Seismic loss dynamics in three major Asian cities using a macro-level approach based on indicators of socioeconomic exposure

The current and future wealth maps are based on the network level representing the economic exposure of loss accounts to the current and future population networks from the reference. 25 obtained by dasymetric mapping using urban maps in ref. 8. A cellular device-based urban growth model called SLEUTH26 was used in ref. 8 for forecasting urban area (30m networks for Jakarta and Metro Manila, 90m networks for Istanbul due to the size of the administrative area) in 2030. SLEUTH model leverages the given urban area extracted from satellite images and many other predictors of change (i.e. regression, transport, area excluded) ) affecting urbanization trends. It uses brute force calibration to generate a set of the most appropriate growth parameters, namely regression, reproduction, diffusion, diffusion and road gravity. The growth parameters define four bases for SLEUTH growth which are spontaneous growth, new spreading center growth, edge growth, and knock-influenced growth. During titration, a shape index called the Lee-Sallee index is used to select the best coefficients. The Lee-Sallee index shows spatial congruence, defined as the ratio of the intersection to the union of the ground-real metropolitan area and the simulated urban area.

In each urban growth model, four inputs for urban areas and two inputs for the road network were used as suggested by SLEUTH, and only the business-as-usual scenario for forecasting the future urban area was considered in ref. 8. Water bodies were used to define the excluded area. After obtaining the most suitable set of growth coefficients in ref. 8, it is observed that the dominant growth type is Edge Growth in Jakarta and Metro Manila while it is a combination of Edge Growth and the growth of the new diffusion center in Istanbul. The projected urbanization prospects in 2030 were used in ref. 25 to divide the extrapolated population of each megacity by following several assumptions. Details of these assumptions can be found in ref. 25 with current and future population density maps.

We obtained wealth maps at the network level (Supplementary Figs 1–3) following the definition of economic exposure proposed by Jaiswal and Wald18 as given in Eq. 1:

$$ {{{{{\rm{Economic}}}}}\; {{{{{\rm {Exposure}}}}}}_{{{{\rm {network}}}}}}} = \alpha \ times {{{{\rm {per}}}} } \, {{{{{\rm {capita}}}}}} {{{{\rm {GDP}}}}}_{{{{\rm {country}}}}}} \ time {{{{{\rm {Population}}}}}} _ {{{{{\rm {network}}}}}} $$

(1)

Therefore, current and future economic exposure networks were derived by multiplying the present (eg, 2016 for Metro Manila and 2018 for Jakarta and Istanbul due to data availability) and future (ie, 2030) GDP per capita with corresponding population networks and correcting the factor exposure α (i.e., share of per capita wealth/per capita GDP) proposed in ref. 18. This correction factor explains the discrepancy between national wealth and the economic value of the exposed assets. Present values ​​of nominal GDP per capita at the country level were obtained from World Bank data (available at https://data.worldbank.org/), and projections of real GDP per capita in 2030 (in 2010 prices) were collected ) from the United States Department of Agriculture (USDA), International Macroeconomics Data Sets (available at www.ers.usda.gov). Then, real GDP per capita in 2030 was converted from 2010 prices to 2016/2018 prices with the benefit of a price index (i.e. current GDP/real GDP) using 2010 as the base year from the World Bank data. Supplementary Table 2 summarizes current and future values ​​of GDP per capita and exposure correction factors from reference. 18 countries selected in our study, namely Indonesia, the Philippines and Turkey.

To assess the dynamics of spatio-temporal change in wealth at earthquake risk, we fitted current and future wealth maps with a probability of 10 and 2% to exceed the seismic risk maps in ref. 8. We used classic PSHA to generate grid-based hazard maps and risk curves by using the OpenQuake Engine (available at www.globalquakemodel.org) 32,33 along with the Southeast Asia Continental Earthquake Model (2018) 34 for Jakarta and Metro Manila, and an earthquake model Middle East (EMME14) 35 for Istanbul. The main reason for choosing a classic PSHA-based risk assessment rather than a probabilistic event-based approach in this study is to perform a network-based comparison between current and future urban networks after urbanization analysis rather than an asset portfolio assessment, 37. It should also be noted that the classical risk assessment PSHA-based is more computationally efficient. Therefore, site-by-site loss-overflow curves were calculated based on the hazard curves, and spatial correlation was not considered in the ground motion residuals. Slope-based shear wave velocities, Vs30, were also taken into account by the USGS for soil amplification during the analysis. The peak ground acceleration (PGA) values ​​were then converted to modified Mercalli intensities (MMIs) using ground-to-intensity conversion equations (GMICEs) proposed by Worden et al. It is worth noting here that the spatial resolution of the seismic hazard maps is the same with the spatial resolution of the wealth maps (ie, 30 m for Jakarta and Metro Manila and 90 m for Istanbul). For network-level risk curves and loss estimation, we grouped smaller networks into 270 m networks to reduce computation time.

