Visualising data: Seeing is Believing - CS Forum 2012
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When patterns and connections are revealed between numbers, content and people that might otherwise be too abstract or scattered to be grasped, we’re able to make better sense of where we are, what it might mean and what needs to be done.
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Visualising data: Seeing is Believing - CS Forum 2012
- 1. Visualising Data Seeing is Believing Richard Ingram @richardjingram
- 2. Observe
- 3. Observe Analyse
- 4. It’s worked to our adaptive advantage in competition with other animal species.
- 5. We’ve developed a greater variety of ways to communicate and express ourselves.
- 7. Paleolithic painting of giant elk Lascaux, southwestern France (c. 15,300 BC)
- 9. Upper Paleolithic painting of bison Altamira cave, northern Spain (c. 23,000 BC – c. 33,000 BC)
- 11. Da Vinci’s Vitruvian Man Pen and ink on paper (c. 1487)
- 13. Florence Nightingale’s coxcomb diagram Showed the causes of death in the British Army in the Crimea (1858)
- 15. Copernicus’s heliocentric universe diagram He literally moved heaven and earth to draw it (c. 1543)
- 17. Ptolemy’s theory of a heliocentric universe Everything revolved around us, and that suited just fine.
- 19. On the Revolutions of the Celestial Spheres Contains thousands of years worth of astrological data (c. 1543)
- 22. The data deluge Power to the people
- 24. “The release of data is a cornerstone of how to strengthen the role of citizens and government, and recast the relationship between the two.” ~ Sir Tim Berners-Lee Interview with The Guardian (2010)
- 26. “Having the data is not enough.You have to show it in ways that people both enjoy and understand.” ~ Dr. Hans Rosling The Joy Of Stats (BBC Television, 2011)
- 27. Free tools for data extraction, exploration and visualisation Gephi (gephi.org) Google Chart Tools (developers.google.com/chart/) Google Fusion Tables (google.com/fusiontables/) Google Refine (code.google.com/p/google-refine/) Lucid Chart (lucidchart.com) ManyEyes (www-958.ibm.com) OpenStreetMap (openstreetmap.org) ScraperWiki (scraperwiki.com) Tableau Public (tableausoftware.com/public/)
- 28. Tableau Public Lucid Chart tableausoftware.com/public/ lucidchart.com Google Chart Tools ManyEyes developers.google.com/chart/ www-958.ibm.com
- 30. Gephi gephi.org
- 32. OpenStreetMap Google Fusion Tables openstreetmap.org google.com/fusiontables/
- 35. Visualising data... ...allows us to quickly explore and analyse possible relationships between things and how they vary together.
- 36. Visualising data... ...can be an effective way to amplify and simplify the communication of our recommendations and arguments to other audiences.
- 38. Visualising data The hidden people networks
- 39. Internal author interviews Who to speak to? Org charts can help.
- 40. Internal author interviews How do we reveal those hidden relationships?
- 41. Sociogram Visualises the structure and patterns of group interactions. Connection
- 42. (Edge) Person
- 43. (Node)
- 44. Sociogram Visualises the structure and patterns of group interactions. Connection
- 45. (Edge) Person
- 46. (Node)
- 47. Sociogram Can reveal community clusters and calculate network science parameters like degrees of separation. More
- 49. =
- 51. degree
- 52. Sociogram Can reveal community clusters and calculate network science parameters like degrees of separation. More
- 54. =
- 56. degree
- 57. Sociogram Can reveal community clusters and calculate network science parameters like betweenness centrality. Higher
- 58. network
- 61. Higher
- 62. betweenness
- 63. Sociogram Can reveal community clusters and calculate network science parameters like betweenness centrality. Higher
- 64. network
- 67. Higher
- 68. betweenness
- 69. Log file entries Whoa, what in the name of thunder does this all mean? #Fields: date time c-ip cs-username s-ip s-port cs-method cs-uri-stem cs-uri-query sc-status cs(User-Agent) 2012-09-03 00:10:19 XXX.XXX.X.211 clarke_n XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=84 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:10:39 XXX.XXX.X.17 olson_b XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:11:12 XXX.XXX.X.40 zajac_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:20 XXX.XXX.X.29 arecchi_f XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=168 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:50 XXX.XXX.X.107 chalmers_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:52 XXX.XXX.X.178 harding_a XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:14:38 XXX.XXX.X.107 chalmers_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=73 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav)
