Kaggle datasets in Rhino

datasets

melbourne housing dataset.csv from kaggle.

this was using that older dataset (link to kaggle) with 9 features. coordinates were polled from Google’s geocoding API based on the address in the dataset.  I believe the updated dataset provides coordinates too, possibly using the same method described.

number of rooms

showing different ranges of number of rooms per unit

kopt

‘if i’m looking for houses that are between 5 to 7 rooms, the newest ones are the light yellow ones on the northeast edge of the city.’

other details that are inherent by linking an excel datasheet to coordinates in the real world:

contour

height of property, contour of surrounding land

watershed

flow of water through property and general direction of water flow. (flash floods, landslides possibly?)

street view

pictures around the site (from Google Street View)

google directions

of course, different methods of transport to and from the city

clouds

weather data at a given time (historical data is paywalled, so i couldn’t access it)

soil_cropped.gif

soil conditions and suitability for certain forms of construction

aussie

…and some really zoomed out GIS level datasets (i’m still wondering what to do with them.)

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Author: Tang Li Qun

Design Architect, Computational Designer, 3D Generalist, Machine Learning enthusiast, Tinkerer

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