using Bayesian probability theory. It has been used to discover new equations that describe physical phenomena, such as the movement of fluids or the behavior of materials under stress. Researchers supporting the expansion of these methods say we’re on the cusp of “GoPro physics”, where a camera can point at an event and an algorithm can identify the underlying physical equation.
According to Wood’s article, machine scientists are being used in fields such as biology, chemistry and materials science to make new discoveries and accelerate scientific progress. For example, a team led by scientists at London-based artificial intelligence company DeepMind has developed a machine learning model that suggests the properties of a molecule by predicting the distribution of electrons within it.
If you are interested in the state of the art related to these approaches Liu et al. (2023) recently published the article “Data, measurement and empirical methods in the science of science”, which includes the Guimerà and Sales-Pardo experience. The publication is available in:
Free abstract digital background image, public domain CC0 photo.
ChatGTP was launched by OpenAI last November. Since then, it has been widely recognized as a game-changing tool for research and everyday life. In this article by Dymaptic, 6 Ways to Use ChatGPT in Your Everyday GIS Work, you can find out how the most famous chatbot could be integrated into your daily use of GIS. Among the most relevant, ChatGPT could help you decide which tool to use. It is also capable of comparing GIS applications, writing basic code, and explaining what GIS is to a non-specialist.
