Tag: Data

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Complexity Digital twins Simulation Urban

The Rise of Digital Twins

Last March, Nature Computational Science published an issue dedicated to digital twins. From the simulators used by NASA in the Apollo missions to today’s applications in urban planning, the concept of digital twins is more alive than ever. 

Since that first application, digital twins have been nurtured by significant advances in computing power, data generation and the emergence of a wide range of methods and tools for building the ‘living models’. These developments have extended the possibilities of digital twins from industry and engineering to other fields such as social sciences, biomedicine, climate science and others, which bring their own challenges, requirements and discussions.

This issue of Nature Computational Science highlights the most recent developments in this flourishing field, bringing together expert point of views on the needs, gaps and opportunities for implementing digital twins in different subjects. In the context of urban planning, Michael Batty presents an article that begins with a discussion of the definition of digital twins and argues that the gap between real and digital is not the same for physical assets than for social and organizational systems. Batty claim, as well as the need to integrate humans in the loop design and use of digital twins, reminding as cities could be inherently unpredictable.

If you are interested in this issue, follow this link:

https://www.nature.com/collections/feicjiideh

Follow this link to read Michael Batty article:

https://www.nature.com/articles/s43588-024-00606-7

Source: Busakorn Pongparnit / Moment / Getty Images

Categories
Artificial Intelligence Networks physics Programming SCIENCE

Empirical methods and the ability to find law of physics in raw data 

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and Marta Sales-Pardo, who discovered a cause of cell division – the process that drives the growth of life – using an unpublished and novel tool, a digital assistant they called a “machine scientist”.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:

https://www.nature.com/articles/s41562-023-01562-4

If you are interested in Guimerà et al. (2022) “machine scientist” you can follow this link:

https://www.science.org/doi/full/10.1126/sciadv.aav6971

Image source: https://www.quantamagazine.org/

Categories
Complexity Economy Geography Research project Valuation

Measuring the Value of Location Data: A Step-by-Step Guide

Measuring the Value of Location Data: A Step-by-Step Guide

The UK’s Geospatial Commission – part of the Department for Science, Innovation and Technology – has recently published a framework to advise the UK government on the most productive and economically valuable uses of geospatial data. This effort is a result of commitments made in the UK’s Geospatial Strategy, published in 2020 and reiterated in the 2022/23 Annual Plan of the Commission.

To address this major challenge and given the growing importance of location data in sectors as diverse as urban planning, transport and public services. The Geospatial Commission presents a step-by-step approach to effectively measure the value of location data. It emphasizes the importance of considering both economic and non-economic factors in the valuation process. 

The framework includes aspects such as data quality, usability, accessibility and societal impact to provide a holistic view of the value of location data. According to the Commission, “assessing the value of location data is difficult because: (i) value is often only realized when location data is combined with other data sets, (ii) value varies depending on the intended use, and (iii) value can be difficult to predict”. 

In response to these challenges, the methodology for measuring the value of location data covers key components such as data collection, data management, data analysis and data use. It also emphasizes the integration of location data with other datasets and the use of advanced technologies, such as artificial intelligence and machine learning, to unlock its full potential.

The framework guide, a summary of its steps, its application and results can be found at the following link:

https://www.gov.uk/government/publications/measuring-the-economic-social-and-environmental-value-of-public-sector-location-data

Image: UK Geospatial Commission