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Data Sense-making: navigating the world through the lens of information design

ABSTRACT: Every day, we're inundated with a staggering amount of information that continues to grow exponentially. How can we process all these inputs and grasp even a fraction of the available knowledge? In my talk, I'll offer a personal reflection on information design and its tools for accessing the world's complexity without necessarily simplifying it. And, why not, I'll also share how I planned a data-driven visit to my favorite theme park. BIO: Alessandro Zotta is an Information Designer who is the Head of Data Visualization at Accurat and teaches Data-Driven Design at NABA.

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Data Sense-making
Speck&Tech #60 January 31st, 2024
Navigating the world through the lens of information design
Alessandro Zotta
hello@alessandrozotta.it
Data Sense-making Speck&Tech
Alessandro Zotta
Information Designer
Head of Data Visualization
+
Accurat Dataschool Lecturer
+
Data-Driven Design Lecturer
Pillar Detail
Mappe del sapere, visual data (Maps of Knowledge, Visual Data) — Triennale Design Museum, Milan 2014
Pillar Detail
Mappe del sapere, visual data (Maps of Knowledge, Visual Data) — Triennale Design Museum, Milan 2014
Pillar Detail
Mappe del sapere, visual data (Maps of Knowledge, Visual Data) — Triennale Design Museum, Milan 2014
Pillar Detail
Selection of works at Politecnico di Milano

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Data Sense-making: navigating the world through the lens of information design

  • 1. Data Sense-making Speck&Tech #60 January 31st, 2024 Navigating the world through the lens of information design Alessandro Zotta hello@alessandrozotta.it
  • 2. Data Sense-making Speck&Tech Alessandro Zotta Information Designer Head of Data Visualization + Accurat Dataschool Lecturer + Data-Driven Design Lecturer
  • 3. Pillar Detail Mappe del sapere, visual data (Maps of Knowledge, Visual Data) — Triennale Design Museum, Milan 2014
  • 4. Pillar Detail Mappe del sapere, visual data (Maps of Knowledge, Visual Data) — Triennale Design Museum, Milan 2014
  • 5. Pillar Detail Mappe del sapere, visual data (Maps of Knowledge, Visual Data) — Triennale Design Museum, Milan 2014
  • 6. Pillar Detail Selection of works at Politecnico di Milano
  • 7. Knight Foundation Google News Lab Infratel Italia & MISE Tech Transparency Project Scientific American Crosswalk Lab IBM World Economic Forum
  • 9. I am an information designer Data Sense-making Speck&Tech Alessandro Zotta January 31st, 2024
  • 10. Why do I love working with data? Alessandro Zotta January 31st, 2024 Data Sense-making Speck&Tech
  • 11. Why do I love working with data? story puzzle + Alessandro Zotta Data Sense-making Speck&Tech January 31st, 2024
  • 12. Why do I love working with data? story puzzle + Alessandro Zotta Data Sense-making Speck&Tech January 31st, 2024 solve enigmas find connections understand the meaning
  • 13. Why do I love working with data? story puzzle + Alessandro Zotta Data Sense-making Speck&Tech January 31st, 2024 solve enigmas find connections understand the meaning sense-making
  • 14. Why do I love working with data? Alessandro Zotta Data Sense-making Speck&Tech January 31st, 2024 going on an adventure discover facts and insights shed light on important matters story puzzle + sense-making
  • 15. Why do I love working with data? story Alessandro Zotta Data Sense-making Speck&Tech January 31st, 2024 puzzle + sense-making going on an adventure discover facts and insights shed light on important matters shareability
  • 16. Why do I love working with data? Alessandro Zotta Data Sense-making Speck&Tech January 31st, 2024 information designers readers story shareability puzzle + sense-making
