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Entries Tagged 'design' ↓

The Math Behind Peoplemaps

For the last few years I’ve been working on mapping out social relationships in cities with my project I had the chance to speak about this work recently at the TED Global conference, and the talk was featured this week on the website. This has led to many, many inquiries about the project, how it works, and the limits of its applicability.

So I wanted to take an opportunity to explain in high-level terms how I’m doing this work, what it tells us, and what it does not. I’ll attempt here to cover the approach in enough detail so others can reproduce it, without going into deep implementation details which are frankly not important to understanding the concepts. However, I’m happy to collaborate with anyone who would like to discuss implementations.

Theoretical Foundation

In the real world, each of us has personal relationships: our family, friends, co-workers, people we interact with in social settings, children and their parents and friends, and the like. The conventional wisdom is that we can maintain roughly 150 real-world relationships — this is called the “Dunbar Number,” named after the anthropologist who identified this phenomenon. Some people may have more, some people less, but 150 is probably average.

In an online setting, people may have many more relationships — perhaps a few thousand that they interact with in some meaningful way. However, the offline, real-world relationships that people have will likely be an overlapping subset of the relationships they have in an online setting. Online relationships are often more flexible (they can be global and operate at all times of day), though not always as meaningful. Still, if you are able to look at people’s online relationships in a specific geography, you have at least a proxy for understanding their offline, real-world relationships as well.

The ability to infer things about both offline and online interactions is derived from the principle of homophily, which sociologists identified as the powerful tendency for people to cluster into groups of people who are similar to themselves. Colloquially we know this through the saying, “birds of a feather flock together,” and it is powerfully demonstrated in network data.

So if we accept the notion that people do, in fact, have relationships that both shape and are shaped by their interactions, then it follows that there may be some ways to measure these relationships with at least some level of fidelity. Social network data appears to offer at least a window into these real-world relationships, though each dataset has biases which are not yet well understood. However, by comparing results from different networks, we can start to get a sense for what those biases might be.

This is where the state of the art for this research is right now: trying to understand how these different data sets are biased, the nature of those biases, and whether the biases are material in terms of distorting the “real world” network we are trying to understand. However, this lack of perfect understanding isn’t preventing people from using this data to do all kinds of things: from recommending movies to helping you find “people you may know” to identifying terror cells using cell-phone “metadata.” All of these activities use essentially the same approach.

These Maps Are NOT Geographic

A quick caveat: while we typically think of city maps as geographic, these maps are explicitly NOT geographic in nature. Rather, they are showing communities and their relationships to each other. The position of communities relative to the page is always arbitrary, their position relative to each other is determined by the presence or lack of relationships between them. I bring this up now to dissuade any notion you might have that these maps are geographic, despite whatever resemblance (real or imagined) they might have to geography. This all said, there are ways to tie these maps back to geography and use it as an additional investigative tool, but you should assume that all discussion of maps here is non-geographic, unless otherwise noted.

Gathering the Data — and Avoiding Bieber Holes

Depending on the dataset you are looking to explore, the exact details of how you gather data will vary somewhat; I have used data from Twitter, Facebook, LinkedIn, AngelList, email, and other sources. In all cases, you will want to gather a set of “nodes” (people, users, or companies, depending on the data source) and “edges” (relationships between them — typically “friend” or “follow” relationships.)

For the case of the city network maps, this is the approach I have used with Twitter data:

  1. Define a target geography using a geographic bounding box.
  2. Determine a set of keywords and location names that users inside the target geography may use to identify their location/geography.
  3. Identify a set of “seed” users within the bounding box, or which otherwise appear to verifiably be within the target geography.
  4. Determine which user identifiers are followed by a given user, and record that in a list that maintains the number of followers for a given user identifier.
  5. When a given user identifier is followed by a number of people exceeding a threshold, request the full user information for that user to see if it appears to be within the target geography.
  6. If that user is within the target geography, then feed it into step 4, requesting the user identifiers followed by that user.
  7. Repeat steps 4-7 until there are few new additions to the dataset.

