I was only able to go to the Saturday talks in the CoCities conference. By all accounts, I seriously missed out on Sunday.
I wanted to write a nice wrap up of the ideas that I thought were hottest, but the weekend is over and all of the time has been sucked into a black hole of real life.
This was a great conference. I was really impressed by the organization. Especially the splendid quantity of outlets! Most importantly, the talks were excellent and the conversation was even better.
Here are the only three things I would improve for next year:
Coffee. There was a bit of a caffein distribution bottleneck. One of the organizers, Peter Bihr, told me his tale of woe, how they had tried so valiantly to have wonderful coffee, but were thwarted at every turn. I suggest that a simple self-serve coffee system can be all right, but everyone knows I am not a coffee person. I only drink it therapeutically.
Balance in topics. One data geek would do nicely. I am a data lover myself, but I having three talks (four, depending on how you count) about data collection and use means that there were topics that didn't get the attention they deserve. Which brings me to...
Sustainability. This is a big piece that was not talked about satisfactorily. Sure, the smart office building is in this realm, but it would have been really good to hear something about how technology is used to keep cars out of cities, or how technology is used to the benefit of urban agriculture. What is the place of nature in the city?
Warren Ellis
Mostly a science fiction writer
No slides! Hooray!
Roadside Picnic is a Russian Sci-fi novel. Science fiction exists to cast a shadow over the present. We are haunted by what has not yet happened, especially so at this conference. The film based on the novel Roadside Picnic is called Stalker. What had not yet happened was Chernobyl.
The "Zone of Alienation" is what the Chernobyl area is called.
in 2003 there was a report that there are a couple cafes inside the zone. Bread and Vodka remain as currency for Chernobyl.
The present is influenced by the future.
Ghost hunters are very technical people. They roam around with electromagnetic field readers.
20 years ago, William Burroughs was asserting that the human soul is an electromagnetic field. Science fiction haunting the future. (I bet you could find a Greek talking about the soul as an electromagnetic field.)
Archaeoacoustics is the study of old sounds (mmmmmmmm. Lovely idea. Looking on Wikipedia, it is an even more poetic discipline than it sounds at first. It's trying to read ancient clay objects as if they were vinyl records.)
UFOs are stress imagery.
Fault lines and electromagnetic fields create ghosts of the future. (lovely idea)
The future oozes up through cracks in the ground.
Mirages in time, reflections of the future.
Every city street is an electromagnetic cauldron.
RFID tags create huge electromagnetic fields. It's surprising.
(I guess that he is insinuating that we are unwittingly creating a conscious-altering environment for ourselves. Interesting idea, probably at least partly true.)
Experience-inducing fields.
AR to visualize the spirit track.
Ghost boxes are electromagnetic devices for communicating with ghosts through radio waves. Conversations with things that are not alive. We all do that regularly, with our sensors and devices and all.
Mostly, we are giving the gift of the digital city to our ruling classes (inviting fascism, as I was thinking earlier today)
We are depending on these incompetent people without vision to design our digital infrastructures. Sometimes the authorities are so incompetent that they are benign. Governments are not good at technology, which sometimes works to our advantage.
Don't give the keys to our digital infrastructure away.
People like the ones at this conference will create these concepts and these structures, but if we give them over to government and authority, they will turn around and guillotine us with them.
Don't get carried away.
Whose streets are these?
Our streets.
(I love how this closing keynote was in many ways a through-the-looking-glass version of the opening keynote. Well done.)
Dietmar Offenhuber
MIT
In the 1990s, we thought that the future would be virtual. No more travel. The death of the cities was predicted. Previously, Frank Lloyd Wright thought that telecommunications would spell the end of cities.
But exactly the opposite has happened. Cities are our future. Urbanization is continuing and accelerating.
The SENSEable City Lab
Not architecture
Not a media lab
It's in an Urban Studies dept.
Kevin Lynch theorized that there are 5 elements needed to map urban environments:
paths
edges
districts
nodes
landmarks
Urban studies, research, and proliferation of data:
There is an idea that data is just out there and all we have to do is visualize it. But also important is who collects the data, where and when is it collected, how does it relate to reality in general. How do we generate and use data in this context?
Connections, Venice Biennale Project, 2006
They took a real-time telecommunications data set. The data visualization is shaped by privacy concerns; the data is aggregated and corsened to protect people's privacy.
Phone data can be used to find out things about locals vs tourists, cars vs pedestrians. For example, if we know where the pedestrians are, we can send the busses to them instead of making the pedestrians chase the busses.
(Is it wrong that I get tired of how data visualizations all look similar? It's the tools dictating the visualization. More questions of representation and how our tools and our culture--and our tools as part of our culture--dictate what we see in a normative, negative way.)
The New York Talk Exchange
How does NY relate to the rest of the world?
(Erik Hogan's--not sure of name--visualizations are a bit fresher-looking. Cool that this is shown right after I said I was getting bored. It's like the good karma fairy was listening!)
How does globalization unfold on the neighborhood level? In Brooklyn, you can see how immigrant families use their cell phones. They did an ethnographic study on how immigrants use their cell phones. There are many social implications to this work.
Trash Track, 2009
This is an example where generating the data itself is very hard and expensive.
In this case, there was no data set. Global supply chains are highly automated and tracked, but at the other end of the system, for trash removal, this is not the case. Citizens know little about what happens to their trash. Even the professionals in the waste management field have spotty knowledge about how the system works. The data sets break down at the intersections when trash moves from one company to another, one process to another. This allows for abuses, like the international trade of electronic waste.
They decided to follow individual items from pick up to end. Using rfid was not possible because there is no ifrastructure to read and follow them. They used active location sensors which could transmit their location through the phone network, using cell id and gps. They recruited 500 volunteers.
Volunteers each gave 20 objects to donate in Seattle.
The tagging process was a nasty business, how to keep the sensor attached to the objects? They had to protect the sensors with insulation foam.
Volunteers then could follow the objects in real time.
They followed the items for 6 months and finished with a map of 3k objects. The electronics and hazardous waste travels most. Sometimes the trajectories are erratic. We see that some electronic waste traveled across the country and back to get to the same end spot as another piece of waste that traveled directly. This shows where there is room for improved efficiency.
There is EPA data showing landfills and recycling centers. When mapped, it mirrors population density and rural populations. They mapped the sensor data with the landfill data.
Another aspect was to observe the volunteers. They followed the items on their own. People understand very well how to read a gps trace.
Copenhagen Wheel, 2009
There are more bikes than people in Copenhagen, and the city is aiming to replace vehicle traffic with bikes.
They wanted to use bikes as sensors, but they didn't want to attach a lot of sensors to bikes or riders. They wanted something compact. So, they designed a sensor wheel that could be attached to any bike. The back wheel has a motor to support your bike efforts and also senses air quality, noise, etc.
You can't have air quality and noise sensors at knee level, so they are rethinking this thing now.
At this time there are 50 bikes in production. You can access your own data collected by smartphone application or web. You can choose to share your data or not. In return for collecting this data, you get real-time feedback about your behavior, road conditions, traffic, etc.
(want)
Models of data collecting are very important to think about.
Matt Biddulph
Nokia
How does big data connect theories about cities to results and to products we can actually make?
We build our understanding about systems by making models. The problem with models is that to make them work we simplify to falsehood.
All models are wrong but some are useful.
We are at the end of theory. The science of the future will not make progress by making models. Instead of making models, we can now gather data from the real world and analyze the real world.
People are city biology. People make cities too complicated to model. When you look at the movement of bikes around London, it looks like a city breathing.
The street is platform. We can't see all the data that is in the street. We can't measure them. Mobile devices are the city sensors. Phones are packed full of sensors. Phones are no longer blind, they are networked and smart.
The flip side to having all this data is how to process it and make it useful. At some point you can't process all the data you collect with the power you have. Google is trying to tackle this problem on the web. Indexing billions of web pages and data sets.
Map-->Reduce.
Take some data, map it, then reduce it.
The mapping and the reducing stage can be broken up and done in parallel. Turn the problem into something piecemeal and manageable. Add more machines as you have more data, so that it scales.
(Why do we need to process all of the data. Scaling to the size of the world sounds like a fool's errand. Whatever happened to the sample? Processing more data than you need just because you can is idiotic. I'd like to understand in which situations the use of big data is really useful and productive and in which situations it is geek masturbation... Not that I am against such a thing all the time, but I do think it is important to be honest about what we are doing and why.)
