In was complemented by the development of GIS[3] technologies

In the past decade, different factors like the
advancements made in wireless communication technologies, the growing universal
acceptance of location-aware technologies including mobile phones and smart
tablets equipped with GPS1
receivers, Sensors placed inside these devices, attached to cars and embedded
in infrastructures, remote sensors transported by aerial and satellite
platforms and RFID2 tags
attached to objects was complemented by the development of GIS3
technologies to result in the availability of an increasing amount of data with
content richness which can be exploited by analysts. With the emergence and
growing popularity of social networks and location-aware services, the next
step was combining these two technologies which resulted in the introduction of
location based social networks4
(Kheiri, Karimipour, & Forghani, 2016). Since such networks act
as a bridge establishing a connection between a user’s real life and online
activities (Kheiri et al., 2016), data obtained from them
is considered among one of the most important resources of spatial data and
presents a unique opportunity for researchers in business-related fields to
precisely study consumer’s behavioral patterns.

Consequently, with the introduction of LSBNs, the
question of optimal store placement like many other scientific problems has
entered a new era with fast, diverse and voluminous data, terms that are
usually used to describe big data. Liu and his colleagues (Liu et al., 2015), introduced the term
“social sensing” for describing the process and different approaches of
analyzing spatial big data in an individual scale. The use of the term
“sensing” in describing this process, represents two different aspects of such
data. First, this kind of analog data can be considered as a complementary
source of information for remote sensing data, because they can record the socio-economic
characteristics of users whereas remote sensing data can never offer these kind
of descriptive information. Second, such data follow the concept of Volunteered
geographic information5
(introduced by (Goodchild, 2006)), meaning that every
individual person in today’s world can be considered as a sensor transmitting
data as they move. Accordingly, Researchers in the past decade have focused
some of their efforts on exploiting LSBN data to solve the retail store
placement problem. Other than one or two cases, most of the research done in
this area has taken advantage of the new advancements in feature selection. Based
on the unique attributes and the type of information that can be retrieved from
LBSN data, a number of features that influence retail store popularity are
defined and then used to predict the popularity of given stores. Accessibility,
distance to downtown, area popularity, neighborhood entropy, venue density, the
effect of complementary products/services, competitiveness, Jensen quality,
transition density, transition quality and incoming flow are some of the most
important features derived from the related literature. Karamshuk and his
colleagues (Karamshuk, Noulas, Scellato, Nicosia, & Mascolo,
2013), asses the
popularity of three different coffee shop and restaurant chains in New York
city with the use of two different type of features (geographic and mobility
features) and via data retrieved from the popular LSBN; Foursquare6.
They compare the results obtained by using each individual feature for
popularity prediction with the results of combining the features with a machine
learning feature selection technique (RankNet algorithm), and conclude that
using a combination of features offers more accuracy. Wang et al (Feng Wang & Chen, 2016), take advantage of the
user generated reviews on Yelp7
to assess the prediction power of their framework in forecasting the popularity
of a number of given candidates for a new restaurant. Their framework is based
on the application of three different regression models (Ridge regression,
support vector regression and gradient boosted regression trees), to combine
features in order to enhance the prediction process. Yu and his colleagues (Yu, Tian, Wang, & Guo, 2016), attempt to tackle
another aspect of the store placement problem; choosing a shop-type from a list
of candidate types for a given location. They combine features by applying a
matrix factorization technique. Rahman and Nayeem (Rahman & Nayeem, 2017), exploit Foursquare data
in order to compare the results of the direct use of features and a combination
of features offered by a support vector machine regression, and demonstrate
that the application of the regression model for feature selection offers more
accuracy and better predictability.

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