  • 17. What makes information design so beautiful Feed curiosity Share knowledge Learn something Data Sense-making Speck&Tech Alessandro Zotta January 31st, 2024
  • 18. What makes information design so beautiful Feed curiosity Share knowledge Learn something Data Sense-making Speck&Tech Alessandro Zotta January 31st, 2024
  • 19. Islands of Adventure Park 2 Universal Studios Park 1 Alessandro Zotta January 31st, 2024 Data Sense-making Speck&Tech Waterpark
  • 20. Questions: Which rides have the longest line? What time of day is best for each ride? What is the best predictor of wait times for our stay? Crowd level Historical wait time Data Sense-making Speck&Tech Alessandro Zotta January 31st, 2024
  • 21. Pillar The dataset — touringplans.com
  • 22. Crowd calendars — Multiple sources Data Sense-making Speck&Tech
  • 23. Data Sense-making Speck&Tech Color key Mardi Gras Days Longer wait times <- less people more people -
  • 24. Data Sense-making Speck&Tech Spring Break Longer wait times
  • 27. Filtered view by crowd level expected for days of visit Data Sense-making Speck&Tech
  • 28. Manual resulting view Data Sense-making Speck&Tech Alessandro Zotta
  • 29. Filtered view by crowd level expected for days of visit Data Sense-making Speck&Tech
  • 32. Data Sense-making Speck&Tech Holiday planning? Send your inquiries to hello@alessandrozotta.it
  • 33. Build data-driven answers and stories Alessandro Zotta Feed curiosity January 31st, 2024 Data Sense-making Speck&Tech
  • 34. What makes information design so beautiful Feed curiosity Share knowledge Learn something Data Sense-making Speck&Tech Alessandro Zotta January 31st, 2024
  • 35. Data Sense-making Speck&Tech DensityDesign Final Synthesis Lab — Politecnico di Milano Analysis of a controversy PHASE 1 PHASE 2 PHASE 3 Research website Communication Public installation Public Data Collection Print + Microsite Data Scraping & Analysis
  • 36. Data Sense-making Speck&Tech DensityDesign Final Synthesis Lab — Politecnico di Milano Analysis of a controversy PHASE 1 PHASE 2 PHASE 3 Public Data Collection Print + Microsite Research website Data Scraping & Analysis Communication Public installation
  • 37. Societal Challenges identified by the EU, 2016 migration climate change adaptation radicalisation inequality and unemployment free movement of people and goods Data Sense-making Speck&Tech DensityDesign Final Synthesis Lab — Politecnico di Milano
  • 41. First draft on paper, Density Design Lab, 2016 Data Sense-making Speck&Tech Behind the scenes
  • 42. Data Sense-making Speck&Tech Production snapshots, mostly using NodeBox and Adobe Illustrator
  • 43. Data Sense-making Speck&Tech On Their Way, published on La Lettura - Corriere della Sera, December 31st, 2016 Gold at Kantar Information is Beautiful Awards Best of Show & Gold at Malofiej International Awards
  • 44. On Their Way, published on La Lettura - Corriere della Sera, December 31st, 2016 Data Sense-making Speck&Tech
  • 45. Master understanding of world phenomena Alessandro Zotta Learn something January 31st, 2024 Data Sense-making Speck&Tech
  • 46. What makes information design so beautiful Feed curiosity Share knowledge Learn something Data Sense-making Speck&Tech Alessandro Zotta January 31st, 2024
  • 47. Goalkeepers Report — Bill & Melinda Gates Foundation Data Sense-making Speck&Tech
  • 48. Goalkeepers Report — Bill & Melinda Gates Foundation Data Sense-making Speck&Tech
  • 49. Goalkeepers Report — Bill & Melinda Gates Foundation Data Sense-making Speck&Tech
  • 50. Goalkeepers Report — Bill & Melinda Gates Foundation Data Sense-making Speck&Tech
  • 51. Pillar Detail Goalkeepers Report — Bill & Melinda Gates Foundation Data Sense-making Speck&Tech
