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Location-based services (LBS) are widely deployed. When the implementation of an LBS-enabled service has evolved, regression testing can be employed to assure the previously established behaviors not having been adversely affected. Proper test case prioritization helps reveal service anomalies efficiently so that fixes can be scheduled earlier to minimize the nuisance to service consumers. A key observation is that locations captured in the inputs and the expected outputs of test cases are physically correlated by the LBS-enabled service, and these services heuristically use estimated and imprecise locations for their computations, making these services tend to treat locations in close proximity homogenously. This paper exploits this observation. It proposes a suite of metrics and initializes them to demonstrate input-guided techniques and point-of-interest (POI) aware test case prioritization techniques, differing by whether the location information in the expected outputs of test cases is used. It reports a case study on a stateful LBS-enabled service. The case study shows that the POI-aware techniques can be more effective and more stable than the baseline, which reorders test cases randomly, and the input-guided techniques. We also find that one of the POI-aware techniques, cdist, is either the most effective or the second most effective technique among all the studied techniques in our evaluated aspects, although no technique excels in all studied SOA fault classes.