Mobile Social Location (Web Directions @media version)

Mobile Social Location Matt Biddulph, Nokia icons by http://www.famfamfam.com/lab/icons/silk/

Photo by mackarus - http://ﬂic.kr/p/7x6aHm

Connected Photo by mackarus - http://ﬂic.kr/p/7x6aHm

Always with you Photo by mackarus - http://ﬂic.kr/p/7x6aHm

Full of sensors Photo by mackarus - http://ﬂic.kr/p/7x6aHm

Where have we been?

Street data We’ve always had static information in public space that’s designed to be interpreted by users of that space. photo: http://ﬂic.kr/p/9CKCw

Ratings and Reviews There have always been websites that let users rate and review places - sites like TripAdvisor have a huge amount of data collected over years. And yes, Beijing has an official star-rating committee for toilets.

Mobile Content In the early days of small mobile devices, apps like Vindigo delivered static content by syncing to an internet-connected computer. The device is blind - it doesn’t know where its user is or anything about the environment in which it’s used. It has to be told. The interfaces on these apps are usually very simple and fast, partly due to the low power devices, but also because they’re designed to be used in a hurry in public space.

Maps Traditional symbolic representations of space ...

Maps ... are now widely available online to build into apps

Navigation Turn-by-turn navigation - usually provided by Navteq, Teleatlas and Google - is the hot topic in online maps right now. Photo by Perfesser - http://ﬂic.kr/p/4cbfmj

Checking in If we can translate a user’s location into something meaningful, we can add layers of information on top.

Checking in Lots of interesting apps are based around the idea of “checking in” at a location rather than simply recording GPS tracklogs.

Place, not lat/long A simple lat/long coordinate is not enough for most people-oriented apps. We need a way to turn a GPS read into a human-meaningful place such as a cafe, office or home. APIs and datasets to do this are starting to appear. Photo by paalia - http://ﬂic.kr/p/6sAzuf

Place, not lat/long “electronic acquisition pays no attention to geography” —Chris Heathcote, 2004 A simple lat/long coordinate is not enough for most people-oriented apps. We need a way to turn a GPS read into a human-meaningful place such as a cafe, office or home. APIs and datasets to do this are starting to appear. Photo by paalia - http://ﬂic.kr/p/6sAzuf

51.5064, -0.1168 Let’s take a journey from a single piece of data using some web APIs

Flickr has a reverse geocoder that turns a lat/long into a place

This gives us many IDs we can use to talk about the lat/long

And the Yahoo Geoplanet Explorer helps us browse some of the attributes of the place

Flickr will show us pictures of the place

Tom Taylor’s Boundaries viewer will show us the shape of the nearby neighbourhoods

Geosetta will translate the place IDs into IDs used by other systes

Geonames indexes Wikipedia by place

And we can see that we’re in the Southbank Centre, home of @media 2010

Where are we going?

Location brokers As location information becomes a core part of many apps, we’re seeing services such as Yahoo Fire Eagle, Google Latitude and Twitter provide a way to selectively share your location with other applications. This can provide a quick bootstrap for a new app, and separate the problem of location acquisition (via many possible devices and channels) from application concerns.

Compass We’ve had GPS for a long time and it’s been in affordable devices for a couple of years. Manufacturers appear to have only recently recognised that an electronic compass adds a lot to the picture that the “blind” phone sees by GPS...

Augmented Reality ... in particular, knowing which way a user is oriented allows more effective overlaying of information onto their local context. Photo by Marc Wathieu - http://ﬂic.kr/p/5ZwuhQ

Sensors The process of making devices less blind doesn’t have to stop at GPS and compass. Projects like Nokia’s Push N900 are encouraging users to augment their devices with new sensors and capabilities using platforms like Arduino. Photo by Rain Rabbit - http://ﬂic.kr/p/6Y8ejj

http://blogs.nokia.com/pushn900/ Sensors The process of making devices less blind doesn’t have to stop at GPS and compass. Projects like Nokia’s Push N900 are encouraging users to augment their devices with new sensors and capabilities using platforms like Arduino. Photo by Rain Rabbit - http://ﬂic.kr/p/6Y8ejj

Visualisation We’re becoming a more information-literate culture, and information visual and data exploration tools are becoming commonplace.

Realtime There’s growing interest in apps that can communicate in both directions between client and server - the return of Push. Protocols like XMPP and Pubsubhubbub are providing a way for an app to push information to users in realtime based on their preferences or their current context. Photo by Hugo! - http://ﬂic.kr/p/2yr85

The social graph(s) bopuc moia tominsam mattb moleitau See http://www.hackdiary.com/2010/02/10/algorithmic-recruitment-with-github/ for this section

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carlo rozza SteveMarshall FND NeilCrosbyginader themattharris tomyan philhawks radleywright richardhodgson norm phae greut simonmaddox psd drewm gillesruppert BenWard cbetta tommorris natbat garethr dwhittle dhilton mojodna the myt evilstreak pjbarry voodoochild AndrewDisley willi iamdanw andybeeching sterveld georgebrock simonw samsoir rik mikesten richardkee dsingletonskarab molily danieljohn mattb webiest atl sanna fidothe Jonty Allinthedata russss jensy superfeed memespring rux straup jaygooby monkchips vancae jonocole jwheare james filipeamore chrismear hubgit jystewart evangineer fredrikmollerstrand dougma eartle baseonmars thommay harry-m

http://gephi.org/ http://measuringmeasures.com http://jung.sourceforge.net/ There’s a lot of hard computer science around processing graphs. The Jung library makes this a lot easier.

Buy it!

http://lucene.apache.org/mahout/ There’s also a lot of hard computer science around machine learning. Mahout is building scalable Hadoop-based libraries for recommendation, clustering, collaborative ﬁlter and auto-classiﬁcation.

