Fine scale population density data and its application in risk assessment

Abstract

Population density is one of the key parameters for assessing the magnitude of population exposed to risk, and the better quality data we have, the better the assessment of risk. The aim of this study is to elaborate a high-resolution spatially distributed population density grid, which estimates population at the commune scale with a reliability of over 90%. The novelty of the approach is population density estimation in a regular European grid, based on buildings vector data collected in the national topographic database. Using abductive reasoning in combination with statistics and spatial analysis, the authors extract approximate information about a population from the large-scale topographic data. Moreover, linking the obtained population data with the cadastral databy unique building identifierallows for regular, quick and census surveyindependent updates of the population surface. A shortcoming of the approach is the issue of the possible existence of two houses per family, which leads to an overestimation of population. However, in the study area it affected only two of the total 14 communes by 7%-9%.

Mapping the Sensitivity of Population Exposure to Changes in Flood Magnitude: Prospective Application From Local to Global Scale

“…Flood maps with a coarse resolution compared to the population maps can be downscaled by a water volume distribution approach and projected onto a digital elevation model with a higher spatial resolution following the procedure by Winsemius et al (2015). Coarse population data can be downscaled and disaggregated to the spatial resolution of the flood hazard maps according to the procedures outlined in Stevens et al (2015), Calka et al (2017), Lloyd et al (2017), and Tatem (2017).…”

“…A further high-resolution settlement layer was compiled by Tiecke et al (2017). An overview of the available gridded population datasets and their spatial resolutions is given by Calka et al (2017) and Stevens et al (2015).…”

Dasymetric Modelling of Population Distribution – Large Data Approach

“…An access to high resolution data on population distribution is needed for a wide range of applications related to urban and transport planning (Benn 1995, Murray et al 1998, Pattnaik et al 1998, resources management (Gleick 1996), disaster/relief mitigation (Bhaduri et al 2002), assessment of human pressure on the environment (Weber, Christophersen 2002) and quantifying environmental impact on population (Vinkx, Visee 2008). Reliable information on population distribution is also essential for characterizing population at risk from natural hazards (Dobson et al 2000, Chen et al 2004, Tralli et al 2005, Thieken et al 2006, McGranahan et al 2007, Maantay, Maroko 2009, Mondal, Tatem 2012, Berke et al 2015, Tenerelli et al 2015, Calka et al 2017 and for public health applications such as disease burden estimation and epidemic modelling (Hay et al 2005, Tatem et al 2008, 2011, 2012).…”

Estimating Hilly Areas Population Using a Dasymetric Mapping Approach: A Case of Sri Lanka’s Highest Mountain Range

“…Accurate mapping of population distribution has become very important in a variety of applications, such as urban and regional planning, disaster management, resources and facility allocation, risk-assessment, and socioeconomic development policy [1][2][3][4][5][6][7][8][9][10][11][12]. This is particularly true for developing countries with a rapid population growth and inaccurate or unavailable census data.…”