Pixels and Place

by Kate O'Neill

  The same may be true online, too.

  Data and digital connectedness in communities like Twitter, Facebook, and other gathering spaces link a communities inhabitants across time. And data and digital connectedness link a city’s inhabitants across time, too, often through these same channels.

  In the next section, we’ll examine some patterns and practices for adopting integration of physical and digital experiences from cities around the world.

  Cities and The Smart City Imperative

  The notion of smart cities is alluring, as it promises conveniences and advancements we’ve seen imagined in science fiction. A great deal of opportunities are already available with what is possible today, but it takes most cities a herculean effort of bureaucracy to muster the changes to civic infrastructure needed to take advantage of them. Still, a great many initiatives are underway, some sponsored by corporate giants like Google and IBM who have products and services geared at cities (and who stand to benefit from a more connected populace).

  We can think of the Internet of Things in this context, not so much as smart refrigerators and smart toasters, but instead as IP-enabled embedded devices connected to the Internet—including sensors, machines, active positioning tags, radio-frequency identification (RFID) readers, building automation equipment, and much more.

  Some of the most promising applications of data and the Internet of Things as they are being applied and will be applied in cities have to do with:

  Assistance for residents and visitors who are disabled and differently-abled, such as those with visual impairments, through location data and beacon guidance and other technology enhancements to wayfinding and navigation

  “Smart” parking alerts and monitoring to assist drivers in finding nearby spaces to save their time, maximize parking utilization, and decrease the amount of greenhouse gases being emitted by cars circling blocks looking for a parking spot

  Transit reliability and predictability have huge impacts, too, and will be of particular value to the city’s lower-income residents who rely on public transit

  I saw the latter play out in Chicago, along Michigan Avenue. Bus shelters everywhere tend to have a list of routes that stop at the station, so that’s nothing new; but these shelters included digital displays showing the buses en route to the station and how long before each arrived. That technology is in place in other cities, and in other infrastructure in other cities. For example, most of New York City’s subway stations include signs that show which trains are approaching and when, and NYC’s MTA does track that data for buses and makes it accessible online but not visibly at the stops and shelters. That said, the buses are what connect people across the grid at a most granular level, and many cities, including NYC, are missing this level of signage for buses to allow riders better ability to plan their routes and arrival time.

  This is a basic, fairly low-level implementation of data in place, but whereas many tech advances benefit the wealthy and privileged first, this is an initiative that cuts across socioeconomic classes and helps the lower-income and working classes who often depend more heavily on public transportation.

  Elements of Smart Cities

  When we talk about smart cities, we’re really talking about a combination of any of the following elements or more:

  Traffic flow enhancement, where sensors can not only monitor traffic flow, as they already do, but along with predictive analytics, can trigger alerts to be sent to drivers to seek alternate routes when certain routes are congested.

  Smart services, like parking and transit enhancements. For example, data could inform the driver of—or, in an autonomous vehicle context, simply guide the car to—available parking spaces, the selection of which can be optimized for either proximity to destination or lowest overall price. If the parking app is granted visibility into the driver/passenger’s calendar, of course, it potentially makes the decision seamless.

  Smart environment, such as monitoring pollution levels, measuring tremor levels for earthquakes, and sending relevant notifications to citizens about relevant issues.

  Smart security, such as home and building automation that is guided remotely and/or by data-based rules.

  Structural health, where buildings can self-report the need for maintenance issues, along with other necessary information.

  Smart logistics, which encompasses a wide range of intelligent data-driven services for a variety of purposes—like triggering garbage trucks to come around and empty bins when a sensor reports that they are full—to improve the living conditions for citizens and reduce maintenance costs for cities.

  And to some extent, the decentralization and/or democratization of public places.

  These may be achieved through a combination of sensors, location-aware services, ubiquitous or widespread open mobile access everywhere, and other technologies.

  Public/Private Partnerships

  Increasingly, open data projects are being funded through public/private partnerships.

  This makes the infrastructure available in a timeline and scale that might not be possible with taxes or civic budgets, and it can be seen as goodwill for the company that sponsors it. Google and their Fiber program are one such example, where putting high-speed internet in place in cities across the country (and around the world) may be only marginally profitable for them; but ensuring that as many people as possible can use data-rich services ultimately means that their data stores are richer. For a company that monetizes data in a wide variety of ways, that could well be worth the investment.