“scalable, Apache licensed machine learning libraries” http://lucene.apache.org/mahout/ There’s also a lot of hard computer science around machine learning. Mahout is building scalable Hadoop-based libraries for recommendation, clustering, collaborative ﬁlter and auto-classiﬁcation.

Careful where you go

Red dot fever Schuyler Erle coined the term “red dot fever” - the naive tendency to plot datapoints on maps without thinking through the design implications. It’s very easy to ﬁre up a map API and add markers to a map without realising how unclear the representation can become. Information can often be processed by clustering or ﬁltering before being mapped. Indeed, maps aren’t always the best representation of place data.

Gapminder World Chart 2006 Low income countries Middle income countries High income countries Singapore 3 Japan Iceland Finland Czech Sweden Republic Slovenia Spain Italy Germany Luxembourg 4 Hong Kong Austria Netherland Oman Norway Portugal Greece Denmark France Healthy Canada Switzerland 5 Malaysia Israel Cyprus Belgium Australia Andorra Ireland Malta New Zealand UK San Marino United Arab Emirates Jersey South Korea Cuba Chile Croatia Taiwan Macao Monaco USA Liechtenstein 7 Hungary Bahrain Brunei Poor Isle of Man Rich Bosnia & Herzegovina Estonia Faeroe Is. Fr. Polynesia Poland Slovak Rep. Saudi Arabia Guam Serbia & Cayman Is. Lithuania Virgin Islands Macedonia Belarus Montenegro N.Mariana Is. Bahamas (U.S.) Puerto Rico 10 Costa Rica American Libya Kuwait Bermuda Sri Lanka Jamaica Netherlands Ant. Samoa Barbados Montserrat Ukraine Thailand Latvia New Caledonia Sick Mauritius Greenland Uruguay Nauru Albania Fiji Bulgaria St. Lucia Colombia Belize Argentina Moldova Turks & Caicos St. Vincent & Venezuela Island British Virgin Qatar Aruba Jordan Grenadines Dominica Palau Islands China Trinidad & 20 Vietnam Tonga El Salvador Russia Mexico Tobago Panama St. Kitts Syria Tunisia & Nevis Anguilla Solomon Islands West Bank Ecuador Grenada Antigua & & Gaza Paraguay Seychelles Barbuda Nicaragua Honduras Armenia Peru Dominican Rep. Romania Brazil 30 North Uzbekistan Korea Philippines Tuvalu Algeria Suriname Turkey Cape Verde Maldives Egypt Samoa Marshall Is. Lebanon Mongolia Georgia Micronesia Guatemala Kazakhstan Population (millions) Indonesia Iran Botswana Kyrgyz Rep. Vanuatu Iraq Morocco Regions <1 1 50 India Namibia Comoros Americas 10 Tajikistan Nepal Arab states Bolivia Turkmenistan 100 Zimbabwe Bangladesh Bhutan Azerbaijan South Africa Asia & Pacific 70 Eritrea Laos Yemen Lesotho Guyana Kiribati Gabon Europe & Central Asia 1 000 Madagascar Cambodia Haiti Papua New Guinea Sudan Swaziland Mayotte Sub-Saharan Africa Senegal 100 Ethiopia Gambia Ghana Kenya Mauritania 2006 data for countries & territories from Myanmar Togo Tanzania Timor Leste Pakistan UN Statistics Division, Institute for Health Metrics Guinea Benin Côte d'Ivoire and Evaluation and others sources as stated in: Sao Tome Congo, Rep. "Gapminder documentation 2007:1" at: & Principe Cameroon www.gapminder.org/publications. Malawi Uganda (Brazzaville) Central African Rep. Rwanda Zambia Liberia Mozambique Free to copy for non-commercial use BY: $ Burkina Faso Burundi Djibouti E-mail: info@gapminder.org Niger Chad 200 Guinea Bissau Mali www.gapminder.org Congo DR Somalia Afghanistan Nigeria Angola Equatorial Guinea (Kinshasa) Sierra Leone 300 100 200 300 500 1 000 2 000 3 000 5 000 10 000 20 000 30 000 50 000 Money Gross National Income per capita - in US dollar by exchange rate (log) Maps aren’t the only way to show geo-data

Outdoors It’s important to be humble as a mobile developer. Never forget that your application may be used in the street, in parallel with another app or activity, and for less than 30 seconds at a time. Your app may be the irritation standing in the way of someone getting the information they need right now. Photo by JanneM - http://ﬂic.kr/p/6sjM3e

“A typical user pulls a device out of a pocket or bag and uses it for a few seconds, or maybe a few minutes, before putting it away again.” —iPhone Application Programming Guide

Data entry Phones are usually not great data entry devices. When we built the Dopplr Social Atlas mobile application, we allow users to record places they like with a minimal interaction - only two taps are required. We upload these pings to the Dopplr website and complete the data gathering through the website at a later time. This allows us to use large widgets such as maps and autocomplete that would not be practical on the small screen.

Roaming Be aware that (particularly outside North America) many apps are used outside their phone’s home country. Roaming data charges are still disturbingly high and not everyone is organised enough to swap SIM cards at the airport when they travel. Be conservative with your use of data.

“35 ways to ﬁnd your location” Chris Heathcote, Etech 2004 There’s a great set of slides from Chris Heathcote reminding us that GPS isn’t the only way to ﬁnd your location. There are many other technical and cultural approaches. http://conferences.oreillynet.com/cs/et2004/view/e_sess/4657

A phone isn’t just a pocket computer that runs apps. It’s a bridge between the internet and the real world.

Thank You Matt Biddulph, Nokia

Mobile Social Location (Web Directions @media version)

by Matt Biddulph on Jun 21, 2010

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