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Instead of location polling, use location subscription event. I.e. onLocationChanged()
Use the accuracy "medium".
The table below outlines the accuracy options per platform:
lowest = 500m - 3000m
low = 500m = - 1000m
medium = 100m - 500m
high = 0 - 100m
best = 0 - 100m
bestForNavigation = 0 - 100m -> Optimized for navigation
Use a location clustering method to reduce the # of location data to record. Sample logic is given below:
What if we decide on the alert radius? Is it 100m or 200m or etc? If we think of this in latlong, 1 second ~= 25m. I.e. if we round off the users latlon by last 2 seconds, we can roughly say the person is in a square of 4 x 4 seconds (i.e. 100m x 100m). It's like laying a mesh of 100 x 100m2 on top of Sri Lanka and we mark which square the user is in.
Also, we can keep a moving window of 3-5 past squares and avoid flip-flop between 2 squares, when the user is in a boundary.
The text was updated successfully, but these errors were encountered:
Instead of location polling, use location subscription event. I.e. onLocationChanged()
Use the accuracy "medium".
The table below outlines the accuracy options per platform:
lowest = 500m - 3000m
low = 500m = - 1000m
medium = 100m - 500m
high = 0 - 100m
best = 0 - 100m
bestForNavigation = 0 - 100m -> Optimized for navigation
Use a location clustering method to reduce the # of location data to record. Sample logic is given below:
What if we decide on the alert radius? Is it 100m or 200m or etc? If we think of this in latlong, 1 second ~= 25m. I.e. if we round off the users latlon by last 2 seconds, we can roughly say the person is in a square of 4 x 4 seconds (i.e. 100m x 100m). It's like laying a mesh of 100 x 100m2 on top of Sri Lanka and we mark which square the user is in.
Also, we can keep a moving window of 3-5 past squares and avoid flip-flop between 2 squares, when the user is in a boundary.
The text was updated successfully, but these errors were encountered: