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For implementing the map and database behind ir we used Google's map and Fusion Tables APIs.....

Evaluation:

Unfortunately, due to the nature of our project, we were unable to get in contact with three users from target audience of our project (since the user population is fairly small).  Instead, we used one target audience member as well as two students taken from the MIT population (although not from the 6.813 roster).  The most important effect that this may have is that our target user population would like have extensive training with the software and would also be much more intimately familiar with the problem at hand.  With that in mind, this is the information we presented each of our users with prior to the test:

Briefing:

Imagine you are analyst at Boingo looking to make additions to your WiFi towers in South Africa.  You want to look at population and rainfall data in that region to find optimum places to add towers.  Already loaded into your interface, you have 3 already existing data sets, and one dataset of new additions.
Useful background information:
Satellites (going up in 2013-2014) will provide WiFi coverage over Africa, but their frequency range can't transmit signals through rain.  WiFi towers do work in rain.  Many African tribes are nomadic, moving around in search of water and food.  Your job is to find locations for new towers to maximize the number of people with WiFi access, taking into account the data.

Demo:

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http://www.youtube.com/watch?v=tfDULO83dpU

Tasks:

Task 1: View the relationships between two of these sets on the map.
Task 2: Find three regions without WiFi coverage, and add new towers there.
Task 3: Analyze their costs and move the most expensive towers to places with the highest population and highest rainfall.

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Reflection:

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