Category Archives: Place

Personal Data Geographies

Our phones track our personal geographies. This enables dystopian surveillance, but also provides an interesting layer of biographical data that we haven’t had access to previously. My personal perspective is that if other actors (cellphone companies, marketers, governments) are going to have access to this information, I should at least be able to view and analyze this data, too. That’s why I’m thankful that Google exposes this data to end-users through the Location History page, and also allows outputs of raw geodata.

I’m going to use this data as a personal reflection aid, sort of the way social media data helps power TimeHop‘s semi-automated moments of reflection. I’m also experimenting with artistic visualizations (as in, actual paint and paper). But to start, I’ve taken the data from the 5 or so months that I’ve lived in New York, imported it into Google Earth, and created a GIF of my geographic footprint:

year-nyc
Continue reading Personal Data Geographies

Play into a Broader World View with Terra Incognita

My colleague Catherine D’Ignazio is one of those rare people who manages to create beautiful art and clever software while remaining incredibly down to earth. I’ve been helping out here and there on her Media Lab Master’s thesis, Terra Incognita. Here’s an overview of the project I wrote up for the Internews Center for Innovation & Learning.

We’re building a news game that helps you explore a wider swath of the globe than you may have before. Terra Incognita: 1000 Cities of the World is a game delivered by Chrome browser extension. When you open a new tab, you’ll be prompted to read a news story from one of the top 1,000 global cities. You’ll also get credit for news stories you read on a limited set of news sites. You can get early access to Terra Incognita today.

Terra Incognita screenshot

Continue reading Play into a Broader World View with Terra Incognita

On feeling comfortable in new places

It can take some time (2.5 years?) before you really feel comfortable living in a new city. Some people jump right in, others need time. Even though I’m pretty nomadic and love things like bikeshares and coworking spots and Amazon Prime for purposes of pretending I live in places I don’t, I’m emotionally more in the latter camp.

I moved to San Francisco yesterday to try it out for a bit, and even though I’ve been all over the area on previous trips I haven’t been a good explorer these past 24 hours. For example, today I made the conscious decision to take a right hand turn for the sole purpose of breaking my one-street life thus far. It’s Catherine D’Ignazio’s thesis in real-time — she’s working to create a Fog of War for real life to encourage geographic serendipity.

I’m chatting with a friend in a new city going through the same thing. Her city is colder and darker. But I told her I’d write up my list of shortcuts to feeling like you belong somewhere. Here’s what I’ve got: Continue reading On feeling comfortable in new places

Smarter Cities, Better Use of Resources?

Dr. Lisa AminiIf you’ve read a magazine or traveled through an airport in the last couple of years, you’ve probably seen ads for IBM’s Smarter Cities initiative. Today in our Post-Oil Shanghai course, we got to learn about some of the projects behind the very public campaign. Dr. Lisa Amini is the first director of IBM Research Ireland, based in Dublin. They focus on creating urban-scale analytics, optimizations, and systems for sustainable energy and transportation.

Lisa’s group focuses on transforming cities with:

  1. Sensor data assimilation: how do we ensure data accuracy, and account for the volume of data that comes in from sensors deployed at a metropolitan scale?
  2. Modelling human demand: how do we design a robuest enough model to reliabily infer demand and peoples’ use of city infrastrucure
  3. Factor in uncertainty: we’re talking about humans, here.

Sensor Data
Smarter CitiesWe have a massive amount of diverse, noisy data, but our ability to use it productively is quite poor.

One reason Lisa’s team is based in Ireland is that Dublin shared their municipal data on energy, water, and other core services. IBM wanted to focus on making use of available data, not laying down new sensors. Lisa shows us a map generated by bus data. The data is much more granular at the city center than in the suburban outreaches. And even downtown, the GPS isn’t terribly accurate, and sometimes locates buses smack dab in the middle of the River Liffey. This complicates efforts to infer and improve the situational awareness.

Bus bunching is a major problem in cities. Buses that begin ten minutes apart get slowed down, and end up clustered in bunches, with long waits in between. One goal is to dynamically adjust schedules and routes to compensate for predictable bunching conditions.

