# Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions

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## Abstract

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## 1. Introduction

- Presenting the capabilities of integrating a freely available dataset gathered from VGI (without having to inspect individual buildings) to derive a probabilistic exposure model for residential buildings in Valparaíso and Viña del Mar (Chile), based on the inferred building footprint area for certain typologies.
- In addition to better characterising the building composition, this approach has improved its spatial representation by downscaling a coarser existing exposure model onto higher-resolution regular grids. Then, a large magnitude earthquake scenario can be defined, and a related set of exhaustive seismic ground motion fields through the variation of some of its driving parameters can be constructed.
- Use the former set of groung motions along with the three exposure models as inputs to independently calculate the direct economic losses that are expected from the building portfolio subjected to such a worst-case earthquake scenario. This vulnerability assessment allows us to propagate and compare the uncertainties embedded in the exposure models with respect to the parameters used to constrain the seismic ground motions.

## 2. Context of the Study Area

**Figure 1.**Location of the study area within (

**a**) Chile, (

**b**) the Valparaíso region (grey) and Valparaíso province (red), and (

**c**) communes of Valparaíso and Viña del Mar. (

**d**) Detailed image of the two communes, showing Sentinel-2 images for 21 September 2019 (downloaded from the Copernicus Open Access Hub of the European Space Agency (ESA)). Map data © Google Earth 2019. Edited from [50] and [59].

## 3. Materials and Methods

#### 3.1. Delimitation of the Urban Area and Some Initial Features

#### 3.2. Building Exposure and Vulnerability Models for Valparaíso

#### 3.2.1. The Initial Commune-Based SARA Exposure Model with Merged Classes

- We did not consider the class “UNK” (unknown) because it lacked observable attributes. Its proportion (~10%) was redistributed to the other classes.
- We combined five pairs of classes into a more generic enclosing typology that had a similar taxonomic description and only differed in their storey ranges. These were ER-ETR-H1 within ER-ETR-H1-2, MCF-DNO-H1 within MCF-DNO-H1-3, MUR-ADO-H1 within MUR-ADO-H1-2, W-WLI-H1 within W-WLI-H1-3, and W-WS-H1 within W-WS-H1-2.

^{2})) were derived from the “reference average area per dwelling” values as a function of the construction quality reported in [67] for Chile. They were 70 m

^{2}, 80 m

^{2}, and 70 m

^{2}for the upper, middle, and lower construction quality, respectively. Aligned with the construction practices in Valparaíso and with the last two Chilean seismic codes (NCh433 Of.72, [70] and NCh433 Of.96 [71]), the earthen, masonry, and non-ductile (excluding RC) types were assumed to have a lower construction quality, wooden and non-ductile RC classes had a middle one, and ductile RC classes had an upper quality. The values of such categorizations were multiplied by the number of dwellings per class and then divided by their respective average numbers of storeys. Finally, they were divided by the number of buildings per class to obtain the inferred building footprint area per typology. This procedure is illustrated in Table A1.

^{2}) values of the reclassified typologies are reported in Table 2.

^{2}. This type of procedure is comprehensively described in [72]. The disaggregation assumed a fixed number of nighttime residents per building typology. The highest spatial resolution of the aggregated exposure model of the original SARA model was made available at the third administrative division of Chile (commune). This means that our area of interest (Valparaíso and Viña del Mar) was only composed of two large geo-cells (Figure 3). Hence, one of the shortcomings of the SARA model is that the spatial distribution of buildings is unknown, because all of the exposure information is provided at the centroid of each geo-cell. Thus, this assumption disregards the outcomes from land use classifications, which is particularly relevant for Valparaíso, as can be seen from Figure A1. Although this resolution could be sufficient for regional seismic risk estimates [73], it would not be adequate for more detailed analysis using local ground motions or when aiming for future urban requalification (e.g., [74]). Thus, a direct downscaling of such information into a more detailed resolution was needed. This was initially carried out in [50]. This process is explained in the following section.