- 70. Log file entries: a closer look Important elements in bold: date/time stamp, client ip address, username, and content accessed: 2012-09-03 00:09:53 XXX.XXX.X.104 russell_g XXX.XXX.X.103 80 GET / admin/pages/content.php?id=12 Cmd=contents 200 Mozilla/4.76+[en]+ (X11;+U;+Linux+2.4.9-ac7+i686;+Nav)
- 71. Log file entries: a closer look Important elements in bold: date/time stamp, client ip address, username, and content accessed: 2012-09-03 00:09:53 XXX.XXX.X.104 russell_g XXX.XXX.X.103 80 GET / admin/pages/content.php?id=12 Cmd=contents 200 Mozilla/4.76+[en]+ (X11;+U;+Linux+2.4.9-ac7+i686;+Nav)
- 72. Log file entries: a closer look Important elements in bold: date/time stamp, client ip address, username, and content accessed: 2012-09-03 00:09:53 XXX.XXX.X.104 russell_g XXX.XXX.X.103 80 GET / admin/pages/content.php?id=12 Cmd=contents 200 Mozilla/4.76+[en]+ (X11;+U;+Linux+2.4.9-ac7+i686;+Nav)
- 73. Log file entries: a closer look Important elements in bold: date/time stamp, client ip address, username, and content accessed: 2012-09-03 00:09:53 XXX.XXX.X.104 russell_g XXX.XXX.X.103 80 GET / admin/pages/content.php?id=12 Cmd=contents 200 Mozilla/4.76+[en]+ (X11;+U;+Linux+2.4.9-ac7+i686;+Nav)
- 74. Log file entries: a closer look Important elements in bold: date/time stamp, client ip address, username, and content accessed: 2012-09-03 00:09:53 XXX.XXX.X.104 russell_g XXX.XXX.X.103 80 GET / admin/pages/content.php?id=12 Cmd=contents 200 Mozilla/4.76+[en]+ (X11;+U;+Linux+2.4.9-ac7+i686;+Nav)
- 75. Log file entries We want to locate authors accessing the same content within a certain timeframe. #Fields: date time c-ip cs-username s-ip s-port cs-method cs-uri-stem cs-uri-query sc-status cs(User-Agent) 2012-09-03 00:10:19 XXX.XXX.X.211 clarke_n XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=84 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:10:39 XXX.XXX.X.17 olson_b XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:11:12 XXX.XXX.X.40 zajac_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:20 XXX.XXX.X.29 arecchi_f XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=168 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:50 XXX.XXX.X.107 chalmers_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:52 XXX.XXX.X.178 harding_a XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:14:38 XXX.XXX.X.107 chalmers_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=73 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav)
- 76. Log file entries We want to locate authors accessing the same content within a certain timeframe. 2012-09-03 00:10:39 XXX.XXX.X.17 olson_b XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:11:12 XXX.XXX.X.40 zajac_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:50 XXX.XXX.X.107 chalmers_s XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav) 2012-09-03 00:13:52 XXX.XXX.X.178 harding_a XXX.XXX.X.103 80 GET /admin/pages/ content.php?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- ac7+i686;+Nav)
- 77. Log file entries We want to locate authors accessing the same content within a certain timeframe. 00:10:39 XXX.XXX.X.17 olson_b XXX.XXX.X.103 80 GET /admin/page ?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- av) 00:11:12 XXX.XXX.X.40 zajac_s XXX.XXX.X.103 80 GET /admin/page ?id=37 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- av) 00:13:50 XXX.XXX.X.107 chalmers_s XXX.XXX.X.103 80 GET /admin/ ?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- av) 00:13:52 XXX.XXX.X.178 harding_a XXX.XXX.X.103 80 GET /admin/p ?id=174 Cmd=contents 200 Mozilla/4.76+[en]+(X11;+U;+Linux+2.4.9- av)
- 78. Log file entries We return to the relative safety of the spreadsheet.
- 79. Log file entries We return to the relative safety of the spreadsheet.
- 80. Log file entries We return to the relative safety of the spreadsheet.
- 82. Gephi gephi.org
- 86. Big mess Let’s run a layout algorithm.
- 90. That’s better... But who are the most influential?
- 100. So, what did we learn?
- 101. We were able to easily spot the different community clusters to which people were connected.
- 102. Visualising the log file data helped us create an alternative org chart.
- 103. Using network science parameters we saw which individuals held the most influence over multiple groups.
- 105. Visualising data Relationships between content
- 106. For many of us, published web content is increasingly formed of flexible modules scattered across documents and locations, but they are seldom read that way.
- 108. Frequently trodden paths Mapping the movement of users between web pages.
- 109. Frequently trodden paths Weightier nodes and edges indicate key paths and stops.
