Tranformative capacities, 06/03/2024, Alexander Dunkel

<!-- first page, leave blank --> Notes: - introduction

Geosocial media as ### <span class="highlight">Social-Ecological-Technological Systems (SETS)</span> > "[...] couplings between social, ecological, and technical components [...] in which algorithms [...] operate" <p class="smallgrey"> Rakova & Dobbe (2023). <br> <em>Algorithms as Social-Ecological-Technological Systems: <br>An Environmental Justice Lens on Algorithmic Audits.</em> <a href="https://doi.org/10.1145/3593013.3594014">DOI</a> </p> Note: - SETS to understand Social Media integration into todays environment --- <img class="plain" alt="Schema: SETS and the facets framework" src="images/2024-03-04_sets.png"/> <div style="position:absolute;text-align:left;top:75px;left:25px;z-index:9999;font-size:xx-large;"> <p class="highlight" style="vertical-align: bottom;background: rgba(255, 255, 255, 0.0);" > System framing: <br>Transformative Capacities (TC) & <br> Social Media </p> </div> --- <img class="plain" alt="Schema: SETS and the facets framework (highlighted TC potential)" src="images/2024-03-04_sets_p2.png"/> <div style="position:absolute;text-align:left;top:75px;left:25px;z-index:9999;font-size:xx-large;"> <p class="highlight" style="vertical-align: bottom;background: rgba(255, 255, 255, 0.0);" > Social Media mediates <br>frequentation patterns, <br>place perception &<br> user interest </p> </div> <div style="position:absolute;text-align:right;bottom:100px;right:25px;z-index:9999;font-size:xx-large;"> <p class="highlight_orange" style="vertical-align: bottom;background: rgba(255, 255, 255, 0.0);" > Visualize <br> Base Components <br> (understanding <br>preconditions) </p> </div> Notes: - Social Media mediates frequentation patterns, place perception and user interest --- <span class="highlight">Five facets of reactions on social Media:</span> Dunkel et al. (2019). _A conceptual framework for studying collective reactions to events in location-based social media._ [DOI](https://doi.org/10.1080/13658816.2018.1546390) Notes: - pointed out in several publications - in practice, no significant change --- ### TC <span class="highlight">Identify weaknesses and opportunities for improvement</span> 1. <span class="highlight">Depth focus</span>: Geosocial media indicator development for Transformative capacities 2. <span class="highlight">Broad impact</span>: Interactive tools for evaluating preconditions by decision makers --- ### Hypothesis (1.): Depth focus <span class="highlight"><em> If transformative change emerges from specific temporal, spatial and social factors coinciding, then geosocial media can be filtered to (1.1) identify, (1.2) monitor and (1.3) leverage situations in which TC are known to emerge. </em></span> --- - <span class="highlight">Goal & Output</span>: Spatial localization & semantic filtering for identification and monitoring of TC within Germany; statistics - <span class="highlight">Focus</span>: development of conceptual foundations; data processing - <span class="highlight">Resolution & area</span>: cities and districts DE; selective comparison EU [Monitoring](https://www.flickr.com/photos/64974314@N08/8140970785/in/dateposted/) | [Quantitative Analysis](https://journals.plos.org/plosone/article/figure?id=10.1371/journal.pone.0280423.g003) | [Filtering](https://www.flickr.com/photos/64974314@N08/23135385683/in/dateposted/) Notes: - Indicator development based on past observations - e.g. Münster: Map Sustanability Discourse over time; extract topics and terms, semantic similarity - comparison cities; regions; visualize relative proportion of topics --- ### Hypothesis (2.1): Broad impact <span class="highlight"><em> If Geosocial media as a technological factor increasingly affects social and ecological systems, understanding and visualization of conditions and effects of information spread, algorithms and AI (the T in SETS) becomes a prerequisite for sustainable urban transformation. </em></span> --- - <span class="highlight">Goal & Output</span>: Overview geosocial media, wide range of possible uses due to low pre-filtering; identification of status quo ("understanding preconditions"), Geosocial Tile Service (Flickr, Twitter, iNaturalist, Instagram) - <span class="highlight">Focus</span>: Overview, information, database, data processing: ethics & law - <span class="highlight">Resolution & area</span>: DE; resolution based on data density [Greater Toronto Area](https://maps.alexanderdunkel.com/?mapid=5) | [Lübecker Bucht](https://flic.kr/p/2pjJoux) | [Oderbruch](https://www.flickr.com/photos/64974314@N08/52883250009/in/dateposted/) --- ### Hypothesis (2.2.): Intersection <span class="highlight"><em> When geosocial media patterns reflect subjective user behaviour (interests, locations, group), quantitative complement to contemporary data sources (e.g., surveys, Earth observation data) can support planners' ability to more holistically identify weaknesses and leverage TC synergies in coupled social-environment-technological systems (SETS) to make change possible and more likely. </em></span> --- - <span class="highlight">Goal & Output</span>: Development and promotion of TC by intersecting with application-related data (areas L, M, R); Interactive Interface - <span class="highlight">Focus</span>: Methodology and tool development; synergy effects through collaboration Data processing - <span class="highlight">Resolution & area</span>: worldwide --- <div style="width:100%;height:100%;position:relative;bottom:-550px;text-align:center;z-index:9999;"> <p style="vertical-align: bottom;background: rgba(255, 255, 255, 0.8)" > <strong>Emoji Tag Map, Campus TU Dresden</strong> </p> </div> <!-- .slide: data-background="images/campus.png" --> Notes: - implementation of privacy aware data structure (package update) - Mapnik rendering, pipeline setup --- Challenges: Topic Filtering & Visualization --- <iframe class="stretch" style="z-index:100;"frameBorder="0" data-src="https://geo.rocks/hexbins/"></iframe> <div class="darkgrey" style="width:100%;height:100%;position:absolute;top:85%;text-align:center;z-index:9999;"> <p style="vertical-align: bottom;background: rgba(255, 255, 255, 0.8)" > <strong>Dominik Weckmüller (PhD), Bonn, Interactive filtering</strong> </p> </div> <!-- .slide: data-background="#232323" --> --- <iframe class="stretch" frameBorder="0" data-src="https://geo.rocks/semanticfinder/"></iframe> <!-- .slide: data-background="#232323" --> --- <div style="height:99vh"> <br> <span class="highlight">Signed Chi</span> <div style="text-align:center;width:100%"> <img class="plain" style="width:55%;height:auto;left:50%;" alt="Chi-square equation" src="images/chi.png"/> </div> <div style="position:relative;"> <p style="vertical-align: bottom;text-align:center" > 1. A <span class="highlight">generic query</span> (expected; e.g. all Flickr images) 2. A <span class="highlight">specific query</span> (observed; e.g. "sunset"-reactions) Dunkel at al. (2023). [DOI](https://doi.org/10.1371/journal.pone.0280423) </p> </div> </div> Notes: This equation was originally developed by Visvalingam (1978), UK Census Unit, to visualize over- and underrepresentation in spatial data. --- <div style="height:50vh;width:50vw;"> <div style="position:absolute;text-align:right;bottom:0;right:50px;z-index:9999;font-size:x-large;"> <p style="vertical-align: bottom;background: rgba(255, 255, 255, 0.0); color:lightgrey; " > Sunset in Oia, Santorini <br> CC BY-SA 2.0 Pedro Szekely/Wikipedia </p> </div> </div> <!-- .slide: data-background="images/flickr_oia_sunset.jpg" --> Notes: - over- and underrepresentation of reactions to the sunset and sunrise worldwide - datasets: 700 Million georeferenced Flickr and Instagram photographs --- <div style="height:99vh"> <img class="plain" style="width:1100px;height:auto;position:absolute;top:50px;left:100px" alt="Sunset-sunrise map worldwide for Instagram" src="images/sunsetsunrise.png"/> <img class="plain fragment" data-fragment-index="1" style="z-index:9998;width:1100px;height:auto;position:absolute;top:50px;left:100px" alt="Sunset-sunrise map worldwide for Flickr" src="images/sunsetsunrise2.png"/> <div style="position:relative;top:550px;z-index:9999"> <p style="vertical-align: bottom;text-align:center" > ▶ consensus among the top-ranked 20% of choices </p> </div> </div> Notes: - striking commonalities, despite the fundamental differences between these two networks - consensus among the top-ranked places to view the sunset and sunrise worldwide - some interesting differences: Burning Man example --- <img class="plain" style="height:500px;" alt="Sunset-sunrise map close-up: Italy" src="images/01_sel_2.png"/> <br> Relative ranking of places (sunset and sunrise) for Central Europe. Source: Instagram Notes: - different form of visualization - confirm expectations: east/west coast patterns --- <img class="plain" style="width:80%;" alt="Sunset-sunrise map close-up: Indonesia" src="images/indonesia.png"/> <br> Relative ranking of places (sunset and sunrise) for Indonesia. Source: Instagram Notes: - identify surprising trends, e.g. Sunrise tours in Indonesia --- <div style="height:50vh;width:50vw;"> <div style="position:absolute;text-align:right;bottom:100px;right:50px;z-index:9999;font-size:x-large;"> <p style="vertical-align: bottom;background: rgba(255, 255, 255, 0.0); color:lightgrey; " > "Sunrise Watchers, Mount Batur, Bali" <br> Howard Somerville/Flickr </p> </div> </div> <!