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Research Agenda

I have been asked on two occasions in the past month to present on the topic of my current and future research activity (both methodological and substantive). These draw together my activities over the past seven years at UCL made up of both PhD and Po…

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AR Controlled Device: Parrot’s AR.Drone

Here is a good item for somebody who has Peter Pan Syndrome.
Parrot’s AR.Drone is an iPhone-controlled quadricopter device. You can control this interesting toy with the very unique interface that iPhone provides and you can even control this object with your iPhone accelerometers. You can sync your iPhone with Pattort’s AR.Drone and then your device will move right along with your iPhone.

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AR Controlled Device: Parrot’s AR.Drone

Here is a good item for somebody who has Peter Pan Syndrome.
Parrot’s AR.Drone is an iPhone-controlled quadricopter device. You can control this interesting toy with the very unique interface that iPhone provides and you can even control this object with your iPhone accelerometers. You can sync your iPhone with Pattort’s AR.Drone and then your device will move right along with your iPhone.

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Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases

Geodemographic classifications provide discrete indicators of the social, economic and demographic characteristics of people living within small geographic areas. They have hitherto been regarded as products, which are the final “best” outcome that can be achieved using available data and algorithms. However, reduction in computational cost, increased network bandwidths and increasingly accessible spatial data infrastructures have together created the potential for the creation of classifications in near real time within distributed online environments. Yet paramount to the creation of truly real time geodemographic classifications is the ability for software to process and efficiency cluster large multidimensional spatial databases within a timescale that is consistent with online user interaction. To this end, this article evaluates the computational efficiency of a number of clustering algorithms with a view to creating geodemographic classifications “on the fly” at a range of different geographic scales.

Muhammad Adnan, Paul A Longley, Alex D Singleton and Chris Brunsdon

Adnan, M., P.A. Longley, A.D. Singleton, and C. Brunsdon. 2010. “Towards Real-Time Geodemographics: Clustering Algorithm Performance for Large Multidimensional Spatial Databases.” Transactions in GIS 14 (3): 283–297. http://dx.doi.org/10.1111/j.1467-9671.2010.01197.x.

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