1.9 liz turner
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The document promotes a company called Iconomical that provides website design and development services. It lists their email and website for contact information. It encourages the reader to learn more about their projects by visiting their website or contacting them via email.
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1.9 liz turner
- 10. !"#$%&"'%(")*+,-
- 13. !"#$%&"'%(")*+,-
- 14. thank you for listening! !"#$%&"'%(")*+,- www.iconomical.com/company/projects/ liz@iconomical.com
Hinweis der Redaktion
- Phone book: Joke: not interesting to read. No tools to do so, but full of interesting information... about demographics, economics and cultural life of the city. Organized according to classifications - alphabet, location, categories. Visualization = ways to analyze data!\n
- Beginning to use dynamic visualization tools daily, to explore data to help us get things done! Plings: places to go and things to do for kids. Interface for exploring a live database. When, where and what? Map and timeline model.\n
- Clicking “things to do” shows Plings according to extracted keywords. Pan around to find keyword clusters! Analysis should be invisible - so easy that users don’t know they’re doing it. What do we need to do to make this happen?\n
- Building blocks. Bricks an essential component of our city. Visible at street level, but never seen on a map. Likewise, might have 1000’s of data items - can’t put all on screen. How to get from the very large to the very small and back again?\n
- Zoom out: 100’s of locations - don’t want to see them all! Aggregate at low resolution - see Plings clusters around urban centers. Investigate data to find self-organizing patterns, and make tools that take advantage of that. Do your homework!\n
- Drill-down is easy on a map. Zoom in and apply some extra filters - find a dance class, see events at the same location, different times. But what happens when a locative map doesn’t fit my data model?\n
- Data must be be shaped and refined. Analyze and prototype! The forms data takes may surprise you. Grows out in different directions. Different domains may need different views. Discover the unexpected. Here are some different ways of looking at government spending...\n
- Tanzanian Budget explorer: Spending hierarchy of ministries and departments, by Function. Mouse-over tool-tip identifies the spending agency. Drill down for more. Great way to show relative quantities on one page. But won’t work for regional spending. \n
- Regional spending on a cartogram. Spending distribution per capita: change to “actual” for an inverse relationship as the colors change - a good example of a relationship that gives context to the data! Click on a region to drill down. Once we’ve organized things into views...\n
- ...Interfaces should lead users through the data in all directions - they learn as they go. Users control their choices, not confused by options. But too little risks missing something surprising! Respect the data - throw away your preconceptions, or the data will trip you up!\n
- Government data hard to understand because abstract classifications. Limit choices and contextualize! Functions and sectors, by central government. Different classifications overlap each other. Interface is structured to do this. \n
- Same sectors, development and recurrent spending. Harder to understand, but useful to see classifications in practice. E.g., >25% development spending on infrastructure! Pivoting between different perspectives controlled for relevance, but allowing myriad views!\n
- To sum up: data comprises building blocks in classifications which need analysis and aggregation; refined in forms which give them context; presented in interfaces which facilitate way-finding through exploration ... basics for successful visual apps!\n
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