-- .slide: data-background="images/batur.png" --> Notes: - obvious: data and behaviour is heavily influenced by online communication trends --- <div style="height:99vh"> <p style="z-index:9999;width:100%;position:relative;top:50px;"><span class="highlight">Global Spread of Information</span><br> <span class="small fragment">Instagram "Mass invasions", Øian et al. (2018), p.1229 ^{<a href="https://doi.org/10.6027/TN2018-534" class="smallsuper fa fa-external-link"></a>}</span></p> <br> <div style="text-align:center;height:500px;z-index:9998"> <img class="plain" style="width:100%;height:auto;left:50%;" alt="Visitation statistics for several vantage points in Europe as measured from Instagram" src="images/comparison_pois.png"/> </div> <div style="width:400px;position:absolute;top:400px;right:0px;text-align:right;z-index:9999;background-color:white"> <em><p style="vertical-align: bottom" class="small"> Frequentation of popular </br>vantage points in Europe </br>from Instagram data </br>Claudia Tautenhahn, </br>Master thesis, TUD 2019/20 </p></em> </div> </div> Notes: - global spread of information also has negative effects on landscapes - examples: overfrequentation at popular tourist spots in Europe - Dark Hedges, known from Game of Thrones - seasonal periodicity + trends

## Event Inventories? Catalog events and temporal valuation patterns for places and regions Notes: - question for this "scoping study" - catalog events and temporal patterns for regions --- Two key tasks: 1. Reduce <span class="highlight">bias</span> in the data to increase representativeness 2. Increase <span class="highlight">precision</span>, to derive more reliable and useful inferences <p class="fragment" data-fragment-index="5">Rapacciuolo et al. (2021), p.1229 ^{<a href="https://doi.org/10.1111/oik.08215" class="smallsuper fa fa-external-link"></a>}</p> Notes: - publication for biodiversity monitoring based on crowdsourced online data - bias and precision often inversely related --- Reduce bias: - through aggregation - for example, by sampling from a larger, more representative number of observers and by integrating data from different platforms Notes: - e.g. combining data from multiple sources - complicates data analysis due to differences in data collection --- Increase precision: - through filtering - for example, by querying based on time, space and semantics Notes: - filtering will reduce available data and thus, representativity --- <div style="height:99vh;"> <br> <strong>Study 1</strong> ^{<a href="https://kartographie.geo.tu-dresden.de/ad/wip/ephemeral_events/html/05_hotspots.html" class="smallsuper fa fa-external-link"> </a>}<br> <img class="plain" style="width:auto;height:40%;left:50%" alt="Overview hotspots (DE map)" src="images/overview_hotspots.svg"/> <div style="position:relative;top:-50px;z-index:9999"> <p style="vertical-align: bottom;text-align:center" > 30 Biodiversity Hotspots in Germany<br> Data Source: Instagram, Flickr, Twitter, iNaturalist<br> </p> </div> </div> Notes: - primary goal: reduce bias, increase representativeness - no filtering, except space --- <div style="height:99vh;"> <br><br> <img class="plain" style="width:80%;height:auto;left:50%" alt="Postcount averages statistics (Flickr, Instagram, iNaturalist, Twitter) over all 12 months for three Biodiversity Hotspots" src="images/2023-08-14_Figures_p1.png"/> </div> Notes: - absolute measurements of observed user activities - striking differences: iNaturalist (Hikers, Nature lovers); - areas popular among families in summer, etc. - corroboration --- <div style="height:99vh;"> <br> <strong>Study 2</strong> ^{<a href="https://kartographie.geo.tu-dresden.de/ad/wip/ephemeral_events/html/02_reddit_nationalparks.html" class="smallsuper fa fa-external-link"></a>}<br> <img