  • 53. May 2022, ScientificAmerican.com 51 50 Scientific American, May 2022 Graphic by Accurat (Alessandro Zotta and Alessandra Facchin) 1970 0 2 4 6 1980 1990 2000 2010 2017 40 0 40 80 120 160 1970 1980 1990 2000 2010 2017 AQUACULTURE CAPTURE Used 9.7 Exported 3.2 Farmed 0.6 MMT Captured 6.2 Imported 6.1 North America Seychelles St. Helena Sao Tome and Principe Congo The Gambia Gabon Ghana Mauritius Sierra Leone Côte d'Ivoire Egypt Morocco Angola Senegal Comoros Benin Cameroon Libya Equatorial Guinea Namibia Mozambique Zambia Malawi Tunisia Uganda Togo Chad Guinea Cabo Verde Nigeria Rwanda Burkina Faso Mauritania Central African Republic Tanzania, United Rep. of Mali South Africa Madagascar Congo Dem. Rep. Liberia Algeria Eswatini Djibouti Zimbabwe Kenya Réunion South Sudan Burundi Botswana Somalia Lesotho Niger Guinea-Bissau Sudan Eritrea Ethiopia Iceland Faroe Islands Portugal Norway Spain Malta Denmark Lithuania Finland France Italy Sweden Luxembourg Latvia Ireland Netherlands Russian Federation Poland Belgium U.K. Croatia Greece Switzerland Germany Austria Montenegro Estonia Slovenia Moldova, Republic of Ukraine Belarus Czechia Slovakia Albania Bulgaria Romania Serbia Hungary North Macedonia Bosnia and Herzegovina Northern Mariana Is. Kiribati Tokelau Palau Cook Islands Nauru Wallis and Futuna Is. Tuvalu French Polynesia Micronesia, Fed.States of Samoa Marshall Islands Solomon Islands Vanuatu Niue Fiji Tonga New Zealand New Caledonia Australia Papua New Guinea American Samoa Guam Greenland St. Pierre and Miquelon Antigua and Barbuda Anguilla Barbados Bermuda Falkland Is.(Malvinas) Turks and Caicos Is. Montserrat Saint Kitts and Nevis Saint Lucia Curaçao Dominica British Virgin Islands Peru Jamaica Guyana Trinidad and Tobago Bahamas Saint Vincent/Grenadines Costa Rica Canada U.S. Panama Cayman Islands Suriname Mexico Bonaire/S.Eustatius/Saba Chile Guadeloupe Belize Martinique Venezuela, Boliv Rep of Saint Barthélemy Uruguay Brazil French Guiana Dominican Republic Ecuador El Salvador Colombia Argentina Haiti Sint Maarten Nicaragua Cuba Paraguay Guatemala US Virgin Islands Honduras Bolivia (Plurinat.State) Saint-Martin Puerto Rico Aruba Grenada Laos Bhutan Armenia Nepal Azerbaijan Turkmenistan Kazakhstan Uzbekistan Palestine Kyrgyzstan Tajikistan Afghanistan Mongolia Cambodia Malaysia Republic of Korea Hong Kong SARMyanmar Macao SAR Singapore Brunei Darussalam Viet Nam Thailand Sri Lanka China Bangladesh Philippines Taiwan Cyprus United Arab Emirates Israel Qatar Bahrain Kuwait Iran (Islamic Rep. of) Saudi Arabia Korea, Dem. People's Rep Georgia Timor-Leste Lebanon Jordan Turkey Yemen Iraq Pakistan Syrian Arab Republic Maldives Indonesia India Japan CAPTURE Freshwater fish Grass carp 5.7 Silver carp 4.8 Nile tilapia 4.6 Cupped oysters 5.3 Japanese carpet shell 4.0 Constricted tagelus 0.9 Whiteleg shrimp 5.4 Red swamp crawfish 2.2 Chinese mitten crab 0.8 Atlantic salmon 2.6 Milkfish 1.5 Rainbow trout 0.9 Gilthead seabream 0.3 Large yellow croaker 0.2 Skipjack tuna 3.4 Anchoveta 4.3 Silver cyprinid 0.3 Nile tilapia 0.3 Nile perch 0.3 Jumbo flying squid 0.9 Yesso scallop 0.4 American sea scallop 0.3 Antarctic krill 0.4 Hilsa shad 0.6 Pink salmon 0.5 Chum salmon 0.3 Saltwater/freshwater fish Crustaceans Saltwater fish Mollusks Freshwater fish Saltwater/freshwater fish Crustaceans Mollusks Saltwater fish Alaska pollock 3.5 Gazami crab 0.5 Akiami paste shrimp 0.4 European seabass 0.3 MMT AQUACULTURE HOW MUCH A COUNTRY EATS Country has coastlines Country is landlocked 5 15 0 Country Name 30 g Daily Consumption of Seafood Protein Grams per person (2017) WHAT WE EAT AND USE Daily Seafood Consumption Grams of protein eaten per person Millions of metric tons Seafood Supply 152.9 19.9 Nonfood Food Seafood Production Methods (2017) Subregional Distribution Total Supply