The Effects of Quality Control on Decreasing Error Propagation in the LandScan USA Population Distribution Model: A Case Study of Philadelphia County

Abstract

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.

Evolution and Influencing Factors of Urban Built-Up Areas in the Yangtze River Delta Urban Agglomeration

“…It uses population census data, Geographic Information System (GIS) data, satellite remote sensing data, and other data sources, as well as geographic modeling algorithms, to simulate and estimate the distribution of population across the globe. Compared to other types of population data, LandScan data not only allows for updates and improvements based on available data sources and technological advances but also provides high-resolution population data, making it widely applied in studies related to urban planning, environmental assessment, disaster management, and energy planning [51,52]. In this study, LandScan data for the YRD urban agglomeration at four time points, namely 2013, 2016, 2019, and 2022, are obtained.…”

The role of spatial representation in the development of a LUR model for Ottawa, Canada

“…It can be argued that an accurate map of human population is essential to municipal planning; even more so for public health planning and healthcare provisions (Hay et al 2005 ). Global disaster management for the developing world has given rise to projects like LandScan (Bhaduri et al 2007 ) and others that are producing dasymetric gridded global population estimates at fine spatial resolutions that compare well with known population distributions in the developed world (Sutton et al 2003 ; Sabesan et al 2007 ; Patterson et al 2009 ). It is clear that accurate data on the spatial distribution of population is fundamental to a number of endeavours (Liu et al 2008 ), but few studies have focused on the question of spatial representation in terms of population distribution and its impact on policies.…”