[{"data":1,"prerenderedAt":226},["ShallowReactive",2],{"work-en-an-aura-of-words":3,"work-cards-en":139},{"id":4,"title":5,"approach":6,"body":50,"brief":57,"card":63,"description":54,"extension":67,"gallery":68,"hero":93,"heroAlt":94,"meta":95,"navigation":96,"outcome":97,"path":101,"problem":102,"reflection":106,"resources":107,"role":125,"seo":134,"status":135,"stem":136,"summary":137,"__hash__":138},"work_en\u002Fwork\u002Fan-aura-of-words.md","An Aura of Words",[7,13,19,32,44],{"label":8,"title":9,"prose":10},"Premise","Small data over the official record",[11,12],"The course's idea that stuck was small data: against the reflex to reach for big datasets, it argues for situated, citizen-generated data, qualitative and grounded in lived experience, and meaningful precisely because it is not produced to satisfy a report. A Google Review is exactly that kind of small data.","So the project's whole stance is a debate, not a verdict. It does not ask whether the parks are good or bad. It asks whose experience counts in how we understand public space, and whether the collective voice of citizens, once made legible, can say something the official channels cannot.",{"label":14,"title":15,"prose":16},"Data","Reviews, scraped and kept whole",[17,18],"The raw material is Google Reviews of five Lugano parks, gathered with a custom Python scraper that loads each park's reviews and pulls the text, the star rating, the date, and the reviewer name. Only reviews with written text were kept, in whatever language they were left in, mostly Italian, with German, English, and French in the mix.","The five parks are deliberately unequal, because that is how attention really falls: Parco Ciani alone accounts for more than 1'600 reviewed words, while little Parco Lambertenghi contributes 65. We kept that imbalance rather than normalizing it away, because the volume of what people choose to say about a place is itself part of the portrait.",{"label":20,"title":21,"prose":22,"artifacts":25},"Lexicon","Six lenses on the language",[23,24],"Every meaningful word is sorted into one of six lenses: the visitor's inner state, the sensory environment, the actions people take, the social context, the physical infrastructure, and tension or complaint. Functional words are ignored; only the words that carry real descriptive weight are kept.","The encoding is a hand-curated lexicon matched word by word across each park's reviews. Each occurrence also records the other meaningful words that appear alongside it in the same review, so the data captures not just which words are used, but which ones tend to appear together.",[26],{"src":27,"alt":28,"caption":29,"decision":30,"width":31},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fartifact-lexicon.jpg","A single Google review with each meaningful word tinted by its lens: quiet in green, families in yellow, benches and playground in orange, neglected boxed in red, with thin lines tracing each term down to the six-lens legend.","The lexicon, applied — one review, matched word by word.","Matching words against a hand-built lexicon, rather than scoring sentiment automatically, is what let the encoding keep a review's ambiguity instead of flattening it to a rating.","wide",{"label":33,"title":34,"prose":35,"artifacts":38},"Encoding","Language as a living aura",[36,37],"Each park's aura is an organic, color-blended blob where the area of each color is the proportional weight of that category in its reviews, with a subtle breathing motion so it reads as a living portrait rather than a fixed chart. Color is the primary encoding, so a reader recognizes a category instantly, without labels.","What gives each aura its character is how the lenses mix. People rarely describe a park in one register; a single review often moves between feeling, sensory detail, and complaint in the same breath. The aura is built to show that blend, not just which lens is largest, but which ones tend to occur together, which is what makes one park's portrait recognizably different from