per Region Asia Asia Europe Europe Americas Africa Oceania Americas Africa Oceania Australia and New Zealand Central Asia Eastern Asia Eastern Europe Latin America and Caribbean Melanesia Micronesia Northern Africa North America Northern Europe Polynesia Southeast Asia Southern Asia Southern Europe Sub-Saharan Africa Western Asia Western Europe 48.4 million live metric tons (MMT) 8.2 MMT Nonfood AFRICA EUROPE AMERICAS ASIA 108.7 MMT Food 14.8 Imports* Exports* Bar height shows amount of wild-caught seafood in region Bar height shows amount of farmed seafood in region 69.2 MMT Bar height shows amount of seafood used Food 7.3 Nonfood 16 Food 3.2 Nonfood 12.4 Food 0.8 Nonfood OCEANIA 1 Food 0.3 Nonfood 21 21 5.6 People in highly populous countries such as the U.S. and India eat relatively little seafood— though these nations are part of regions that produce a fair amount—when compared with tiny countries such as Kiribati and other nations in Oceania. Seafood reliance becomes apparent when production is divided by population size. Seafood species differ in popularity, depending on whether they are farmed or wild-caught. Top Three Species, by Group (2017) A World of Seafood Fish, shellfish and crustaceans have become a gigantic global harvest. More than 177 million metric tons of seafood (excluding plants) came from oceans and fresh water in 2019, the latest year for which data are available. These charts show the regions and countries that produce and use the most. The charts also show how much comes from fish farming—or aquacul- ture—and from wild-catch fisheries, as well as trends in consumption over time. DEMAND ON THE RISE The amount of seafood that people use has been climbing since the 1970s. Most of the growth is in food products, whereas nonhuman food uses (animal feed, for instance, or bait to catch other fish ha e fluctuated n a erage, our daily diet of seafood protein has also increased. Sources: FishStatJ, Food and Agriculture Organization of the United Nations ( https://www.fao.org/fishery/en/statistics/software/fishstatj), accessed March2022;FishStatJ,FoodBalanceSheetsofFishandFisheryProducts (2017 import, export, food and nonfood country-level data);FishStatJ,FAO FisheriesandAquacultureProductionStatistics(aquaculture, capture and species data); FAO Yearbook of Fishery and Aquaculture Statistics 2019. FAO, 2021, accessed March 2022 (1966–2017 data; 2017 country-level fish protein data) These charts do not include data for marine mammals, crocodiles, corals, pearls, mother-of-pearl, sponges and aquatic plants. * Productionmethods—andcross- regionconnectiondetails—arenot available for imports and exports. Data Sense-making SpeckTech Scientific American, May 2022
  • 54. AQUACULTURE Mald HOW COUN Seafood Production Methods (2017) Subregional Distribution Total Supply per Region Asia Europe Americas Africa Oceania Eastern Asia Southeast Asia 48.4 million live metric tons (MMT) 8.2 MMT Nonfood ASIA 108.7 MMT Food Imports* Exports* Bar height shows amount of wild-caught seafood in region Bar height shows amount of farmed seafood in region 69.2 MMT Bar height shows amount of seafood used 21 People in countries India eat though th of regions amount— tiny coun and other Seafood r apparent is divided
  • 55. Used 9.7 Exported 3.2 Farmed 0.6 MMT Captured 6.2 Imported 6.1 North America Ic Fa Kiribat Europe Americas Africa Oceania Australia and New Zealand Central Asia Eastern Europe Latin America and Caribbean Melanesia Micronesia Northern Africa North America Northern Europe Polynesia Southern Europe Sub-Saharan Africa Western Europe AFRICA EUROPE 14.8 7.3 Nonfood 16 Food 3.2 Nonfood 12.4 Food 0.8 Nonfood OCEANIA 1 Food 0.3 Nonfood * Productionmethods—andcross- regionconnectiondetails—arenot available for imports and exports.