Another project looks to improve traffic signalling, not just for public transportation, but all traffic. Scientists are finding links between vehicle emissions and health, and certain urban corridors and certain times see a dangerous buildup of pollutants. Smarter planning could help us at least prevent this buildup from taking place around schools and hospitals.

Lisa talks about Big Data, but also Fast Data. It’s continuously coming at you, and if you can leverage it in realtime, it can be much more useful than a study conducted well after the fact. Her team is working on technology to make use of data as it comes in, and construct realtime models and optimizations. They can see bus lane speed distribution across an entire city of routes and a fleet of a thousand buses, accounting for anomalies past and present.

Sensor data’s great, but what do you do when a segment of the bus route is flashing red? Often, a disruption requires a person going to the scene to find out what’s wrong. The IBM team is experimenting with using Natural Language Processing data to determine if the cause of traffic is a Madonna performance at the O2 Centre. They can analyze blogs, event feeds, and telco data. Twitter isn’t useful for this yet because of the low percentage of Twitter users with geocoding enabled on their tweets.

Congestion is a significant contributor to CO2 emissions, so proative traffic control is becoming an important tool. Europeans are more and moer concerned about the livability of their cities, and even when they’re not, the EU Commission is happy to regulate. Cities are excited to avoid paying heavy fines, and invest in technologies that help avoid such costs.

At the individual level, congestion charging only works if there’s a reasonable alternative to driving. Even then, Praveen notes, it creates equity issues, where the wealthy can afford to drive into the city, and the poor cannot.

Lisa’s team is also modeling coastal quality and circulation patterns. One of the big problems is the treatment of water in waste plants. These plants treat the water to a static chemical index, and then release the water back into the world. Marine life dies, and we have toxins in the water, because
Large rainfall creates road runoff into the wate system. And tidal conditions can push water back upstream and hold the toxins in place, killing marine life. People are deploying sensors across the water systems, which is a huge improvement on annual testing conducted by a diver. But sensors don’t work incredibly well underwater – they’re limited by range and “fouling” of data.

New technology uses light sensors to understand the movement of water, which, combined with other sensors and de-noising models, can produce a cleaner picture of what’s happening across the bodies of water.

Modelling Human Demand
How people move, interact, and how they prefer to consume resources.
They can improve city services by taking advantage of telco data, smart car data, and other private and public information. The findings show a surprising sapital cohesiveness of regions. Geography still plays a huge role in how we look for services, communicate, and travel. Cellphone path data can illustrate points of origin that can better inform the planning of transportation paths. Political lines are a particularly ineffective way to organize services.

In the energy space, two trends have converged: First, we have more and more renewable energies, but they’re only available when the wind blows and the sun shines (efforts to store this energy notwithstanding). Our fossil fuel power plants require careful management, and must be gradually. Ireland could actually use more windpower than they currently do, but it would have adverse effects on the traditional plants.

The second trend is smart meters, which provide much more information on how energy is used. This allows for demand shaping, dynamic pricing, and smart appliances that act based on this information. But the energy companies are structured around predicting national energy demands, and follow very conservative policies that optimize for fulfilling peak demand. Energy companies are learning to forecast energy demands for pockets, rather than huge regions, and to take advantage of reneweable energy sources with pilot projects. They foresee running hundreds of thousands of dynamic energy models, rather than their current one-model-that-rules-them-all.

A project with Électricité de France simulates massive amounts of realistic smart meter demand data to test future scenarios. They’re building additive models based on human events like holidays and residential vs. commercial energy usage. The IBM Research team has build complicated flowcharts to identify compelling datastreams.

Uncertainty
Utility leaders are forced to make decisions that are fraught with risk and uncertainty. It’s not just optimization, but social welfare and balancing competing costs. Lisa would like to incorporate the notion of risk into the technological systems. When your phone tells you there’s a 10% chance of rain today, it’s not very actionable information. Medical tests and treatment plans can be equally infuriating in that they fall short of complete predictability. How do you communicate information that carries risk with it so leaders can make decisions?

