fig 1 - sss10...0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 2.5 3.5 4.5 paris hong kong lugano geneva singapore...
TRANSCRIPT
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Paris
Hong Kong
Lugano
Geneva
Singapore
London
NYC
Milan
Canberra
Florence
Moscow Chicago
Denver
KEY RESULTS
• This research finds that the worldwide airplane network degree centrality rank-size
distribution exhibits a power law with the alpha parameter equal to 0.774. This is
consistent with other spatial datasets.
• This research also finds a positive association between house price and degree
centrality for each city.
Power Laws in the Worldwide Airplane Network and its Association with House Price
Law Stephen Space Syntax Laboratory, The Bartlett School of Architecture
[1] Batty, M., (2013), The New Science of Cities, MIT Press, Cambridge, MA.
[2] OpenFlights, (2014). OpenFlights Database. Available under the Open Database License. www.openflights.org
BACKGROUND Many variable’s probability distribution in nature follows the bell-shape normal distribution
that centres on the mean with 95% of the data within two standard deviation. Its frequency
distribution is as follow.
In contrast variables in space whose probability distribution often follows a skewed
distribution where the majority of the elements have low values and few elements have
high values. These distribution can be approximated by a power law distribution. Its
frequency distribution is as follow.
The normal distribution is characterised by random evolution over long periods of time.
The skewed distribution on the other hand is shaped by direct competition. Example
includes population in cities, skyscraper height, income or the transportation network.
(Batty, 2013) This research will explore this tendency at the inter-country level using the
worldwide airplane network and secondly to explore its association with house price.
RESEARCH APPROACH This research makes use of open data from openflights.org. The dataset contains an origin
and destination matrix from all cities airport to all cities airport of the world differentiated by
different airlines.
• First, we group all the city’s airport into one city so all the flights going to Narita and Haneda would be
grouped into Tokyo.
• Second, we create a worldwide airplane network.
• Third, we calculate degree centrality for each of the city.
• Fourth, we plot the rank size distribution for this network.
• Fifth, we visualise the degree centrality of the network
• Sixth, we produced a log-log plot between house price with the worldwide airplane degree centrality in
exploring its association.
𝑓 𝑥𝑖 ~ 𝑥𝑖−∝
𝑓 𝑥𝑖 ~ exp [−𝜇 𝑥𝑖 − 𝑥 ]
Fig 1. Degree Centrality of worldwide airplane network
Fig 2. Worldwide Airplane Degree Centrality Rank Size Distribution
Fig 3. House Price and Worldwide Airplane Connectivity
Fig 3
Fig 2
Fig 1
y = 5431.6x-0.774 R² = 0.9422
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Log House Price
Log
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