Abstract: Understanding the movements of a population throughout the 24-hour day is critical when directing disaster response in an urban area. An emergency situation can develop rapidly, and understanding the expected locations of groups of people is required for the success of first responders. Recent advances in modern consumer technologies have facilitated the generation, sharing and mining of an extensive amount of volunteered geographic information. Users leverage inexpensive smart devices, pervasive Internet connections and social media services to provide data about geospatial locations. Using an enterprise system, it is possible to aggregate this freely available, geospatially enabled data and create a population estimation with high spatiotemporal resolution, via a heat map. This investigation explores the effects of different temporal sampling periods when creating such estimations. Time periods are selected, estimations are generated for several large urban areas in the western United States, and comparisons of the results are shown/discussed.