Doing this process once produces a “first draft” data set, which can then be visualized for inspection — to look for improper inclusions, obvious exclusions, and any particular data artifacts or pathologies.

At this stage, various problems may appear. As an example, if you are trying to visualize Birmingham UK, you will likely end up with some data for Birmingham, Alabama, due to legitimate confusion which may exist between the two communities. At this point, you can modify the test used to determine whether something is inside the target or not, and regenerate the dataset, as well as perform additional data gathering iterations to get more of the “right” data. This process typically takes a few iterations to really drill down into the data you’re looking for.

One persistent problem I’ve come across is a phenomenon called Bieber Holes, which are essentially regions of the network occupied by Justin Bieber fans. They are so virulent, and their networks so dense, that only aggressive exclusion filters can prevent the algorithm from diving down into these holes and unearthing millions of Beliebers — only a fraction of which may pass location tests. Anyway, I’ve developed good techniques to avoid Bieber holes (and similar phenomena) but it’s a reminder that when working with data from the public with algorithmic approaches, editorial discretion is required.

Laying out the Network Graph

There are dozens of algorithms for laying out network graph data, each optimized to illustrate different properties of the graph. Since I’m primarily interested in homophily and clustering, I’m looking for layouts that express communities of relationships. A good way to do that is to use a force-directed graph layout algorithm; with this approach, relationships act like springs (expressing Hooke’s law), and each user or node repels nearby nodes (expressing Coulomb’s law). By iteratively drawing the graph based on these forces, the graph will eventually reach a steady state which exhibits the following properties:

  1. People with many relationships between them will be arranged into tight clusters.
  2. People with the fewest relationships between them will appear at opposite edges of the graph.
  3. People who have many relationships at both ends of the graph will appear in the middle.
  4. Clusters with few or no relationships between them will appear very far apart on the graph.

You can think of this in very simple terms. If you have a room full of 10 people, there can be a total of 45 relationships between them (n * (n-1)/2, since someone is not friends with themselves). If every person is friends with every other person, this network will appear as a perfect and symmetric “ball” under the rules of a force directed graph layout.

Likewise, let’s suppose that same room of 10 people was grouped into two groups of 5 people, and that those two groups hated each other and refused to speak, but each member of each group knows every other member of their own group. You would see two perfect “ball” layouts (each with 10 relationships expressed), but with no connection between them.

When we visualize data from a city in this way, we are essentially measuring the separateness of communities — whether we see several of these separate groups or whether we see one unified community.

Note that in the example with 10 people in one group, we have a total of 45 relationships, while in the example with two groups of 5, there are only 20 (2 x 10). Scaling that up to a city of 500,000 people, if the city was fully meshed, there would be 124,999,750,000 total relationships, while if that city is segregated into two groups of 250,000, there can only be 31,249,875,000 relationships in each group, or 62,499,750,000 total relationships across both groups, which is a little less than half of the number of relationships possible if the two groups merged.

Detecting Communities and Adding Color

Within the network, we can use algorithms to detect distinct communities. Communities are defined by the number of shared relationships within a given subgroup. We use an algorithm called the Louvain community detection algorithm, which iteratively determines communities of interest within the larger network, and assigns a community membership to each user accordingly.

We can then assign each of these assigned communities a color. For my work so far, I have assigned these colors arbitrarily, with the only goal of visually differentiating one community from another. This helps to generate an aesthetically pleasing visual representation.

While communities often correlate to clustering exhibited by the layout algorithm, for nodes that are not clearly members of only one cluster, color can be used to indicate their primary community affiliation. This is helpful in the visualization because generates blended color fields that can give a sense of the boundary between two communities.

For example, a group primarily concerned with politics (blue) and a group primarily concerned by music (yellow) may have a mix of both blue and yellow nodes in the space between those distinct communities, and you can get a sense that those people are very interested in both communities and which their primary affiliation might be.

Assigning Node Size with In-Degree

In a graph of Twitter users, it can be helpful to indicate how many people are following a given user within our specific graph (note that this is calculated for our subgraph, not taken from a user’s “global” follower count as displayed by Twitter.)

