Disasters, whether natural or anthropogenic, can be catastrophic and deadly. Without readiness and a well-planned response from local agencies and public health authorities, the number of casualties and the damage to the infrastructure can have long-term effects on the economy and the well-being of the community and country. The main obstacle to developing robust disaster plans and estimating hazard impacts is the availability of region-specific data. This dissertation encompassed the creation of a data repository of geospatial and demographic data in support of emergency response and risk assessment tools such as Re-PLAN and RAMP. Several hazard specific geospatial data types were integrated and joined with the main demographic data source, the American Communities Survey (ACS). The proposed Bridge-Spatial Alignment function aims to address the spatial and data misalignment between the demographic datasets describing at-risk populations and the hazard specific region of interest. The proposed methodology has been shown to be equally accurate or better than existing boundary approximation methodology for estimating the populations of arbitrary spatial regions where the scale of the target spatial resolution is larger than the source units. Although the LandScan-Based disaggregation method proved adequate for the problem in the Spatial Alignment case study, it was shown to be lackluster as a predictor of the population distribution for Census Data.