Omaha Coffee Shops!

Today’s topic? Highlighting the power of mashing up Factual data, BatchGeo  geo-encoding, and A.PlaceBetween.Us meetup flashmob planning.

Today’s chosen real-world “pain point” app? Discovering coffee shops, that magical elixir, when out & about in beautiful Omaha, NE.

This map shows Omaha’s 125+ coffee destinations (independents & Panera Bread). Map layers are selected via the legend’s category pins & the “all categories” view are restored by clicking the legend’s black area.

View Omaha Coffee Shops (pins) in a full screen map

This second embedded map showcases BatchGeo’s clustering capabilities. Zooming in or clicking a cluster icon will expand cluster(s) into sub-clusters &/or individual coffee shop locations.

View Omaha Coffee Shops (clusters) in a full screen map

Factual provides many other data sets, all of which can be explored via their API. For example one method of exploring Factual’s API is by pasting the below string into Apigee’s generic console & pushing GET.

This API URI query string is provided by Factual when interacting with their data via the “Get API query” button to simplify developer forays. Also, for reference, each info pop-up card on the above maps contain:

• Coffee Shop Name (hyperlinked)
• Address & Phone Number
• Coffee Shop Category
• Directions Link via Google
• Meet-up Planner via A.PlaceBetween.Us
• Link to edit raw data sourced via Factual
• Fisheye image powered by Google Places

For more posts on solving real-world pain points, subscribe to ThoughtPuzzle via email, feed, or follow. Enjoy!

Microsoft Office – convert trial from unlicensed product to activated license

Interesting ‘undocumented feature’ encounter yesterday…

Sometime ago, I’d downloaded the 60-day trial software suite of Microsoft Home & Office 20110, which had since expired and converted to ‘unlicensed product’, i.e., I could open files in MS Word, Excel, PowerPoint, OneNote, & Outlook, but could not save files, import/export data, or create new items.

• After yet again evaluating the trifecta of OpenOffice or LibreOffice, coupled with Mozilla Thunderbird & Lightning, yet again came to  conclusion that MS Office, and in particular, Outlook, remains as my personal information manager (PIM) of choice. Testing included converting exploring Thunderbird / Lightning after importing several gigabytes of mail, appointments, from PST files. Some big gaps…
  • import didn’t include [hundreds?] of contact pictures
  • lack of task folder / hierarchy other than multiple calendars
  • copying / pasting across documents within OpenOffice did not retain consistent formatting
  • couldn’t store attachments in contacts, i.e., limited ability to use Thunderbird & Lightning as a wiki
• Decision – purchase license of MS Home & Office (& re-downloading the suite, being sure to keep the same filename at Microsoft’s suggestion), I entered the license key on pop-screen that appears after opening Office. However…

Tweet Tributaries – Wiki Waters

Text analysis side project for aspiring developers, researchers, or startups. Construct novel apps to trawl Twitter’s tributaries or Wikipedia’s waters and DARPA may be interested.

Click to read the app suggestions

Google Chrome + Google Reader – More Magic, Please

Browse via Chrome. Track via Reader. What could be. Some extended thoughts. Wait, doesn’t auto-discovery make using feeds easy?

No, not as implemented in Chrome and Reader. Practically speaking, a user requires the computing knowledge equivalent of driving stick shift.

First up – feeds, those mystical links to RSS and Atom, which are commonly used on blogs and news sites. Unfortunately, they’re often ‘hidden’ by web masters, who only post them  via auto-discovery &/or bury them.

Click to discover about feed-reading with Chrome & Reader.

App Discovery – Filter Bubbles and Random Selection

An interesting challenge is finding useful &/or novel. apps to install on my mobile phone. During a recent app foraging session, I happened to also be contemplating the duality of filter bubbles and leveraging randomness. Approaches include:

• Suggested apps – surf ‘installers of this app checked out these apps’ at App Brain, App Market, or App Store. Akin to Amazon or New York Times recommendations. Limited with respect to finding ‘novel apps’, i.e., stays ‘in the bubble’.
• Recommender lists – for instance, an App-of-the-Day site – again, within human-curated filter bubble.
• Functional keywords – semi-useful, think of domain words, e.g., compass, and explore discovered apps.
• Random keywords – semi-fun, think of a random word, e.g., Camelot, and explore discovered apps.

Some app stores generate random app suggestions, akin to Wikipedia’s random page. A ‘random app launcher’ is also allegedly available. However, these approaches don’t leverage human input, and hence offer little to no entertainment value. My goal was to construct an engaging ‘random app experience’ that combined randomness with some novel use of human input.

The ah-ha moment click to read New Year’s toast to randomness.