  In 2016, Siemens announced a partnership with Sri Lanka, with the intent of making Sri Lanka a smart city and a commercial, naval, and aviation hub of Asia. In addition to analyzing opportunities for the city on a macro scale, the initiative will be seeking solutions to common issues in dense urban areas, like waste management, traffic, as well as focusing on resolving issues in slums and with environmental issues.

  Open Data Initiatives

  The Open Data movement is based on the idea that some data, mostly public data held by governments at the federal, state, and local levels, but also some scientific and academic data, should be openly available and free for anyone to use, analyze, and re-publish. (A related term, Open Government, is sometimes used interchangeably, but some Open Data advocates say this term should more strictly relate to the transparency of data specifically about government practices and policy—such as voting records and the funding sources for campaigns and elected officials.) In making this civic data available, governments allow for more innovative public-facing solutions to be developed.

  On January 20, 2009, his first day in office, President Obama signed the Open Government Initiative, mandating that a great deal of government data become and remain available. This was followed in 2013 by Project Open Data, which provided resources and processes for storing civic data at the city, state, and federal levels to make these data sets publicly available. The administration maintains a website, data.gov, which serves as a repository for these downloadable data sets. At the time of this writing, there were over 5,100 data sets available in the local catalog for data relating to US cities—including crime statistics, business licenses, real-time traffic information, and many more. Anyone can download these data sets and cross-reference one against another, potentially discovering a pattern that might turn up a useful insight. And anyone who represents a government entity can upload a data set to the catalog, too.

  By sharing these data sets in this way, new discoveries may emerge that lead to innovations to improve quality of life in cities and around the world.

  Other data sources and insights

  A variety of other sources of data are publicly available for download, as well. For example, bike share programs have risen in popularity in cities around the world. New York City’s Citi Bike program alone had ten million rides in 2015. Citi Bike also makes system data available for mapping and analysis purposes.85 The data they make accessible includes start and end times, ri
de duration, and start and end stations, all of which could spell out some behavioral patterns at a macro level. And for subscribers, they include birth year and gender, so it’s possible to see demographic trends, as well. They include a unique identifier for each bike in the system, but there is no identifier for the system user, so the kinds of analysis that might see subscriber patterns over time isn’t possible with this public data. But no doubt Citi Bike has that data and can analyze it themselves.

  The Citi Bike system data as viewed in Microsoft Excel

  Naturally, we might start to wonder about what this data suggests about connection to place and how the human data being tracked demonstrates it. It stands to reason that there are, for example, commuter uses of the service. On his blog, software developer Todd Schneider demonstrates this with some analysis of the freely available rider data.86

  Image source: toddwschneider.com

  I find it interesting that the peaks of the two spikes don’t line up perfectly. The gap there allows us to speculate about some scenarios that might make up the difference. Some of the people who ride from the outer boroughs to work in Manhattan in the morning (the red peak) may be meeting friends for drinks after work, or attending networking functions in the evening, and choosing to ride home later in the evening (note that the blue line is higher than the red past the peak), or take the subway home, or a cab. Maybe Brooklynites (and others) who work in Manhattan just work longer hours than Manhattanites who work in Brooklyn. That seems like a silly potential explanation, but it’s kind of a provocative idea, and a puzzle we could begin to solve with more data. Once again, data can raise as many questions as it answers.

  Balance and Flow

  Every person will have a different threshold for adventure and excitement, but one of the promising ideas in designing experiences for humans with behavioral data insights is the chance to provide novel opportunities amidst the everyday.

  For example, there’s this great sense of serendipity about new places, and exploring the unknown. But there’s also a great risk in going somewhere unfamiliar and taking the chance of not knowing how to get back.

  This relates back to the part early on about GPS navigation and our innate sense of direction and observation of landmarks. (Recall that people had less ability to recall landmarks when they were using an “ego-centric” mapping experiences.) As it turns out, people may need to be able to explore to get their bearings and understand a place.

  Perhaps one way to think about a “smart city” is simply as a city in which a visitor can get lost and not feel lost where there’s enough infrastructure to guide them back.

  At any rate, it’s an interesting goal in almost any context: to design experiences so that a person can get lost without feeling lost.

  Design experiences so that a person can get lost without feeling lost.

  One lesson to take from cities is to design for “flow.” In other words, to think not just about throughput and traffic maximization—not just about sales—but to remove obstacles that people might encounter as they go about their routines.

  Most cities were designed around the car. But as cities have grown in density, cars are increasingly inefficient forms of transportation. Public transit like buses and trains is far more efficient, so in growing cities, there is a need for what is known as transit-oriented design. That’s design that takes into account where subway stations and resources overlap; where bus routes are taken into consideration in the location scouting for a hospital; or where at least the transit authority is in discussions with the hospital about extending a bus route to the hospital. Good urban planning keeps these things in mind.