- 110. Frequently trodden paths Weightier nodes and edges indicate key paths and stops. Page
- 111. (Node)
- 112. Frequently trodden paths Weightier nodes and edges indicate key paths and stops. User
- 113. flow (Edge) Page
- 114. (Node)
- 115. Frequently trodden paths Weightier nodes and edges indicate key paths and stops. User
- 116. flow (Edge) Page
- 117. (Node)
- 118. Frequently trodden paths Weightier nodes and edges indicate key paths and stops. User
- 119. flow (Edge) Page
- 120. (Node)
- 123. Web server logs Store information on each request we make. Referring Resource New request (Previous page) (Next page) Source: doriantaylor.com/visualizing-paths-through-the-web
- 124. Web server logs Haven’t we been here before? Not quite. XX.XXX.XXX.86 [30/Aug/2012:11:09:27 +0100] GET /contact-us/ HTTP/1.1 200 14728 - http://www.crosscountrycoaches.com/destinations/ Mozilla/5.0 (Windows NT 6.0; rv: 14.0) Gecko/20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:29 +0100] GET / HTTP/1.1 200 12007 - http:// www.crosscountrycoaches.com/destinations/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:29 +0100] GET /contact-us/view-your-ticket/ HTTP/1.1 200 14084 - http://www.crosscountrycoaches.com/ Mozilla/5.0 (Windows NT 6.0; rv: 14.0) Gecko/20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:37 +0100] GET /services/ HTTP/1.1 200 13428 - http://www.crosscountrycoaches.com/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:38 +0100] GET /login/ HTTP/1.1 200 17284 - http:// www.crosscountrycoaches.com/services/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:42 +0100] GET /reprint-your-ticket/ HTTP/1.1 200 27788 - http://www.crosscountrycoaches.com/services/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:42 +0100] GET /services/terms-and-conditions/ HTTP/ 1.1 200 11638 - http://www.crosscountrycoaches.com/reprint-your-ticket/ Mozilla/ 5.0 (Windows NT 6.0; rv:14.0) Gecko/20100101 Firefox/14.0.1
- 125. Web server logs: a closer look Important elements in bold: client ip address, next page and previous page: XX.XXX.XXX.86 [30/Aug/2012:11:09:29 +0100] GET /contact-us/view-your-ticket/ HTTP/1.1 200 14084 - http://www.crosscountrycoaches.com/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:42 +0100] GET /reprint-your-ticket/ HTTP/1.1 200 27788 - http://www.crosscountrycoaches.com/services/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1
- 126. Web server logs: a closer look Important elements in bold: client ip address, next page and previous page: XX.XXX.XXX.86 [30/Aug/2012:11:09:29 +0100] GET /contact-us/view-your-ticket/ HTTP/1.1 200 14084 - http://www.crosscountrycoaches.com/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:42 +0100] GET /reprint-your-ticket/ HTTP/1.1 200 27788 - http://www.crosscountrycoaches.com/services/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1
- 127. Web server logs: a closer look Important elements in bold: client ip address, next page and previous page: XX.XXX.XXX.86 [30/Aug/2012:11:09:29 +0100] GET /contact-us/view-your-ticket/ HTTP/1.1 200 14084 - http://www.crosscountrycoaches.com/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:42 +0100] GET /reprint-your-ticket/ HTTP/1.1 200 27788 - http://www.crosscountrycoaches.com/services/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1
- 128. Web server logs: a closer look Important elements in bold: client ip address, next page and previous page: XX.XXX.XXX.86 [30/Aug/2012:11:09:29 +0100] GET /contact-us/view-your-ticket/ HTTP/1.1 200 14084 - http://www.crosscountrycoaches.com/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1 XX.XXX.XXX.86 [30/Aug/2012:11:09:42 +0100] GET /reprint-your-ticket/ HTTP/1.1 200 27788 - http://www.crosscountrycoaches.com/services/ Mozilla/5.0 (Windows NT 6.0; rv:14.0) Gecko/ 20100101 Firefox/14.0.1
- 129. Web server logs Cleaning up the data. Most non-human activity can be removed by searching for: Bingbot (google.com/bot.html) Googlebot (bing.com/bingbot.htm) Any mentions of ‘bots’, ‘spiders’, and ‘crawlers’
- 130. Web server logs Extract the URL data and add to a spreadsheet.
- 131. Web server logs Extract the URL data and add to a spreadsheet.
- 132. Web server logs Extract the URL data and add to a spreadsheet.
- 133. Cross Country Coaches The entirely fictitious UK-wide bus and coach operator. Airports Attractions Holiday camps and Direct to terminal amusement parks Day trips Events Towns and cities Sports and music
- 134. Cross Country Coaches The entirely fictitious UK-wide bus and coach operator. Airports Attractions Holiday camps and Direct to terminal amusement parks Journey Planner Purchasing e-tickets Day trips Events Towns and cities Sports and music
- 136. Gephi gephi.org
- 140. An even bigger mess Let’s run a layout algorithm.
- 144. That’s better... Let’s pick out the influential pages.
- 154. Export node data This will come in handy later
- 155. So, what did we learn?
- 156. Visualising the web server log data provided us with a different perspective of the stories we’re telling our audiences.
- 157. Filtering the data took away the visual complexity and revealed to us the key paths and stops.
- 158. Exporting the node data generated a page- level inventory of the most frequently accessed content.
- 160. Visualising data Playing with numbers
- 161. Utilising internal data Bringing data from your own investigations into play.
- 162. Exported node table Manually feed in data from your own investigations.
- 163. Exported node table Manually feed in data from your own investigations.
- 164. Exported node table Manually feed in data from your own investigations.
- 165. Exported node table Manually feed in data from your own investigations.
- 166. Exported node table Manually feed in data from your own investigations.
- 167. Exported node table Manually feed in data from your own investigations.
- 168. Exported node table Automatically feed in data from your own investigations. Exported node table + Content inventory = Extended node table
- 171. What can we do with this additional data?
- 174. Q. Is there a perceived difference in the quality of content maintained in-house and through external partners?
- 181. Cross Country Coaches The entirely fictitious UK-wide bus and coach operator. Airports Attractions Holiday camps and Direct to terminal amusement parks Day trips Events Towns and cities Sports and music
- 182. Cross Country Coaches The entirely fictitious UK-wide bus and coach operator. Airports Attractions Holiday camps and Direct to terminal amusement parks Day trips Events Towns and cities Sports and music
- 189. Utilising external data Bringing public data into play.
- 193. Regional breakdown of tourism value Only available in handy PDF format [!].
