Accurate description of the distribution of housing units within sub-County geographies is an important component of small-area population estimation. This paper pilots the use of the Pearl-Reed logistic model to predict housing unit growth in urban Census tracts in Bernalillo County, New Mexico for 2007. The model is based upon 1990 to 2000 growth rates, constrained with respect to a priori estimates of an upper-limit of housing units that could potentially be built within a tract based on its land area. In spite of the simplistic nature of this model, it is found to perform quite well. Further development based on incorporation of additional economic, demographic, and sociologic data would likely improve the model substantially; however, in this study the model out-performed standard trend extrapolation procedures for the study area and displayed error measures comparable to those reported in the literature for extrapolation methods in general.

Sinkholes cause subsidence and collapse problems for many transportation infrastructure assets. Subsequently, transportation infrastructure management agencies dedicate a considerable amount of time and money to detect and map sinkholes as part of their asset management programs. Traditionally, sinkholes are detected through area reconnaissance, which includes visual inspection of a site to locate existing sinkholes or device inspection of a site to locate potential sinkholes or previously filled sinkholes. Another method for sinkhole detection is through a review of maps such as geological maps. These methods are expensive, time-consuming, and labor-intensive. Recent advances in remote sensing, especially airborne light detection and ranging (LiDAR), allows for the examination of the change in the Earth's surface elevation accurately and rapidly. The focus of this study is to develop a conceptual framework for sinkhole detection and mapping with airborne LiDAR. This conceptual framework lays the foundation for the future application of airborne LiDAR for sinkhole detection and mapping.

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