Assessing population exposure for landslide risk analysis using dasymetric cartography

Abstract

Abstract. Assessing the number and locations of exposed people is a crucial step in landslide risk management and emergency planning. The available population statistical data frequently have insufficient detail for an accurate assessment of potentially exposed people to hazardous events, mainly when they occur at the local scale, such as with landslides. The present study aims to apply dasymetric cartography to improving population spatial resolution and to assess the potentially exposed population. An additional objective is to compare the results with those obtained with a more common approach that uses, as spatial units, basic census units, which are the best spatial data disaggregation and detailed information available for regional studies in Portugal. Considering the Portuguese census data and a layer of residential building footprint, which was used as ancillary information, the number of exposed inhabitants differs significantly according to the approach used. When the census unit approach is used, considering the three highest landslide susceptible classes, the number of exposed inhabitants is in general overestimated. Despite the associated uncertainties of a general cost-benefit analysis, the presented methodology seems to be a reliable approach for gaining a first approximation of a more detailed estimation of exposed people. The approach based on dasymetric cartography allows the spatial resolution of population over large areas to be increased and enables the use of detailed landslide susceptibility maps, which are valuable for improving the exposed population assessment.

Combining Social Vulnerability and Physical Vulnerability to Analyse Landslide Risk at the Municipal Scale

“…In order to assess the exposure of the population and the buildings, the resident population of each BGRI subsection provided by the census of the INE was considered, as well as the buildings location that was provided in vector format by the Loures Municipality (Direcção de Projecto do Plano Director Municipal, or DPPDM). Because the number of residents per building was not available, the resident population per BGRI was distributed into each residential building of the BGRI by dasymetric mapping in function of the area of each building [44], in order to estimate the number of residents in each susceptibility class and in each combined vulnerability class. In addition, the physical vulnerability calculated for each BGRI unit was ascribed for the total buildings existing in the BGRI.…”

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

“…The estimated population density map appears to have a more scattered pattern than other population maps that use a single ancillary variable. This is because a large number of classes from all the layers can be mixed and considered to judge the population distribution through the disaggregation process within the multi-layer multi-class approach (6,8,16 classes for slope, altitude, and land use, respectively). Also, it should be noted that this dasymetric mapping represents the fine-grain division of hilly area population disaggregation by preserving the pycnophylactic property of population mapping [52], which maintains the same total population in each source zone.…”

“…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.…”

Contrasting Population Projections to Induce Divergent Estimates of Landslides Exposure Under Climate Change

“…Existing literature proposed a multi‐indicator analysis based on the demographic data in administrative units, such as provinces or municipalities, to estimate the exposure of elements at risk to landslide by incorporating the landslide susceptibility or frequency of landslides occurrence within the same areas (Gariano et al., 2017; Martha et al., 2021). Several studies used dasymetric mapping procedures to refine the estimation of the spatial distribution of elements at risk to provide comparatively fine‐scaled estimations of landslide exposure (Emberson et al., 2021; Garcia et al., 2016; Pellicani et al., 2014; Santangelo et al., 2021). Efforts have attempted to estimate landslide exposure at continental or global scales (Emberson et al., 2020; Schlögl & Matulla, 2018).…”