In a joint project with the World Bank, USAID, and numerous other partners, there are now 6 TB hard drives on the ground in Haiti with mapping tools and satellite and remote imagery data being shared with the Haitian government. Read more about the project on the FortiusOne blog.

Schuyler Erle and Tom Buckley will be heading down on Tuesday to provide on the ground support between the government agencies and the community.

A tremendous thank you to the numerous individuals and groups that helped and provided tools or data: World Bank, San Diego State University / Calit2, Internet2, Georgetown University, DigitalGlobe, Delta State University, Sahaha, Crisis Mappers, OpenStreetMap, NOAA, Ushahidi, DevelopmentSeed, TelaScience, STAR-TIDES, CrisisCommons, USAID, GeoCommons, OpenSGI, GeoEye.

The last 2 days have been filled with coordinating various efforts in gathering information and volunteers responding to the massive Haiti earthquakes of January 12. The analysis team at FortiusOne has put together a news dashboard highlighting the event and current response efforts.

There have been several tremendous groups that have actively been contributing data and tools both with remote developers and responders on the ground. CrisisMappers, CrisisCommons, Ushahidi, OpenStreetMap, just to name a few.

Many data providers have been making their data freely available. This is most notable when looking at Mikel’s screenshots of OpenStreetMap before the quake and after volunteers began tracing over historic maps and newer satellite imagery from Digital Globe and GeoEye.

Other efforts:

• Ushahidi Haiti is crowd-sourcing reports. You can send a text message to 447624802524, send an email to haiti@ushahidi.com, or send a tweet with the hashtag/s #haiti or #haitiquake.
• The CrisisCommons Wiki has a list of available data and organizations
• Sahana has a form to list offices and organizations that are working on the ground
• GeoCommons search for Haiti has all the datasets and maps that people have contributed for download as Spreadsheet, Shapefile, KML, and more
• OpenStreetMap’s Project Haiti has a list of datasets and people tracing data

There is clearly a movement to openly share data from numerous data sources: governments, organizations, Web sites, individuals, and devices. Users are more easily able to publish data through collaborative sites, or find and download data that they can use to remix, reapply, reuse, and extend. The trajectory of open data sharing and utilization parallels the development of open-source, where the potential magnified impact of open sharing and collaboration yields far great outcomes.  

However, unlike the open source world, the legal and cultural frameworks in which to share data have not yet emerged. In code, there are a gamut of well known and widely used licenses: GPL, BSD, MIT, Apache, and more. While each has unique characteristics, their overall meaning and implications are easily understood by developers and comply with business operations that wish to use open-source software. The licenses are each unique, and can sometimes be confusing, yet there is a small vocabulary of regular licenses that developers can easily picked and choose.

In the media world, Creative Commons developed an ingenious mechanism of licenses with clear verbage and branding that makes it readily accessible by nearly anyone. With miscible license options such as “Share-Alike”, “No-Derivatives”, and “Non-Commercial”, media producers and consumers can clearly mark appropriate uses of their works. The impact is most clearly seen on content sharing sites such as Flickr and Slideshare where users can choose from a very small list of licenses to publish information, or search for information under certain terms.

Because of these attractive features of understandability, small set, and branding, Creative Commons is increasingly utilized to openly share data. However, the Creative Commons apply only to creative works: stories, songs, photographs, and other media and as such are not truly valid when applied to databases.

Instead, the current landscape of data licenses are all completely unique, incompatible, and difficult to understand. This situation is further complicated by the existing data business ecosystem that thrives on charging large amounts of money to write, verify, and mix unique data licenses and prescribe legal uses of multiple combined data sources.

The implications are that the situation persists and groups that would like to share, or use, open data are relegated to complex, and expensive, legal counsel, or must accept risk and hope they remain within compliance, or at least outside of notice.

There are currently only two potential solutions currently in development. Creative Commons has developed CC0, (”C, C, Zero”) – which essentially removes all copyright from a work or data. Flickr utilizes the CC0 license for their derived boundary datasets.