  • 56. Laos Bhutan Armenia Nepal Azerbaijan Turkmenistan Kazakhstan Uzbekistan Palestine Kyrgyzstan Tajikistan Afghanistan Mongolia Cambodia Republic of Korea Hong Kong SARMyanmar Macao SAR Singapore Brunei Darussalam Viet Nam Sri Lanka China Bangladesh Philippines Taiwan Cyprus Israel Qatar Bahrain Kuwait Iran (Islamic Rep. of) Saudi Arabia Korea, Dem. People's Rep Georgia Timor-Leste Lebanon Jordan Turkey Yemen Iraq Pakistan Syrian Arab Republic Maldives Indonesia India Japan Freshw Grass c Silver c Nile tila Cupped Japane Constr Whitel Red sw Chines Crusta Mollus AQUACULTURE HOW MUCH A COUNTRY EATS Country has coastlines Country is landlocked 5 15 0 Country Name 30 g Daily Consumption of Seafood Protein Grams per person (2017) WHAT WE EAT AN Total Supply per Region ASIA People in highly populous countries such as the U.S. and India eat relatively little seafood— though these nations are part of regions that produce a fair amount—when compared with tiny countries such as Kiribati and other nations in Oceania. Seafood reliance becomes apparent when production is divided by population size. Seafood species differ in po on whether they are farme Top Three S
  • 57. Seychelles St. Helena Sao Tome and Principe Congo The Gambia Gabon Ghana Mauritius Sierra Leone Côte d'Ivoire Egypt Morocco Angola Senegal Comoros Benin Cameroon Libya Equatorial Guinea Namibia Mozambique Zambia Malawi Tunisia Uganda Togo Chad Guinea Cabo Verde Nigeria Rwanda Burkina Faso Mauritania Central African Republic Tanzania, United Rep. of Mali South Africa Madagascar Congo Dem. Rep. Liberia Algeria Eswatini Djibouti Zimbabwe Kenya Réunion South Sudan Burundi Botswana Somalia Lesotho Niger Guinea-Bissau Sudan Eritrea Ethiopia Portugal Malta Finland France Sweden Netherlands Poland Belgium U.K. CroatiaEstonia Slovenia Ukraine Albania Romania Northern Mariana Is. Kiribati Tokelau Palau Cook Islands Nauru Wallis and Futuna Is. Tuvalu French Polynesia Micronesia, Fed.States of Samoa Marshall Islands Solomon Islands Vanuatu Niue Fiji Tonga New Zealand New Caledonia Australia Papua New Guinea American Samoa Guam Silver c Nile tila Nile pe Jumbo Yesso s Americ Antarc Hilsa sh Pink sa Chum s Freshw Saltwa Crusta Mollus Gazam Akiami AFRICA EUROPE 16 Food 3.2 Nonfood 12.4 Food 0.8 Nonfood OCEANIA 1 Food 0.3 Nonfood * Productionmethods—andcross- regionconnectiondetails—arenot available for imports and exports.