We can do that by making the “dot” associated with each user bigger or smaller based on the number of followers. This has no real effect on the shape of the network, but it can be helpful in determining what kinds of users are where, and how people have organized themselves into communities of interest.

Determining Community Interests

After you have a colored graph with communities and clusters, it’s time to try to figure out what these clusters seem to be around. The first thing to do is to start by manually inspecting nodes to see who they are — often starting with the biggest nodes first. Typically you’ll find that people often organize themselves into groups like these: sports, music, mainstream media, politics, food, technology, arts, books, culture, and the like. These clusterings vary somewhat from city to city, but you’ll see some common patterns between cities.

After communities are detected, we can start to monitor traffic coming out of these communities to look for topics of conversation and other characteristics which may be helpful in explicating the observed clustering. For instance, we can gather a corpus of Twitter traffic for each community, recording:

  1. hashtags
  2. commonly shared links
  3. languages in use
  4. operating systems in use (desktop vs. mobile et al)
  5. client software in use
  6. geographic coordinates (as provided in GPS)
  7. age of user accounts
  8. user mentions

With this kind of data recorded as a histogram for each community, we can start to get a pretty good sense that a given group is mostly concerned with sports, music, politics, and the like. By working with a collaborator who is well-versed with the culture of a given place, we can also get a sense of local subtleties that might not be immediately obvious to an outside observer. These insights can be used to generate legends for a final graph product, and other editorial content.

Investigating Race

Another phenomenon is very apparent in American cities: people separate by race. American cities like Baltimore and St. Louis are polarized into black and white communities, with some people bridging in the middle. These cities present roughly as eccentric polygons, with differences in mesh density at each end of a racially polarized spectrum. Relatively prosperous European cities, like Munich or Barcelona, present more like “balls,” with no clear majority/minority tension displayed. Istanbul, by contrast, shows a strong divide between the rich, establishment and a large emergent cohort of frustrated young men.

In many American cities, we do observe strong racial homophily in the data. For example, in the data for a city like Baltimore, people at opposite ends of the spectrum are generally strongly identifiable as “very white” or “very black.” This is a touchy subject, and it’s difficult to discuss this topic without offending people; however, what we are aiming to do here is to try to understand what the data is showing us, not make generalizations or prescriptive measures about race.

One way to ground this discussion about race is to look at the profile photos associated with user accounts. We can gather these photos in bulk, and when displaying them together, it becomes quickly clear that people have often organized themselves around skin color. When they have not organized themselves around skin color, they are organized around other cultural signifiers like fashion or style. This can give us a concrete sense that certain communities consist primarily of one race or another. However, this is not to suggest that outliers do not exist, or to make any statement either about any given individual — and certainly does not suggest an inverse relationship between race and the ability or proclivity to participate in any given cluster or clusters.

The Final Product

Once we have refined the data, identified communities, investigated trends and topics within communities, and potentially looked at photos, profiles, and other cultural signifiers, we are in a position to annotate the network map with editorial legends. This is a fundamentally human process. I generally use a tool like Photoshop or Keynote to annotate an image, but this could be done in a number of ways. Once this step is done, a final product can be exported.

This entire process of taking data from one or more biased sources, refining data, and then using a human editor who also has a bias produces a final end product which is comparable to an “op-ed” in a newspaper: it’s an expression of one possible mental model of the world, informed by a combination of facts, errors, and pre-existing opinions. It’s up to the reader to determine its utility, but to the extent it offers a novel view and the reader deems the data and editorial biases acceptable, such renderings can be an informative lens through which to see a city.

A Note About Bias, Inclusions, and Exclusions

A common criticism of large-scale social network analysis is that it is not representative because it is biased in some way. A typical statement is, “I don’t use Twitter, so the results don’t include me, therefore I question the validity of the approach.” Likewise, people may say the same thing about LinkedIn or Facebook, or start into a long explanation of how their online habits are very different from their offline reality. These are important facts to consider, but the question is really whether network analysis is representative enough to start to deliver information that we didn’t have until now.