another's.",[39],{"src":40,"alt":41,"caption":42,"decision":43,"width":31},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fartifact-encoding.jpg","A single park's color-blended aura beside the six-category legend, the area of each color showing its proportional weight.","The aura — six lenses, blended by proportional weight.","Encoding category weight as blended area, not bars, kept the data poetic and legible at once, which is the whole point of the piece.",{"label":45,"title":46,"prose":47},"Journey","From one review to your ideal park",[48,49],"The interface is a guided scrollytelling journey. It opens over a drift of real review snippets, lays out the six-category framework, then runs a methodology lab where you watch a single review get taken apart word by word, each meaningful term lighting up in its category color as a running tally builds the aura in front of you.","From there it opens out. A map places the five auras on Lugano, sized by review volume; a comparison view sets their chromatic signatures side by side; and a per-park word map shows every categorized word as a color-coded network, so you can see how themes interconnect. It ends by handing the method to the reader: a prompt asks what your ideal park would be, and as you type, your words are classified live against the same six lenses. You stop being an audience and become another voice in the data.",{"type":51,"value":52,"toc":53},"minimark",[],{"title":54,"searchDepth":55,"depth":55,"links":56},"",2,[],{"role":58,"year":59,"host":60,"scope":61,"shipped":62},"Data pipeline & front-end","2026","SUPSI · Making Use of Data","Team of five","Live visualization + participatory layer",{"title":64,"image":65,"alt":66},"An Aura of Words — Lugano's parks, told by their reviews","\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fadjacent.jpg","Lugano's five parks shown on a map, each as a color-blended organic aura sized by review volume.","md",[69,73,77,81,85,89],{"src":70,"alt":71,"caption":72},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fgallery-01-opening.jpg","The opening of the interface: the project title over a drifting backdrop of real Google Review snippets.","Opening — grounded in real citizen sentiment.",{"src":74,"alt":75,"caption":76},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fgallery-02-framework.jpg","The six-category framework explained: inner state, sensory environment, action, social context, infrastructure, tension.","The framework — six semantic lenses.",{"src":78,"alt":79,"caption":80},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fgallery-03-comparison.jpg","All five park auras shown side by side at equal size with volume removed, so only their color balance can be compared.","Comparison — five auras, sized equally so only the color speaks.",{"src":82,"alt":83,"caption":84},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fgallery-04-map.jpg","Lugano's five parks on a map, each a color-blended aura sized by its volume of reviews.","The map — five auras, sized by review volume.",{"src":86,"alt":87,"caption":88},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fgallery-05-wordmap.jpg","A per-park word map: every categorized word shown as a color-coded network of interconnected themes.","Word map — how a park's themes interconnect.",{"src":90,"alt":91,"caption":92},"\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fgallery-06-contribution.jpg","The contribution prompt: a reader types their ideal park and the words are classified live against the six lenses.","Contribution — the reader becomes another voice.","\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fhero.jpg","An Aura of Words: Lugano's five parks on a map, each shown as a color-blended organic aura whose size reflects its volume of citizen reviews.",{},true,[98,99,100],"A live scrollytelling visualization: five comparable park auras on a map, a methodology lab that builds an aura from one review in real time, and per-park word maps that show how themes interconnect.","A faithful encoding of citizen language at scale, keeping its texture, subjectivity, and contradictions intact rather than collapsing it into a rating or a