#### 3.2.2. Preliminary Model: A Simple Downscaling Using Spatial Disaggregation of Population

#### 3.2.3. Ancillary Data Available for Valparaíso

#### OpenStreetMap (OSM)

#### Data Collection of Taxonomic Attributes in Valparaíso and Building Classification

#### 3.2.4. Bayesian Exposure Model for Valparaíso

#### 3.2.5. Comparison of Exposure Models Available for Valparaíso

- The first model (Section 3.2.1) is quite similar to the original SARA model, since it maintains its spatial representation over the administrative units. Its composition consists of the combination of similar classes in terms of their height.
- The second model (preliminary downscaled model, Section 3.2.2) constitutes the spatial disaggregation of the former onto higher resolutions of regular grid cells (500 m × 500 m). The total number of buildings was estimated by disaggregating the population at the block level from the 2017 official Chilean census.

#### 3.3. Generation of Seismic Ground Motion Fields for an Earthquake Scenario

#### 3.3.1. Ground Motion Prediction Equation (GMPE)

#### 3.3.2. Site Term (Spatial Distribution of Vs_{30})

_{30}values) as the only proxy. The sensitivity analysis of this term was performed considering three Vs

_{30}conditions:

- Assuming Vs
_{30}values of 600 m/s uniformly distributed throughout the study area. This emulated the presence of a moderately homogenous weathered rock with similar values assumed for the seabed rocks (Figure 13a).

#### 3.3.3. Spatial Correlation Model

_{30}values from microzonation (Figure 12c) and the Montalva et al. (2017) GMPE for three conditions:

- Uncorrelated ground motion fields (Figure 14a);

_{30}, and spatial correlation. The three exposure models formerly presented were addressed as the fourth element within this sensitivity analysis to calculate their seismic vulnerability. These models, along with their fragility functions, were assembled in order to fulfil the data formats required by the Assetmaster and Modelprop software [100]. They produced inputs which, together with the ground motions provided by Shakyground, were used by the DEUS engine [101] to estimate the damage and losses. The replacement cost values suggested in [69] and loss ratios per damage state (i.e., 2%, 10%, 50%, and 100%) were used. Therefore, 81 parametric combinations for risk assessment were generated per ground motion realisation.

**Figure 12.**Median values of the Mw 9.1 earthquake for the three IMs of (

**a**) PGA, (

**b**) S.A (0.3 s), and (

**c**) S.A (1.0 s) using the Montalva et al. (2017) GMPE [92] and the seismic microzonation available for the area [95].The earthquake hypocentre is shown as a white dot. The rupture plane is represented by a green rectangle.

## 4. Sensitivity Analyses of Scenario-Based Seismic Risk Assessment

_{30}values, imposed the largest values.

_{30}parametrisations. Moreover, the arrangement of the normalised risk metrics of the preliminary downscaled model (Figure 15b) presented a smoother shape in contrast with the other two exposure models. This feature was a contribution of having a more spread-out aggregation (Figure 7) made of an unrealistic portfolio composition (e.g., very few walled, high-rise, reinforced concrete buildings). Finally, the Bayesian-derived exposure model (Figure 15c) produced the highest estimated losses due to its comparative larger building counts. It might appear that the difference in these counts induced a linear increment of the losses with respect to the preliminary exposure model. However, this trend was not entirely linear. This was due to the different spatial distributions of the buildings (Figure 10) and having larger proportions of walled medium-rise RC buildings (Figure 11) which, despite being more resistant to ground shaking (Figure 4) have higher replacement costs (Table 1).

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Spatial Delimitation of the Urban Area and Available Data Sources for Building Exposure Modelling

**Figure A1.**Land use classification for the study area. Map data © Google Earth 2020. Figure modified from [50].

**Figure A2.**Categories according to the available input data in the study area, with 136 grid cells represented by green colour having information on build-up height according to [50] as well as the OSM footprint area (Section 3.2.3), 51 grid cells represented with blue colour having information about the build-up height and density from remote sensing data products (as studied by [50]), 158 grid cells represented by orange colour having a complete information only about the footprint area from OSM, and 39 grid cells represented by pink colour having no input information. Map data © Google Earth 2020. Figure modified from [50].

## Appendix B. Assumptions Followed in the Prelimary Model to Obtain Building Counts from Population and Footprint Areas per Typology

**Table A1.**Procedure followed in deriving the building footprint area values for each SARA typology in Valparaíso according to the descriptions in Section 3.2.1.