To answer this, it’s helpful to think about this in terms of recent history. In 2004 or 2007, it probably wasn’t helpful to say anything in particular about data from social networks, because none of them had enough penetration to deliver insights beyond a very biased community of early adopters: whether it was geeks in the case of Twitter, or young music fans in the case of MySpace, or college students in the case of Facebook.

As these networks have continued to evolve, however, their penetration is increasing rapidly. They are also accreting a great deal of historical data about people and their position in social networks which give us clues as to their offline interactions. This accretion of data will only continue and begin to paint a more complete and multi-dimensional picture of our culture — especially if you correlate the data from multiple social networks.

I believe we are now at a point where these data sets are large and detailed enough to offer important insights about our “real world” culture. This belief is based on two facts. First, it’s possible to get a good working image of a community by gathering data over just a few hours. While we make every effort to gather as much data as is realistically possible before making statements about a community, the effect of adding more data is additive: we add members to the community, but we do not fundamentally alter its shape.

To understand this effect, it is helpful to consider an analogy from astronomy. A more powerful telescope can yield a better, sharper image of a star formation, but it neither changes the shape of the star formation or our basic understanding of its structure. Better tools simply yield more detailed data. I believe we are at a point where we have enough data to begin to understand structures, and that more data will yield more detailed understanding — but not alter the fundamental shapes we are beginning to uncover. This line of thinking is helpful in comprehending “exclusions.”

On the subject of “false inclusions,” these are generally sussed out in the layout and review process, but all of the graphs I have produced have generated a limited number of false inclusions as a practically inevitable artifact of the process. As a result, any statements one may make about a specific individual based on this kind of analysis may or may not be valid: they should be viewed through the lens of the biases disclosed alongside the visualization. However, repeated experience has shown that removing false small number of false inclusions does not have a material effect on the community structure.

On the subject of “network bias” (I don’t use x, therefore conclusions are not valid), when comparing data from Facebook, Twitter, LinkedIn, AngelList and other sources, the same patterns of homophily are exhibited. While each network has its own bias (LinkedIn towards professionals, Twitter towards both youths and professionals, Facebook towards Grandparents), if we limit analysis to a given geography, we will inevitably see homophilic tendencies which are correlated in each network.

While it’s difficult to speak about this in detail yet, as obtaining full comparable data sets from each source is currently quite challenging, early investigations indicate that the same patterns are exhibited across all networks. This squares up with the notion that what we are really doing in examining these networks is but a proxy for real world relationships. Ultimately, these networks must converge into something that approximates this abstract, Euclidian reality, and as we get more data and correlate it, it’s likely that a unified data set will closely reflect the actual geometry of our communities.

Future Research Directions

This kind of analysis is forming the basis for the emergent field of “computational sociology,” which is currently being explored by a variety of researchers around the world. This work has a number of important implications, and poses questions like:

  • What do we mean by diversity? If we should be looking to bridge networks, is race a helpful proxy for that function? Or should we be looking to develop new better measures of diversity?
  • What is the nature of segregation? Is physical segregation a product of our social networks? Or is it a manifestation of them?
  • What is the role of urban planning? Is our social fabric something that’s shaped by urban planning, or are our cities simply a manifestation of our social fabric?
  • What kind of interventions might we undertake to improve a city’s health? Should they be based first on creating and improving relationships?
  • What is creative capital and how can we maximize it? If creative capital is a byproduct of relationships between people with diverse backgrounds, then we should be able to increase creative capital by orders of magnitude by bridging networks together and increasing the number of relationships overall.

The level of detail we are able to extract from network data in cities is more detailed than any data source we have ever had, and can supplement many other indicators currently used to measure community health and make decisions about resource allocation. For example, the census may tell us someone’s race, family name and physical address, but it tells us very little about their participation in the social fabric. And if we believe that the city is primarily manifested as the sum total of social relationships, then clearly data surrounding social interactions is more useful than other attributes we may harvest.