  Part of applying this in integrated experience design is to think about how you accommodate multi-modality. For example, in cities throughout the United States, there are instances where, to merge onto a bridge, bicyclists have to cross car traffic at an intersection. Not only does that scenario pit bicyclists against motorists, but it also reveals an inherent bias for the cars. Websites that favor desktop browsers over phones and other mobile devices feel somewhat related to this paradigm (even if the desktops pose significantly less threat of harm to the phones). That extends, too, to brand interactions that don’t attempt to interact with people in integrative physical and digital interactions.

  The only way to scale a place or a system is by accommodating multiple modes of access in context-relevant ways. If your system doesn’t adapt, it won’t scale.

  Another lesson from cities about access has to do with parking in a downtown area. Prohibitive parking costs may affect whether people who live outside of downtown spend time and money in the area. On the other hand, you could look at rising parking costs as an indicator that the downtown areas isn’t as accessible by other modes as it could be. What are the implications, economically or otherwise, of optimizing your systems for infrequent use?

  It’s going to depend on your business and your brand: in a hotel geared at business travelers, it probably makes sense to design around frequent visitors to make the experience very familiar and comfortable for returning guests, but along the way also make things simple for those who are new to the brand. In an app relying on adoption by a critical mass of new and occasional users, on the other hand, the simplicity of the experience is key to ongoing success, but there still has to be something in it for the returning people.

  In any case, what this gets at, fundamentally, is the need to think and design at scale—to consider what happens when something succeeds, and not let success be the downfall of a place or a system.

  As most places and systems scale, it’s imperative to keep people moving, which brings us right back to the metaphors of movement and stillness. (See the earlier section on “Movement Versus Stillness.”) Part of our experience of a place is with our kinesthetic sense through its metaphors of movement or stillness, which means we can make meaning from motion, whether that motion is physical or imagined. Disconnected experiences have a sense of unease; integrated experiences don’t always move fast, but they move forward deliberately.

  ***

  One interesting facet of this, metaphorically, is that the best way to get meaningfully lost in a city you want to explore is on foot. Biking is fine and certainly gets you farther faster, but walking gives you more options and agility. And cars, buses, and subways are only so useful; they can get you from one part of town to the other, but to really take in the detail on a block-by-block level, you’re better off on foot.

  What’s the analogy online? What does this tell us about experiencing online spaces and community?

  There’s a certain intentionality about it. You have to go into it with a mindset that you yourself are as much a part of the surroundings as everything you see.

  In the old days, you could go online, hit a bookmark, do what you needed to do, and get out. That was one way of experiencing the internet. That was 1.0.

  You could wade in and explore, immerse yourself, allow yourself to “surf” and float, then go where the links take you. That takes up an expansive amount of time. That was 2.0. (Or was it 1.5?)

  Or you can be social and contribute. 3.0. (Or was that 2.0?)

  Or you can experience the internet and everything it has spun off and empowered as an integrated part of your day-to-day life, being prompted with timely and relevant notifications by your devices, summoning a virtual assistant or search engine to answer questions as they arise, even having your surroundings adapt to you as you move through space. You move through space powered by data; you leave data in your wake; you are the digital self you create. Maybe this is what 4.0 is, or maybe it doesn’t matter anymore what “version” it is. This isn’t just about the internet anymore; this is our real life now. That’s the experience we’re heading into.

  CHAPTER TWELVE

  Epilogue: Where Do We Go From Here?

  From smart fitting room mirrors to emailing a wrench to space, these are sweeping, big ideas, many of which fundamentally change our expectations of ev
eryday encounters and interactions. This is not the realm of science fiction, although it sometimes feels like it. To use a metaphor of place to describe a state of being, this is where we are.

  So if this is where we are, where do we go from here?

  We embrace the integration, and we do so while creating meaningful experiences—the kind that we ourselves might want to have.

  The Challenge: Follow the Integrated Human Experience Design Philosophy

  The challenge is to execute these ideas while following the Integrated Human Experience Design philosophy:

  Make meaningful use of metaphor

  Blur the lines, with intention

  Recognize the humanity in the data

  Furthermore, don’t forget that:

  Analytics are (often about) people. It’s important to remind ourselves of the human beings represented by the data we collect and analyze.

  Relevance is a form of respect, so show people what you offer that you think they’ll want to see. But remember discretion is a form of respect, too. So gather the data you need, but don’t be creepy.