  • 58. Sketches — Scientific American, May 2022 Data Sense-making SpeckTech Alessandro Zotta
  • 59. Details — Scientific American, May 2022 Data Sense-making SpeckTech Alessandro Zotta
  • 60. Photo © Paul Carroll, Unsplash
  • 61. Photo © Hugh Broughton Architects
  • 62. Kickoff workshop at AA School of Architecture, London, 2019 Data Sense-making SpeckTech
  • 63. Kickoff workshop at AA School of Architecture, London, 2019 Data Sense-making SpeckTech
  • 64. Data Sense-making SpeckTech Kickoff workshop at AA School of Architecture, London, 2019 EVOLUTION OF ARCHITECTURAL FOOTPRINT OF STATIONS ON ANTARCTICA 1,920 1,900 1,940 1,960 ATS 1,980 2,000 2,020 Opened STATION LIFECYCLE LEGEND Expanded (Original Size Increased) Transfered between Countries Temporary Closed Destroyed Renovated / Rufurbished Extended (New Facilities Added) Rebuilt Belgrano III Belgrano II Belgrano I Brown Casey Decepcion Esperanza Marambio Matienzo Melchior Petrel Primavera San Martin Camara Carlini Orcadas Aboa Bellinghausen Bharati Bahia Luna Station Camara Caleta Potter Carlini Carvajal Concordia Dallmann Davis Dirck Gerritsz Laboratory Dr. Guillermo Mann Druzhnaya IV Ferraz Frei Gabriel González Videla Gabriel de Castilla Georg-von-Neumayer-Station Neumayer III Neumayer III Great Wall Henryk Arctowski Jang Bogo Johann Gregor Mendel King Sejong Kohnnen Kunlun Leningradskaya Machu Picchu Maitri Baia Terra Nova Mario Zucchelli Mario Zucchelli Mawson Mirny Molodezhnaya Vechernyaya Novolazarevskaya Oazis Progress O’Higgins Pedro Vicente Maldonado Prat Princess Elisabeth Professor Julio Escudero Risopatrón Yelcho Russkaya SANAE IV Scott Base St. Kliment Ohridski Syowa Taishan Troll Vechernyaya Vostok Zhongshan Dumont d’Urville Amundsen-Scott South Pole Artigas Fossil Bluff and Sky Blu Halley II Halley IV Halley V Rothera Juan Carlos I McMurdo Palmer Ruperto Elichiribehety Signy Vernadsky Wasa Halley VI Halley III Halley I UK UA RU PL UK CL Amundsen-Scott South Pole AR AU BV BE BR BG CL CN CZ EC FI FR/IT FR DE IN IT JP NL NZ NO PE PL BV KR RU ZA ES SE UA UK UY US 51054 9200 1150 130 2559 4000 16200 8979 108 1800 872 1500 4000 48 7480 7500 3605 3930 5183 4815 200 908 288 12786 15175 221 980 1800 108 22000 22855 1944 15 10 9 4 4 3 3 3 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Number of Stations / per country (no.) Size of Sum of Stations / per country (sqm) 2 Estonia Turkey Venezuela Romania Cuba Guatemala Pakistan Greece M a l a y s i a Denm ark Austria M onaco C a n a d a S w i t z e r l a n d P o r t u g a l Papua New Guinea Mongolia North Korea Kazakhstan Belarus Colom bia Iceland Hungary Norway South Africa Spain United Kingdom E c u a d o r F i n l a n d U n it e d S t a t e s Poland Peru Sweden Czech Republic Ukraine Chile Argentina Uruguay Australia Brazil China B e l g i u m R u s s i a France Germany B u l g a r i a India Italy Japan South Korea Netherlands New Zealand A n t a r c t i c T r e a t y S y s t e m Life Sciences D a t a M a n a g e m e n t Antarctic G eographic