High-resolution population distribution data are critical for successfully addressing important issues ranging from socio-environmental research to public health to homeland security, since scientific analyses, operational activities, and policy decisions are significantly influenced by the number of impacted people. Dasymetric modeling has been a well-recognized approach for spatial decomposition of census data to increase the spatial resolution of population distribution. However, enhancing the temporal resolution of population distribution poses a greater challenge. In this paper, we discuss the development of LandScan USA, a multi-dimensional dasymetric modeling approach, which has allowed the creation of a very high-resolution population distribution data both over space and time. At a spatial resolution of 3 arc seconds (*90 m), the initial LandScan USA database contains both a nighttime residential as well as a baseline daytime population distribution that incorporates movement of workers and students. Challenging research issues of disparate and misaligned spatial data and modeling to develop a database at a national scale, as well as model verification and validation approaches are illustrated and discussed. Initial analyses indicate a high degree of locational accuracy for LandScan USA distribution model and data. High-resolution population data such as LandScan USA, which describes both distribution and dynamics of human population, clearly has the potential to profoundly impact multiple domain applications of national and global priority.

The objective of this research is to compute population estimates by age and sex for small areas whose boundaries are different from those for which the population counts were made. In our approach, population surfaces and age‐sex proportion surfaces are separately estimated. Age‐sex population estimates for small areas and their confidence intervals are then computed using a binomial model with the two surfaces as inputs. The approach was implemented for Iowa using a 90 m resolution population grid (LandScan USA) and U.S. Census 2000 population. Three spatial interpolation methods, the areal weighting (AW) method, the ordinary kriging (OK) method, and a modification of the pycnophylactic method, were used on Census Tract populations to estimate the age‐sex proportion surfaces. To verify the model, age‐sex population estimates were computed for paired Block Groups that straddled Census Tracts and therefore were spatially misaligned with them. The pycnophylactic method and the OK method were more accurate than the AW method. The approach is general and can be used to estimate subgroup‐count types of variables from information in existing administrative areas for custom‐defined areas used as the spatial basis of support in other applications.

Geospatial data sciences have emerged as critical requirements for high-priority application solutions in diverse areas, including, but not limited to, the mitigation of natural and man-made disasters.

Geospatial technologies and digital data have developed and disseminated rapidly in conjunction with increasing computing efficiency and Internet availability. The ability to store and transmit large datasets has encouraged the development of national infrastructure datasets in geospatial formats. National datasets are used by numerous agencies for analysis and modeling purposes because these datasets are standardized and considered to be of acceptable accuracy for national scale applications. At Oak Ridge National Laboratory a population model has been developed that incorporates national schools data as one of the model inputs. This paper evaluates spatial and attribute inaccuracies present within two national school datasets, Tele Atlas North America and National Center of Education Statistics (NCES).Schools are an important component of the population model, because they are spatially dense clusters of vulnerable populations. It is therefore essential to validate the quality of school input data. Schools were also chosen since a validated schools dataset was produced in geospatial format for Philadelphia County; thereby enabling a comparison between a local dataset and the national datasets.Analyses found the national datasets are not standardized and incomplete, containing 76 to 90 percent of existing schools. The temporal accuracy of updating annual enrollment values resulted in 89 percent inaccuracy for 2003. Spatial rectification was required for 87 percent of NCES points, of which 58 percent of the errors were attributed to the geocoding process. Lastly, it was found that by combining the two national datasets, the resultant dataset provided a more useful and accurate solution.

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LandScan USA is a 90 m population distribution model that is used for a variety of applications, including emergency management. Models should have a measure of accuracy; however, the accuracy of population distribution models is difficult to determine due to the inclusion of multiple input datasets and the lack of quantifiable, observable (validated) data to confirm model output. Validated data enables quantification of: (1) overall model accuracy and (2) changes in model output at different levels of quality control. This article examines the effect of quality control for two national school datasets incorporated as input in LandScan USA for Philadelphia County, Pennsylvania; which had a local, validated school dataset available. The effect of each stage of quality control efforts utilized throughout the LandScan USA process were assessed to determine what level of quality control was required to have a statistically significant change of the model's population distribution. The typical level of quality control for LandScan USA resulted in 36% of schools being moved to the correct location and 20% of missing student enrollments were found, compared to 87% and 98% respectively for the validated dataset. The costs of increasing quality control resulted in a six‐fold increase in labor time; however, the additional quality control did not produce statistically significant improvements in the LandScan USA model. Thus, typical quality control efforts for schools in LandScan USA produced a population distribution similar to the validated level of quality control, and can be applied with confidence for policy, planning, and emergency situations.

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