Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Choosing the right home location definition method for the given dataset   Ever since first mobile phones equipped with GPS came to the market, knowing
the exact user location has become a holy grail of almost every service that
lives in the digital world. Starting with the idea of location based services,
nowadays it is not only important to know where users are in real time, but
also to be able predict where they will be in future. Moreover, it is not
enough to know user location in form of latitude longitude coordinates provided
by GPS devices, but also to give a place its meaning (i.e., semantically label
it), in particular detecting the most probable home location for the given
user. The aim of this paper is to provide novel insights on differences among
the ways how different types of human digital trails represent the actual
mobility patterns and therefore the differences between the approaches
interpreting those trails for inferring said patterns. Namely, with the
emergence of different digital sources that provide information about user
mobility, it is of vital importance to fully understand that not all of them
capture exactly the same picture. With that being said, in this paper we start
from an example showing how human mobility patterns described by means of
radius of gyration are different for Flickr social network and dataset of bank
card transactions. Rather than capturing human movements closer to their homes,
Flickr more often reveals people travel mode. Consequently, home location
inferring methods used in both cases cannot be the same. We consider several
methods for home location definition known from the literature and demonstrate
that although for bank card transactions they provide highly consistent
results, home location definition detection methods applied to Flickr dataset
happen to be way more sensitive to the method selected, stressing the paramount
importance of adjusting the method to the specific dataset being used.

31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg