Spatial Autoregressive Model for Population Estimation at the Census Block Level Using LIDAR-derived Building Volume Information

The collection of population by census is laborious, tune consuming and expensive, and often only available at limited temporal and spatial scales Remote sensing based population estimation has been employed as a viable alternative for providing population estimates based on indicators that make use of two-dimensional areal information of buildings or one-dimensional length information of roads The recent advancement. of LIDAR remote sensing provides die opportunity to add the third dimension of height information into the modeling of population distribution This study explores the use of building volumes derived from LIDAR as a population indicator Our study shows the volume-based model consistently outperforms area and length-based models at the census block level. Additionally, the study examines the impact of spatial autocorrelation, the presence of which violates the independence assumption of the traditional OLS models To address tins problem, a spatial autoregressive model is employed to account for the spatial autocorrelation in the regression residuals By incorporating the spatial pattern, the volume-based spatial en or model achieves a goodness of fit (R2) of 85 percent, with a significant improvement in model performance and estimation accuracies in comparison with its OLS counterpart. The study confirms building volume as a more valuable indicator and estimator for block level population distribution, especially if an appropriate spatial autoregressive model is adopted.