A revolution in sensor technology is introducing new problems that must be addressed by visualization research. These technologies will enable earthquake engineers to monitor tectonic plate activity, park rangers to respond more effectively to wildfires, and marine biologists to uncover mysteries from the ocean depths using a network of small, lightweight sensors collecting data in real-time. Visualizing data from these sensor networks is crucial to understanding the information the data represents. However, data collected from such networks are scattered through space and time without explicit connectivity information, posing difficult challenges to researchers and engineers. While past research on such meshless data has focused primarily on interpolation schemes, we propose the development of visualization algorithms and processing frameworks in which existing interpolation schemes can be applied. This work not only plays a significant role in supporting the continued development of sensor network technology; it fills a major gap in visualization research, since effective direct methods for meshless data visualization did not previously exist. Meshless methods can be applied to existing problems encountered in visualizing data with a mesh, giving a set of common techniques that can be applied to a broad class of data sets.