- 195. PDF to Excel Online Zamzar pdftoexcelonline.com zamzar.com
- 208. Priorities?
- 209. So, what did we learn?
- 210. Furnishing our exported node table with data from our own investigations allowed for deeper dives into our sample.
- 211. Importing external data provided us with possible new angles and ideas.
- 212. Adding interactive elements helped us develop a basic narrative around our data.
- 214. Visualising data You can do it!
- 215. Don’t be afraid of data. Treat it as something to play with and explore.
- 216. Don't worry about not having all the answers. Uncertainly can be a great way of raising new questions.
- 217. Create something that could be of use to others and you might find you get help and cooperation back.
- 219. Recommended reading Semiology of Graphics: Diagrams, Networks, Maps by Jacques Bertin The Data Journalism Handbook FREE! edited by Jonathan Gray, Liliana Bounegru, and Lucy Chambers Designing Data Visualizations by Noah Iliinsky and Julie Steele Information is Beautiful by David McCandless Envisioning Information by Edward R. Tufte
- 220. Image sources and credits Mount Teide (palmstorys.com/bilder/Englishversions/Tenerife.html) Stratovolcano cross-section by Woudloper (http://commons.wikimedia.org/wiki/ File:Stratovolcano_cross-section.svg) The Expression of the Emotions in Man and Animals (various) (http:// commons.wikimedia.org/wiki/The_Expression_of_the_Emotions_in_Man_and_Animals) Paleolithic painting of giant elk (http://commons.wikimedia.org/wiki/ File:Lascaus,_Megaloceros.jpg) Upper Paleolithic painting of bison by Ramessos (http://commons.wikimedia.org/wiki/ File:AltamiraBison.jpg) Vitruvian Man (http://commons.wikimedia.org/wiki/ File:Da_Vinci_Vitruve_Luc_Viatour.jpg) Diagram of the causes of mortality in the army in the East (http:// commons.wikimedia.org/wiki/File:Nightingale-mortality.jpg)
- 221. Image sources and credits cont... On the revolutions of the celestial spheres (http://ads.harvard.edu/books/ 1543droc.book/) Geocentric model of the solar system (http://commons.wikimedia.org/wiki/ File:Ptolemy_Sky.jpg) Tim Berners-Lee by Silvio Tanaka (http://commons.wikimedia.org/wiki/File:Tim_Berners- Lee_CP.jpg) Hans Rosling (http://novartisfoundation.org/platform/content/element/3967/2336.jpg) Nicolaus Copernicus (http://commons.wikimedia.org/wiki/File:Nikolaus_Kopernikus.jpg) Isaac Newton (http://commons.wikimedia.org/wiki/ File:Sir_Isaac_Newton_by_Sir_Godfrey_Kneller,_Bt.jpg)
Editor's Notes
- \n
- humans -> observe + analyse -> contents -> world around us -> unique + astonishing\n\n*BUT* form + communicate -> verbal + visual concepts\n
- principle factor -> adaptive advantage -> competition -> other animal species\n
- one respect -> superior -> other animals -> we've developed -> greater variety -> systems of communication\n\n*AND* one = art\n
- earliest known examples -> human expression -> demonstrate -> our ability -> chaotic + complex environments -> under control -> magic of art\n\n*BECAUSE* depict something = transform it -> whatever form/shape -> want\n
- *THOUGH* no one knows for sure -> real purpose? = cave paintings = experts believe -> primarily wild animals (cows, deer, horses, elk + bison) -> attempt to control or ‘tame’ them\n\n*INTERESTING* domesticated -> 1000s years\n
- if art + other forms -> creative expression = transform + interpret -> science = identifier + unifier\n\nfew better collisions -> 2 cultures = diagram\n
- world = full of famous + recognisable diagram\n\nmost potent -> ability -> express complex ideas simply -> intellectual + artistic beauty -> power -> shift perspectives -> change minds\n
- 500+ years ago -> (little known) Polish cleric -> Nicolaus Copernicus -> came to revolutionise -> we see -> our place -> universe\n
- 1000s years -> scholars + religious scriptures -> steadfast -> belief -> we (earth) = static centrepiece of universe = everything revolved around us\n\n*SO* against weight -> established teachings + opinions -> *HOW* back up theory?\n
- scan pages -> life's work = contains pages of tabular data and calculations\n\n*AS WELL AS* using = own astrological observations = recalculate planetary positions -> 1000s year's worth data from past astronomers = underpinned diagram\n\n
- despite weight (foundations) = beauty lies -> simplicity -> you or I could draw it\n
- for someone -> benefited -> data from diff sources -> would've welcomed -> data revolution\n
- for someone -> benefited -> data from diff sources -> would've welcomed -> data revolution\n
- for someone -> benefited -> data from diff sources -> would've welcomed -> data revolution\n
- for someone -> benefited -> data from diff sources -> would've welcomed -> data revolution\n
- barriers (lowered) -> ourselves -> rich datasets -> information -> communities + politics + governments\n\n*IF* access to -> numbers + statistics = what's really going on = we can start make things better -> local + national + Inter'l level\n
- barriers (lowered) -> ourselves -> rich datasets -> information -> communities + politics + governments\n\n*IF* access to -> numbers + statistics = what's really going on = we can start make things better -> local + national + Inter'l level\n
- barriers (lowered) -> ourselves -> rich datasets -> information -> communities + politics + governments\n\n*IF* access to -> numbers + statistics = what's really going on = we can start make things better -> local + national + Inter'l level\n
- barriers (lowered) -> ourselves -> rich datasets -> information -> communities + politics + governments\n\n*IF* access to -> numbers + statistics = what's really going on = we can start make things better -> local + national + Inter'l level\n
