Jane Jacobs on Transportation

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I recently finished reading the new(ish) compilation of Jane Jacobs’ shorter writings, Vital Little Plans.

Jacobs’ writing is the closest things modern American planners have to holy scripture. Sadly, for those of us who spend our time thinking about transportation, there hasn’t been an enormous amount of her work to chew on. Reading Vital Little Plans was a chance to glean more of what Jacobs had to say about the way we move through our cities.

There is one transportation issue where Jacobs’ position is fairly well known: her much-publicized opposition to urban expressways. And, indeed, you’ll find many passages in the book denouncing highway investment that sound strikingly contemporary:

“Los Angeles, where at rush hour the cars on the great freeways crawl at 10 miles an hour, the same speed the horse and buggies used to achieve, where the poor have no practicable way to reach jobs, where the exhausts have turned the air into a crisis, where expressways, interchanges and parking lots occupy some two-thirds of the drained and vacuous downtown.”

A City Getting Hooked on the Expressway Drug, 1969

This is well known Jacobs territory. It’s not surprising that the patron saint of walkable neighborhoods opposed new automobile infrastructure in cities. But some of her other positions are startling.  Readers might be surprised to learn, for example, that Jacobs was at least as ferocious a critic of public transportation management as she was of highway investment.

In her 1969 piece, Strategies for Helping Cities, she describes a transportation “status quo that is predicated on inconvenient, deteriorating, obsolete public transit” and highlights the “unwillingness of local government to permit competition to its services […] in public transportation.”

This was not a one-off critique of public transport planning. In The Real Problem of Cities, a speech given on the inaugural 1970 Earth Day celebration, she excoriated the entire idea of centralized government control of transportation systems:

“The surest way to arrange that the status quo is not going to be disturbed, that development is not going to occur, that a problem with us now is going to be with us indefinitely, is to centralize responsibility for defining it and for administering funds directed to its solutions. The very strategy itself is fatally at odds with a goal of problem-solving.”

Perhaps her most scathing critique of public transportation systems was written almost 25 years later, after Jacobs had relocated to Toronto. There, in 1994, she helped co-found a still-active group called the Consumer Policy Institute, and penned a letter focused on the problems of public transit in Toronto. It’s worth reading in full:

“Affordable, convenient public transit is vital, yet Canadian cities are plagued with costly, inadequate systems. Time and again, transit managements and politicians with public funds at their disposal embrace foolish, extravagant policies while ignoring common-sense alternatives and neglecting innovative thinking. Those decisions are paid for in higher fares, lost customers, rotten service, tax subsidies and lost opportunities.

It used to be reasoned that public service monopolies would benefit from lack of ‘wasteful’ competition and economies of scale. They don’t. The post office is a notorious example. Only when that monopoly began to break down did many badly needed innovations from independent businesses become available. Or consider long-distance passenger rail services: they are a disgrace, forever deteriorating yet becoming more costly.

Good service delivery must be responsive to customers’ ever-changing needs, not protected from customers by limiting their choices or evading failure by winning government favors. Hopping the gravy train is no way to run a railroad or any other successful commercial service.”

First Letter to the Consumer Policy Institute, 1994

The use of “gravy train” to describe public investment will resonate with those readers familiar with Toronto’s disgraced former mayor Rob Ford. Indeed, the general tone of Jacob’s piece – questioning the wisdom of public management of transportation – is more reminiscent of writing from the political right than the political left. As demonstrated frequently throughout Vital Little Plans, one of the most satisfying elements of Jacobs’ writing is her ability to scramble simplistic assumptions about political alignment and urban policy.

So if not the traditional right/left political divide, what motivated Jacobs’ pointed and repeated criticism of public transport management? Reading through the various selections in Vital Little Plans, it becomes clear that the common enemy she is attacking is top-down, centralized control of complex systems.

For followers of Jacobs, this should not come as a surprise. The founding ethos of her seminal work, The Death and Life of Great American Cities, is that city success flows from the bottom up. There, she casts the villain as government planners who have a utopian vision of what cities should look like; they destroy functioning urban fabric because they are too far removed from its use to even notice that it is succeeding.

Here, in various selections from Vital Little Plans, Jacobs’ argument against public monopoly control of transportation is similar: a single government agency is liable to miss opportunities for innovation, be biased to the status quo, and ignore changing consumer demands.

