  Fairness in data-driven decision-making studies scenarios where individuals
from certain population segments may be unfairly treated when being considered
for loan or job applications, access to public resources, or other types of
services. In location-based applications, decisions are based on individual
whereabouts, which often correlate with sensitive attributes such as race,
income, and education.
  While fairness has received significant attention recently, e.g., in machine
learning, there is little focus on achieving fairness when dealing with
location data. Due to their characteristics and specific type of processing
algorithms, location data pose important fairness challenges. We introduce the
concept of spatial data fairness to address the specific challenges of location
data and spatial queries. We devise a novel building block to achieve fairness
in the form of fair polynomials. Next, we propose two mechanisms based on fair
polynomials that achieve individual spatial fairness, corresponding to two
common location-based decision-making types: distance-based and zone-based.
Extensive experimental results on real data show that the proposed mechanisms
achieve spatial fairness without sacrificing utility.