Interconnected water systems, with water treament plants, households, and geographical features demanding different priorities. Water utilities spend enormous amounts of energy moving water from one place to another, losing between 20-70% of the water along the way. We need to begin considering these systems as integrated, and acknowledge the risk and uncertainty inherent within them. When you start working on any one aspect of city services, you quickly involve other departments.

There have been many studies on providing energy and water information to homeowners to encourage conservation. The biggest change you can make? It’s not laundry or watering your lawn. Fix your leaky faucet.

Culture matters, too. Europeans expect more of their government, and citizens get up in arms when a resource like water becomes metered.

Rather than produce a perfect formula and answer to a question like municipal water demand levels, they have built models that allow for imperfections in data and can optimize for cost or service delivery.

An area of hope is to target non-experts with well-communicated information and visualizations of existing data.

What would you do if you had a city’s worth of data?
Lisa’s team is working to convince the city of Dublin to release more municipal data for others to make us of, following in the footsteps of Data.gov, Washington, DC, and San Francisco.

The Social City project seeks to better understand the social context of people in a city to better understand why certain groups of people aren’t getting the resources they need. The conditions in which these people live could be major drivers of why

Q&A

Sandra: Have you thought about incentivizing people, rather than just providing information?
Lisa: Incentives don’t need to be financial. One study found that knowing how other people like you behave has the ability to change individual behavior.

Praveen: Is it feasible to offset the cost of installing smart meters with the energy savings it provides?
Lisa: Right now, it’s still a net loss, because you still don’t have systems on the energy utlity side to take advantage of smart meters. But utilities know their time to adjust is limited, and governments are helping utilities to see that their time is limited.

People like to see immediate changes when they alter their behavior. Anything you can do to show people a change early in the feedback loop can be powerful (anecdotally).

Q: Can we do a better job of choosing sites for our buildings?

Lisa: There’s great data for this, but there are a lot of difference influencers. The telco data and the social context projects, for example, show just how many factors are at play. People may take advantage of a welfare system, but in the data, we often see them pop up once, and then disappear. They may register under different names. Cities know their service centers aren’t meeting peoples’ needs, often because of inconvenience of location.

Zack: There are a lot of policy implications in your work, and new technologies at play. You’re also in a position to educate policymakers and advocate for specific policies. What kind of barriers do you run into talking to those folks?

Lisa: Where it works is when you find some city leaders who are incredibly passionate about trying to do better and fix their city or some aspect of their city. Predominantly, people take these jobs because they do care about the city and services and infrastructure and making that better. The challenge is that a lot of policy and politics and regulations at larger levels that an individual leader can’t work around. Bus drivers’ union leaders were initially upset about the city sharing the Dublin buses’ GPS data. Are you going to spy on my lunchbreak?? Cities have histories and personalities and election cycles. Some people are afraid that the data will paint a negative picture of their work. Lisa compares it to the tide: sometimes you just can’t command it due to its scale. Leaders can’t yet prove a return on investment on a project because there’s so much uncertainty.

Smart Customization vs. Mass Production

Liveblog of Ryan C.C. Chin’s PhD thesis defense at MIT Media Lab

Ryan came to MIT in 1997, and got a Master’s in Architecture, and then at the Media Lab, before entering into the Lab’s Ph.D program. He took leave for 18 months to work on the CityCar project.

Ryan’s thesis examines smart customization, and the scientific differences between mass customization and traditional mass production. Is one better than the other? Is one more sustainable?

The CityCar is customizable on a number of levels: its base design, its adaptability to its environment (city), and its individual parts’ modularity.

Ryan hasn’t only worked on cars; he’s also studied customization of dress shirts. He chose shirts because of their low cost, frequency of use, and relatively easy traceability (see SourceMap).

Ryan started with an online customer survey of nearly 1,000 people. People have three types of dress shirt, with regards to fit: standard, made-to-measure with your measurements, and custom-tailored, designed specificaly for oyu. The average male has 14.2 dress shirts for work, but we don’t wear them all. Very few of us own only custom shirts, whereas 76% of respondents owned only standard shirts.

