LandScan USA: a high-resolution geospatial and temporal modeling approach for population distribution and dynamics

Abstract

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 human carbon budget: an estimate of the spatial distribution of metabolic carbon consumption and release in the United States

“…LandScan (Dobson et al 2000, Bhaduri et al 2007) is a grid-based population dataset that estimates where people are located around the world at a resolution of 30 arcseconds (*1 km 9 1 km at the equator). LandScan combines data on population, road networks, digital terrain, schools, prisons, commercial and residential buildings, and nighttime lights with high-resolution satellite imagery to estimate where people are located during the day and during the evening.…”

Reviews of Geospatial Information Technology and Collaborative Data Delivery for Disaster Risk Management

“…Although this feature is useful for any phase of DRM, it is especially useful for planning purposes. Although satellite remote sensing can be used to observe physical characteristics of the land surface, several studies have developed socioeconomic datasets relevant to vulnerability assessment, such as urban expansion [32,33], population [34], and gross domestic product (GDP) [35,36], by using a combination of satellite-based data and socioeconomic statistics.…”

“…Several studies presented frameworks and practices of spatio-temporal population mapping by measuring people's movement between daytime and nighttime [34,97] and spatio-temporal patterns of commutes between home and work [98,99]. The frameworks are extensible to other data for better accuracy and broader regions.…”

Analyses of school commuting data for exposure modeling purposes

“…There was no way to test this assumption given the data available to us. We used the LandScan USA model (Bhaduri et al, 2007) at a 1 km resolution to spatially decompose US census data to ''fill in the gaps. ''…”

“…Land cover/land use is one of the most relevant attributes used in this endeavor; see Monmonier and Schnell (1984). The Dasymetric modeling approach employed in LandScan population distribution models has been discussed in detail by Dobson et al (2000); Bhaduri et al (2007).…”