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#movementecology

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🌍 after watching "The Core" the other day (fluff B-movie involving the earth's magnetic field), I was reminded to update my #rstats International Geomagnetic Reference Field {igrf} package. The package now uses version 14 parameters as released late 2024.

bluegreen-labs.github.io/igrf/

bluegreen-labs.github.ioInternational Geomagnetic Reference FieldThe 14th generation International Geomagnetic Reference Field (IGRF). A standard spherical harmonic representation of the Earth's main field.

New paper with Beate Zein
&
@jedalong :

⭐A new data-driven paradigm for the study of avian migratory navigation⭐

We propose how the multi-modal multi-scale nature of navigation could be studied w/ data mining, machine learning & AI.

link.springer.com/article/10.1

SpringerLinkA new data-driven paradigm for the study of avian migratory navigation - Movement EcologyAvian navigation has fascinated researchers for many years. Yet, despite a vast amount of literature on the topic it remains a mystery how birds are able to find their way across long distances while relying only on cues available locally and reacting to those cues on the fly. Navigation is multi-modal, in that birds may use different cues at different times as a response to environmental conditions they find themselves in. It also operates at different spatial and temporal scales, where different strategies may be used at different parts of the journey. This multi-modal and multi-scale nature of navigation has however been challenging to study, since it would require long-term tracking data along with contemporaneous and co-located information on environmental cues. In this paper we propose a new alternative data-driven paradigm to the study of avian navigation. That is, instead of taking a traditional theory-based approach based on posing a research question and then collecting data to study navigation, we propose a data-driven approach, where large amounts of data, not purposedly collected for a specific question, are analysed to identify as-yet-unknown patterns in behaviour. Current technological developments have led to large data collections of both animal tracking data and environmental data, which are openly available to scientists. These open data, combined with a data-driven exploratory approach using data mining, machine learning and artificial intelligence methods, can support identification of unexpected patterns during migration, and lead to a better understanding of multi-modal navigational decision-making across different spatial and temporal scales.

Open PhD position:🌟A fully-funded PhD on dolphins' movement in the Adriatic Sea🌟 as a collaboration of St Andrews University and Morigenos.org

Application DL 9/2/2024, see Current Funded Opportunties here: st-andrews.ac.uk/geography-sus

Project description: st-andrews.ac.uk/assets/univer

Every once in a while, I stumble over a paper that has really nice figures, that make me curious to learn how they were created

Fiona Lippert et al.'s "Learning to predict dynamics from weather radar networks" is definitely one of them

Luckily they provide their plotting code at github.com/FionaLippert/FluxRG for all of us to learn from

Cool to see more users from 🐮 😎

Gu, C., Liu, L., Zhang, Y. et al. Understanding the spatial heterogeneity of pressure in the Three-River-Source Region on the . J. Geogr. Sci. 33, 1660–1680 (2023). doi.org/10.1007/s11442-023-214

"The ’s velocity transducer collected the moving speed information, and we used (...) a package for , to obtain the daily and

Interested in movement analysis? Join us at the Disruptive Movement Analysis (DMA’23) GIScience conference
workshop in Leeds, UK, on 12 Sept. Featuring short research & vision papers, student competition, data challenge and a special issue of @ijgis

More info:

Workshop CfP: somayehdodge.info/amd-giscienc

GIScience conference:
giscience2023.github.io/

#MovementAnalysis
#HumanMobility #MovementEcology
#GIScience #GPS #SpatialDataScience
#SpatialBehaviour
#GISchat

Somayeh DodgeDMA 2023By Somayeh Dodge