At Tobias & Tobias we can’t solve problems like climate change, greenhouse gases, the extinction crisis or the crude oil soaking the Louisiana coastline. But we can do our part.

We’ve been trying to persuade staff to waste less paper, use less energy, and send less waste to landfill. To do this our design team has created a poster series called Green Tips that we’ve placed at strategic locations around the office. They’re irreverent, not po-faced, and we thought we’d make them available to the world at large.

Green Tips posters are available in PDF format for easy printing. Just right-click on the thumbnails below and select “Save As”.

Green Tip #1 – Save Paper (Download PDF)

Green Tip #2 – Your Machine (Download PDF)

Green Tip #3 – Recycle (Download PDF)

One notion that I take exception to, however, is that the iPad signals the death of the keyboard and that touch interfaces are destined for ubiquity.

Now, I’m no technological conservative – I’ve been using touch-screen phones since before the iPhone came out. But I think a more fundamental point is being missed here, which is that the roles computers play in our lives are multiplying greatly.

Computers used to play a relatively limited set of roles which could be supported with a common set of interface models, mainly centred around the keyboard and the mouse. The keyboard & mouse setup worked when the computer operator was sat at a desk, had enter lots of data from a large character set, and needed direct access to many (maybe even several hundred) on-screen controls offered by their applications.

Today, not every computer user is sat at a desk and in the need state described above. Computer users might be on the other end of a phone line from the machine itself, operating it through a (notoriously infuriating) voice interface. They might be delivering a parcel and collecting the recipient’s signature using a handheld computer’s (notoriously infuriating) pen interface. And of course the computer user might be using a personal device like a smartphone, which needs to be small and light and whose functions don’t require the sort of  intricate and precise interactions supported by the keyboard/mouse combo.

But this doesn’t change the fact that some computer users will still be in situations where the keyboard and mouse paradigm is appropriate. Therefore the keyboard will not die.

What things like the iPad illustrate is that we are using computers more than we used to – in a wider number of contexts and for a wider range of reasons. They don’t replace what we already have, they’re just a new addition to our collection of tools.

For what it’s worth, there are things about Buzz I’d like to change. Conversations shouldn’t be treated so much like emails, for example, with “read” and “unread” states – this brings “inbox anxiety” into the equation, something Twitter was wise to discard. And users could benefit from more fine-grained control over privacy settings.

Inbox anxiety with Google Buzz - I'm not looking forward to having hundreds of unread "Buzzes"

But I’m happy to put these thoughts to one side: at the moment I’m more interested in the response it’s provoked among my own contacts, many of whom are tech-savvy but not really social web junkies. So far, it’s making me think that Buzz has an appeal for people who are active online but always disliked Twitter and had never heard of Friendfeed.

Buzz has definitely been a conversation-starter in a way that Wave wasn’t. In the first few hours, many posts were as you’d expect – “what is this for?”, “can anyone see this post?”, that sort of thing. Today is day two for Buzz, however, and the conversations have started to move away from these meta topics. In fact they’re slowly starting to resemble the sorts of conversations these people have in real life.

This is very different from Wave, which prompted a few discussions of the “what’s this all about?” variety before being largely abandoned even by early adopter types like myself. Obviously this might happen with Buzz as well – as I said above, today is only day two – but the acceptance trajectory so far seems very different. For example, the risk of being flooded with too much Buzz data seems much greater than that of Buzz falling into disuse.

In many ways I’m tempted to think that Wave has been a kind of public beta for Buzz. MG Seigler at TechCrunch is thinking along similar lines in this post, If Google Wave Is The Future, Google Buzz Is The Present. Buzz certainly explains why Wave had no Gmail integration, something I wondered about at the time.

Once again, it’s early days with Buzz. But my own anecdotal experiences so far make me suspect that – despite the contrary opinions of various mavens and competitors – it’s going to be a fixture in the social media landscape for some time to come.

Some extensive googling led me to this extremely useful post on Labnol, where I learnt about how to use the ImportXML function in Google Spreadsheets. Before too long I’d cracked my problem. In this post I’m going to explain how you can do it too.

Data you can extract from Twitter

This walkthrough will teach you how to extract two types of Twitter data using Google Spreadsheets – tweets and links.

Tweets are extracted using the Twitter Search API in conjunction with ImportFeed. This allows Twitter search results to be extracted into a spreadsheet format.

Links are extracted using the Backtweets API in conjunction with ImportXML. The Backtweets API allows you to find any links posted on Twitter even if they’ve been shortened using services like bit.ly or tinyurl.

I’m in a hurry, can I just do this right now?

