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#SpatialDataScience

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Anita Graser 🇪🇺🇺🇦🇬🇪

It's been a pleasure contributing to the "Spatial Data Science Languages" preprint spear-headed by @edzer together with @martinfleis @paleolimbot @robinlovelace @nowosad @josi @loreabad6
fosstodon.org/@martinfleis/114

FosstodonMartin Fleischmann (@martinfleis@fosstodon.org)"Spatial Data Science Languages: commonalities and needs" - that a preprint 11(!) of us wrote together as one of many outcomes of two workshops held in Münster (2023) and in Prague (2024). It summarised where we are, what we share between R, Python and Julia, what are the common challenges, lessons and recommendations - https://arxiv.org/abs/2503.16686 Big thanks belongs especially to @edzer@mastodon.social who kickstarted the whole initiative! And to all the others who participated! #rspatial #geopython #juliageo
#SpatialDataScience#gischat
Public
Dr Urska Demsar

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.
#navigation#migration#DataScience
Continued thread
Public
Dr Urska Demsar

Both projects are funded via the Iapetus Doctoral Training Progamme and are open to both UK and international candidates.

More info on how to apply here:
iapetus2.ac.uk/how-to-apply/

Deadlines:

The DL for international candidates to contact supervisors is 9 Dec, as the supervisor will need to issue a sponsorship code to a suitable international candidate.

UK applicants do not need a code and have time to apply until 3 Jan 2025.

IAPETUS2Studentship CompetitionApplications for Iapetus 2025 studentships are now open. Please start by looking at our projects page for information on our 2025 projects. We recommend contacting the primary supervisor of your preferred project for more information and to discuss your suitability for the project. Application Process Iapetus will support at least 13 funded postgraduate studentships in 2025.To apply
#PhD#GIS#GISchat
Continued thread
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Dr Urska Demsar

Project 2, supervised by @fbenitez.bsky.social:

Identification of native vegetation species by linking UAV imagery with terrestrial spectroradiometers
iapetus2.ac.uk/studentships/id

This project will develop accurate and efficient spatial data science methods to classify and monitor native vegetation species in subtropical regions from #UAV #multispectral #imagery and field #spectrometer data.

IAPETUS2Identification of native vegetation species by linking UAV imagery with terrestrial spectroradiometers
#PhD#GISchat#SpatialDataScience
Public
Dr Urska Demsar

We have two fully-funded PhD projects in spatial data science at the University of St Andrews:

Project 1, supervised by Dr Tania Mendo:

Bridging the divide between distant water fleets and coastal communities in the biggest unregulated fishery in the world
iapetus2.ac.uk/studentships/br
This project will use spatial data science to identify illegal fishing activities from vessel tracking data and satellite imagery.

IAPETUS2Bridging the divide between distant water fleets and coastal communities in the biggest unregulated fishery in the world
#PhD#GISchat#SpatialDataScience
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