1. Geo-Spotting: Mining Online Location-based
Services for Optimal Retail Store Placement
Dmytro Karamshuk
King's College London
Based on the paper:
D. Karamshuk, A. Noulas, S. Scellato, V. Nicosia, C. Mascolo. Geo-Spotting: Mining Online
Location-based Services for Optimal Retail Store Placement. ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD), Chicago, 2013

2. Optimal Retail Location Problem
Among L possible locations in the city select one where
a new store would be most profitable/popular.

3. Optimal Retail Location Problem
The problem is not new
●
A. Athiyaman. Location decision making: the case of retail service development in a
closed population. In Academy of Marketing Studies, volume 15, page 13, 2010.
●
O. Berman and D. Krass. The generalized maximal covering location problem.
Computers & Operations Research, 29(6):563–581, 2002.
●
A. Kubis and M. Hartmann. Analysis of location of large-area shopping centres. a
probabilistic gravity model for the halle-leipzig area. Jahrbuch für
Regionalwissenschaft, 27(1):43–57, 2007.
●
Pablo Jensen. Network-based predictions of retail store commercial categories and
optimal locations. Phys. Rev. E, 74:035101, Sep 2006.
Our approach:
explore fine-grained and cheap data from LBSN

4. Location-based social networks
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check-in at places
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share with your friends
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receive bonuses for check-ins
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search for places
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leave comments for others

5. Check-ins around the world
over 40M users
over 4.5B check-ins

6. Collecting the Data
Dataset collected in New York
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37K venues
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47K users
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621K checkins
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May – November, 2010
accounts for »25% of the original data

7. How popular is a venue?
The distance between the two places is only few hundred meters

8. How popular is a venue?
Distribution of check-ins per place
Geographic distribution of venues
size = #checkin
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popularity can be several orders of magnitude different from place to place
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probably it depends on the location and types of places

9. Popularity and type of venue
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different types and chains of
venues have different usage
patterns
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we cannot compare check-ins
across venues of different
chains but we can across
individual chains
Number of check-ins per place for
individual chains of restaurants

10. Co-location with other venues
How frequently we observe a Starbucks close to a
railway station?
Does it influence the popularity of a restaurant?
Pablo Jensen. Analyzing the localization of retail stores
with complex systems tools. IDA ’09, pages 10–20, Berlin,
Heidelberg, 2009. Springer-Verlag.

11. User mobility between places
How many users go to a Starbucks
after railway station?
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there is correspondence between colocation and mobility patterns
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but also many discrepancies

12. Optimal Retail Location Problem
Among L possible locations in the city select one where new store would be most popular.

13. Define the area
An area is defined as a disc of radius r around a point with geographical coordinates l
The area is described by a set of numeric features
check-ins at venues in the disk.
designed from

14. Geographic features of an area
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density – number of venues in the area
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neighbors entropy – heterogeneity of venue types
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competitiveness – percentage of competing venues

15. Geographic features of an area
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quality by Jensen
–
define inter-types attractiveness coefficients
–
weight surrounding venues by their attractiveness

16. Mobility features of an area
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area popularity – total number of checkins in the area
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transition density – intensity of transitions inside the area
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incoming flows – intensity of transitions from outside areas

17. Mobility features of an area
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transition quality
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define transition coefficients for each type
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weight venues according to the product of coefficient and
check-ins volume

18. Ranking problem
Use area features
to rank all areas in a given set L
according to their potential popularity.
Compare with the ground truth: ranking of places basing
on their actual popularity.

19. Evaluation metrics
Compare the predicted and ground truth rankings.
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Top-K locations ranking – use NDCG@K
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Accuracy of the best prediction – Accuracy@X% of having the
best predicted store in the Top-X% of ground truth ranking
We explore random cross-validation approach and report
average values across all experiments.

20. Performance of individual features
NDCG@10
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some indicators are general across various chains while some are chain-specific
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the lack of competitors in the area play positive role as do the existence of place attractors
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performance of In.Flow is in accordance with the fact that McDonalds attract more users
from the remote areas

21. Considering fusion of factors
Explore the fusion of features in a supervised learning approach
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regression for ranking – conduct regression using Linear Regression,
SVR or M5P and then rank according to regressed values
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pair-wise ranking – learn on pair-wise comparison using neural
networks RankNet
Use the same evaluation methodology as for individual features.

22. Results of the supervised learning
NDCG@10
Individual features
Supervised learning
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supervised learning has better performance than the the best individual feature
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the combination of geographic features and mobility features yields better result than
the combination of geographic features alone
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regression to rank with SVR is the best performing technique

23. The best location prediction
Supervised learning
Individual features
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supervised learning yields reliable and significantly improved result
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the best prediction lies in top-20% of the ground truth ranking with
probability over 80%

24. Implications
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we show how fine-grained data from location-based social networks
can be effectively explored in geographic retail analysis
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this can inspire further works in location-based advertising, developing
indexes of urban areas, provision of location-based services etc. etc.
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particularly we see a lot of potential in the approach of measuring user
flows from check-ins in various applications
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we also faced some challenges when scaling this approach to other
chains and cities

25. Thank you for your attention!
Dmytro Karamshuk
King's College London
follow me on Twitter: @karamshuk