If you just want to do it – instead of learn how to do it – just open this Google spreadsheet I’ve created.  You’ll need to make your own local copy so you can edit it. Instructions can be found in the spreadsheet itself.

How to extract tweets containing links

The instructions below will help you create a Google Spreadsheet that pulls in and displays the time, username and text of all tweets containing links to a specified page. Because it uses Backtweets, these tweets will be retrieved even if they used shortened URLs from services like bit.ly or tinyurl.

1. Create a new spreadsheet in Google Documents.
2. Enter column labels in this order: “Search criteria”, “Timestamp”, “Username” and “Tweet text” in cells A1 to D1.
3. In cell B2, underneath Timestamp, insert the following formula:
   

   =ImportXML(”http://backtweets.com/search.xml?itemsperpage=100&since_id=1255588696&key=key&q=”&A2,”//tweet_created_at”)

4. In cell C2, underneath Username, insert the following formula:
   

   =ImportXML(”http://backtweets.com/search.xml?itemsperpage=100&since_id=1255588696&key=key&q=”&A2,”//tweet_from_user”)

5. In cell D2, underneath Tweet Text, insert the following formula:
   

   =ImportXML(”http://backtweets.com/search.xml?itemsperpage=100&since_id=1255588696&key=key&q=”&A2,”//tweet_text”)

6. Now paste a search query into cell A2 – say, http://www.google.com. After a few seconds, you should see columns B, C and D fill up with tweets, looking something like the image below:



7. The formulas pasted into cells B2, C2 and D2 all reference the URL in cell A2. This means that whenever you paste anything new into A2, the search results should refresh.
8. Also, you can paste parts of URLs into A2 – not just entire ones. This is useful for seeing all links to a specific directory on your site, for example.

Finally, this tool can only extract 100 results at a time – but it is possible to set it up to retrieve more than that. Look at my sample Google Spreadsheet if you want to do this.

Extracting tweets from Twitter search results

The method for doing this is identical to the above, but uses the ImportFeed function instead of ImportXML.

1. Create a new spreadsheet in Google Documents.
2. Enter column labels in this order: “Search criteria”, “Timestamp”, “Username” and “Tweet text”. For the rest of this walkthrough, I’m going to assume that these labels are in cells A1 to D1, but in reality you can put them wherever you like
3. In cell B2, underneath Timestamp, insert the following formula:
   

   =ImportFeed(”http://search.twitter.com/search.atom?rpp=20&page=1&q=”&A2, “items created”)

4. In cell C2, underneath Username, insert the following formula:
   

   =ImportFeed(”http://search.twitter.com/search.atom?rpp=20&page=1&q=”&A2, “items author”)

5. In cell D2, underneath Tweet Text, insert the following formula:
   

   =ImportFeed(”http://search.twitter.com/search.atom?rpp=20&page=1&q=”&A2, “items title”)

6. Type a search query into cell A2 – say, “Hoth.” Hit enter and the results will load. It should look something like this:
7. Things will go wrong if you insert characters like # or @ into the search query. To get around this, type %23 instead of # and %40 instead of @. This will allow you to search for hash tags and usernames.

I haven’t been successful in generating more than 20 search results per request, but you can get around this using the page number parameter in the ImportFeed query string. See my own Google spreadsheet to find out how to do this.

I hope these instructions are useful – if you have any comments, questions or feedback, please let me know in the comments.

Traditional economic reports, on the other hand, look at events that have taken place. Unemployment figures tell us how many people are claiming benefits rather than how many people have been put at risk of redundancy. Average house prices are based on completed transactions, not how many people are currently looking to buy. So while we can be fairly confident of these reports, they don’t provide us with particularly current insights.

This trade-off between confidence and currency was, in the past, largely academic as analysing current data was almost impossible. But in the age of the real-time web, this might be about to change: maybe patterns in search behaviour can give us a glimpse of future patterns in the economy.

We first became interested in this topic back in spring 2009, so we analysed search patterns for two sets of keywords as the UK economy went into recession. We looked for relationships between these search patterns and related economic indicators, and listed some tentative predictions based on what we observed.

House prices

In April 2009, we looked at volumes for 23 keywords that homebuyers might use, including buying a home, cheap mortgage and mortgage providers. UK search volumes for these keywords were then compared to house prices.

House prices charted against search volumes for 23 related keywords, from January 2004 to April 2009. Sources: Nationwide, Google Insights

Searches typically decline as autumn ends before rebounding in January. But in 2008, the January rebound was lacklustre and the decline came in spring – much earlier than usual. This was in line with house prices, which peaked in late 2007 and dropped severely from spring 2008.