ranking.","A participatory layer that classifies a reader's own \"ideal park\" live against the six lenses, turning the audience from consumers of the data into contributors to it.","\u002Fwork\u002Fan-aura-of-words",[103,104,105],"A public park is officially described through institutional channels: planning documents, maintenance reports, facility inventories. Those records tell you what is physically present, and almost nothing about how people actually experience the place, what they feel there, or what meaning they give it.","The project starts from the opposite premise. The language a citizen uses in a review, written voluntarily just after a visit, holds a richer and more honest portrait of a park than any official record. It is informal, affective, and unprompted, and that informality is the feature, not the flaw.","The catch is that this language is vast, unstructured, and scattered across hundreds of reviews. On its own it cannot be read at a glance. The challenge was to make it legible without scrubbing away what makes it valuable: its texture, its subjectivity, its contradictions.","The idea I keep from this project is small data. Where the instinct is to reach for the biggest dataset available, the more interesting move was to take a small, messy, human one seriously: voluntary reviews, full of feeling and contradiction, and to make them legible without sanding off the parts that make them honest. Treating data as something that gains meaning through interpretation and dialogue, rather than as a fixed fact, is what turned the project from a visualization into a debate, and the contribution prompt is what closes the loop, handing the method back to the people the words came from.",[108,112,116,121],{"type":109,"title":110,"url":111},"demo","Live visualization","https:\u002F\u002Fjerem-marti.github.io\u002FMAInD-Making_Use_of_Data-2026-Lugano_Parks\u002F",{"type":113,"title":114,"url":115},"github","Source, scraper, and lexicon","https:\u002F\u002Fgithub.com\u002Fjerem-marti\u002FMAInD-Making_Use_of_Data-2026-Lugano_Parks",{"type":117,"title":118,"poster":119,"src":120},"video","Project walkthrough","\u002Fimages\u002Fwork\u002Fan-aura-of-words\u002Fvideo-poster.jpg","\u002Fvideos\u002Fan-aura-of-words\u002Fwalkthrough.mp4",{"type":122,"title":123,"url":124},"pdf","Park poster","\u002Ffiles\u002Fan-aura-of-words-poster.pdf",{"led":126,"contributed":129,"notTouched":131,"team":133},[127,128],"The data pipeline: scraping the reviews and the lexicon-based classification that encodes them","Front-end implementation of the scrollytelling and the live visualization",[130],"The semantic framework, the category validation, and the editorial direction with the team",[132],"Parts of the visual identity and interface design, shared within the team","Team of five (Annabelle Conron, Nerea Asensio, Nicholas Vos, Jérémy Martin, Julie Alme) for Making Use of Data at SUPSI.",{"title":5,"description":54},"live","work\u002Fan-aura-of-words","A scrollytelling portrait of Lugano's five green spaces, built entirely from the words of the people who visit them. It encodes thousands of citizen reviews into six semantic lenses to give each park a color-blended 'aura', then asks the only question that matters: is this how you see this park?","pzuwoZ2wl-7VwaLoIWVAmM2YwyiYT2jcj4bKaNkN0xY",[140,146,148,154,160,166,172,178,184,190,196,202,208,214,220],{"path":141,"card":142},"\u002Fwork\u002Fa-ta-dispo",{"title":143,"alt":144,"image":145},"À ta Dispo — matching event volunteers with organizers","The hand-drawn À ta Dispo wordmark, a coral logo on a pink sticker shape, on a plain coral background.","\u002Fimages\u002Fwork\u002Fa-ta-dispo\u002Fadjacent.jpg",{"path":101,"card":147},{"title":64,"image":65,"alt":66},{"path":149,"card":150},"\u002Fwork\u002Fbeau-rivage",{"title":151,"alt":152,"image":153},"Beau-Rivage Eco-Redesign — a 95% lighter luxury hotel site","The redesigned Beau-Rivage Palace site: restrained, elegant, far lighter than the original.","\u002Fimages\u002Fwork\u002Fbeau-rivage\u002Fadjacent.jpg",{"path":155,"card":156},"\u002Fwork\u002Fbereal",{"title":157,"alt":158,"image":159},"Re-coding