mobile geo social

In other news, GeoMeme now measures real-time local trends based on both MySpace and Twitter content.

GeoMeme uses the new Real-Time Stream API from MySpace to tap into the flood of geo-located updates being posted by MySpace users all around the world.

MySpace content is mashed up with tweets from a number of mobile Twitter apps, and located onto a Google Map. Local trends are identified using semantic analysis services from Yahoo.

A couple of example GeoMemes generated by all this real-time geo-located content: Rihanna beats Lady Gaga in New York, and Avatar beats Hurt Locker in Los Angeles.

I popped into the OpenAustralia Hackfest at the weekend to learn and talk about some of the latest developments in the Gov2.0 revolution.

There are now some quite interesting public datasets available, and the developer community is hard at work turning this data into useful APIs, and building innovative applications to consume the data.

Some of the notable apps to emerge from OpenAustralia include:

• It’s Buggered, Mate – from the Canberra Hackfest, a geo app to crowdsource the reporting of broken public infrastructure.
• Suburb Matchmaker – the winner of the Sydney Hackfest, a tool to help you find your ideal suburb to live in.
• FridgeMate – currently winning the MashupAustralia contest and only a couple of days away from the $10,000 prize. FridgeMate lets you assemble a map of local public amenities to stick on your fridge door. My advice to the Creative Possums behind FridgeMate would be to look at using the Zazzle API so people could buy the actual fridge magnet.

My own presentation focussed on some mobile, geo and social technologies to create location-aware mobile mashups to share OpenGov content with friends on Twitter, friends on Facebook, and *real* friends on a t-shirt. Here’s the deck:

When hiring Engineers, I always look for evidence of pet projects, so recently I thought it was fair to create one of my own: GeoMeme, the fun way to measure and share real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that ‘love’ beats ‘hate’ in Manhattan:

GeoMeme is a desktop web application and also a location-aware mobile web app for iPhone and Android phones.

Implementing the mobile version of GeoMeme as a web app has some advantages and disadvantages, compared to building native iPhone &/or Android applications.

Native apps are great because they currently offer the deepest integration to the full capability of the phone, for example using device APIs to access Contacts, the Camera Roll, an Accelerometer, or the GPS chip. For some applications, this deep device integration is essential and so a native application is beneficial.

On the other hand, emerging HTML5-based mobile browsers are aiming to standardise integration to such device APIs, starting with Geolocation APIs; meaning that location-aware mobile web apps are now becoming viable. Aligned with this development is the new version of the Google Maps API. v3 has been greatly simplified since v2, and is now optimized for use on mobile phones. Less is more.

The deciding factor for me choosing to build a mobile web app for GeoMeme rather than a native app was development speed. A mobile web app enjoys far greater code re-use from the desktop web version, and it is possible to push regular updates and improvements to users, without having to wait for appstore approval or for users to upgrade.

I believe this need for development speed is common among a good proportion of mobile apps that are still in ‘rapid iteration’ or ‘release early, release often’ mode, so this post is intended to share some of the techniques used in GeoMeme with developers wanting to build their own location-aware mobile web apps.

Let’s build an example location-aware mobile web app called ‘Here I Am!’, for the photographically challenged. The app will present some local photographs (from Panoramio) which can be shared with friends on Twitter or Facebook.

Where on earth is that mobile phone..?

The first job of a location-aware mobile app is to work out where on earth the mobile phone currently is. Unfortunately, at the time of writing, there is still no universally reliable and accurate solution for a mobile web app to detect the location of the mobile phone it is running on. However the following partial solutions can be combined to good effect:

(more…)

GeoMeme is a pet project of mine. It’s a web app, and also a mobile web app for iPhone and Android, that measures real-time local twitter trends.

Visitors to GeoMeme choose a location on the map, and two search terms to compare. GeoMeme then measures and compares the number of matching tweets within the bounds of the map, based on public data from a number of mobile twitter apps.

As an example, GeoMeme can work out that ‘District 9′ beats ‘Inglorious Basterds’ in Manhattan.

As well as offering users the normal pan and zoom controls to move the map around, GeoMeme also introduces an innovative geo-autocomplete control which is powered by the geocoder service from Google Maps v3 API and the new Static Maps v2 API.

This blog post shares some details of how the geo-autocomplete control works, and offers some code so you can build your own geo-autocomplete controls.

1. Based on a partial location typed by the user, obtain a list of possible matching locations:

If the user has already typed ‘San’ into a form field, we can obtain a list of possible matching locations by passing this partial location to the geocoder service from Google Maps v3 API, as follows:

(more…)

I am pleased to announce the launch of GeoMeme, the fun way to measure and share real-time local twitter trends.

I got thinking about this when a recent Los Angeles earthquake was being measured in tweets per second rather than using the Richter Scale.

Then came the Magnitwude Calculator as a standard way to measure the magnitude of Twitter trends.

[Then came twotspot.com but that domain name was just too damn rude, so it was quickly renamed to GeoMeme.]

What does GeoMeme do?

GeoMeme measures real-time local twitter trends.

Tweeps are located on the map using public data from a number of iPhone twitter apps. When twitter launches its geolocation API, that will be used to locate even more people on the map.

GeoMeme measures and compares how many people on the map are tweeting about each of your two search terms:

The ‘magnitude’ of each search term is equal to the number of unique people tweeting per hour per square kilometer, so it increases when more people are tweeting in a smaller area.

Example: if 100 different people in an area of 10km² have tweeted about ‘love’ in the last 2 hours, the magnitude is 5.0 (100 divided by 10 divided by 2).

So you can search for ‘love’ and ‘hate’ and GeoMeme works out which one “beats” the other with the higher magnitude.

The default search terms are and smiley faces which provides a good measure of local happiness, as an example.

Can I use my iPhone?

Sure, or your iPod Touch. Here’s the screenshot:

Give me an example!

Thanks to some early coverage on The Register, Mashable, and Google Maps Mania, and winning Mashup of the Day on ProgrammableWeb, we’re off to a flying start. I’m glad GeoMeme is hosted on Google App Engine for scalability.

Here’s a selection of the most popular GeoMemes so far:

• beats in New York City
• Mega Shark beats Giant Octopus in LA
• Snow Leopard beats Windows 7 in Cupertino
• bridge beats swimming in San Francisco Bay
• wtf beats ftw in Washington

How does it all work?

I will leave the details of how it all works to another post, stay tuned for that.

2009 has already seen some big Twitter moments, including Michael Jackson’s death and memorial service, #iranelection, Oprah’s mainstreaming, and the race between @aplusk and @cnn to reach 1 million followers.

But how can we objectively measure and compare the scale of such things?

A little while ago I got thinking about this when a Los Angeles earthquake was being measured in tweets per second rather than using the Richter Scale.

And now here is my solution, the Magnitwude Calculator, which measures the current magnitude of tweets on any topic within any location.

Please have a fiddle. Type in a search term or select from the autocomplete list of currently trending topics, move the map around, and tell me what you think:

You can link directly to the Magnitwude Calculator at http://hitching.net/magnitwude

When I found out that the human race was speeding up, by around 10% over the past decade, I deliberately walked around slower that day.

A study carried out in the early 1990s demonstrated that pedestrians’ speed of walking provides a reliable measure of the pace of life in a city, and that people in fast-moving cities are less likely to help others and have higher rates of coronary heart disease.

Using identical methods to those employed in the previous work, the present day research teams discovered that the pace of life is now 10% faster than in the early 1990s. The biggest changes were found in the Far East, with the pace of life in Guangzhou (China) increasing by over 20%, and Singapore showing a 30% increase, resulting in it becoming the fastest moving city in the study. More here from Richard Wiseman.

I find that rate of evolutionary acceleration a bit unnerving. And here I am, sleepless in Sydney at 4am, waiting for baby to arrive and now wondering if there’s any geo dimension to all of this. So here goes:

1. A Google spreadsheet of the data
2. geo-coding from Geonames, imported into the spreadsheet using importXML
3. a flickr photo of someone walking in each city, imported into the spreadsheet using importFeed via a Yahoo Pipe. The photos don’t look too releveant, maybe I need to clean my pipe.
4. some Google Maps code generated from Pamela Fox’s Spreadsheet Map Wizard

Space/Time poverty: how fast do people walk, where?

Click on the markers or list to see how fast.

Hello world! Okay, first thing to do here is to restore some ancient blog artifacts.

In 1997, I built a website for Dragoman, the adventure travel company, in return for a 3 month road trip through Central America. En route, we published an online travelogue onto the Dragoman site so that our friends and families back home could keep up to date with our adventures.

We found the odd internet cafe, even a couple with a scanner to digitize some of the Polaroid photos we’d taken. But mostly it was a big challenge to find computers with a modem and an internet connection.

The most memorable deployment was in Tegucigalpa, hiding behind a false wall in a small bookstore, trying to upload HTML files at a baud rate somewhere around the speed of morse code. In Tegucigalpa in 1997, there was a waiting list to get phone numbers because the local exchange only had 5 digits to play with. The owner of the shop explained that whenever a local died, there was a rush to get the phone number, so it’s really hard to get connected to the internet. “Some people around here would kill for an internet connection!”

This is possibly the first ever group travel blog, lost many years ago to some website redesign but re-published here again in it’s original glory.

One objective of this is to find and reunite all the wonderful people who were on that fateful trip, the Central American Explorer which departed Panama City on 6th November 1997.

My second objective; as part of the restoration process I will be embedding some digital maps showing parts of the trip, in an attempt to find the best digital map trip widget out there. I have recently been working on such a thing at Lonely Planet, so we’ll start with that:

Here’s all the Dragoman posts.