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#giscience

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Public
Tuomas Väisänen 📼🧟‍♂️

We wrote a small piece on our open #Erasmus #StudentMobility data for a Finnish GIS professional magazine, Positio, with @miladmzdh, Oula Inkeröinen and Olle Järv. It is in #Finnish though.

It just came out:
positio-lehti.fi/2025/05/avoim

Positio · Avoimella opiskelijavaihtoaineistolla uusia näkökulmia Euroopan aluekehitykseen - Yli 2.2 miljoonan opiskelijavaihdon geokoodattu aineisto Euroopasta saatavilla! | PositioIhmisten liikkuvuus on saanut paljon huomiota paikkatietoalalla viime vuosina eikä syyttä. Liikkuvuutta kuvaavia aineistoja käytetään laajalti eri
#GIScience#Paikkatieto#Positio
Public
Anita Graser 🇪🇺🇺🇦🇬🇪

PhD position Geographic Information Visualization and Analysis (GIVA) “GeoNavLearn” with Prof. Sara I. Fabrikant

jobs.uzh.ch/job-vacancies/phd-

UZHUZH: PhD position Geographic Information Visualization and Analysis (GIVA) “GeoNavLearn”With 28,000 students and 9,000 employees, the University of Zurich (UZH) is the biggest university in Switzerland. As a member of the League of European Research Universities, it counts amongst the leading European universities. The UZH offers a culturally diverse and inspiring working environment and promotes international collaboration in research. The Geographic Information Visualization and Analysis (GIVA) group at the University of Zurich, Faculty of Science, invites applications for a 4-year PhD position with the SNF-funded Project “GeoNavLearn: Towards geographic context-adaptive mobile geographic information displays (mGIDs) to support navigators' spatial learning” led by GIVA head Prof. Dr. Sara I. Fabrikant. The initial appointment is for the first year, with a three-year extension.
#GISjobs#GISChat#GIScience
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HeiGIT

HeiGIT and Urban Big Data Centre are hosting a joint workshop on “𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀” at the 𝗚𝗜𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗰𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝟮𝟬𝟮𝟱: giscience2025.org/workshops.ht
We are looking forward to your submissions on participatory mapping and your experiences engaging with communities!

📣We will also be giving a talk on “𝗔𝘀𝘀𝗲𝘀𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗠𝗮𝗽𝗽𝗶𝗻𝗴”: giscience2025.org/abstracts.ht

See you there!

Call for Contributions GIScience 2025: Mapping with Communities
#GIscience#GIScience2025#ParticipatoryMapping
Continued thread
Public
Tuomas Väisänen 📼🧟‍♂️

Following that is @tadusko presenting his work on the rise and fall of Flickr and use of its data in #GIScience at the #LBS2025 conference.

Key takeaway is that #Flickr is highly biased data produced in the Global North by a select few highly active users, but widely used in research because it is the only major social media platform that still allows data collection using its API. The question remains whether the data should be used.

Brilliant work all in all!

Tatu presents his work at Location Based Services conference.
Tatu shows how the amount of Flickr data shared is reducing but the amount of research using the data is increasing.
Replied in thread
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OpenHistoricalMap

@grimalkina Next month’s #StateOfTheMapUS will have a healthy mix of the human and tech sides, both grassroots and institutional, that would be relevant to those in or adjacent to #GIScience. Our friends at @OpenStreetMapUS offer discounts for academic affiliation, and they just started raising funds to hopefully offer scholarships for affected government employees too. en.osm.town/@OpenStreetMapUS/1

OSM Town | Mapstodon for OpenStreetMapOSM US (@OpenStreetMapUS@en.osm.town)Attached: 1 image Many government agencies have seen recent reductions in spending, limiting participation in conferences like SotM US. Events like ours are critical for building relationships and sharing knowledge. Government partners are valued stakeholders in collaborations for #opendata. OSM US wants to fill that gap, and we need your help! This fund will support scholarships for US-based current and recent government employees to attend #StateoftheMapUS2025. 🔗 https://openstreetmap.app.neoncrm.com/forms/-sotmus-gov-fund
Public
Tuomas Väisänen 📼🧟‍♂️

A quick illustrative #map on the implications of using different types of centroids in regional #demography #mobility and #migration studies.

Think about what you are emphasizing.

By using population-weighted centroids, you can emphasize the population perspective in the analysis instead of the geometry, potentially improving your analysis considerably. The distance difference is stronger in sparsely populated and remote regions.

A map of some European countries on the NUTS 3 regional level, where each region contains a geometrically calculated centroid, and a population-weighted centroid. That is, the centremost geographical point of the region. In Northern Europe, the distance difference between the two types of centroids can be hundreds of kilometres, which can have massive implications in studies which use centroids to calculate distance-based metrics. Using the geometric centroid will not provide representative metrics of the region from the perspective of the population. The distance difference in Central European NUTS 3 regions is much less considerable, and the differences are measured in kilometres instead of hundreds of kilometres.
#GIScience#Geospatial#cartography
Public
Tuomas Väisänen 📼🧟‍♂️

I have struggled with many IDEs with their lack of good integration of interactive #Python.

VSCode and its derivatives are nice, but they do not play ball with my workflow. I can get what I want to work only partially, and it is much slower than my current workflow. Running one line at a time in an interactive window is excruciating.

Isn't it common in #DataScience and #GIScience to have an interactive workflow with a lot of back and forth reiterations with multiple line executions?

Public
Anita Graser 🇪🇺🇺🇦🇬🇪

I really enjoyed the interdisciplinary collaboration sparked by the gAia project on predicting #landslides, resulting in our recent #preprint:

📝 "Visualizing uncertainties in landslide susceptibility modeling using bivariate mapping"

nhess.copernicus.org/preprints

nhess.copernicus.orgBrief communication: Visualizing uncertainties in landslide susceptibility modeling using bivariate mappingAbstract. Effectively communicating uncertainties inherent to statistical models is a challenging yet crucial aspect of the modeling process. This is particularly important in applied research, where output is used and interpreted by scientists and decision makers alike. In disaster risk reduction, susceptibility maps for natural hazards are vital for spatial planning and risk assessment. We present a novel type of landslide susceptibility map that jointly visualizes the estimated susceptibility and the corresponding prediction uncertainty, using an example from a mountainous region in Carinthia, Austria. We also provide implementation guidelines to create such maps using popular free and open-source software packages.
#QGIS#RStats#GIScience
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