class="plain" style="width:80%;height:auto;left:50%" alt="Reddit National Park statistics" src="images/barplot_nationalparks.png"/> <div style="position:relative;top:-50px;z-index:9999"> <p style="vertical-align: bottom;text-align:center" > 20 US Nationalparks <br> Data Source: Reddit<br> Comments (red) & submissions (blue) </p> </div> </div> Notes: - Data availability: Georeferences available at different granularities - single data source, limited representativeness --- <div style="height:99vh;"> <img class="plain" style="width:60%;height:auto;object-fit:contain;left:50%" alt="Reddit National Park statistics (Joyplot)" src="images/joyplot_nationalparks.png"/> <div style="position:relative;top:-100px;z-index:9999"> <p style="vertical-align: bottom;text-align:center" > Relative seasonal communication patterns (Joyplot) </p> </div> </div> Notes: Mountain peaks are used as a metaphor for the volume of monthly patterns that deviate from the norm (the average monthly frequency for each park), and distance/order and absolute height of the peaks are used to symbolize the total available volume of data, which increases with distance --- <div style="height:99vh;"> <br> <strong>Study 3</strong> ^{<a href="https://kartographie.geo.tu-dresden.de/ad/wip/ephemeral_events/html/04_milvus_maps.html" class="smallsuper fa fa-external-link"></a>}<br> <img class="plain" style="width:50%;height:auto;object-fit:contain;left:50%" alt="Milvus milvus map and statistics for Europe" src="images/milvus.png"/> <div style="position:relative;top:-50px;z-index:9999"> <p style="vertical-align: bottom;text-align:center" > Observations of the red kite (Milvus milvus) in Europe <br> Data Source: iNaturalist<br> 2007-2022 </p> </div> </div> Notes: - identify long-term temporal trends for specific ephemeral values - filtering significantly reduces the available variance, richness, and representativeness of the data --- <div style="height:99vh;"> <img class="plain" style="width:100%;max-height:80%;object-fit:contain;left:50%" alt="Temporal chi evaluation for Milvus milvus and three regions" src="images/2023-08-31_Figures.png"/> <div style="position:relative;top:-50px;z-index:9999"> <p style="vertical-align: bottom;text-align:center" > Temporal signed-chi evaluation for three regions </p> </div> </div> Notes: - expected trends (all photographs) vs. observed trends - difficult to interpret - shifts in platform use and community fluctuations - recommendation: benchmark species; or benchmark group

<div style="height:99vh;"> <br> <strong>Outlook: Towards <em>Event Inventories</em></strong><br> <span class="small">Poor data availability</span> <img class="plain" style="width:250px;height:auto;position:absolute;top:20%;left:0;right:0;margin-left:auto;margin-right:auto;" alt="Nevada wikidata map" src="images/nevada.png"/> <img class="plain fragment" data-fragment-index="1" style="z-index:9998;width:250px;height:auto;position:absolute;top:20%;left:0;right:0;margin-left:auto;margin-right:auto;" alt="Saxony wikidata map" src="images/saxony.png"/> <div style="position:relative;top:40%;z-index:9999"> <p style="vertical-align: bottom;text-align:center" > Wikidata SPARQL Query: Events ^{<a href="https://kartographie.geo.tu-dresden.de/ad/wip/ephemeral_events/html/03_wikidata_event_query_nevada.html" class="smallsuper fa fa-external-link"></a>} </p> </div> </div> Notes: - combine different data sources to build Event Inventories, e.g. Wikidata - very little available: town fairs, festivals, but also wildfires and mass shootings - includes personal observations - geosocial media data helps to underpin observations with data --- <div style="height:99vh;"> <br> <strong>Outlook: Towards <em>Event Inventories</em></strong><br> <span class="highlight">What works well</span> <ul style="font-size:xx-large;text-align:left;width:80%;"> <li>visualization of periodic, recurrent, and seasonal patterns</li> <li>small scale contexts (regional, continental or global)</li> <li>well-defined events</li> </ul> <span class="fragment"> <span class="highlight">Challenges</span> <ul style="font-size:xx-large;text-align:left;width:80%;"> <li>varying specificity as a key challenge in capturing landscape change through user-generated content</li> <li>fluctuating user groups and platform incentives</li> <li>data retrieval, API access</li> </ul> </span> </div> --- Thank You <!-- .slide: data-background="#232323" -->