He then studied how people actually acquire mass customized products vs. mass produced products. 94% of respondents drove to buy their shirt. 63% of us clean our shirts in the washing machine, but mainly because it’s wrinkled, not because it’s dirty.

The main reason we return shirts is that they don’t fit properly. Online, mass-produced shirt retailers see a 40% return rate. That drops to 20% return rate in offline mass-produced shirt stores. Mass customized retailers see only a 5-10% return rate.

Whether it’s sold online or offline, mass produced shirts are made pretty much the same way. But when you order online, the delivery of a shirt to your home by truck produces huge CO2 savings over you driving to the store yourself.

With made-to-measure dress shirts, nothing gets produced until your order comes in, at which point the order goes to a QA center in China, where an electric scooter brings it to the factory. The carbon costs add up as your shirt is flown DHL to the US.

When you get a shirt custom-tailored, the tailor comes to your office to fit you and your coworkers, and then sends the order to Hong Kong. The shirts are made and flown back to the tailors’ studio, which then delivers the shirt and makes additional alterations. This back and forth adds some carbon costs.

The vast majority of the the CO2 involved in delivering your shirt comes in the last few miles, where you drive to a store. The mass-produced shirt ordered online has the lowest carbon count, followed by made-to-measure shirts ordered online.

Ryan also conducted a post-transaction customer use study using two washable RFID chips inserted into the collar stays on dress shirts. What happens after you acquire the new clothing? They built an RFID tracking system and embedded it into the office environment. Subjects would see a green confirmation light when the shirt they were wearing registered with the RFID readers.

The team cataloged a selection of sample shirts and sold them to employees at Fidelity and MIT Tech Review. They collected thousands of RFID reads over the course of the summer and color-coded a grid (or calendar, really) of how often each shirt was worn in the office.

Patterns emerge

People wear their favorite shirts on consecutive days, often in the same order. Ryan calculated an ideal shirt utilization rate: the number of shirts you own divided by number of days you need to wear a shirt. But we favor certain shirts and shun others. Some of us achieve equal distribution, though, working through our wardrobes systematically (“first in, first out”). One man reported that he gets dressed each morning by literally going right-to-left through his closet. Another man saved his custom-tailored shirt for a big board meeting, like a power tie, and felt the desired effect. A third guy wears only his cheap shirts, knowing that he or his children are likely to stain it, while the nicer shirts are never used at all. Others save their nice custom-tailored shirts for out-of-office occasions where Ryan’s RFID readers couldn’t scan them, like weddings and dinners.

On average, we don’t wear about 20% of our shirts at all. The mass production shirts got worn a lot, and were generally considered favorites, even over custom-tailored shirts. Ryan attributes this puzzle to better craftsmanship in mass-produced shirts, and fewer opportunities to wear custom-tailored shirts.

Lessons Learned:

  • We should move goods, not people, as much as we can. 16-ton UPS trucks are 24 times more efficient than a personal automobile for delivering goods.
  • Pull-based marketing dramatically reduces inventory. $300 billion in lost revenue in textiles wasted on stocks, transportation of goods, and heavy discounts. Build-to-order automobiles are only 6% of the US market, while it represents 50% of the European market.
  • Persuasive interfaces help people make the right choices. Showing the environmental effects of fast shipping vs. slow shipping works on us.
  • We need to miniaturize retail environments. The big box stores have become . Apple has begun deploying urban boutiques, where the highlight is experiencing the product, not stacking boxes.
  • Customizable Clones: Take the top 5 shirts you wear, the ones you love and the ones that fit, and make the rest of your shirts like those. These shirts are the iterative product of the trial and error represented by the rest of your wardrobe.
  • Local production is controversial. The labor cost is still about 2.5 times higher, even when you account for transportation costs.
  • Smart materials, like the Apollo Fabric, reduce the amount of energy the textiles require after it’s produced. Few retailers know Ministry of Supply claims to be anti-microbial and wrinkle-free, meaning fewer trips to the drycleaners, and higher shirt utilization.

Responsible Consumerism would allow us to create the ideal wardrobe, at the intersection of our own desires and environmental benefits. Ryan suggests a carbon label, like US FDA’s nutrition labels, showing the consumer the amount of carbon involved in the clothing article’s production, lifetime use, cleaning, and recycling.

