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1. Activity triangles: a new approach to measure activity spaces

   https://doi.org/10.1007/s10109-023-00414-z  

   Sila-Nowicka, Katarzyna ; Fotheringham, A Stewart ; Demšar, Urška ( October 2023 , Journal of Geographical Systems)

   There is an on-going challenge to describe, analyse and visualise the actual and potential extent of human spatial behaviour. The concept of an activity space has been used to examine how people interact with their environment and how the actual or potential spatial extent of individual spatial behaviour can be defined. In this paper, we introduce a new method for measuring activity spaces. We first focus on the definitions and the applications of activity space measures, identifying their respective limitations. We then present our new method, which is based on the theoretical concept of significant locations, that is, places where people spent most of their time. We identify locations of significant places from GPS trajectories and define the activity space of an individual as a set of the first three significant places forming a so-called “activity triangle”. Our new method links the distances travelled for different activities to whether or not people group their activities, which is not possible using existing methods of measuring activity spaces. We test our method on data from a GPS-based travel survey across three towns is Scotland and look at the variations in size and shape of the designed activity triangle among people of different age and gender. We also compare our activity triangle with five other activity spaces and conclude by providing possible routes for improvement of activity space measures when using real human movement data (GPS data).

    

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2. Protease Activity Analysis: A Toolkit for Analyzing Enzyme Activity Data

   https://doi.org/10.1021/acsomega.2c01559  

   Soleimany, Ava P. ; Martin-Alonso, Carmen ; Anahtar, Melodi ; Wang, Cathy S. ; Bhatia, Sangeeta N. ( July 2022 , ACS Omega)
3. Explosive Magnetotail Activity

   https://doi.org/10.1007/s11214-019-0599-5  

   Sitnov, Mikhail ; Birn, Joachim ; Ferdousi, Banafsheh ; Gordeev, Evgeny ; Khotyaintsev, Yuri ; Merkin, Viacheslav ; Motoba, Tetsuo ; Otto, Antonius ; Panov, Evgeny ; Pritchett, Philip ; et al ( June 2019 , Space Science Reviews)
4. QuActive: A Grammar Based Micro-Activity Modeling Framework for Detecting Activity, Monitroing Activity Quality and Notifying the User

   Emi, I. ; Mondol, A. ; Stankovic, J. ( April 2017 , ICCPS2)
5. Clustering Honeybees by Its Daily Activity [Clustering Honeybees by Its Daily Activity]

   https://doi.org/10.5220/0007387505980604  

   Acuna, Edgar ; Palomino, Velcy ; Agosto, José ; Mégret, Rémi ; Giray, Tugrul ; Prado, Alberto ; Alaux, Cédric ; Conte, Yves ( February 2019 , Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods)

   In this work, we analyze the activity of bees starting at 6 days old. The data was collected at the INRA (France) during 2014 and 2016. The activity is counted according to whether the bees enter or leave the hive. After data wrangling, we decided to analyze data corresponding to a period of 10 days. We use clustering method to determine bees with similar activity and to estimate the time during the day when the bees are most active. To achieve our objective, the data was analyzed in three different time periods in a day. One considering the daily activity during in two periods: morning and afternoon, then looking at activities in periods of 3 hours from 8:00am to 8:00pm and, finally looking at the activities hourly from 8:00am to 8:00pm. Our study found two clusters of bees and in one of them clearly the bees activity increased at the day 5. The smaller cluster included the most active bees representing about 24 percent of the total bees under study. Also, the highest activity of the bees was registered between 2:00pm until 3:00pm. A Chi-square test shows that there is a combined effect Treatment× Colony on the clusters formation. 

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