  Pervasiveness of tracking devices and enhanced availability of spatially
located data has deepened interest in using them for various policy
interventions, through computational data analysis tasks such as spatial hot
spot detection. In this paper, we consider, for the first time to our best
knowledge, fairness in detecting spatial hot spots. We motivate the need for
ensuring fairness through statistical parity over the collective population
covered across chosen hot spots. We then characterize the task of identifying a
diverse set of solutions in the noteworthiness-fairness trade-off spectrum, to
empower the user to choose a trade-off justified by the policy domain. Being a
novel task formulation, we also develop a suite of evaluation metrics for fair
hot spots, motivated by the need to evaluate pertinent aspects of the task. We
illustrate the computational infeasibility of identifying fair hot spots using
naive and/or direct approaches and devise a method, codenamed {\it FiSH}, for
efficiently identifying high-quality, fair and diverse sets of spatial hot
spots. FiSH traverses the tree-structured search space using heuristics that
guide it towards identifying effective and fair sets of spatial hot spots.
Through an extensive empirical analysis over a real-world dataset from the
domain of human development, we illustrate that FiSH generates high-quality
solutions at fast response times.
