The Apple VS Google and Microsoft

Market rivalry between the three most prominent technology companies Apple, Google and Microsoft (listed here in an alphabetic order, without any bias) has been well documented in the media. Each organisation has its loyal group of followers but also equally large group of critics. The reality for most of us is that we have to use bits and pieces of technologies, tools and services from all three suppliers. Comparing financial metrics gives an interesting perspective on each competitor but between the figures, my very biased and stereotypical view of those three companies…

Rank Company Market capitalization($ B)
2 Apple 319
5 Microsoft 233
16 Google 195

My impression of Microsoft is that it is ubiquitous in the personal computer world since overwhelming majority of desktop computers and laptops run on Microsoft software. You may not like Microsoft, or even totally hate it when their software crashes on you, yet you have no other choice but to use it. Microsoft software is a memory hungry beast and impossible to tinker with (forget trying to separate the pieces!) but that integrated “package” is so loved by “project manager” type of developers – just click on a few tick-boxes to configure individual pieces and “it all should work” (never mind how efficient it is and what it does under the hood).

Rank Company Total enterprise value ($B)
4 Apple 289
18 Microsoft 202
30 Google 164

Apple, on the other hand, is “cute and flashy” (pun intended), and practical to the point of pain (you can’t do things any other way but the Apple way, but they put a lot of effort into interface design and user interaction functionality so it kinda grows on you over time). The development environment is limited to a “toy world” of smart apps and is not a domain where any “serious stuff” can happen. For now, but who knows how far Apple will be able to push the boundaries with their “cloud initiatives”.

Rank Company Total physical assets ($M)
748 Microsoft 7,800
750 Google 7,760
931 Apple 5,870

Then there is Google, very plain (in comparison to Apple) and messy (in contrast to Microsoft) but still mostly free and “unbounded” (although sadly, things are starting to change on that front). You can totally get lost in the maze of Google products and services. The downside is that you may never own the “fruit of your hard work” if the company drops support for a specific product (due to “cloudy” and proprietary nature of many of Google products, unfortunately you cannot get the source code and continue on your own). But oh my, when it works, it works. If “it” doesn’t do something now, there is a good chance that this extra functionality will be added sooner or later (pity you never know when…).

Rank Company Revenue ($B)
78 Apple 76
100 Microsoft 67
365 Google 30

All in all, each company has its strengths and limitations, and their respective successes can be measured in different ways, as those financial metrics demonstrate…

Rank Company Total employees (thousands)
40 Microsoft 27.6
51 Apple 25.4
102 Google 15.1

3 Crime Maps: Point collation


Quick update of the Placemark Clustering project: we’ll be doing user tests using the uk police crime map later this summer (discussed below) comparing it to a chloropleth grid (translation = head map based on grid, I explain further here)
In thinking about this I’ve hunted down some examples and I thought it would be interesting to name check 3.

No Collation: The first map is Oakland Crime Spotting (bottom inset in figure) that is very similar to San Francisco Crime mapping, reviewed here. Unlike the other two maps it attempts no point collation at all, I image the authors would argue that they deal with the problem by providing sophisticated filtering tools to reduce the point density. However, it doesn’t help if the user wants to get an overview picture of crime across the area the map covers.
Traditional Choropleth: Switching to the the UK, the Metropolitan Police (=London for non UK readers) offer a choropleth map based on wards and subwards (top left insert). I regard this as the traditional approach. Notably it doesn’t show actual figures for postcodes, only sub wards – a sub ward is a collection of postcodes. My problem with this is that almost no one knows the boundaries of wards and sub wards so its a strange way to split the city up. (Aside: in my experience, Londoners tend to split London up based on tube stations)
Point Collation: The UK police offer a national map which uses point collation (top right insert). This is the main one we’re planning to test as IMHO it isn’t an effective way to visualise the data (related post). It offers a finer grain of data – you don’t actually see the true location of the crime but it is collated down to the postcode level. In London, a postcode is roughly equal to a single street.