In the first few months of 2009, however, search volumes enjoyed a far stronger January rebound than in the previous year – so we hypothesised that house prices would bottom out or even start to rise again in the middle of 2009. Let’s look at how accurate that hypothesis turned out to be.

House prices charted against search volumes for 23 related keywords, from January 2004 to December 2009 Sources: Nationwide, Google Insights

Sure enough, the search volume resurgence was accompanied by house price growth throughout 2009. But you’ll notice that search volumes soon tapered off, with a particularly steep fall after August. Our revised hypothesis, then, is that house prices will initially plateau and then drop again. We’ll revisit the statistics in spring 2010 to see how things turn out.

Financial difficulties

The second set of keywords we analysed was related to impending financial difficulties such as joblessness, debt and insolvency. They included signing on, mortgage arrears and debt problems, and were compared to the UK jobless rate.

UK unemployment rate charted against search volumes for 24 related keywords, from January 2004 to April 2009 Sources: Office for National Statistics, Google Insights

These search volumes dip at the end of each year before rising in January – and the rise in early 2008 was more pronounced than in previous years. The jobless rate started climbing three months later, suggesting that in this case search patterns might anticipate economic statistics. We observed that search volumes had dropped significantly in the first few months of 2009, so our hypothesis was that the jobless rate would stabilise but not drop between April and July. The chart below shows what actually happened.

UK unemployment rate charted against search volumes for 24 related keywords, from January 2004 to April 2009 Sources: Office for National Statistics, Google Insights

The unemployment rate has indeed stabilised, wavering between 7.7% and 7.8% since early June, suggesting that our original hypothesis was valid. And search volumes have kept on dropping throughout 2009. If search trends do anticipate economic reports in this case, we should see the unemployment rate drop steadily between now and spring 2010. Again, we’ll revisit these figures in April to see if this happens.

Conclusion

Our hypotheses from April 2009 were largely borne out as the year progressed: the drop in house prices was reversed and unemployment rates stabilised. So maybe there is some truth to the notion that search patterns can shed some light on forthcoming economic change.

But these hypotheses were in tune with the economic mood of the time. Many commentators were talking about green shoots and a V-shaped recession – there was a feeling that recovery was just around the corner. Today, we remain in what has become the longest-running recession in recorded history and there is considerable uncertainty about what 2010 will bring.

Our new hypotheses are less likely to be tainted by current economic consensus, precisely because no real consensus seems to exist right now. For this reason, the idea of search predicting the future will be seriously tested as the year unfolds. Don’t forget to come back in April 2010 to see the results for yourself.

This is why their marketing focuses so much on music, culture and lifestyle. It’s why O2 customers get priority tickets to concerts at the arenas bearing their name. It’s why Orange customers get half-price cinema tickets on Wednesdays. And it’s why T-Mobile runs that insufferable campaign about Josh and his ever-growing band.

Join the b(r)and: T-Mobile want to be associated with music and lifestyle

Customers are being encouraged to associate the brands of mobile operators with a certain type of lifestyle experience instead of just voice and data. This experience extends from the marketing to exclusive content services and even the interfaces and feature sets of the handsets themselves.

In this sense, mobile telcos are offering their customers a walled garden, in which the mobile internet is presented as part of a convenient package branded Orange, AT&T, T-Mobile or O2. If your internet memory goes back as far as the mid-1990s this might sound slightly familiar. But in the next ten years this walled garden is due to come under direct assault.

Charlie Stross has posted an excellent, thought-provoking piece looking at how the next ten years might pan out for the mobile industry – and making it sound in some ways like a technology rehash of the Great Game, with Apple and Google as the chief protagonists.

As Stross sees it, Apple and Google both want to destroy the walled garden built by telcos but for different reasons and in different ways. As a premium marque, Apple wants to work with telcos while preventing their brands from adulterating the Apple experience:

Apple don’t want to destroy the telcos; they just want to use them as a conduit to sell their user experience… [they] want to maintain the high quality Apple-centric user experience and sell stuff to their users through the walled garden of the App Store and the iTunes music/video store

Google, on the other hand, wants people to view more of its ads. To make this happen, Google wants to fundamentally reshape the mobile industry:

I think Google are pursuing a grand strategic vision of destroying the cellco’s entire business model… turning 3G data service into a commodity… getting consumers to buy unlocked SIM-free handsets [like the Nexus One]… and ultimately do the Google thing to all your voice messages [through Google Voice] as well as your email and web access.

