"Megacities" are characterized by large populations (at least 10 million) and interdependent infrastructure, demographic, economic, and government networks (the four pillars). To be successful in future operations, the military must expand its understanding of megacities and their networks. In particular the Joint Warfare Analysis Center (JWAC) is interested in these megacity networks and their implications for potential urban operations. We develop a methodology to group like megacities into five clusters. With 33 variables describing the four pillars, we construct a data set using over 90 data sources for 41 large urban areas. This work greatly expands previous work in both the number of cities studied and the number of variables used. We also study clustering sensitivity to missing values by generating an ensemble of 5,000 clusterings based on randomly imputed missing values. We compare these to clustering without imputation, the ensemble consensus or average clustering, and clusterings from previous studies in addition to identifying which cities are sensitive to missing values. Our work not only informs JWAC of the similarities and differences between the 41 cities studied, it provides a method to identify for which cities, more data collection is warranted, and it provides a blueprint for future work in this area. As the U.S. military prepares for the future, senior leaders and analysts alike expect that urban environments will play an increasing role in the operations we conduct. People are migrating to urban areas and the littorals at an increasing rate, which makes understanding the structure and unique challenges of working in these densely populated regions increasingly important. We use a combination of clustering techniques and sensitivity analysis to methodically categorize megacities and large urban areas using a data set constructed using over 90 sources that includes 41 cities throughout the world. This analysis is intended to inform the Joint Warfare Analysis Center (JWAC) on the relationships between megacities and provide insights into the ways in which they are similar or different. It also provides JWAC with a blue print for this type of analysis. Specifically, we give the details and reasoning for how we construct the data set and show how to handle and understand the effects of the inevitable missing values. In Chapter II, we address the current literature discussing the megacities and their networks as well as some of the modeling techniques that have been applied to understanding them. We also describe how our work adds to the discussion and deepens our collective understanding of the unique challenges associated with studying megacities. In Chapter III, we describe how we decide which variables to use, how we collect the data, and particular challenges we face in collecting data. We address our methodology in Chapter IV including how we calculate the distances between observations, the algorithm we select, and our data imputation technique. The results and analysis for our study appear in Chapter V where we show our baseline clusters, how the clusters shift when we impute missing values, the variability in the clustering ensembles, and the consensus (average) clustering resulting from our sensitivity analysis. Finally, we use Chapter VI for conclusions and to address where future researchers can further contribute to the study of megacities.