Gevorg Yeghikyan

@lexparsimon

I am ['an urban data scientist', 'machine learning urban mobility', 'data-driven urban planning', 'a Roman among Greeks']

Vrijeme pridruživanja: listopad 2017.
15 fotografija ili videozapisa Fotografije i videozapisi

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  1. prije 11 sati

    Imagine the breaks out at a random location in the . How will it spread? What can be done to contain it? Read the full post: Visualisation in and

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  2. 21. sij

    What will happen when swarms of flood our and why should planners care. ..the problem of finding the best locations for drone stations in a . Thanks to for osmnx, and Read on:

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  3. 26. pro 2019.

    Did you know that a surprisingly small number of taxi vehicles is enough to cover a large portion of a city? Read on: Thanks to for the awesome osmnx, and for visualisation.

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  4. 25. stu 2019.

    price distributions in , , , , , and [scales differ across the maps]

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  5. 24. stu 2019.

    The end of work? Despite and , global workforce has doubled in the past decades reaching 3.5 billion - much faster than population growth. Unless automation takes place uniformly, will we face a shortage, given the limited pool of rural population?

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  6. 4. stu 2019.

    Infomap detection on the origin-destination of London car trips on a typical day.

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  7. 2. stu 2019.

    car origin-destination flow in Rome.

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  8. 1. stu 2019.

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  9. 29. lis 2019.

    would be interesting to hear your thoughts:)

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  10. 29. lis 2019.

    Which of the following is more likely to become history in 10-15 years from now? Why?

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  11. 18. lis 2019.

    Tentative answer: because in the case of geoeconomic shocks, until the very end we refuse to believe in their possibility.

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  12. 18. lis 2019.

    Why are we scared of the impact of and more than the geoeconomic shocks inherent to the capitalist world-system? WWI&II, deindustrialisation, collapse of the USSR have all caused millions to suffer. Why is the (supposed) suffering inflicted by scarier?

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  13. 17. lis 2019.

    Visualised convergence in distribution of a random variable.

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  14. 15. lis 2019.

    Increased computational capability is also leading statistics in a new direction, away from using "classical" results which are really approximations to avoid computational issues. - US army research office Source with their research program:

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  15. 12. lis 2019.

    An absolutely amazing deepfake! I counted at least 12 overlays. To those who wonder if there will be automated ways to detect deepfakes, the answer seems to be: not anytime soon! Source:

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  16. 11. lis 2019.

    Just wrote a short blog post reproducing in Python the core argument in 's and 's paper, showing how absence of personal risk in asymmetric fat tailed environments can put entire systems in danger.

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  17. 7. lis 2019.

    Code for the visualisation:

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  18. 7. lis 2019.

    How to plan and manage cities not only to survive but also to benefit from such events? Do we need a new paradigm for urban planning and planning in general? What will it look like? Maybe with the concept antifragility () at its core?

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  19. 7. lis 2019.

    However, things become even more interesting, if one introduces rare "shock events", during which the spatial organization changes dramatically. Imagine a city completely unprepared for such events - political, social, cultural change, natural hazards, etc.

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  20. 7. lis 2019.

    an initial random spatial distribution, ghettos form over time. This is an example of a complex system where the system's behaviour as a whole may be completely different from what one might expect from the behaviour of its parts (policy makers?economists? urban planners?)

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