How can customization improve the utilization rates of all the things we produce and own? And how do we scale this customization to the scale of a city?

Ryan attributes his inspiration to the late William J. Mitchell, and the huge number of people that worked on the CityCar and other projects.

Is the era of mass customization over?
Ryan points to Joseph Pine’s continuum of mass production, customization, lean production, and craft. All are necessary.
The number of customized things is going to increase, but what’s ideal? Standard, mass-produced goods work for many purposes (like Ryan’s current outfit). But there are huge cost and environmental savings to customization. Whether or not everyone feels these costs and benefits will depend on actual environmental policy. Everyone would love a custom shirt, but the average mass produced shirt is $20, while custom tailor shirts can easily cost $80. Custom needs to become more economical.

Ryan recommends that we receive a copy of the data generated by the full body scans the TSA requires of us. We could use that data for custom clothing, health, and other purposes.

Ryan foresees an “apparel genome,” where all of our clothing is tagged and machine readable, leading to insights about how we choose our outfits, what additional outfit configurations we could create from our existing clothing, and so on. I’ve begun using SuperCook, where I catalog the food in my pantry, and the app informs me what recipes will utilize my CSA-delivered eggplants. It’s not a big stretch of the imagination to consider doing the same for our clothing.

Customized goods fit into the broader trends of rent-rather-than-own, where an increasingly urban population favors access over ownership and proximity over storage space.

Microsoft made something cool: Photosynth

I’ve heard the new Windows phone is awesome, too, but haven’t had a chance to play with it yet.

At first, it seemed strange to me that Photosynth, an incredible and free panorama-snapping iOS app, was developed by Microsoft. But then I began uploading my panoramas to Photosynth.net and happily agreed to share my panoramas on Bing Maps, as well, and I quickly realized that Microsoft did, in fact, have self interest in this endeavor. They’re populating Bing Maps with great 180-360 degree panoramas of famous and beautiful places.


The view from the top of Parque das Ruinas, in Santa Teresa, Rio de Janeiro.

Google added this element to Google Maps years ago when it purchased Panoramio in 2007, but like many Google-acquired start-ups, the service has sort of languished ever since (relative to modern efforts). It’s a good sign for Google Maps that Panoramio photos are no longer a primary feature, but not much has changed about Panoramio itself. There’s a neat API, at least, and a great collection of geo-tagged photos, but Google should probably introduce a competing app to rejuvenate submissions.


El Ateneo Grand Splendid, a former theatre in Buenos Aires, was spared destruction and given new life as the flagship bookstore of El Ateneo.

Anyway, Photosynth is awesome enough to make me re-install Silverlight and dust off my Windows Live (Hotmail) ID. Even though I took panoramas all over South America without 3G or Wifi access, when I went to upload them tonight, the photos’ metadata quickly brought up a list of nearby places to choose from. It was amazing how easy it was to assign each photo to a destination I am now 8,000 miles away from, without even consulting a map.


The Panama City, Panama skyline as seen from the harbor.

I’ve been through every iteration of panoramas in the last 20 years, with varying degrees of pain:

1997: I excitedly purchase a Kodak Advanced Photo System camera for its simple panoramic mode. My parents graciously pay a premium each time get my wide aspect ratio memories developed.
2002: My first Canon Digital Elph doesn’t have a panoramic mode, but I use clunky software programs to digitally stitch and assemble panoramas. The overlapping edges don’t look great.
2004: My next Canon point and shoot has a panoramic mode included in the firm/hardware, which consists of holding your elbows as steady as you can while rotating your torso enough to achieve a 30% overlap. The bundled panorama utility software stitches them together. Again, results are not great.
2005: I see that my friend in Leeds, England simply prints 4x6s and manually arranges them on her wall, as people have done for 150 years. I’m a little depressed how superior the analog treatment is.
2011: Microsoft releases the Photosynth app. It’s simple to use and is yet another smartphone app that makes the iPhone a justifiable replacement for actual cameras with far higher quality components.


Experimenting with a macro shot of my cafe carioca.