Inform ation G e o - s c ie n c e s In te rn at io na l M ar iti m e O rg an iz at io n Wo rld Wid e Fun d for Nat ure World Meterolo gical Organiz ation Interna tional Teleco mmun ication Union Well Developed Programs In te rn a ti o n a l A s s o c ia ti o n o f A n ta rc ti c a T o u r O p e ra to rs Un ite d Na tio ns En vir on me nt Pro gra m In te rn go ve rn m en ta l O ce an og ra ph ic C om m itt ee In te rn a ti o n a l H y d ro g ra p h ic O rg a n iz a ti o n A n ta rc ti c a n d S o u th e rn O c e a n C o a li ti o n P h y s ic a l S c i e n c e s In te rn a ti o n a l U n io n fo r C o n s e rv a ti o n o f N a tu re Initial Stage Programs Associate Members Special Contributors Honorary M em bers U n i o n M e m b e r s National Committees A T C M C C A M L R C C A S O th e r co n ve n tio n s A C A P T h e W o rk in g G ro u p o n E c o s y s te m , M o n it o ri n g , a n d M a n a g e m e n t S ta n d in g C o m m it te e o n Im p le m e n ta ti o n a n d C o m p li a n c e C o m m is s io n S t a n d in g C o m m it t e e o n A d m in a n d F in a n c e O t h e r S c i e n t i f i c C o m m i t t e e T h e W o rk in g G ro u p o n S ta ti s ti c s , A s s e s s m e n ts , a n d M o d e lli n g A d v i s o r y C o m m i t t e e A rt ic le 2 F re ed om of sc ie nt ifi c re se ar ch Article 7 Inspections Article 8 Jurisdict ion of citizens hip Ar tic le 3 Sc ie nt ifi c C oo pe ra tio n Ar tic le 4 Te rri tor ial so ve rei gn ty A rt ic le 1 P e a c e fu l p u rp o s e s Arti cle 5 Nuc lear acti vity Article 6 Geog raphic al cover age Artic le 9 Tre aty Mee ting s Art icle 10 Ac tivi tie s co ntr ary to Tre aty Ar tic le 11 Di sp ut es be tw ee n Pa rti es A rt ic le 12 M od ifi ca tio n an d du ra tio n A rt ic le 1 3 R a tif ic a tio n a n d e n tr y in to fo rc e A rt ic le 1 4 D e p o s it io n Expert Groups I n t e r n a t i o n a l C o u n c il F o r S c ie n c e E x e c u t iv e C o m m it t e e S t a n d i n g C o m m i t t e e s COMNAP SCAR Madrid Protocol C E P Th e Wo rki ng Gr ou p on Fis h Sto ck As se ssm en t Consultative countries Non-consultative countries Groups Individual Secretariat Instrument Formal Informal Appointment Observation Reports Representation Convention THE ANTARCTIC TREATY SYSTEM Formal Informal Appointment Observation Reports Representation Convention Treaty Protocols/Conventions Meetings Consultative Countries Non-Consultative Countries Groups Secretariat Individual Committee for Environmental Protection Antarctic Treaty Consultative Meeting Agreement on the Conservation of Albatrosses and Petrels Convention for the Conservation of Antarctic Seals Convention on the Conservation of Antarctic Marine Living Resources Council of Managers of National Antarctic Programmes Scienti c Committee on Antarctic Research CEP ATCM ACAP CCAS CCAMLR COMNAP SCAR THE ANTARCTIC TREATY DENMARK ROMANIA PAPUA NEW GUINEA HUNGARY CUBA AUSTRIA GREECE NORTH KOREA CANADA COLOMBIA SWITZERLAND SLOVAKIA TURKEY VENEZUELA ESTONIA BELARUS MONACO PORTUGAL