These distinct strategies both threaten the mobile telcos, who stand to lose any emotional connection they have with their customers either way. But this doesn’t mean that Apple and Google are going to be bedfellows:

Apple’s iPhone has been good for Google: iPhone users do far more web surfing — and Google ad-eyeballing — than regular phone users. But Apple want to maintain…  the walled garden of the App Store and iTunes… [and] Google can’t slap their ads all over those media. So it’s going to end in handbags at dawn … eventually.

The piece (here’s the link again by the way) has me thinking that the coming decade in mobile networks will be much like the previous decade was in land-line internet service provision.

If Charlie Stross is right, the idea of the telco as provider of an experience will not last the decade, meaning that flash mobs, Orange Rock Corps and Josh Ward will become nothing but a dim and distant memory. And customers will hopefully have greater choice over how they use mobile networks, which would be nothing but a good thing in my opinion.

But an exchange during the following Q&A session stuck in my mind later. One of our directors asked a question: is the mobile app destined to be a transitory phenomenon, something that will fade away as mobile browsers become capable of delivering the same functionality?

The speaker was adamant that this was not the case and that mobile apps were here to stay. He felt that Google’s increasing preference for mobile browser apps over native apps was misguided and that Google were wrong on this one. Mobile browsers were so far from rivalling the functionality of native apps that it wasn’t even worth thinking about.

I was tempted to counter this point by bringing up the iPhone’s support for HTML 5 and starting a detailed discussion about in-browser capabilities. But this wasn’t the main subject of the talk and I’m in no way an expert on HTML 5, so I decided to keep my mouth shut instead.

In the weeks since the talk, however, I’ve often found myself turning this question over and over again in my head. And the more I think about it, the more I feel that mobile apps are basically doomed – or at least I hope they are.

Don’t get me wrong – they play an important role. It’s good that so many people today see phones as devices for more than just calling or texting, and the iPhone and its suite of native apps is largely to thank for this. But in the longer run, the publication and distribution model they are based on has to go.

The idea of tying software to a single hardware platform is anachronistic, uncompetitive and limits user choice. This is bad enough when you’re dealing with computers, but it’s even worse when the devices are as personal as mobile phones. People should be free to choose a different phone without needing to buy new versions of the software tools that have become integral to their lives.

Aside from user choice, there’s a more practical reason why the native app model is unsustainable. Developers won’t want to keep maintaining multiple codebases for the apps they produce, especially when there’s the option of building an equally functional in-browser app which any standards-based client can run. And although Apple might hope to render this point irrelevant by establishing monopolistic domination of the smartphone market, relieving developers of the need to consider other platforms, current research indicates that they won’t succeed.

The smartphone OS market will be more fragmented in 2012 than in 2009

A more fragmented smartphone OS market will increasingly compel developers to support separate codebases for Windows Mobile, RIM, Android, Symbian and the iPhone. But as mobile browsers become capable of delivering similar interactivity, serious developers will become inclined to start using the browser as the platform instead. This will be a good thing for users and the industry alike.

If I’m correct and native apps do fade away over time, we may look back on the era of pointless mobile apps as just one among many strange blips in the history of technology. But despite some early rumblings from notable developers, native mobile apps will be with us for some time yet – and, in the medium term at least, they still have an important role to play in encouraging mainstream adoption of the mobile internet.

Edit: This article was later reposted on Android and Me and attracted numerous comments. Click here to see the conversation on Android and Me

Edit 2: Stephen Fulljames shared a couple of links related to this post. PhoneGap is a toolkit for developing mobile apps in HTML & JavaScript. And this post from front-end consultant Peter-Paul Koch provides some background to his work with Vodafone on mobile browser compatibility and W3C widgets.

The study involved 66 graduate students with either normal or corrected vision being given a short story to read online. A preliminary reading test was carried out on participants so the study could predetermine their reading speed. Different text layouts were used, such as multiple column, full justification and so on. Study participants were tested for both reading speed and reading comprehension.

• Reading speed: Multiple-column layouts impaired reading speed when text was left-justified. However, left-justified text was read more quickly in a single column layout than full-justified text. The highest reading speed was 269.33 words per minute for two-column, full-justified text.
• Reading comprehension: No significant variation was found across the different text formats.
• Fast versus slow readers: Faster readers benefited most from the 2-column, fully-justified layout. Slow readers benefited from 1-column, left-justified text.

The study was perhaps limited by the fact that the participants, as undergraduates, were heavier readers of online text than the average member of the population. I’d be interested to see if any similar studies have been carried out with a larger sample size, broader age range and a more representative mix of internet ‘natives’ versus internet ‘newbies’. Does anyone know of any? If I find some I’ll post them here.