- offshoots -> emergence -> new + powerful tools = interrogating + presenting data.\n\nallow us = communicate findings -> in a manner -> audiences = can understand + act upon\n
- offshoots -> emergence -> new + powerful tools = interrogating + presenting data.\n\nallow us = communicate findings -> in a manner -> audiences = can understand + act upon\n
- offshoots -> emergence -> new + powerful tools = interrogating + presenting data.\n\nallow us = communicate findings -> in a manner -> audiences = can understand + act upon\n
- offshoots -> emergence -> new + powerful tools = interrogating + presenting data.\n\nallow us = communicate findings -> in a manner -> audiences = can understand + act upon\n
- These tools (basic level) = free to access and use\n
- From -> graphics software = produce interactive visualisations\n
- …tools = visualising + manipulating -> data -> graph form\n
- …tools = mapping location data\n
- …tools for scraping + retrieving + converting data\n
- I've found -> visualising data -> effective way -> test buoyancy theories\n\n*ALSO* gain deeper understanding -> hidden processes -> exist (organisations)\n
- *ALSO* I've found -> effective method -> amplifying + simplifying -> communication -> my recommendations + arguments -> people effect change in organisations\n
- \n
- \n
- \n
- \n
- (one of) effective methods employed -> discover + analyse -> lifecycle -> organisation's content -> conduct series 1-on-1 interviews -> key members -> authoring team\n\n*BUT* locating -> right people -> multi-departmental organisation = isn't always easy\n
- consulting -> hierarchal org charts = useful\n*BUT* don't (always) reveal -> hidden relationships -> forged by content\n*NOR* always show -> real influence lies -> organisation\n
- *THIS* a sociogram -> put simply = visualised social network\n\n*IT CAN* powerful tool = discovering deeper meanings behind -> relationships + communities -> within a network\n
- *SO* each node (bubble) = person within network\n\n*AND* each edge (line) = connection between two people\n
- used = reveal community clusters = might not always be reflected -> org chart\n\n*AND* used = calculate network science parameters = degrees of separation\n\n*SO* the more connections -> individual has = higher their degree\n
- *ALSO* used = calculate betweenness centrality\n\n*OR* I prefer to call it = influence\n\n*SO* more connections -> individual has -> different community clusters = higher their betweenness\n
- most CMSs + intranet servers -> generate + store -> sets log files = record activity (users)\n\n*WHEN* opened = might look like this\n
- focus on -> single entry\n\n*SO* data we're interested in (bold) = date + time, client ip, username, content accessed\n
- focus on -> single entry\n\n*SO* data we're interested in (bold) = date + time, client ip, username, content accessed\n
- focus on -> single entry\n\n*SO* data we're interested in (bold) = date + time, client ip, username, content accessed\n
- focus on -> single entry\n\n*SO* data we're interested in (bold) = date + time, client ip, username, content accessed\n
- focus on -> single entry\n\n*SO* data we're interested in (bold) = date + time, client ip, username, content accessed\n
- focus on -> single entry\n\n*SO* data we're interested in (bold) = date + time, client ip, username, content accessed\n
- focus on -> single entry\n\n*SO* data we're interested in (bold) = date + time, client ip, username, content accessed\n
- *BACK TO LOG FILE* we're looking for = same content accessed by different authors -> within (certain timeframe) = here are 2\n\ns_zajac came along 1/2 hour later from b_olson to access the same content = there’s one connection\n\n*SO* we extract these usernames…\n
- *BACK TO LOG FILE* we're looking for = same content accessed by different authors -> within (certain timeframe) = here are 2\n\ns_zajac came along 1/2 hour later from b_olson to access the same content = there’s one connection\n\n*SO* we extract these usernames…\n
- *BACK TO LOG FILE* we're looking for = same content accessed by different authors -> within (certain timeframe) = here are 2\n\ns_zajac came along 1/2 hour later from b_olson to access the same content = there’s one connection\n\n*SO* we extract these usernames…\n
- *BACK TO LOG FILE* we're looking for = same content accessed by different authors -> within (certain timeframe) = here are 2\n\ns_zajac came along 1/2 hour later from b_olson to access the same content = there’s one connection\n\n*SO* we extract these usernames…\n
- *BACK TO LOG FILE* we're looking for = same content accessed by different authors -> within (certain timeframe) = here are 2\n\ns_zajac came along 1/2 hour later from b_olson to access the same content = there’s one connection\n\n*SO* we extract these usernames…\n
- *BACK TO LOG FILE* we're looking for = same content accessed by different authors -> within (certain timeframe) = here are 2\n\ns_zajac came along 1/2 hour later from b_olson to access the same content = there’s one connection\n\n*SO* we extract these usernames…\n
- *BACK TO LOG FILE* we're looking for = same content accessed by different authors -> within (certain timeframe) = here are 2\n\ns_zajac came along 1/2 hour later from b_olson to access the same content = there’s one connection\n\n*SO* we extract these usernames…\n
- …add them -> 2 columned spreadsheet\n\nfirst usernames -> under ‘Source’ -> second -> under ‘Target’\n\n*FINALLY* save as .csv\n
- …add them -> 2 columned spreadsheet\n\nfirst usernames -> under ‘Source’ -> second -> under ‘Target’\n\n*FINALLY* save as .csv\n