MALAYSIA PAKISTAN ICELAND KAZAKHSTAN GUATEMALA MONGOLIA NON CONSULTATIVE PARTIES 1998 M AD R ID PR O TO C O L EN TER ED IN TO FO R C E 1991 M AD R ID PR O TO C O L SIG N ED 1961 EN TER ED IN TO FO R C E 1959 AN TAR C TIC TR EATY SIG N ED ARGENTINA CHILE FRANCE NEW ZEALAND NORWAY UNITED KINGDOM BELGIUM JAPAN RUSSIA SOUTH AFRICA UNITED STATES POLAND GERMANY CHINA BRAZIL INDIA URUGUAY EQUADOR ITALY SWEDEN AUSTRALIA FINLAND PERU SPAIN SOUTH KOREA NETHERLANDS UKRAINE BULGARIA CZECH REPUBLIC CONSULTATIVE PARTIES SOUTH KOREA ECUADOR GREECE NORTH KOREA CANADA ICELAND PORTUGAL AN TAR C TIC AN D SO U TH ER N O C EAN C O ALIT IO N AU C C AS C O N FER EN C E R EVIE W RUSSIA JAPAN FRANCE ARGENTINA AUSTRALIA CHILE NEW ZEALAND NORWAY UNITED KINGDOM BELGIUM UNITED STATES SOUTH AFRICA BRAZIL NETHERLANDS POLAND CHECH REPUBLIC UKRAINE C C AS C O N FER EN C E G R EEN PEAC E: 'W O R LD PAR K AN TAR C TIC A' 1957-1958 IN TER N ATIO N AL G EO PH YSIC AL YEAR ATS SEC R ETAR IA T ESTABLIS H ED 2007 - 2008 IN TER N ATIN AL PO LAR YEAR C EP FIR ST M EETIN G 2048 / C EP TO TH E ATS C O U LD BE R EVIE W ED FR AR C L N O N Z U K TER R IT O R IA L C LAIM S DENMARK ROMANIA PAPUA NEW GUINEA HUNGARY CUBA AUSTRIA COLOMBIA SWITZERLAND GUETAMALA SLOVAKIA TURKEY VENEZUELA ESTONIA BELARUS MONACO MALAYSIA PAKISTAN MONGOLIA KAZAKHSTAN PL DE CN BR IN EC UY IT SE FI PE ES KR NL UA BG CZ BULGARIA GERMANY URUGUAY ITALY SPAIN PERU INDIA CHINA FINLAND SWEDEN KP 1980 C C AM LR EN TER ED IN TO FO R C E 1980 C C AM LR SIG N ED 2004 AC AP EN TER ED IN TO FO R C E 2001 AC AP SIG N ED 1972 C C AS SIG N ED DE IT FI PK 1978 C C AS / EN TER ED IN TO FO R C E CONSULTATIVE MEMBERS NON-CONSULTATIVE MEMBERS ANTARCTIC TREATY CONSULTATIVE MEETING UNITED KINGDOM NEW ZEALAND FRANCE NORWAY AUSTRALIA CHILE ARGENTINA PROTOCOLS CONVENTIONS ATS RATIFIED ATS ENTERS INTO FORCE TIMELINE OF THE ANTARCTIC TREATY SYSTEM MEETING OF EXPERTS DIPLOMATIC CONFERENCE SPECIAL ANTARCTIC TREATY CONSULTATIVE MEETING 2020 2010 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 1900 2030 2040 2050
  • 65. Antarctic Resolution — Giulia Foscari, UNLESS Image courtesy of UNLESS © Delfino Sisto Legnani
  • 66. Antarctic Resolution — Giulia Foscari, UNLESS Image courtesy of UNLESS © Delfino Sisto Legnani
  • 67. Data Sense-making SpeckTech 30th anniversary of the Protocol on Environmental Protection to the Antarctic Treaty Inauguration Round Table, Madrid, 2021
  • 68. Antarctic Resolution Open Access — Giulia Foscari, UNLESS Image courtesy of UNLESS © Delfino Sisto Legnani
  • 69. Create opportunities for others to access information Alessandro Zotta Shareability January 31st, 2024 Data Sense-making SpeckTech
  • 70. Feed curiosity Share knowledge Learn something Master understanding of world phenomena Build data-driven answers and stories Create opportunities to access information Information design Alessandro Zotta Data Sense-making SpeckTech Questions?
  • 71. Alessandro Zotta Thank you for your attention Data Sense-making SpeckTech January 31st, 2024