Given her love of bottom-up organization and distributed decision making, I suspect that Jacobs would have been a supporter of ‘informal transit’ systems like collectivos, matatus, dollar vans, or jeepneys, although there’s no writing about them in this volume. Those systems tend to be composed of thousands of independent actors, each with a financial incentive to meet consumer demand, and without centralized control of routes or individual driver behavior. While that has often made them the enemy of public authorities, I imagine it would have made Jacobs a fan.

Her thirst for new ideas, did, it’s worth noting, lead her to support some transportation ideas of dubious viability. Most notably, she expressed enthusiasm for a system that sounded like the cultish Personal Rapid Transit. She also waxes poetic at various points about cable-cars and high speed ferries.

The key takeaway for me is not so much that she thought any of these schemes would necessarily be successful. Her broader point was that restricting the ability for new ideas to be attempted always leads to stagnation, in urban transportation as in society at large. In one passage of The Real Problem of Cities, immediately after excoriating the New York City Transit Authority for ignoring potential new technologies, she underscored the paramount importance of the search for creative new solutions:

“To maintain the status quo is impossible, in this or in most other things. In most activities, and certainly taking society as a whole, we must be creative or else resigned to decay. This is not simply an imperative of modern economies. It is an imperative of the human condition itself.”

The Real Problem of Cities, 1970

Jacobs died in 2006, so she didn’t get the chance to see most of the recent wave of new technologies in urban transportation – carsharing, bikesharing, ridesharing, or autonomous vehicles. While I suspect she wouldn’t not have gone in for techno-utopian claims that any one solution would save the city, I imagine she’d be cheered by the fact multiple different solutions were both proliferating and competing.

Her writing in Vital Little Plans makes clear that she took supreme pleasure in challenges to the status quo. Whatever she might make of the individual players, I suspect she’d be heartened that there’s more being challenged in urban transportation today than there has been in a very long time.

In San Francisco, a Glimpse of the Future of Transit

What if transit agencies were as nimble in providing service as riders are in using it?

I recently moved to San Francisco, and I was lucky enough to find an apartment that’s only a few short miles from work. As a lifelong public transport commuter, I made a point of checking out the transit options.

It didn’t look great; they all involved bus-to-bus transfers.  All the literature suggests that people really hate transfers, especially for services like buses that tend to have longer and less reliable headways.

So I turned to apps. The real time information powering Citymapper, Transit App, and Moovit is not new, but I didn’t have much use for it when I lived in New York. My daily commute was so obvious I didn’t need additional information – I walked to the A train.

In San Francisco, things changed. There are dozens of different combinations that might prove to be my fastest route on any given day (see below). Which one is faster depends on a long list of variables – traffic congestion, the different wait times for express or local service – and, critically, the chances that my dropoff time from one route is convenient to my pickup on another. This last point is critical, because knowing it in advance directly eliminates one of the issues with bus transfers: not knowing how long you’ll wait for the second bus.

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That’s important, because I have 3 or 4 different options for the first bus I board in the morning (for those in San Francisco – the 1, 1BX, 38, 38R, 2, and 33). With real time information in hand, I can hop on whatever first bus leg will get me to work quicker, including knowledge about the future location of the second bus. After experiencing it for the first time, it’s clear that real time information and predictive software is already helping to make transit more attractive, even in places where things like bus-to-bus transfers are required.

But at a more profound level, it’s highlighting changes to transit operations that may be on the horizon. In San Francisco, I no longer really care about routes in any conventional sense. I don’t care about a schedule, I don’t really care about ‘stops’, and I don’t really care where one bus drops me off or another picks me up. As long as those things can be reliably conveyed to me when and where I need them, they don’t need to be fixed in time or space.

In one important sense, bus routes are a way to communicate information about service to riders: “Stand here, get this bus, go there”. That makes sense when you don’t have a more dynamic way to communicate, which we didn’t when we invented bus routes 200 years ago. It’s obviously also a logical structure in places where you have or need dedicated infrastructure, as streetcars once did and as my old subway commute in New York City still does.

Clearly, though, routes have had a lot of staying power even beyond those places. Despite enormous urban development changes, most of the bus routes in major cities still look a lot like the streetcar routes that they replaced almost 100 years ago.

Now, sophisticated riders are able to hop between routes, knitting together new kinds of service with real time information. But that’s only a one-way flow – riders are getting smarter.

