While several data aggregation techniques have been proposed for sensor networks, an understanding of the performance of various data aggregation schemes across the range of spatial correlations is lacking. We analyze the performance of routing with compression in sensor networks using an application-independent measure of data compression (an empirically obtained approximation for the joint entropy of sources as a function of the distance between them) to quantify the size of compressed information, and a bit-hop metric to quantify the total cost of joint routing with compression. Analytical modeling and simulations reveal that while the nature of optimal routing with compression does depend on the correlation level, surprisingly, there exists a static clustering scheme which can provide near-optimal performance for a wide range of spatial correlations. The implication, that there exist relatively simple energy-efficient aggregation schemes for correlated sources has much practical importance.