Convert binary attributes into rich numericals

[bkowshik]

Ref https://github.com/mapbox/gabbar/issues/43

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In https://osmcha.mapbox.com/47414802/, a place=village was converted to place=town.

At present, the context we give the machine learning model about this modification is along with other attributes are:

• place: 1
• place_old: 1
• place_modification: 1
• harmful = 1

But, the model has no knowledge on what the modification was to make an effective prediction on whether the feature modification was a 👍 or a 👎. So, how about we convert the binary value representing the modification into better numerical's to aid the model to make a more informed decision.

Popularity from TagInfo

TagInfo provides values for what percentage of place features have say a city as the value. Ex: 0.43% of all place objects on OpenStreetMap are place=city. With this, the model will currently get the following attributes:

• place_new: 24.92% - Percentage of place=village
• place_old: 2.21% - Percentage of place=town

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cc: @batpad @geohacker

[bkowshik]

We now have percentages from TagInfo as values. Ex:

• tourism tag in newVersion of feature has value which 5.54% of tourism features have in OSM
• boundary tag in oldVersion of feature has a value which 89.37% of boundary features have