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So, for us as researchers, educators and citizens, the rise of new spatial technologies over the last half decade or so is really nothing short of revolutionary.

Specifically, I mean the range of interactive internet and mobile technologies that enable collection, compilation, mapping and dissemination of spatial data by vast numbers of people.

So, for instance, the Google Maps API, a fabulous example, a freely available web mapping service that allows nearly anyone with a web connection to compile and visualize information on a map and share that.

So, we've seen map mashups for, used for anything from coordinating austerity protests in the UK, sharing environmental data on the impact of things like gas fracturing, or disseminating map-based artworks that protest evictions for urban redevelopment in China.

That's some great new work by a scholar called Wen Lin.

I also refer to applications like crisis mapping that some of you in this research group are working on, another use application of these digital tool sets.

In crisis mapping, volunteers are using open source tools to build digital data sets for relief workers and government following natural disasters.

The classic example of that is, of course, OpenStreetMaps' effort to crowdsource a base map of Haiti following the earthquake in 2010.

We see other crisis mapping efforts that collect and map real-time, on the ground, observations by citizens, such as in cases of, say, election fraud, voter intimidation and violence.

I also refer to location-based services and the growing number of location-based services that engage us and our smartphones in what I think of as geosocial applications, or as a student of mine once put it, egospatial applications.

Those geographic services that let us publicize our presence at a particular location and learn about the presence of others that we know or might like to know.

A classic example is, of course, Foursquare, but Twitter and Facebook are increasingly focused on disseminating geosocial applications, and then there's a host of services that repackage and work with location-coded data from those services.

These examples constitute the component parts of the geoweb, and I think of that as, in the first part, new forms of data and representation, things like geotagged text and images, geo-visualization that is not strictly or traditionally cartographic.

Another component of the geoweb are its new ways of producing geographic data, such as crowdsourcing and mashup, and then, of course, the hardware and software components that enable all of that.

And the claims that we've seen made about the geoweb in recent years run the gamut from utopian to dystopian and everything in between.

So, you know, we've seen the geoweb heralded as a democratization of cartography, a leveling of the digital divide.

Some have called it a liberation technology that's capable of undergirding progressive social movements.

On the other hand, it's been decried for advancing new forms of harassment and surveillance, for worsening already unequal access to digital information and digital forums for civic participation.

And I'll return to those themes of social impact in a few minutes.

But first, I want to underscore the tremendous challenge that the geoweb poses for GI science, theory, and practice developed over the last three decades.

So the nature of the geographic dimension of the data in the geoweb can be more complex.

GIS is traditionally rooted in modeling absolute location on the surface of the earth with geographic locators that, at least in early forms of GIS, can ultimately be expressed in quantitative terms.

With the geoweb, the geographic component of a given data artifact may well be that traditional form, but it also may be linguistic and location may be relative.

So a place name, Rice Lake, can be used as a geographic identifier or location expressed in relative terms near Rice Lake.

And, you know, as use of these kinds of data in a digital environment, whether for analysis, for data integration or mapping, these linguistic and relative locational terms are much harder to handle in a digital environment.

So near, of course, is highly context dependent as to whether or not we're talking about near this podium or near a burning boxcar.

Right.

Secondly, data structures are considerably different in many geoweb applications than they are in conventional GIS with implications for functionality.

So the geoweb is not based around the relational database and the tabular data of conventional desktop GIS.

And apologies to those of you who do object oriented programming.

That's another kettle of fish entirely.

In particular, the geoweb tends not to have the very tight linkage between the map and the data.

In conventional GIS, of course, every object in your data in your visualization has a comparative record, corresponding record in your database.

And that, of course, is the essence of the kinds of analytical operations you can do in a GIS.

In the geoweb, we can have in some applications one without the other.

You can have a map object that is not linked to an extensive database that you can query for analysis.

Or you can have a collection of georeferenced data that are not linked to a geovisualization.

So the geotagged tweets that are created when you use Twitter's geo application are not natively visualized.

And third, conventional measures and practices for ensuring data reliability and accuracy are not typically appropriate in a geoweb environment.

The traditional approaches were built around a top-down structure for producing and vetting spatial data.

So the practices of agencies like the UK Ordnance Survey, the US Geological Survey, and their assurances of a certain degree of reliability.

User-generated content on the geoweb is at the other end of the spectrum.

This is bottom-up data development.

And we're seeing the need for new accuracy and reliability approaches.

There's all kinds of interesting ones emerging that I don't have time to detail here today.

Automated or algorithmic techniques that flag or throw out data that are simply impossible by virtue of when compared against the context of a data set.

Or Web 2.0 techniques that we saw pioneered by, say, eBay user ratings, that sort of thing.

But the more technical aspects of GI science are only part of the picture when we think about some of the implications of the geoweb.

And I think for a largely human geography audience, they're ones that we can leave at this level for now.