- …add them -> 2 columned spreadsheet\n\nfirst usernames -> under ‘Source’ -> second -> under ‘Target’\n\n*FINALLY* save as .csv\n
- import our data in Gephi = tool = visualising + manipulating -> data -> graph form\n
- we have a big mess = randomly distributed nodes and edges\n\nwe’ll run a layout algorithm, which positions our nodes in an aesthetically pleasing way\n\nI use Force Atlas 2\n
- we have a big mess = randomly distributed nodes and edges\n\nwe’ll run a layout algorithm, which positions our nodes in an aesthetically pleasing way\n\nI use Force Atlas 2\n
- we have a big mess = randomly distributed nodes and edges\n\nwe’ll run a layout algorithm, which positions our nodes in an aesthetically pleasing way\n\nI use Force Atlas 2\n
- this looks better! = formed of defined community clusters\n\nTo help pick out the most important individuals in the network, we need to rank this by ‘Degrees of separation’\n
- this looks better! = formed of defined community clusters\n\nTo help pick out the most important individuals in the network, we need to rank this by ‘Degrees of separation’\n
- this looks better! = formed of defined community clusters\n\nTo help pick out the most important individuals in the network, we need to rank this by ‘Degrees of separation’\n
- we’ve ranked degrees by colour. The ‘greenest’ nodes have the highest degree\n\nNow let’s find out who are the most influential by ranking the network by ‘Betweenness Centrality’\n
- we’ve ranked degrees by colour. The ‘greenest’ nodes have the highest degree\n\nNow let’s find out who are the most influential by ranking the network by ‘Betweenness Centrality’\n
- we’ve ranked degrees by colour. The ‘greenest’ nodes have the highest degree\n\nNow let’s find out who are the most influential by ranking the network by ‘Betweenness Centrality’\n
- Despite the colour and size of nodes helping us to see who are the most influential in this network, it is still too busy\n\nso we’ll filter out the weaker points of the network\n
- Despite the colour and size of nodes helping us to see who are the most influential in this network, it is still too busy\n\nso we’ll filter out the weaker points of the network\n
- Despite the colour and size of nodes helping us to see who are the most influential in this network, it is still too busy\n\nso we’ll filter out the weaker points of the network\n
- These look like the most influential people we should be talking to\n\nThey might have been deep on an org chart but in this context they are very important to us.\n
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- we (able) see = different community clusters -> these people belonged to -> identified -> our graph\n
- in effect we created = alternative org chart\n\n*AND* revealed = another power structure within an organisation\n
- studying sociograms might = enable large organisations -> understand which individuals -> connecting remote communities\n\n*AND* very useful = those of us -> air-dropped into organisations -> limited time to learn -> people + culture\n
- Let's look = how content relationships -> visualised\n
- Let's look = how content relationships -> visualised\n
- Let's look = how content relationships -> visualised\n
- Let's look = how content relationships -> visualised\n
- though many of us = prepare web content -> isolated docs + locations -> ready for assembly -> they are = rarely read that way\n\n*SO* fully appreciate -> story we're telling (audiences) = we might -> take a look from above...\n
- ...like this this graph = rendering = most frequently trodden paths -> through a website\n\nby studying it = possible glean significant information -> our content + relationships between it\n\n*BUT* before we make one = me explain what we're looking at\n
- ...like this this graph = rendering = most frequently trodden paths -> through a website\n\nby studying it = possible glean significant information -> our content + relationships between it\n\n*BUT* before we make one = me explain what we're looking at\n
- ...like this this graph = rendering = most frequently trodden paths -> through a website\n\nby studying it = possible glean significant information -> our content + relationships between it\n\n*BUT* before we make one = me explain what we're looking at\n
- ...here’s a simplified example:\n\n*SO* each node (bubble) = web page\n\n*AND* each edge (line) = flow of users between pages (traffic)\n
- ...here’s a simplified example:\n\n*SO* each node (bubble) = web page\n\n*AND* each edge (line) = flow of users between pages (traffic)\n
- *SO* larger nodes = higher PageRank = (essentially) higher importance -> relation other pages (set)\n\n*AND* thicker edges = frequency of flow -> between pages -> this direction\n
- *SO* larger nodes = higher PageRank = (essentially) higher importance -> relation other pages (set)\n\n*AND* thicker edges = frequency of flow -> between pages -> this direction\n
- Indebted = Dorian Taylor -> this technique\n\nread everything he's ever written, particularly his essay for contents magazine\n
- In his article = "visualising paths through the web" -> Dorian points out -> web servers = log information -> each available referring resource (prev. page) + each new request we make (next page)\n
- let's take a look -> sample web server log = looks a lot like = log file sample we saw earlier *BUT* = subtle differences\n
- focus on -> two entries\n\n*SO* we want -> locate each referent-referrer connection (next + previous page) = come under matching client ip addresses\n
- focus on -> two entries\n\n*SO* we want -> locate each referent-referrer connection (next + previous page) = come under matching client ip addresses\n
