1. NextPlace: A Spatio-Temporal Prediction Framework for Pervasive Systems
Salvatore Scellato1, Micro Musolesi, Cecilia Mascolo1, Vito Latora, and Andrew T. Campbell2
1 Computer Laboratory, University of Cambridge, UK
2 Department of Computer Science, Dartmouth College, USA
Pervasive’11

2. The ability to predict future locations of people allows
Motivation
The ability to predict future locations of people allows
 A rich set of novel pervasive applications and systems
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Leisure events reports and notification
With pervasive technology, these could be implemented
in a more effective way
avoiding the delivery of information to uninterested users
providing a better user experience

3. NextPlace
A new prediction framework based on nonlinear time series analysis
For forecasting user behavior
In different locations
From a spatio-temporal point of view
Estimate the duration of a visit to a certain location and of the interva ls between two subsequent visits
When they visit their most important places
Do not focus on the transitions between different locations

4. Predictability & Intuition
Any prediction of future user behavior is based on the assumption of determinism
Determinism: future events are determined by past events
Human activities are characterized by a certain degree of regularity and predictabi lity
Because in human societies daily and weekly routines are well-established
Intuition
The sequence of important locations that an individual visits every day is more or less fixed
Example
if a woman periodically goes to the gym on Mondays and Thursdays, she may change her routine for those days, but the changed routine will be more or less the same over different weeks.
Two steps of Next Place
How to isolate the user’s significant places
How to estimate future times of arrival and residence times in the different sig nificant places
Omit the detailed nonlinear prediction model

5. Significant Place Extraction: GPS
Many solutions have been presented in the literature [2,11,18]
Intuition: permanence at a place is directly proportional to the importance that is attrib uted it by the user
Approach: 2-D Gaussian distribution weighted by the residence time at each GPS point
The value of the variance for the Gaussian distributions: 10 meters
Frequency map
Contains peaks which give information about the position of popular locations
Significant places: regions that are above a certain threshold T
(a) Frequency map
(b) Significant places

6. Significant Place Extraction: WiFi
Intuition: the most frequently seen access points are natural candidates to re present significant places
Methodology
Determine a significant place if the user has a sequence of at least n visits to the access point
In the setting: n = 20

7. Predicting User Behavior
Algorithm description
The history of visits of a user to each of its significant locations is considered
For each location, try to predict when the next visits will take place and for how lon g they will last
Procedure
Create two time series from the sequence of previous visits
C = (c1, c2, …, cn), time series of the visit start times
D = (d1, d2, …, dn), time series of the visit duration
Search in the time series C sequence of m consecutive values (ci-m+1, …, ci) that are closely similar to the last m values (cn-m+1,…,cn)
Estimate next value of time series C by averaging all the values ci+1 that follow each found sequence
Select corresponding sequences (di-m+1, …, di)
Estimate next value of time series D by averaging all the values di+1 that follow thes e sequence
Do not consider type of visit place, the visit purpose, correlation of visit place s, …

8. Example
Last three visit of a certain user to a location
Monday at 6:30pm
Monday at 10:00pm
Tuesday at 8:15 am
Find sequences that are numerically close to (6:30pm, 10:00pm, 8:15 pm)
i.e., (6:10pm, 9:50pm, 8:35am) and (6:35pm, 10:10pm, 8:00am)
Assume that the next visits that follow these subsequences
Start at 1:10pm and 12:40pm
Last for 40 and 30 minutes
Estimate the next visit at 12:55pm for 35 minutes

9. Validation: Datasets
Cabspotting: movement traces of taxi cabs in San Francisco with GP S coordinates of approximately 500 taxis
CenceMe GPS: during the deployment of CenceMe[21], at Dartmout h College with GPS
Dartmouth WiFi: extracted from SNMP logs of the WiFi LAN of Dart mouth College campus
Ile Sans Fils: a non-profit organization which operates a network of free WiFi hotspots in Montreal, Canada. Over 45,000 users with 140 h otspots

10. Validation: Careful Choice of suitable threshold
GPS-based: threshold T for frequency map
T: a fraction of the maximum value of the frequency map
T=0.10 for Cabspotting
T=0.15 for CenceMe GPS

11. Validation: Predictability Test
Mean quadratic prediction error
𝜀= 1 𝑁 𝑛=1 𝑁 ( 𝑠 𝑛 − 𝑝 𝑛 ) 2
sn = time series
pn = predicted values
Predictability error: error / variance^2
If this ratio is close to 1, the mean quadratic prediction error is large  no determinism is present
If this ratio is close to 0, the mean quadratic prediction error is small  a high degree of determinism

13. Evaluation Methodology NPm : NextPlace with m = 1, 2, 3
M1, M2: first-order and second-order Markov-based
L: NextPlace with linear predictor
Definition of correctness
If we predict that, at time T, the user will be at location L at time TP = T + delta T
Correct only if the user is at L at any time during the interval [TP – theta, TP+theta]