Can the phone be our sole source of data? (I don't understand this question. It can be, sure. Should it be? Why should it be?) Does the information translate back into useful and desirable data? Can I get a feel for a city I have never been to because of the data we've sifted and sorted from mobile phones?
We wanted to test the data to see if we can observe patterns and structures that we can show to be true.
For example, we looked at search patterns. What did people look for and where were they when they looked for it? We discovered that people search for Ikea on their phones when they are near Ikea, and we also saw Prenzlauerberger yuppies looking for Ikea from home. Also, we saw a spike on Saturday.
(The real question then is how does this information get back to the user in a way that is beneficial to her and not just helping a corporation to learn how to target advertising.)
When you search you are revealing your goals, but in a mobile phone, we also have a lot more passive context.
The Starbucks index. By processing POI data, we can tell you meaningful things about where you are now. For example, we can tell you how Starbucksy an area is.
If we look at how many times each map tile on ovi.com is loaded, we get an attention map of the world. Which parts of the city do people care about? If we look at patterns from the use of the drive navigation application, we see something else. We see where people who don't know their way around are going and when. Commuter data would be different.
You can use cities you know to check the viability of the data sets so that you can also make predictions about cities you don't know. Validating common-sense predictions.
Design with data... where is the product... how do we make it make sense?
Mike Kuniavsky "Smart Things" is a good book
What happens when the intelligence of the web is embedded in stuff in the real world? Information has a grain and different kinds of data are appropriate for different products. Data as a new raw material (love).
***
Question: I want a company to store my data and give it back to me so I can use it with another service. (Oh, God yes!)
Matt's response, from a personal point of view, not Nokia's. His personal philosophy is that yes, this should be the case. You should be able to get your data out.
(I didn't like it when Matt prefaced that statement by saying that he works at Nokia because his start up was acquired. That may be how he got there, but if that is why he's still there, he should get another job. Anyhow, It was an uncomfortable question and he handled it with aplomb overall.)
Anil Bawa-Cavia
Social technologist, former last fm developer
Urbagram
His previous work at last FM was about big data, mapping, and collaborative filters.
Living cities are mostly invisible.
We think of cities as what is visible: roads, buildings, infrastructure. But cities are also made of flows and interactions: Poeple, goods, vehicles, etc. What are the interactions between these duel entities?
The living city is unplanned and is in conflict with the planned pavement. Everything unplanned is the living city.
Desire paths of data.
interactions
boundaries
flows
Thinking about the social life of the city, they analyzed data from Foursquare (that's a pretty specific subset of citizens) When you visualize this data, you see social hubs and walkable cells. Most people are happy to walk 7 minutes.
You can use this data to compare the social lives of different cities (so far, this data is not telling me anything I didn't already know about Manhattan, London, or Paris. Srsly. Anybody living there could have drawn these maps without any data set whatsoever.)
Cities feel fragmented when you can't walk from A to B.
Paris has fewer big clusters. It is more continuous than NY or London. Parisian foursquare users don't cross the Perif' (Nobody does. Again, nothing new to anybody who lives in Paris)
They were given access to cell phone data to study. They mapped out who is calling whom most. This is an example of a way of redrawing boundaries.(Now this is interesting!) Communication boundaries rather than political boundaries. (You know I love everything that hits political boundaries with the fuzzy warmness of more meaningful boundaries.)
You can find ways of making real pictures of neighborhoods.
Flows
He showed a beautiful animated map of the London bus system. When you look at this, you see the historical center of the system affecting the flow still today.
Build a macroscope. Take a step back and see the city as a single entity.
(really lovely visualization here). This comes from rfid data from people using train cards. They have 2 million public samples and they also get extra research data. 300 million trips. (Wow)
They also looked at real-time bike share data. Volumes of flows, which bike stations are full and which are empty when.
(I would like to hear more concrete examples of how this big data modeling is used to solve real concrete problems or discover real useful conclusions that surprise us or are counter-intuitive. So far, the data is only reinforcing for the most part what any regular member of the living city knows intuitively. The pictures are pretty and all, but... I do want to know what the purpose is. Also, when is processing the data worth the effort is what I am wondering. If it only tells you what you already know, how is it useful? In predicting things about places you don't know? Maybe. Anil gave a good concrete example of using the public transit data to minimize system disruptions, but his real answer was that he is a researcher so it is not his responsibility to find applications for the information. That is up to engineers, designers, and industry.)
Making the living city tangible and knowable is their goal. When we know the living city, we can make the infrastructure responsive to it.
(This is an interesting goal, and a pretty good one on its surface. I do have the feeling that there is a tendency to say, "Here's data! What can we do with it?" instead of saying, "Here is an urban failure! What kind of data could help me fix it?" I'm a pragmatic designer-person, so this rubs me the wrong way.)
Ton Zijlstra
Networked Living, open government, giving skills and tools to the people.
Spice up your city-Just Add Open Government (slideshare)
Cities are unpredictable, but recognizable at the same time. They are complex, adaptive systems.
Serendipity. Creativity. Excitement.
Government, on the other hand, feels boring. Open government is not about opening up concrete silos, or mere transparency. What this is really about is the same kind of digital disruption that changed the music industry. If government doesn't open from the inside, it will be opened from the outside.
Participation and Open Data
This is not about town hall meetings followed by representational action. Participation is really about living in your community. Urban farming, for example. There were no grocery stores in inner-city Detroit which sold fresh vegetables, so people started growing vegetables in the middle of the city. That is participation.
Opening public data. This data could be used in many other ways. This is about tapping an abundant resource which nobody has access to. This is big data.
(Weird that he is using a Flickr api project to illustrate a point about open government data. Isn't there a great government data visualization example?)
EU documents are used to train google translator.
PSI-->Beurocrats for public data. It already is the law that public data should be usable, but practice does not always follow. Problem: open standards, machine-readability.
Participation is the path to data reuse. Government as a platform.
Your local environment is your natural place of action.
Examples: data about health inspection reports are connected to restaurants in a usable way. Citizens report civic issues or suggestions on a map.
Data often gets used in a useless way, to tell you nothing new or to make pretty pictures with no real readable information at all. Zijlstra calls this "flat" use of data.
It gets really interesting when you COMBINE data sets. (hells ya)
Example of cholera outbreaks and water sources on a map of London to discover the contamination source (this is a pre-digital example, so maybe the machine-readable bit is not really important)
Citizen-generated noise-pollution grid example. Private citizens sharing data is interesting.
Corporations are also starting to share data about the provenance of their ingredients.
The point is, that when government opens its data, it encourages private citizens and corporations to augment and add to that data.
In Rotterdam, you can get an alert from a particular air quality sensor and set your own sensitivity. It helps you decide when it is safe for you to go out in a meaningful way.
We feed data into devices which act on the environment and then create their own data sets and feed them back into the system. (some examples would have been nice here. The one he gave was a bit slippery for me)
"What's the problem I want to address," is the first question. Finding the data and building the way to use it should follow from that. Start with your sphere of influence and your own problems. Figure out what kind of data you need and then go to the government and get it. All you need is a single civil servant who is interested in what you are doing. It's people, not government. People are easy to approach (this is the most important point!)
***
Question about Wikileaks
Wikileaks is what happens when government is not responsive.
Government should do its own leaking.
Question: How do you get data into government? How do you get government to use the data you create?
It can be very difficult when the citizen data doesn't tell the story that the government is interested in telling.
Pitt Moos, Smart Car marketing guy.
Mr. Moos is wearing some hott leather pants. I missed the beginning of this talk coming back late for lunch.
He's giving us the history of Smart cars, what they've learned.
There are a lot of complications with electric cars. Difficulties in crossing borders. (Not much talk about how dirty electricity really can be. Batteries, new technology, etc. Battery technology is one of the hardest and most important keys to our future. That would be a great talk for this conference.)
Cars can be charged at any 220 V household plug. The infrastructure is, in fact, already there. You can charge it overnight at home. 8 hours is long enough.
Getting in to China is difficult because they don't like to let foreign technology in.
Car to Go started in Ulm. This is Smart's own car sharing service. Pay by the minute. 80% of the rides people make are when they discover a car and then think of an errand they can do. (For the moment this works with non-electric cars. Electric cars will be less flexible.)
Paris wants an electric car sharing system by 2012. Smart lost the bid, but the system will be coming.
Smart is also working on cars and mopeds for individual mobility in cities.
The appearance of the Smart car is dictated to a great deal by its size. They do plan to make a four-seater. The 1.2 million current customers are very happy with the current look.