Wireless sensor networks monitor phenomena that vary over the spatial region the sensor network covers. The sensor readings may also be dual-used for additional purposes. In this paper we propose to use the inherent spatial variability in physical phenomena to support localization and position verification. We first present the problem of localization using general spatial information fields, and then propose a theory for exploiting this spatial variability for localization. Our Spatio-Correlation Weighting Mechanism (SCWM) uses spatial relationships of measured physical phenomena to determine an appropriate subset of environmental parameters for better locationaccuracy. We next present the Flex-EP algorithm, which supports our theoretical model for performing localization. Finally, we provide an experimental evaluation of our approach by using a collection of physical phenomena measured across one hundred locations inside a building. Our results provide strong evidence of the viability of using general sensor readings for location applications.