Many types of event sequence data exhibit triggering and clustering properties in space and time. Point processes are widely used in modeling such event data with applications such as predictive policing and disaster event forecasting. Although current algorithms can achieve significant event prediction accuracy, the historic data or the self-excitation property can introduce biased prediction. For example, hotspots ranked by event hazard rates can make the visibility of a disadvantaged group (e.g., racial minorities or the communities of lower social economic status) more apparent. Existing methods have explored ways to achieve parity between the groups by penalizing the objective function with several group fairness metrics. However, these metrics fail to measure the fairness on every prefix of the ranking. In this paper, we propose a novel list-wise fairness criterion for point processes, which can efficiently evaluate the ranking fairness in event prediction. We also present a strict definition of the unfairness consistency property of a fairness metric and prove that our list-wise fairness criterion satisfies this property. Experiments on several real-world spatial-temporal sequence datasets demonstrate the effectiveness of our list-wise fairness criterion.