But what if transit agencies were as nimble in adapting service? Could buses decide to skip stops or turn local into express based on how many riders were already in the vehicle and where they were going? How about dynamically rerouting buses to avoid traffic congestion? What about deciding on the number vehicles in service based on real time demand?

Before you can even start thinking about that kind of dynamic service, agencies need to overcome one obvious hangup: a lack of fine grained data on their own riders. Riders are getting a lot of information from agencies via new APIs and apps, but not much is flowing back the other way.

That’s one reason I was excited to read about a new MBTA partnership with Transit App, which seems to be bridging this gap and providing user information back to the agency. That’s an exciting first step. If agencies are going to get more nimble and more able compete with the private car (let alone the private autonomous car), much more of that will be required. Without fine grained information on how its own riders are using their service, I can’t see how public transit agencies are able to thrive in a future marketplace of on-demand options.

Just to point out an obvious constraint to changing service patterns – you can’t start skipping stops and dispensing with the idea of routes until everyone is using a device that can convey information in real time (a smartphone, or something like it). Today, I suspect the number of daily bus riders using an app on a regular basis is still quite low.

And another disclaimer – in places where buses are already fast, frequent, and full, dynamic service changes probably won’t produce all that much benefit. But that’s a small slice of today’s transportation picture. Only about 2% of travel in the US is completed on public transportation. I think there are a lot more places that would benefit from nimbler bus service, and that’s a good thing for the potential growth of shared rides in the future.

All of these concepts are not wholly new – the International Transport Forum has modeled the most efficient possible public transport structure for Lisbon. While it kept the metro in place, it eliminated traditional fixed route bus service and replaced it with on-demand minibuses and shared taxis. A recent report by ITDP and UC Davis argued that transit will have to evolve and use smaller, on-demand vehicles where they make sense, in order to meet our long term climate objectives.

But what’s changed for me since I moved to San Francisco is that my commute is starting to give me just a hint of what that future might look. It’s exciting to see up close.

Montreal to NYC by Bus and Rail

The trip from Montreal to New York is surprisingly inconvenient. I had time to ponder potential improvements during a recent 12 hour train ride home. Here’s what I came up with.

I’m from Montreal but live in New York City, so I frequently make the trip between the two. For those who haven’t had the privilege, there are currently two ways to make the trip for those who can’t afford a flight or don’t want to drive: the bus, and the train. Both could use an upgrade.

First, the train. The train’s scheduled run time is about 11 hours, although my anecdotal experience suggests that an additional hour is not an uncommon delay. That means the trip is frequently as long as 12 hours, almost double the length of the driving alternative. The other struggle is frequency – there’s only 1 train per day, leaving in the morning and arriving in the evening.

Why is the train so unimaginably slow? There are two basic reasons. One is that the track north of Albany is slow and single tracked. New York to Albany is nearly half the mileage of the whole trip but takes only 2 hours and 20 minutes at its fastest. The stretch from Albany to Montreal takes 8 to 9 hours, despite covering roughly the same distance.

The bus is quicker, and completes the whole trip in a scheduled time of about 8 hours at its fastest. There are two reasons the bus is slower than a car – it makes a mysteriously long (30-45 minutes) service stop in Albany, and it takes longer to cross the border than a passenger car typically does. As with anything that travels on roads, it can also suffer from significant delays getting into and out of the congested core of Manhattan.

Is there a way to improve the two options to make them more competitive with the car? Although some have proposed a multi-billion dollar HSR program, there’s a much quicker approach that would yield enormous benefits for both riders and transport operators at minimal cost.  See the table below for a summary of the current situation.

Road Distance Train Travel Time Bus Travel Time
New York – Albany 150 miles 2h 20m 3h+ 
Albany – Montreal  220 miles 8h 30m 4h 30m+

The train is quickest south of Albany. It also provides the additional benefit of a reliable and congestion-free approach to Manhattan’s urban core. As an added bonus, it also has spectacular views of the Hudson for its entire length. The bus wins by an enormous time margin north of Albany. Why not combine the two to get the best of both?

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The proposal is simple – passengers at Albany would transfer to a bus to complete the trip to Montreal (or vice versa) and shave about 4-5 hours off of of today’s train journey. Transfers between modes can be a pain, but they don’t have to be. The Albany train station could be configured to make the bus transfer quick and easy, cross ticketing (ie a single ticket for bus and rail) would make it seamless, and the buses could be timed to depart immediately after train arrivals. The best case scenario could get the total travel time below 7 hours and get travel time roughly to parity with the private car, with the added benefit of a congestion free approach to Manhattan’s Penn Station.

