News by Subject Technology & Engineering

Munich is home to the world's first fully automated sensor network for measuring urban greenhouse gas emissions based on ground-based remote sensing of the atmosphere. It has been developed by scientists in the group of Jia Chen, Professor of Environmental Sensing and Modeling at the Technical University of Munich (TUM). Now, anyone can view the measurement data via an Internet platform.

A team of researchers led by Matti Pirinen of the University of Finland used more than 18,000 Finnish samples to take an incredibly detailed look at how Finnish genetics have shifted, year by year, from the 1920s to the 1980s. They describe their results in a new paper published March 4th in PLOS Genetics.

Researchers examined e-scooter use in Washington, D.C. and found that built environment and demographics both matter. Tourist attractions, hotels and metro stops are all predictive of higher destinations. Scooter traffic is almost all in the downtown area, near the Mall, the White House and Congress. Younger median age, percentage of bachelor's degrees and population density each were positive predictors for both trip origins and destinations. This model will help transportation planners figure out what drives e-scooter use.

The unprecedented cost of the 2018 Kilauea eruption in Hawai'i reflects the intersection of distinct physical and social phenomena: infrequent, highly destructive eruptions, and atypically high population growth, according to a new study led by University of Hawai'i at Mānoa researchers.

The University of Toronto Engineering research team used city and survey data to map Toronto's entire cycling network - including the new routes - and found that additional bike infrastructure increased low-stress road access to jobs and food stores by between 10 and 20 per cent, while boosting access to parks by an average of 6.3 per cent.

Sharing the press release that I hope you will find interesting: A research team from Skoltech (Russia) and FBK (Italy) presented a method to derive 4D building models using historical maps and machine learning (ML). It is useful for understanding urban phenomena and changes that contributed to defining our cities' actual shapes.