Discerning Stable Structures vs. Topical Discussions

Likewise, many researchers have been using social network data like Tweets to characterize conversations around a topic; and it is true that by harvesting massive quantities of Tweets, one can discern trends, conversation leaders, and other insights. However, this type of analysis tends to be topical and shift a great deal depending on whether people are active online, and contains biases about why they might be active online. Regardless, this kind of research is well worth understanding, and may ultimately lead to a better understanding of network structure formation. However, I want to differentiate it from the kind of inquiry I am pursuing.

So, rather than “you are what you tweet,” I believe the truth is something more like “you tweet what you are.” Network follow structures tend to be very stable; while they do change and evolve over time, one interesting feature of the process I am using is that it tends to be very stable over time and the results of analysis are repeatable. That is to say, if I analyze Baltimore one day, and then repeat the analysis a month later, I will obtain a comparable result. This is not likely to be the case with semantic analysis as topics and chatter may change over the course of a few weeks.

Tracking (and Animating) Changes Over Time

Because network structure analysis is fundamentally labor intensive and somewhat difficult to compute, it’s difficult to automate and to perform on an ongoing basis. For example, it would be nice in Baltimore to know whether things are getting “better” or “worse.” Right now, only by taking periodic snapshots and comparing them can we get a sense for this. It would be helpful to be able to animate changes over time, and while this is theoretically possible, tools to do this have to be built by hand right now. My hope is to apply such tools to the process and dramatically increase our ability to monitor network structure in real time, and spot trends.

Ultimately, this will give us some ability to see our social structure change in near real-time, developing a sense as to whether interventions are having a positive effect, or any effect at all.

Healthy vs. Unhealthy Network Patterns – and Brain Development

Dr. Sandy Pentland, an MIT researcher who is perhaps the leading figure in the field of computational sociology, has suggested that there are certain patterns that characterize “healthy” networks, as well as patterns that characterize “unhealthy” networks.

Healthy networks are characterized by:

  • Frequent, short interactions
  • Broad participation by all nodes and meshing
  • Acknowledgement of contributions
  • A tendency to explore other parts of the network

By contrast, unhealthy networks exhibit the opposite patterns: broadcast vs. peer-to-peer interactions; fewer network connections; isolated subnetworks; a lack of exploration of other parts of the network.

Perhaps the most telling finding is that these patterns are not limited to just human networks, but also appear in other colony-based organisms, like bees. It appears that there are universal patterns of network health that apply to life in general.

The other important finding is that lack of network exploration affects brain development in young people. Specifically, young people who grow up in an environment where network exploration is not valued tend to exhibit structural changes in the brain which appear to dampen their desire to explore networks as adults. This produces a multi-generational effect, where children who grow up in isolated networks tend to persist in isolated networks, and to pass that on to their children.

This suggests that one possible intervention is to promote network exploration at an early age across the entire population. How we might do this is certainly open to discussion, but it seems to be a very powerful tool in breaking down divisions in our social networks.

What’s next?

As this project advances, we are gathering ever-larger datasets. This requires more and more computational power and distributed algorithms for visualization. If this is something you’re also working on, I would like to talk to you — please contact me. There are some interesting challenges in scaling this up but there are interesting opportunities emerging to apply these approaches!

Mapping Your City

We have a long list of places that we’re looking at mapping, and trying to prioritize opportunities. If you have a mapping project you would like us to consider, please contact us. We will try to get back to you as quickly as possible, but in general, we’re looking for projects that can make a serious social impact. It takes a serious amount of effort to generate these maps, so be thinking about possible partners who could potentially fund this work and help advance this science.

Don’t Change the World, Select a Better One

Much has been said about entrepreneurs and their oft-stated ambition to “change the world.” Making money is nice, they say, but they really want to “put a dent in the universe,” as Steve Jobs once said.

Some see this as high-minded conceit: Change the world? Through tweets and hashtags? Photo oversharing? A world where everyone is constantly staring at their phones? Is this change actually for the better? All valid questions, to be sure.

I’ve given this a lot of thought over the years, and while “changing the world” might be a bit of an overreach, one thing is true: Entrepreneurs are the people who get to decide which universe everyone else lives in.