Aside from the clear passenger travel time benefits, there would be lots of operational gains for both bus and rail services to this change. As of right now, one of the train’s sources of delay is that it swaps out an electric locomotive for a diesel locomotive at Albany to complete the trip north to Montreal. This typically takes as long as 30 minutes. If the train ran only between Albany and New York, it could stay exclusively on electric power and gain back a large number of wasted man hours.

Even more beneficial, the two train sets currently providing 1 daily round trip on the 12-hour Montreal to New York segment could run 4 round trips on the the 3 hour trip from Albany to New York. Just by turning the trains back at Albany, you’d get 4 times more service over the Albany to NYC line using the same train equipment and crew. That’s a significant increase to the 13 daily round trips currently being offered on that line. 

Much like turning back trains at Albany improves Amtrak’s efficiency, preventing buses from entering New York City would makes buses more efficient. The traffic around New York City is severely detrimental for the operating efficiency of buses. Aside from making bus travel slower on average (which reduces the number of passenger miles a bus can serve in a given hour), congestion is unpredictable. That unpredictability requires bus operators to build additional slack into the bus schedule for buses heading into New York City. Keeping buses on the relatively uncongested Albany-Montreal branch would allow operators to tighten up schedules and run more frequent service at the same cost.

I used the word ‘operators’ deliberately – there’s no reason that multiple bus operators shouldn’t be encouraged to run the new Montreal-Albany route. Given that the goal here would be to attract choice riders, bus operators could compete to offer the best service – luxury buses, large seats with more legroom, etc. 

Even for those who believe that enhanced rail infrastructure is the ultimate solution, this intermediate and flexible step could help build ridership for mass ground transportation (ie, alternatives to air and private car travel) over time, helping make the justification for further investment more obvious.

Another advantage of creating the bus/rail combination is that service levels could be dramatically improved and tailored to demand.  Amtrak currently offers 1 train per day – it’s sometimes sold out at holidays, but it’s likely not terribly well utilized at most other times of year.  With the new model, there would now be 17 trains per day running between Albany and New York, and each could be combined with a bus leg to Montreal. This list of 17 departures would create a whole new realm of options. As just one example, a bus operator could launch a service from Montreal at 1AM to catch the 5:05AM train from Albany to NYC that arrives in NYC at 7:30AM.

There’s one other improvement that would make any option dramatically better: a dedicated border crossing at the Montreal bus station. There’s precedent for this – airline passengers already clear customs at the Montreal airport, even when departing for the United States. Clearing customs at the bus station (rather than the border crossing) would allow for much more certainty in bus travel times (by avoiding an unpredictable border), allowing for further tightening and cost efficiency for bus operators and better experience for riders. I’m sure there are 100s of obstacles to getting that done, but it’s a no brainer from a transport operator and rider experience perspective.

So what would all this cost? I don’t know, but infinitely less than a rail upgrade would cost to get anywhere near the travel time benefit described above. There are definitely obstacles to getting this done, but I think there’s something to the idea of a combination bus-rail alternative between NYC and Montreal.

Open Data in China: Some Assembly Required

As part of a recent project, my team has been looking to get a sense of the spatial patterns of population growth across China over the last ten years. Given the massive urban migration underway across China, understanding these patterns at a reasonable level of disaggregation is critical to developing sensible plans for future growth.

Luckily for us, like most other countries, China periodically carries out a complete national census (most recently in 2010) which collects detailed population data down to the county (more than 2800 across China) and ‘township’ level (more than 47,000 across China and roughly analogous to a US census tract). We were also lucky that the data had been published only a couple days before we started looking for it (in mid March 2013).

We happily set off to purchase the data, which came in two volumes – one for county level statistics and one on township level data. Unfortunately, the data was available in only one format – pictured below. For those who spend a lot of time glued to laptops ensconced in machine-readable data, this data format is colloquially known as a Book. I.e., it’s a book. A thick, massive book.

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The photo below shows a shot of one of 100s of pages contained the township data I described above. On the left is the township name, and across the columns runs population by age and gender.

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So, the data is ‘open’ – i.e. you can have a look at it. And, certainly, if you were interested in knowing exactly how many females aged between 50-64 lived in your township, this might not be a completely inconvenient way to find out (although you’d have to drop about $400 for the book itself). But clearly, for almost any other purpose you can imagine, this a far from ideal format.

The first step to a more user-friendly future is having the data in some type of digital database – no small task in itself. Even with that done, though, carrying out any type of spatial analysis would require a spatial dataset to join the data with. Neither of the datasets above are released with a spatial boundary file.

In some cases, open geospatial datasets are available for use in mapping the data above. The excellent Global Administrative Areas (GADM) dataset, for example, provides county level administrative boundaries in China. Having worked to match this source with Chinese data in the past, however, I can tell you that’s it not always as easy as it sounds given the slight changes in borders and naming conventions that switch between data sets and years. But clearly GADM is light years ahead of starting from scratch.

In the case of the township level data (equivalent to the census tract level in the US) there is simply (to the best of my knowledge) no available geospatial file to link the data to. In other words: 47,000+ data points, no locations. This drove one prominent geospatial company to manually enter the data into a spreadsheet and manually geocode the location of each of the 47,000+ township locations from hardcopy sources assembled from 100s of local governments. Anecdotally, I understand this took 3 full time people working for almost a year to complete.

They did this for the 2000 census and were able, from that data, to derive the richest and most detailed data set available on Chinese urbanization. Despite the amazing work done by the company, their data set is clearly now a key part of their added value as a consulting firm, and given their substantial investment of time and money in creating it, they are (quite understandably) not about to give it away.

Separately, the Michigan University China Data Center does similar work matching published Chinese census data to spatial boundaries for Chinese counties. In 2000, the last year this data appears to be available, they offer detailed data for all 2800+ counties in the country. The price tag? $38,000. $38,000 for census data – the bread and butter and free starting point for every demographic analysis done here in the US. This is not a knock on the China Data Center – I’m sure their time and cost investment to produce the data set is substantial. But it strikes me as odd that a data set created by the government of China is sold by a US institute for a $38,000, putting it out of reach for all but the wealthiest research institutes in China or anywhere else. Even if it could be purchased, restrictive licensing prevents the type of data mashups that are becoming so powerful elsewhere.

It is ultimately reasonable that countries like China have an interest in protecting some sensitive data from the public where absolutely required. And geospatial data remains a sensitive issue in China, as evidenced by resistance to OSM and geospatial data collection more broadly. But in the case of census data, the data is already being made public, just not in a way that is able to actually generate useful and interesting analysis. Because of this decision, private firms and institutes outside China are able to step in provide a costly service, ironically ultimately excluding many Chinese institutes, students, and citizens from using the data themselves to enhance their understanding of China’s demographic evolution.

The relevance of this discussion is heightened by the fact that China is a country still in the midst of the largest wave of urbanization in human history. Massive investments are being made to accommodate growing cities and regions. Understanding population dynamics at a detailed level is critical to properly planning for this growth. No one doubts the ability of China’s researchers, but as I’ve heard at many conferences and in the hallways of many meetings – it’s no good having the techniques and know how if the data is not available. Just a few samples of some analyses done with free US data – for example, on commute times here, or public transport access here – show how valuable open census data can be in enabling broad based dialogue on urban development.

It’s great that China is at least releasing its data, but let’s move to make it digital, open, mappabale, and shareable – and the basis for an understanding of Chinese urban growth that helps lay the groundwork for sound urban investment decision making in the future.

Let’s Build an Open Zoning Data Standard

Zoning – unsexy, unglamorous zoning – is likely the most important tool that government agencies and urban planners have in their arsenal to shape the urban environment. For those blissfully unaware of zoning’s existence – it’s the mechanism by which almost all US cities (with one notable exception) regulate what you can or can’t do with your property. Typical zoning regulations include things like land use (residential, commercial, industrial, etc), a measure of density called Floor Area Ratio (or FAR, shown in the map below), and design regulations like height limits and setback requirements that determine the position of a building on a lot. Things can – and do – get more complicated than this, but these are the essentials of zoning that have been in place since the early 20th century.

Zoning Allowed Density and Transit Stations in Chicago

Zoning Allowed Density and Transit Stations in Chicago

Zoning regulations are not static – in principle, they’re designed to change to reflect a city’s vision of future development, making them one tool by which master planning principles are applied in practice. Changing zoning codes to allow for higher densities in areas that are well served by public transport is, for example, a critical element of the concept of Transit-Oriented Development (or TOD).

