Why building a better iPhone is challenging

So Apple introduces a new iPhone with some updates and everyone is aghast that it isn’t completely redesigned. But it’s getting harder and harder to re-invent mobile devices. I mean they do need to be mobile. Adding more features can lead to increases in size. The iPhone 5 is fit-in-the-pocket comfortable. The Samsung Galaxy Mega is not. You may as well get an iPad mini. Want a bigger camera, say 16 megapixels?, faster processor? That requires a device which is going to eat more power – which means a larger battery, and before you know it the device begins to expand. 16 megapixel photographs also mean that apps will have to work harder to process them, and they’ll need more storage, and likely more memory. What else do you want the iPhone to do? Make you lunch? Drive your car? It’s a mobile device – it does amazing things, but there are limits to what it can (and should) do. Sure compact optics, more efficient processors and better batteries will improve things, but maybe not in the next few development cycles.

The software will likely evolve at a faster pace. Improvements in how the operating system and apps work, increased battery life, and maybe more algorithmic intelligence. The problem is some of these enhancements are transparent to the user, so while it seems as though very little has improved, the opposite is often true. Case in point is Apple Maps. When it first debuted there were many issues with the mapping services. The true genius lies in the use of resolution independent vector maps. This is quite different to the raster images used by others, which aren’t as efficient when used on slow data networks, or networks with low bandwidth. In vector maps, roads, coastlines, and any other data is represented as mathematical lines rather than fixed images. In short the vector images are dynamic, meaning the map labels dynamically reorient themselves, and text scales smoothly, as it too is dynamic. Vector maps save memory, allow maps to be cached on the device, and bandwidth reduced.

Apple Maps is apparently upwards of 5 times more efficient than Google Maps. In an experiment performed by Gizmodo, an identical series of activities were performed using Google Maps and Apple Maps. On Google Maps, the average data download was 1.3MB – Apple Maps came in at 271KB. Reduced data means reduced user costs.

Sometimes the most evolutionary changes are those you can’t see.

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