And this has some other deeper implications.

Quantum Effects

The degree to which quantum physics has any impact on our everyday world is hotly debated by philosophers and physicists. But there are some ways we can interpret quantum effects as entrepreneurs that are interesting.

One theory is that there is an infinite number of parallel universes, and the world we live in is just one of many possible worlds. So who decides which universe the rest of us will live in? To be sure, political and military leaders do, but so do writers, musicians, scientists, inventors — and entrepreneurs.

When you think about it, we all have the power to propose something new. These proposals may be modest, or they may be starkly different from what has come before. Our ability to nurture these new proposals and steer our world into the universe where these new ideas take root is really the fundamental act of entrepreneurship.

So in some sense, entrepreneurs are quantum time travelers — able to steer the world into a new dimension that might not otherwise have existed.

Can you imagine the world we’d be living in if Steve Jobs hadn’t willed the iPhone and iPad into our universe before he passed on? It would be substantially different (and Microsoft’s stock price would certainly be higher).

Entrepreneurs aren’t the only people who have this power to shift us into a new universe: anyone can do it. Whether you’re a nonprofit worker, a lab scientist, a musician or just a rank-and-file employee in a company, there is almost always a way to start with what you have, what you know and who you know, and transform it into something that’s fundamentally new.

It is the collective output of those who dare to make these proposals that defines the shape, rhythm, texture and nature of the world we live in. That puts each of us in a position of incredible responsibility and power; for if each of us is capable of steering the planet in a new direction, then why aren’t we doing it?

Effectual Reasoning

Entrepreneurship researcher Dr. Saras Sarasvathy coined the idea of “effectuation,” a theory that states that entrepreneurs use a particular kind of logic in advancing their proposals.

Essentially, they gather up everything they are, have and know, and then they place a bet on a next step. That bet will typically have a higher upside than downside, and they can afford to continue if they lose the bet.

Following this continuously almost always means you can advance an agenda and make it grow. And Sarasvathy discovered that this is exactly how expert entrepreneurs operate. They don’t typically have a big master plan. They just execute a series of modest bets, and they’re aided by the people and experience they gather along the way. That’s it.

I find all this incredibly empowering; we have a simple recipe that enables us to continuously and collectively select a better universe than the one we are currently living in. And anyone can do it, just by thinking the way entrepreneurs think.

The money that comes from creating a positive change is a nice side benefit, but it’s hardly the point. Money’s just a tool to enable us to keep selecting a better world.

So can we change the world? Perhaps not. But we can each help to choose a better one. And that’s true power.

This piece originally appeared in the December 2013 issue of SmartCEO Magazine (Baltimore).

The Real Republican Adversary? Population Density

The 2012 election demonstrated what many people could have guessed: rural states voted for Romney while densely populated states voted for Obama.

Many have offered explanations — everything from the presence of top universities in cities, to the prevalence of immigrant and African American populations. Perhaps the Republicans should consider running a Hispanic or African American candidate in 2016; but will that really help? Is identity the issue, or is it more about values?

Or is something more basic at work? Studying election results county by county, a stunning pattern emerges.

Population Density: the Key to Voting Behavior?

Curious about the correlation between population density and voting behavior, I began with analyzing the election results from the least and most dense counties and county equivalents. 98% of the 50 most dense counties voted Obama. 98% of the 50 least dense counties voted for Romney.

This could not be a coincidence. Furthermore, if the most dense places voted overwhelmingly for Obama, and the least dense places voted overwhelmingly for Romney, then there must be a crossover point: a population density above which Americans would switch from voting Republican to voting Democratic.

So I normalized and graphed the data, and there is a clear crossover point.

At about 800 people per square mile, people switch from voting primarily Republican to voting primarily Democratic. Put another way, below 800 people per square mile, there is a 66% chance that you voted Republican. Above 800 people per square mile, there is a 66% chance that you voted Democrat. A 66% preference is a clear, dominant majority.

So are progressive political attitudes a function of population density? And does the trend hold true in both red and blue states?

