Population abundance is fundamental in ecology and conservation biology, and provides essential information for predicting population dynamics and implementing conservation actions. While a range of approaches have been proposed to estimate population abundance based on existing data, data deficiency is ubiquitous. When information is deficient, a population estimation will rely on labor intensive field surveys. Typically, time is one of the critical constraints in conservation, and management decisions must often be made quickly under a data deficient situation. Hence, it is important to acquire a theoretical justification for survey methods to meet a required estimation precision. There is no such theory available in a spatially explicit context, while spatial considerations are critical to any field survey. Here, we develop a spatially explicit theory for population estimation that allows us to examine the estimation precision under different survey designs and individual distribution patterns (e.g. random/clustered sampling and individual distribution). We demonstrate that clustered sampling decreases the estimation precision when individuals form clusters, while sampling designs do not affect the estimation accuracy when individuals are distributed randomly. Regardless of individual distribution, the estimation precision becomes higher with increasing total population abundance and the sampled fraction. These insights provide theoretical bases for efficient field survey designs in information deficiency situations.