- focus on -> two entries\n\n*SO* we want -> locate each referent-referrer connection (next + previous page) = come under matching client ip addresses\n
- focus on -> two entries\n\n*SO* we want -> locate each referent-referrer connection (next + previous page) = come under matching client ip addresses\n
- focus on -> two entries\n\n*SO* we want -> locate each referent-referrer connection (next + previous page) = come under matching client ip addresses\n
- cleaning data = case of stripping it -> all non-human visits -> web crawlers = Google bot, Bing Bot and others\n\nfind and replace = your friend and ally\n
- …add them -> 2 columned spreadsheet -> like before\n\nreferring URLs -> under ‘Source’ -> referent URLs -> under ‘Target’\n\n*FINALLY* save as .csv\n
- …add them -> 2 columned spreadsheet -> like before\n\nreferring URLs -> under ‘Source’ -> referent URLs -> under ‘Target’\n\n*FINALLY* save as .csv\n
- …add them -> 2 columned spreadsheet -> like before\n\nreferring URLs -> under ‘Source’ -> referent URLs -> under ‘Target’\n\n*FINALLY* save as .csv\n
- before continue -> introduce -> working example -> remainder (this talk)\n\ncross country coaches = fictitious UK bus + coach operator = generous with their data!\n\nmain services include: airport runs -> day trips to major towns + cities -> attractions -> sporting + music events\n
- all roads (should) -> journey planner = buying e-tickets\n
- *AGAIN* import our data in Gephi = tool = visualising + manipulating -> data -> graph form\n
- we have a BIGGER mess = randomly distributed nodes and edges\n\nwe’ll run the same layout algorithm as before to position our nodes in an aesthetically pleasing way\n\nI use Force Atlas 2\n
- we have a BIGGER mess = randomly distributed nodes and edges\n\nwe’ll run the same layout algorithm as before to position our nodes in an aesthetically pleasing way\n\nI use Force Atlas 2\n
- we have a BIGGER mess = randomly distributed nodes and edges\n\nwe’ll run the same layout algorithm as before to position our nodes in an aesthetically pleasing way\n\nI use Force Atlas 2\n
- this looks better! = formed of defined clusters\n\nTo help pick out the most important pages/content in the network, we need to rank this by ‘PageRank’\n
- this looks better! = formed of defined clusters\n\nTo help pick out the most important pages/content in the network, we need to rank this by ‘PageRank’\n
- this looks better! = formed of defined clusters\n\nTo help pick out the most important pages/content in the network, we need to rank this by ‘PageRank’\n
- Despite the colour and size of nodes helping us to see what is the most influential content in this network, it is still too busy\n\nso, again, we’ll filter out the weaker points of the network\n
- All roads should be leading to Journey Finder\n\nBut many are choosing to leave this process of buying an e-ticket to ‘Contact’ Cross Country Coaches - that’s interesting = something to investigate\n\n\n
- All roads should be leading to Journey Finder\n\nBut many are choosing to leave this process of buying an e-ticket to ‘Contact’ Cross Country Coaches - that’s interesting = something to investigate\n\n\n
- All roads should be leading to Journey Finder\n\nBut many are choosing to leave this process of buying an e-ticket to ‘Contact’ Cross Country Coaches - that’s interesting = something to investigate\n\n\n
- All roads should be leading to Journey Finder\n\nBut many are choosing to leave this process of buying an e-ticket to ‘Contact’ Cross Country Coaches - that’s interesting = something to investigate\n\n\n
- All roads should be leading to Journey Finder\n\nBut many are choosing to leave this process of buying an e-ticket to ‘Contact’ Cross Country Coaches - that’s interesting = something to investigate\n\n\n
- Nevertheless, what we have ourselves is a sample of the most frequently-trodden paths this website = a very useful starting point\n\nSo we’ll export the node data as a .csv as this will come in handy later\n
- \n
- visualising the referring/referent data -> web server logs = provided different perspective -> stories -> we're telling our audiences\n
- by filtering data = removed visual complexity = to reveal key paths + stops users making\n
- *AND* exporting node data = generated sample most frequently accessed site content\n
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- we can add -> further value + depth -> visualisations = informing them -> data from our own investigations\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- Let’s export -> node table -> in addition to page title/descriptions = (manually) add set new columns -> extracted from recent audit\n\ndept responsibility -> individual responsibility -> sourced from (external partners?) -> last updated -> criteria for measuring content quality\n
- *OR* could use = Google Fusion Tables = help quickly merge data across 2 spreadsheets = pairing up 1 (or more) columns -> same values\n
- now we = download CSV -> take it where we please\n
- now we = download CSV -> take it where we please\n
- now we = download CSV -> take it where we please\n
- What could we do -> exported node table -> now we have = additional data?\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- we could use = Tableau Public -> dive deeper into our data\n\ndesktop data visualisation app = quickly create charts + graphs\n\n*BUT* PC software only\n
- let's pose a question\n
- here we have = set of bar charts -> tell us -> what each content source scored (out of 5) = Actionability, Accuracy, Usability -> the content -> responsible for\n\n*IF* single out Tourism Partners = score below average for each measurement\n\n*SO* what content = they responsible for?\n