BeReal — a dual-camera capture and image pipeline","Re-coding BeReal: the composed dual-photo post beside a friend's feed, on a dark mobile interface.","\u002Fimages\u002Fwork\u002Fbereal\u002Fadjacent.jpg",{"path":161,"card":162},"\u002Fwork\u002Fbrushbuddy",{"title":163,"image":164,"alt":165},"BrushBuddy — a monster cube that guides two-minute toothbrushing","\u002Fimages\u002Fwork\u002Fbrushbuddy\u002Fadjacent.jpg","A hand lifts the toothbrush from the dock on top of the BrushBuddy cube, which has a worried yellow monster face.",{"path":167,"card":168},"\u002Fwork\u002Fcultural-trails",{"title":169,"alt":170,"image":171},"Cultural Trails — a full-stack heritage walking app","Two screens from the Sentiers culturels app on the project's green, peach and blue trail ribbon: the explore map of trails across the Lausanne shore, and in-walk navigation following a route on a satellite map.","\u002Fimages\u002Fwork\u002Fcultural-trails\u002Fadjacent.jpg",{"path":173,"card":174},"\u002Fwork\u002Fdatabloom",{"title":175,"image":176,"alt":177},"DataBloom — making the energy impact of digital usage visible","\u002Fimages\u002Fwork\u002Fdatabloom\u002Fadjacent.jpg","The DataBloom flower: ultramarine felt petals on a green segmented stem in a small terracotta pot, on a white studio backdrop.",{"path":179,"card":180},"\u002Fwork\u002Felen",{"title":181,"image":182,"alt":183},"ELEN — a speculative camera for invisible wireless presences","\u002Fimages\u002Fwork\u002Felen\u002Fadjacent.jpg","ELEN, a handheld camera with a translucent orange body and twin white handles, lit on a gray studio backdrop, its deep-blue screen showing through the plexiglass.",{"path":185,"card":186},"\u002Fwork\u002Ffamily-space",{"title":187,"image":188,"alt":189},"UBS Family Space — a family's shared place to manage money","\u002Fimages\u002Fwork\u002Ffamily-space\u002Fadjacent.jpg","UBS Family Space concept: two phone screens on a soft red gradient, a parent's hub for the child's analytics, limits, and goals, and the child's savings-goal screen.",{"path":191,"card":192},"\u002Fwork\u002Fhuman-loci",{"title":193,"image":194,"alt":195},"Human Loci — a synesthetic listening object","\u002Fimages\u002Fwork\u002Fhuman-loci\u002Fadjacent.jpg","Human Loci: a transparent acrylic listening box with three plaster disks for a park, a marina, and a train station.",{"path":197,"card":198},"\u002Fwork\u002Fmeeting-pond",{"title":199,"alt":200,"image":201},"Meeting Pond — connected light objects for distant presence","Two connected Meeting Pond units standing side by side, one glowing blue and the other pink, each showing ripples across a 32×32 LED grid.","\u002Fimages\u002Fwork\u002Fmeeting-pond\u002Fadjacent.jpg",{"path":203,"card":204},"\u002Fwork\u002Fthea",{"title":205,"image":206,"alt":207},"Thea — a calm new sense for living with allergies","\u002Fimages\u002Fwork\u002Fthea\u002Fadjacent.jpg","The Thea wearable suspended in a lattice of orange cords against a cobalt sky, its oblong gauge glowing, an editorial shot from the final presentation.",{"path":209,"card":210},"\u002Fwork\u002Fuefa-female-coaches",{"title":211,"alt":212,"image":213},"Why Not You? — the UEFA female coaches campaign","The Why Not You? campaign: bold display typography over a female coach, with the line Lead Your Passion, Shape the Game.","\u002Fimages\u002Fwork\u002Fuefa-female-coaches\u002Fadjacent.jpg",{"path":215,"card":216},"\u002Fwork\u002Fwama",{"title":217,"image":218,"alt":219},"Wama — machine learning as a sink-side witness","\u002Fimages\u002Fwork\u002Fwama\u002Fadjacent.jpg","Wama's built sink rig at an angle: a tap pouring into a clear basin, the phone in its laser-cut holder showing the character.",{"path":221,"card":222},"\u002Fwork\u002Fwematch",{"title":223,"image":224,"alt":225},"WeMatch — an AX matching service for the WeRoad world","\u002Fimages\u002Fwork\u002Fwematch\u002Fadjacent.jpg","WeMatch title card: 'WeMatch finds your group before you find your trip' in coral and ink on a soft white-to-pink gradient, with the round Matchy agent character.",1783535583770]