Red States and Blue States

Separating the results from red states and blue states, we can see that while each has a slight preference for their ultimate candidate of choice, on a local level voting behavior is still directly correlated to population density.

Studying this graph, two important facts are revealed. First, there are very few cities in red states. Second, the few dense cities that do exist in red states voted overwhelmingly democratic.

Atlanta, New Orleans, St. Louis, Dallas, and Indianapolis are all in red states — and they all voted blue. And there are no true “cities” in red states that voted red. The only cities in red states that didn’t vote blue were Salt Lake City and Oklahoma City. And by global standards, they are not really cities — each has population density (about 1,000/sq. mi.) less than suburban Maryland (about 1,500/sq. mi.).

Historically, one can argue that red states have disproportionately affected election results by delivering a material number of electoral votes.

Red states simply run out of population at about 2,000 people per square mile. St. Louis is the only city that exceeds that density in a red state. It voted overwhelmingly Democratic (82.7%). In contrast, blue states contain all of the country’s biggest and densest cities: Washington DC, New York City, San Francisco, Baltimore, Milwaukee, Philadelphia, Boston, etc.

Red States Are Just Underdeveloped Blue States

As cities continue to grow in red states, those cities will become more blue, and ultimately, those states will become more purple, and then blue. The Republican party says it’s about growth and prosperity; the best way to achieve that in red states is through the growth of cities.

If you follow the red state trend lines, you can clearly see that any dense, fast-growing cities that might emerge in red states will be very likely to vote blue. The few that do already exist already vote blue. How would these new cities be different and cause them to vote red?

Red state voters generally prefer low-density housing, prefer to drive cars, and are sensitive to gas prices. Once population density gets to a certain level, behaviors switch: high-density housing is the norm, public transit becomes more common, and gas use (and price sensitivity) drops.

Red state values are simply incompatible with density.

Cities Are the Future

Globally, cities are growing rapidly as people move from rural to urban areas in search of opportunity. By 2030 it’s estimated that cities will grow by 590,000 square miles and add an additional 1.47 billion people.

Only subsidized suburban housing and fuel prices are insulating the United States from this global trend, and even with these artificial bulwarks, there is no good reason to think that America’s future lies in low-density development.

Density is efficient. Density produces maximum economic output. An America that is not built fundamentally on density and efficiency is not competitive or sustainable. And a Republican party that requires America to grow inefficiently will become extinct.

While the Republican party is retooling in the desert, it should carefully consider whether its primary issue is identity politics or whether its platform is simply not compatible with the global urban future. If that’s the case, an Hispanic candidate running on the same old Republican platform will simply not resonate. The Republican party must develop a city-friendly platform to survive.

Cities are the future and we need candidates from both parties that understand that reality.

The next question: why does population density produce these voting behaviors? Is the relationship causal or correlated? Probably both. I’ll explore this in my next post.

Data Source: US Census 2010 (population density by counties); election 2012 results by County.

The Opportunity Baltimore Is Missing

There’s been an explosion of interest in new “startup accelerators,” incubation, coworking, startup funding, and new-manufacturing efforts in Baltimore in the last few months; unfortunately this appears to say less about Baltimore than it does about the growth in interest in these efforts worldwide.

Here’s a list of some efforts in this space:

  • “Accelerate Baltimore” at ETC Baltimore
  • Accelerator led by Cangialosi and Lane
  • ETC Baltimore itself (Canton and 33rd street)
  • Baltimore Node, Hackerspace on North Avenue
  • Sizeable Spaces, coworking in South Baltimore
  • Capital Studios, coworking on Central Avenue
  • Beehive Baltimore, coworking at ETC Baltimore
  • Accelerator effort being driven by Mike Brenner
  • Accelerator/cyber/techspace in Harbor East, led by Karl Gumtow
  • Innovation Alliance effort being led by Newt Fowler
  • Theater/workspace being discussed by Chris Ashworth/Figure 53
  • Shared warehouse workspace being discussed by Andy Mangold/Friends of the Web
  • Baltimore Angels (Cangialosi et al)
  • Invest Maryland fund (DBED)
  • TEDCO’s Innovation fund
  • Abell Foundation fund (tied to Accelerate Baltimore)
  • Wasabi Ventures fund (investing in city, affiliated with Loyola)
  • Fabrication Lab at Towson University
  • Fabrication Lab at CCBC
  • Fab-lab ideas discussed by John Cutonilli
  • Highlandtown workspace development led by Ben Walsh
  • Mike Galiazzo, pushing Local-Made, (head, Regional Manufacturing Institute)

