“The lights are on, but is anyone home?”: Estimating dwelling distribution in rural Alberta

Abstract

With Canada's increasing population, natural disasters such as flooding events will have an increasing impact on human populations. The severity of these events requires that decision makers have a clear understanding of the flood risks that communities face in order to plan for and mitigate flood risks. One key component to understanding flood risk is flood exposure, an element of which is the presence of structures (e.g., residences, businesses, and other buildings) in an area that could be damaged by flooding. Presently, several resources exist at both the national and global level that can be used to estimate the spatial distribution of structures. These resources are typically generated at global scales and do not account for regional or local data or processes that could enhance the accuracy and precision of exposure estimation in sparsely populated areas. The present study investigates the feasibility of creating a region-specific dwelling distribution model that helps improve estimation of residential structures in rural areas. Herein, we describe a rural dwelling distribution model for the province of Alberta that can be used to assist in the estimation of structural exposure to flood risk. The model is based on a random forest classification algorithm and several publicly available datasets associated with dwelling and population density. The model was validated using visually referenced data collected from earth imagery. The resulting dwelling layer was then evaluated in its ability to spatially disaggregate census dwelling counts, as well as predict dwelling exposure in several scenarios. This method appears to be a useful alternative to globally scaled models, or using the census alone, particularly for rural areas of Canada.