The set of guidelines and recommendations for traditional loss estimation methodologies is mainly based on the method proposed by the Applied Technology Council (1985), which is identified as ATC-1340. The ATC-13 method has two main components, seismic hazard analysis and structural weakness function. Seismic hazard analysis takes the frequent distribution of earthquakes, severe attenuation, and soil conditions into account, while vulnerability analysis requires a detailed inventory of buildings and structures in the area. The expected on-site loss is then determined by ATC-13 as shown in the equation. 2 below:

$$ {{{{\rm{Loss}}}}=\mathop{\sum}\limits _{{B}_{k}}\left[\left\{\mathop{\sum} \limits_{{I}_{i}}P\left({I}_{i}{{{{{\rm{|}}}}}}{B}_{k}\right)* \left(\mathop{\sum} \limits_{{{dr}}_{j}}P\left({{dr}}_{j}{{{{{\rm{|}}}}}}{I}_{i},{B}_{k}\right)* \left({{dr}}_{j}{{{{{\rm{|}}}}}}{B}_{k}\right)\right)\right\}* {V}_{{B}_{k}}\right]$$

(2)

where \({B}_{k}\) is the building type k, \({I}_{i}\) is the intensity level i, \({{dr}}_{j}\) is the expected damage percentage j, and \({V}_{{B}_{k}}\) is the value of all buildings of type \({B}_{k}\).

To our knowledge, the first approach to seismic loss estimation at the macro level was proposed by Chen et al. 3:

$$ {{{{{\rm{Loss}}}}}_{{{{\rm {grid}}}}}} = \mathop {\sum }\limits _{{I}_{j }} P\left({I}_{j}\right)\,\times\,{{{{\rm {MDF}}}}} ({I}_{j})\,\times\ , {g ({{{{\rm{GDP}}}}})\,\times\, {{{{\rm {GDP}}}}}_{{{{{\rm{network}}) }}}} $$

(3)

where \(P \left ({I}_{j} \right)\) is the probability of intensity level j, \({{{{\rm{MDF}}}}} ({I}_{j}) \) is the average damage factor to represent the relationship of exposure to risk and loss given the intensity \({I}_{j}\) and \(g ({{{\rm{GDP}}}}})\) is a function To relate total social wealth to the macroscopic GDP index. The sum over \({I}_{j}\) represents the expected loss in each network considering different possibilities of vibration intensity. \(g ({{{{\rm{GDP}}}}}) \) is assumed to be 4 for low- and middle-income economies, 5 for high-income economies, and 3 for China, India and Japan. Due to limited earthquake data, ref. Suppose 19 that \({{{{\rm{MDF}}}}} ({I}_{j})\) is globally defined by the relationship between density and loss ratio.

Jaiswal and Wald pointed out several limitations of previous macro-level forms such as broad economic categories of vulnerability curves, and the use of GDP which is an indicator of economic activity (i.e. flow) rather than current investment (i.e. inventory). Therefore, through the USGS’s immediate assessment of the Global Earthquake Response System (PAGER), they suggested using the nation-wide vulnerability criteria and exposure correction factor α (that is, wealth per capita/GDP per capita) to adjust for GDP. As shown in the equation. 1. The PAGER system rapidly estimates the population exposed to different levels of vibration intensity along with the range of economic losses following a major earthquake by means of a deterministic approach. The total expected economic loss is estimated as shown in the equation. 4 By summing the product of the loss ratio \(r(s)\), and the total economic exposure at each vibration intensity level, \(s\):

$${E\left({{{{\rm{Loss}}}} \right) = r\left(s \right) \, \times \, {{{{{\rm{Economic}}} }}} \; {{{{\rm{Exposure}}}}}}}_{s}$$

(4)

The loss ratio \(r(s)\) is defined in ref. 18 as below in Eq. 5:

$$ r (s) = \ phi \ left[\frac{1}{\beta }{{{{{\rm{ln}}}}}}(\frac{s}{\theta })\right]$$

(5)

where \(\phi \) is the standard normal cumulative distribution function, \(\theta \) is the mean, and \(\beta \) is the standard deviation of the logarithm normal of the vibration intensity \(s \).

By integrating the probabilistic approach of Chen et al 19 and the deterministic approach of Jaiswal and Wald, we propose here a probabilistic wealth-based macro-level loss estimation approach. The main difference in our proposed approach from other approaches is the integration of exposure correction factor α and the loss ratio definition given in ref. 18 in Chen’s GDP-based probabilistic framework in 19 to calculate current and future probabilistic risk measures AAL and PML. The results of the AAL represent the amount that a state or municipality would have to set aside each year to cover the cost of future disasters in the absence of insurance or other disaster risk financing mechanisms. They also provide a basis for calculating the insurance program premium. PML results are related to the maximum loss that can be expected over a given time period. It represents the amount of reserves that should be available to insurance companies or the government to fend off potential future losses. Then we suggest the following figure shown in the equation. 6 to calculate the AAL and PML based on the economic exposure given in the equation. 1:

$${E ({{{{\rm {Loss}}}}}) _{{{{{\rm {grid}}}}}=\mathop {\sum }\limits_{s}P\ left(s \right)\, \times\, r(s)\, \times \, {{{{{{\rm{Economic}}}}}}\; {{{{\rm {Exposure}}}}}}_{{{{{\rm {grid}}}}}} $$

(6)

where \(s\) is the vibration intensity, \(P \left (s \right)\) is the probability of occurrence of the vibration intensity \(s\), and \(r(s) \) is the loss ratio corresponding to the shaking intensity \( s \).

We obtained the country-level MMI-based vulnerability parameters (eg, \(\theta\) and \(\beta\)) matching for Indonesia, Philippines and Turkey from Jaiswal and Wald18 as summarized in Supplementary Table 3, and curves are shown The vulnerabilities obtained with these parameters are in Supplementary Fig. 5. Based on wealth maps, risk curves and vulnerability curves, we obtained network-level AAL maps, 475- and 2475-year PML maps by following our proposed approach. Next, we pooled the AAL values ​​for administrative level 2, then the AAL values ​​at the megacity level for Jakarta, Metro Manila and Istanbul.

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