Did you know about all of these things? Amazingly, many of the people leading these efforts don’t. Or if they do, they’ve not actually talked to the people involved. To me, this is a problem.

Why? Because folks attempting to gather support for these efforts don’t have all the facts. They either haven’t sat down and listened to people’s motivations, and they’re flying blind. Or it means that they have been unable to sell other like-minded entrepreneurs on their vision, which probably means their vision is not that compelling. And that’s even worse.

But this is not all that’s wrong.

Two Serious Problems

One: there’s a tremendous amount of duplication of effort represented in the list above. Why duplicate all of that administrative, accounting, legal, and governance overhead? By pooling more of these efforts together, that overhead can be minimized and shared.

Two: we don’t have enough human capital to support all of these different efforts. We simply DON’T. Many seem to think it will somehow materialize, but from where I sit, with possibly the widest-angle view of the landscape here of anyone, I don’t see that flow of new startups or even new individuals that can support all of this. It just doesn’t exist.

The Opportunity

Baltimore has an opportunity to become a regional and even international destination for people looking to start or join entrepreneurial enterprises. But for that to happen, we need to have stuff here that can actually become a destination.

And unfortunately, the efforts currently underway are not likely to become that destination because duplicated overhead will keep each effort small and parochial.

However, if more of these efforts pooled their resources and talent – and most importantly identified a BIGGER and more IMPORTANT vision for what it is they are trying to achieve, there would be many positive effects, such as ample governmental and foundation support. And that would be hugely helpful in funneling in the sorely lacking regional and international *human capital* that we so desperately need here!

One Possible Vision

Baltimore has an opportunity to become the hub for digital manufacturing and mass-customization technology on the east coast.

Cangialosi and Lane are already talking about supporting some basic fabrication capabilities at their proposed facility on Key Highway. Gumtow’s effort has placed fab-lab capabilities high on its priorities list. CCBC and Towson have fab-labs, though it’s my understanding they may be underutilized. If you’re going to spend money on fabrication equipment at all, it should be utilized 24×7 in order to maximize the asset.

Something bigger – like taking over the WalMart in Port Covington, or the Meyer Seed Warehouse in Harbor East – could support an accelerator, fab lab, and shared workspace. Thinking a little bit bigger would also have the effect of lowering per-square-foot costs dramatically, and even dramatically altering the real-estate ownership structure.

Baltimore is already home to Under Armour, and at some point in the near future (similar to what happened with it will start throwing off new entrepreneurs with experience in consumer products and manufacturing. Where will they go? Will we keep them here in Baltimore?

Focusing on the intersection of manufacturing and technology is important because it represents the one shot we have at rebuilding even a little bit of a middle class here in Baltimore. Because of that, you’ll find abundant support for such efforts — support that can further reinforce Baltimore’s reputation as an international destination for digital and manufacturing.

The More the Merrier?

I am a fan of placing many, diverse bets rather than making a few large ones. But it’s also important to make strong bets. Unfortunately, Baltimore is right now setting itself up to have many weak positions instead of a smaller number of stronger ones.

I strongly urge the folks leading these efforts to get to know each other and coalesce around a bigger unifying vision that can turn Baltimore into an important regional and international destination for entrepreneurs.

Because without agreeing on a bigger vision, it’s likely that these efforts – each led by well-meaning individuals but with individual motivations – won’t ultimately amount to much, and it would be a shame to waste so much time, effort, and talent.

Thanks to Brian LeGette for his collaboration on some of the ideas underlying this post. Also, everyone on this list is a friend: happy to make introductions and advance the conversation.