  The proliferation of smartphones has led to the increased popularity of
location-based search and recommendation systems. Online platforms like Google
and Yelp allow location-based search in the form of nearby feature to query for
hotels or restaurants in the vicinity. Moreover, hotel booking platforms like
Booking[dot]com, Expedia, or Trivago allow travelers searching for
accommodations using either their desired location as a search query or near a
particular landmark. Since the popularity of different locations in a city
varies, certain locations may get more queries than other locations. Thus, the
exposure received by different establishments at these locations may be very
different from their intrinsic quality as captured in their ratings.
  Today, many small businesses (shops, hotels, or restaurants) rely on such
online platforms for attracting customers. Thus, receiving less exposure than
that is expected can be unfavorable for businesses. It could have a negative
impact on their revenue and potentially lead to economic starvation or even
shutdown. By gathering and analyzing data from three popular platforms, we
observe that many top-rated hotels and restaurants get less exposure vis-a-vis
their quality, which could be detrimental for them. Following a meritocratic
notion, we define and quantify such exposure disparity due to location-based
searches on these platforms. We attribute this exposure disparity mainly to two
kinds of biases -- Popularity Bias and Position Bias. Our experimental
evaluation on multiple datasets reveals that although the platforms are doing
well in delivering distance-based results, exposure disparity exists for
individual businesses and needs to be reduced for business sustainability.