Zoning is powerful tool, but one that’s rarely discussed in detail except by planners, city officials, or those who suddenly realize that the zoning code affects what they can do with their property. Given how critical zoning is to the future of our cities, this conversation deserves to be broader and more inclusive.

Part of the reason for a lack of interest in zoning is the difficulty in actually visualizing and understanding your zoning code. The traditional way to share the zoning code is in static maps or, more recently, data portals that often suffer from all the challenges described here. Neither of these tools are particularly easy, user friendly, or inviting.

Luckily more cities are doing better and opening up their zoning data in geospatial formats for bulk download. I’ve listed the cities I’ve found that do this in Chicago, Washington, DC and New York at the Civic Commons wiki. This is a great step – and I hope more cities are already following or will in the near future (please add to this list if you know of any).

But simply providing the zoning district geospatial data is only half the battle. Actually interpreting what’s in the zoning code – how individual zones regulate the nature of what, where, and how much you can build – is just as critical, and generally more complicated. To get a sense of this – block out an afternoon and read through a typical zoning code (just kidding). What’s needed is a simplified key to decipher the zoning code and breakdown its key elements. In the most basic terms, what we need is a lookup table to link the spatial map of zoning districts to their most critical regulatory elements – permitted land use, density, setbacks, etc. This is exactly what the folks at Open City Apps in Chicago did with Second City Zoning, a project designed to make zoning understandable to the average SimCity player. You can see the simplified zoning district table they created here.

Their zoning district table is fantastic – and their hard work deciphering the data is what allowed me to create quick mashup map from their data to show zoning density near transit. What we need now is to start moving beyond deciphering the individual zoning codes of a given city to building a way to share zoning data in an easily sharable, standardized format. We’ve already seen the power of the open data standard for public transit that I’ve written about before, GTFS. GTFS has in fact been so successful that it’s becoming shorthand for ‘good data standard’ in the broader sense, as evidenced by a recent tweet from Mark Headd, chief data officer for the City of Philadelphia.

So why build an open zoning data standard? A zoning data standard would allow for zoning visualization apps, like those developed for NYC, to be used across jurisdictions. In cities that sprawl across zoning jurisdictions (or even states, as in DC), a zoning standard would allow for an easy analysis and comparison of how, for example, different cities in the DC metro area are supporting transit oriented development. More broadly, standardizing the way zoning data is shared would allow for it to be mashed up and analyzed with things like public transport accessibility analyses like those for NYC done here, possibly providing an analytical input to the way zoning densities are ultimately set.

So what would an open zoning data standard look like? Unfortunately, zoning data is a fair bit more complicated than transit schedule data. Every city defines land use categories with some local idiosyncrasies built in. In addition, things like density controls can actually be limited through overlapping regulations on setbacks, minimum lot size per dwelling unit, height, etc – and which one of these governs may well vary from individual lot to individual lot. In addition, ‘overlay’ zones are increasingly used by cities to supercede an existing zone and allow for different uses and densities, including in DC. So a zoning standard would need a way to not only communicate information, but a method for interpreting the end result of the different rules and processes in a given zone. More broadly, zoning is an evolving practice, and while what I’m describing is principally the Euclidean type of zoning codified in the 1920s, new forms of zoning that focus on form, performance, and other metrics have started to emerge.

It’ll be complicated, but I don’t know any better way to do this than to start. I’ve laid out a simple table for DC’s zoning code, and by the end of tomorrow’s Open Data Day event in DC I’d like to be able to make a map like this for DC. I’ll be at Open Data Day tomorrow in DC (check the projects list) working on the specifics of DC but eager to talk to anyone interested in the broader issue of open zoning data standards. Should be fun.

Transport, Accessibility, and Open Data

Open data and tools are making an old idea more powerful, democratic, useful, and sustainable.

Cities are a tool designed to bring people, goods, and ideas closer together. Transport systems – sometimes successfully, sometimes less so – are what makes these interactions possible. Despite this, most tools in use by planners today focus on measuring things like faster travel speeds, rather than directly measuring how well a region’s constituent parts are connected to one another.

That’s not to say that no tools exist to analyze what’s often referred to as as a city or region’s ‘accessibility’. The maps below show the most straightforward approach – ‘isochrones’ showing how far you can get in a certain number of hours from London on the British Rail system (these ones made by the brilliant folks at MySociety). Similar maps for public transport systems are also available. Clearly, an improvement in the transport system in an area will expand the ‘contour’ for a given time band. When combined with sources of data on the locations of jobs and population – these maps can reveal which areas are most and least accessible to jobs, activities, and population.

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Although this type of work has been carried out in many contexts for years, until recently the data required to make this type of analysis possible, particularly in developing countries, was hard to come by. Where it was collected, it was often stored in expensive, proprietary software systems, used for the purposes of a specific study, and rarely (if ever) shared. Clearly, despite sometimes vast expense, this is not an approach likely to have an impact on furthering the understanding of urban transport challenges.

Luckily, two data sets have been powering a change in the way this type of work is carried out. OpenStreetMap, the global free map of the world, is the most prominent. OpenStreetMap data is collected by a global community of users and is available under a license that allows for open access and use by third parties. OpenStreetMap can be used to map almost anything you can see, including roads, pedestrian footpaths, and building footprints and heights. As well as providing a solid base data set for those starting out on new projects, OpenStreetMap can also be used as a way of collecting new data (as demonstrated by HOT), helping to share the fruits of data collection beyond the uses they were intended for.

The second is the General Transit Feed Specification (GTFS), the emerging data format that many transit providers (particularly in the US) are using to publish line and schedule information to their users. This is what powers the back-end of many rider tools, including google map’s transit app, and the OpenTripPlanner project. Some – though not all – transit agencies that prepare this data share it freely with the world, here.

Neither of these datasets was designed primarily to support ‘back end’ or ‘planning’ applications. They both now have strong communities of users and developers that suggest they’ll both be around for a while serving their original purposes.  But now – and here’s where things get interesting – they’re both starting to be used in projects that look more and more like tools like urban and transport decision makers can use. One example is the recent work by OpenPlans experimenting with an analyst module built on top of the OpenTripPlanner project. An early demo allowed users to visualize the changes in travel time when the DC metro area’s new light rail line goes into operation, and, even more interesting, was used to visualize changes in accessibility after super storm Sandy shut down much of New York’s transit system.

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At institutions like the World Bank, where I’ve spent most of my working days as a consultant over the past three years, these tools and others like them have clear applicability. By using accessibility metrics, it’s possible to directly measure the effect of a new public transport investment on bringing, for example, low income residents a greater ease of accessing a wider variety of employment opportunities. For cities considering the siting of affordable housing, such tools can help prioritize locations according to their transit accessibility. More broadly, the volume of economic activity accessible in a given area has been shown to have an important impact on stimulating economic growth, highlighting the connections between transportation systems and the overall health of the economy.

Just as critically, the goal of studies examining the issues above is never to do something just once – it’s to support analyses that become an ingrained part and parcel of work done locally, in countries and cities around the world. In the old days, with costly project specific data collection and proprietary tools, this was usually unlikely. Now, with open source tools like OpenTripPlanner Analyst building on open data sets like OpenStreetMap and GTFS, there looks to be a much stronger chance that local agencies can learn, master, and adopt the tools to carry out these accessibility analyses themselves.

So that’s part of what’s exciting: open data and tools mean it’s a lot easier for places all over the world, especially where resources are limited, to have access to amazing planning tools and data sets. But maybe more interesting is the fact that these tools, built with and for the wider public, will enable access to this type of transport-land use analysis for a much broader swath of the public than would previously have been engaged.

This is critical, since people in planning offices, and particularly those (like me) who work in one country but support projects in others, often have to make informed guesses about the most crucial issues to analyze. Even with strong public outreach and engagement, there is always likely to be some bias to focus on understanding ‘big’ issues we know tend to be important: ie access by public transport to jobs. But what if the most critical local issue isn’t the number of jobs accessible from a given location, but the number of people within walking distance of a toilet? This, notably, has been a big issue in Mumbai in recent years.  A city wide map of Mumbai showing how many residents are within walking distance of a decent toilet could highlight areas of clear overcrowding. There are doubtless other critical issues planners havent even thought of – but that are out there in the minds of someone confronting the issue right now. The power of the new generation of tools and data is that any local tech developer can help communities adapt available tools and data sets to share these issues both locally and internationally.

There will never be a better informed source on critical planning issues than local residents themselves.  And, by building on global data sets like OSM and GTFS using open source tools, there’s no reason a tool modified by someone in Mumbai couldn’t help a local resident or planner understand the issues being confronted in Beijing, Manila, or Washington, DC. That’s worth getting excited about.