- here we have = set of bar charts -> tell us -> what each content source scored (out of 5) = Actionability, Accuracy, Usability -> the content -> responsible for\n\n*IF* single out Tourism Partners = score below average for each measurement\n\n*SO* what content = they responsible for?\n
- here we have = set of bar charts -> tell us -> what each content source scored (out of 5) = Actionability, Accuracy, Usability -> the content -> responsible for\n\n*IF* single out Tourism Partners = score below average for each measurement\n\n*SO* what content = they responsible for?\n
- here we have = set of bar charts -> tell us -> what each content source scored (out of 5) = Actionability, Accuracy, Usability -> the content -> responsible for\n\n*IF* single out Tourism Partners = score below average for each measurement\n\n*SO* what content = they responsible for?\n
- here we have = set of bar charts -> tell us -> what each content source scored (out of 5) = Actionability, Accuracy, Usability -> the content -> responsible for\n\n*IF* single out Tourism Partners = score below average for each measurement\n\n*SO* what content = they responsible for?\n
- here we have = set of bar charts -> tell us -> what each content source scored (out of 5) = Actionability, Accuracy, Usability -> the content -> responsible for\n\n*IF* single out Tourism Partners = score below average for each measurement\n\n*SO* what content = they responsible for?\n
- here we have = set of bar charts -> tell us -> what each content source scored (out of 5) = Actionability, Accuracy, Usability -> the content -> responsible for\n\n*IF* single out Tourism Partners = score below average for each measurement\n\n*SO* what content = they responsible for?\n
- *SO* let’s return to our intro = Cross Country Coaches\n\nTourism Partners responsible for = content for each town/city destination\n
- by filtering data (further) -> quickly generate (Tableau Public) = breakdown of the scores -> each town + city destination\n\n*AS* these destinations = scored lowest = first in line for review?\n
- by filtering data (further) -> quickly generate (Tableau Public) = breakdown of the scores -> each town + city destination\n\n*AS* these destinations = scored lowest = first in line for review?\n
- by filtering data (further) -> quickly generate (Tableau Public) = breakdown of the scores -> each town + city destination\n\n*AS* these destinations = scored lowest = first in line for review?\n
- by filtering data (further) -> quickly generate (Tableau Public) = breakdown of the scores -> each town + city destination\n\n*AS* these destinations = scored lowest = first in line for review?\n
- by filtering data (further) -> quickly generate (Tableau Public) = breakdown of the scores -> each town + city destination\n\n*AS* these destinations = scored lowest = first in line for review?\n
- could external data = help us prioritise our efforts?\n\nlet's bring some into play\n
- *SO* I downloaded data = domestic tourism stats for 09-11 = VisitBritain\n
- only problem was = data = only available PDF format\n\nthis happens a lot!\n
- *BUT* you can save hours -> re-keying + checking = using tools like these\n\nthese free tools = convert PDF-to-Excel\n
- we can use = Google Fusion Tables = merge our data -> each town + city destination -> with downloaded VisitBritain data\n\nonly inherit the data -> corresponds -> our towns + cities \n\n*SO* let's map the data\n
- we can use = Google Fusion Tables = merge our data -> each town + city destination -> with downloaded VisitBritain data\n\nonly inherit the data -> corresponds -> our towns + cities \n\n*SO* let's map the data\n
- here's a map of British Isles -> generated via Tableau Public\n\nincluded are = bubbles marking -> each town + city -> from our data\n\nTableau public automatically recognised this as location data\n
- *SO* bubble sizes represent = total domestic trips 09-11 (millions)\n\n*AND* colour intensity represents = total spend 09-11 (millions)\n
- *SO* bubble sizes represent = total domestic trips 09-11 (millions)\n\n*AND* colour intensity represents = total spend 09-11 (millions)\n
- *SO* bubble sizes represent = total domestic trips 09-11 (millions)\n\n*AND* colour intensity represents = total spend 09-11 (millions)\n
- *SO* bubble sizes represent = total domestic trips 09-11 (millions)\n\n*AND* colour intensity represents = total spend 09-11 (millions)\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- as expected -> London = largest + darkest bubble = remove\n\nlet's use sliders -> filter out destinations = scored highest for Actionability, Accuracy, Usability\n\nnow we're left = Birmingham + Edinburgh = highest amount of trips + spend = priorities?\n
- \n
- filtering + partitioning -> data -> own investigations = took us deeper into -> data\n\n
- importing -> external data = provided possible new angles + ideas\n
- adding interactive elements = helped develop narrative around -> data\n\n*AND* invites our audiences = delve deeper still\n
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- play + experiment -> your data\n\nhave fun with it -> ask questions of it -> don't fear it\n\n*THEN* = often yield its secrets + stories = surprising ease\n
- we (often) associate numbers = authority and certainty\n\nbut uncertainly = great way -> raising new questions -> sharing them with others\n
- by creating + sharing -> your work -> with others = you might find -> get help + co-operation back\n
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