The other upcoming option is the Open Database License (ODbL), which is being put forth by the OpenStreetMap community in order to create the equivalent Creative Commons, By-Attribution, Share-Alike (CC-BY-SA) for databases.

However, even these two licenses have problems. CC0 is drastic in that it removes all copyright from the data, and so may not work with anything less than full, global release of data. Alternatively the ODbL is criticized for being “too left”, where there is an unclear potential that any utilization of data such as from OpenStreetMap would subsequently have to be released. This is similar to the GPL licenses, or “viral” licenses.

What is missing is a clear set of data applicable open licenses that would allow anyone to easily demarcate the terms of the data they are releasing, and provide confidence to data consumers that they are in compliance with the data rights. The effect will be to allow data to more easily and justifiably be made available as well as tools to interact with this data. It will also address the many questions around collective, or combined, databases and derivative works, such as when deriving vector data from satellite imagery.

A few weeks ago I spoke on a panel at the OGC Summit on Spatial Law and Policy, which is one effort to build a community of developers, companies, data providers, and legal experts to address just such a need. In particular times of disaster response illuminate the immediacy of clear data sharing, which was the focus of the panel, but also more long term use of such data, and derivative works such as in rebuilding and recovery after an event.

I’ve discussed the pitfalls of licensing and Creative-Commons style modules before, which raised some initial questions. But in the year since then nothing has really changed towards this broader vocabulary. Will Creative Commons merely become the de-facto, if non-applicable, licensing? For example, the Ordnance Survey just released data under Creative Commons-Attribution 2.0 UK. I would be interested to hear more about other efforts that are seeking to create a simple, clear set of data licenses. In addition, how else have you dealt with confusing, and complicated data licensing issues in building new datasets, applications, or use cases?

A week ago the New York Times ran an interesting opinion article on the new NYC interactive map. I’m sure it’s been discussed elsewhere, but wanted to make sure and highlight some of the keen insights Mr. Klinkenborg offered. It echoes my feelings that we have done very well at putting static map images into digital interfaces, but are only just beginning to make these maps dynamic and linked – like any medium on the internet – explorable, annotated, and dynamic.

The map itself at first is not very impressive by modern digital mapping expectations. It has simple smooth panning or zoom, with an interface reminiscent of MapGuide style portals.

Where it becomes impressive is after turning on some of the layers of public safety, services, and infrastructure that simple markers that open on hover make it very easy to move around and discover information and links to other municipal databases such as census, architecture, neighborhoods, polling information, lot information, and much more – all without overloading the user. The impressive connection of so much data, especially in a city of the density of New York, is impressive.

The map is an example of simplicity, familiar interfaces, and rich data presentation that As Mr. Klinkenborg states,

There is a pleasing logic to this kind of organization, to layer after layer of data embedded within a scalable map. In a sense, it approximates how we tend to know the world… Think of returning to your neighborhood after a trip or driving to your parents’ house. You can almost feel the increasing depth of your knowledge as the terrain becomes more familiar. What you know isn’t just the superficial arrangement of streets and highways. You have a rich array of geographically organized information, some of it practical — how far to the good grocery store — and some of it emotional.

Obviously I would hope the underlying data is also made available. Imagine if the map existed as a feed of data sources that linked to one another – any queried point returned a GeoJSON item that linked to the Sanitation Collection Schedule in GeoRSS, Elected Officials as hCards, Building outline as a KML, and lot information as GML. The map portal is just a single, simple entry point into this information that the NYC.gov can guide and control. However, the data can and will be available via any number of interfaces that go beyond the device itself, but provide for a seamless integration of this information at our fingertips to query and drape over the very urban landscape as we navigate and interact with it.

Mr. Klinkenborg summarizes,

It’s easy to assume that the real revolution in mapping is the global positioning satellite and Google revolution… But the real revolution lies in the layering of data onto these already kinetic methods of viewing the world. In a very real sense, the virtual planet becomes our index to what we know about the actual planet.

I’m looking forward to future incarnations that include boundaries estimating daily resident happiness, suggesting cultural relevance, and heatmaps of amount of sunlight and sky-view.