syre / inferring-social-networks-from-geographic-coincidences Goto Github PK

The corr feature cannot be implemented right now because of the number of possible spatial locations is extremely large (60+ billion), this can be alleviated with sparse arrays but correlation cannot be computed to our knowledge with sparse arrays!

Is this an issue?

Right now cooccurrences can be counted several times for each spatial and timebin, they should only be counted once (check the crandall paper!)

Start the reverse geocoding script "geocoder_script.py"

When making histograms allow to set a self-defined max value (steps will then count up to that value), and the last step will be a bin of > max val

x-axis is for example [0-50000,50000-100000,300000-350000]

Statistics over co-occurrences

Advantages/Disadvantages of using only 2 decimal degrees precision (ease of computation, might include a spatial bin which covers a lot more area than necessary) vs full precision

Filter data so we only get data where 0 <= accuracy <= y, where y=100.000 (mm)

Maybe y=y+20.000

Right now we classify a friend based on if they have more cooccurrences than 5 and that they are alone in those cooccurrences, is this approach fine? (it doesn't account for people with lots of cooccurrences)

Right now in our code one user can have multiple nationalities, to make spatial binning for a single country possible we need to assign a single nationality for a user (preferably based on which country is most occurring in his location updates)

right now a timebin size is defined by the range (start_time - bin_size/2) to (end_time + bin_size/2) which leads to varying timebin sizes

Create histogram/boxplot for features which we want to investigate, suggestions welcome @handiandi !

Review code for co-occurrences

Friends should perhaps be defined based on percentage of cooccurrences rather than just a fixed value

Histogram for co-occurrences binned by day, where do co-occurrences happen most frequently?