Taxonomy | Dwellings | Buildings | Mean Number of Storeys | Reference Average Area per Dwelling (m^{2}) | Population | Construction Quality | Area m^{2} Dwellings | Floor Area (m) | Floor Area (m^{2}) per Building | Average Floor Area per Building | Sub-Categories |
---|---|---|---|---|---|---|---|---|---|---|---|

W + WS/H:1,2 | 1464.4 | 1464.4 | 1 | 80 | 5420 | Low | 117,152 | 117,152.1 | 80.00 | 73.24 | A |

ER + ETR/H:1,2 | 2348.8 | 5909.5 | 1.25 | 70 | 30,771 | Low | 582,001 | 465,600.8 | 78.79 | ||

MUR + ADO/H:1,2 | 9185.9 | 7472.5 | 1.25 | 70 | 33,997 | Low | 643,013 | 514,410.4 | 68.84 | ||

MUR + STDRE/H:1,2 | 5747.8 | 4928 | 1.25 | 70 | 21,273 | Low | 402,346 | 321,876.8 | 65.32 | ||

MCF/DNO/H:1,3 | 6841.9 | 876.4 | 1.5 | 70 | 3244 | Low | 61,348 | 40,898.7 | 46.67 | 65.56 | B |

MUR/H:1,3 | 2732.5 | 1821.7 | 1.5 | 70 | 10,113 | Low | 191,275 | 127,516.7 | 70.00 | ||

W + WLI/H:1,3 | 16,254.2 | 10,836.1 | 1.5 | 80 | 60,157 | Low | 1,300,336 | 866,890.7 | 80.00 | ||

CR/LWAL/DNO/H:1,3 | 4384.3 | 1096.1 | 2 | 80 | 16,226 | Low | 350,744 | 175,372 | 160.00 | 153.33 | C |

CR/LWAL/DUC/H:1,3 | 1461.1 | 365.3 | 2 | 70 | 5408 | Low | 102,277 | 51,138.5 | 140.00 | ||

MCF/DUC/H:1,3 | 4501.3 | 1125.3 | 2 | 80 | 16,659 | Low | 360,104 | 180,052 | 160.00 | ||

CR + PC/LWAL/H:1,3 | 1016.9 | 203.4 | 2.5 | 80 | 3764 | Low | 81,352 | 32,540.8 | 160.00 | 153.33 | D |

MR/DNO/H:1,3 | 4652.5 | 930.5 | 2.5 | 80 | 17,219 | Low | 372,200 | 148,880 | 160.00 | ||

MR/DUC/H:1,3 | 2077.7 | 415.5 | 2.5 | 70 | 7690 | Low | 145,439 | 58,175.6 | 140.00 | ||

CR/LWAL/DNO/H:4,7 | 4316 | 287.7 | 5 | 80 | 15,974 | Medium | 345,280 | 69,056 | 240.03 | 225.04 | E |

CR/LWAL/DUC/H:4,7 | 2076.6 | 138.4 | 5 | 70 | 76,856 | Medium | 145,362 | 29,072.4 | 210.00 | ||

CR/LWAL/DUC/H:8,19 | 1656.9 | 34.5 | 12 | 70 | 6132 | High | 115,983 | 9665.3 | 280.15 | 280.15 | F |

## Appendix C. Data Collection of Building Attributes in Valparaíso and Their Classification

**Figure A3.**Location of the RRVS building survey in Valparaíso. Map data: figure modified from [59]. Map data © Google Earth 2020.

**Figure A4.**Façade of inspected building (ID = 599 in [81]). Image data © Google Street View 2019.

**Table A2.**Data collection for the building in Figure A4.

Attribute Type | Attribute Value |
---|---|

Material type | MCF |

Material technology | CL99 |

Material property | MO99 |

Lateral load-resisting system (LLRS) | LWAL |

Non-structural exterior walls | EWMA |

Roof shape | RSH2 |

Roof coverage material | RMT6 |

Roof system material | RWO |

Roof system type | RWO1 |

Floor material | FC |

Floor type | FC99 |

Floor connections | FWCP |

Number of storeys | 2 |

Ductility of the LRRS | DU99 |

**Figure A5.**Distribution of attribute values within the GEM V.2.0 taxonomy for 604 inspected buildings in Valparaíso for (

**a**) material type, (

**b**) material technology, (

**c**) non-structural exterior walls, and (

**d**) lateral load-resisting system.

**Table A3.**Observed attribute values incorporated into the four attribute types in Figure A5.

(a) Material Type SRC: Concrete, composite with steel section C99: Concrete, unknown reinforcement EU: Earth, unreinforced ME: Metal (except steel) E99: Earth, unknown reinforcement MUR: Masonry, unreinforced [MUR] S: Steel MR: Masonry, reinforced MAT99: Unknown material CR: Concrete, reinforced W: Wood M99: Masonry, unknown reinforcement MCF: Masonry, confined (MCF) | (b) Material Technology ET99: Unknown earth technology ST99: Stone, unknown technology WO: Wood, other WS: Solid wood MEIR: Iron ME99: Metal, unknown CB99: Concrete blocks, unknown type S99: Steel, unknown CBS: Concrete blocks, solid ETC: Cob or wet construction MUN99: Masonry unit, unknown CLBLH: Fired clay hollow blocks or tiles CLBRS: Fired clay solid bricks CIP: Cast-in-place concrete MATT99: Unknown material WLI: Light wood members CL99: Fired clay unit, unknown type |

(c) Non- Structural Exterior Walls EWPL: Plastic or vinyl exterior walls, various EWO: Material of exterior walls, other EWE: Earthen exterior walls EWG: Glass exterior walls EWCB: Cement-based boards for exterior walls EWC: Concrete exterior walls EW99: Unknown material of exterior walls EWME: Metal exterior walls EWMA: Masonry exterior walls EWW: Wooden exterior walls EWSL: Stucco finish on light framing for exterior walls | (d) Lateral Load Resisting System LH: Hybrid lateral load-resisting system LFM: Moment frame LFBR: Braced frame LFINF: Infilled frame LPB: Post and beam L99: Unknown lateral load-resisting system LWAL: Wall |

**Figure A6.**Resulting fuzzy compatibility scores for the building in Figure A4 with respect to the SARA typologies. The solid and dashed segments represent the equivalent defuzzified values according to the mode, median, or mean values of the triangular fuzzy numbers.

**Figure A7.**Distribution of the SARA typologies for the sample constituted by 604 surveyed buildings randomly distributed throughout Valparaíso (Figure A3).

## Appendix D. Basic Overview of the Probabilistic Exposure Modelling Approach

^{2}(mean value in Figure A8a) in the given model?” From Figure A8b, it is possible to observe that once the likelihood term was solved for that value, it was maximised at 94%. That area value was within the type of low-rise buildings, which was the value selected for such a category. Therefore, the chosen percentages for all of the observed buildings were the ones for which the likelihood function was maximised, as defined by Equation (A10).

**Figure A8.**(

**a**) Footprint area distribution for a given grid cell, with a mean ~81 m

^{2}. (

**b**) The posterior distribution obtained for that grid cell after having maximised the likelihood function which, in this case, was for low rise buildings. Adapted from [28].

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**Figure 2.**Flowchart outlining the input data gathered, the processes, and the three resulting residential building exposure models for Valparaíso (Chile) we will be presenting throughout this study. These exposure models, together with the parametric ground motions, are used as inputs for the sensitivity analysis in scenario-based seismic risk in the last stage of this study.

**Figure 3.**Building counts at the commune level for 15 SARA building classes (after having combined similar typologies (Section 3.2.1)). The colour scale represents the material type (green = wooden; orange = masonry; blue = reinforced concrete).

**Figure 5.**Inferred ranges of residential building counts in the study area. This outcome was obtained from the spatial disaggregation of nighttime residents at the block level as reported by the official 2017 Chilean census.

**Figure 6.**Spatial distributions of the building counts for the six subcategories in the range A–F obtained from the preliminary model. The latter involves a simple downscaling using the dasymetric disaggregation of the population based on the use of nighttime residents at the block level as reported by the official 2017 Chilean census.

**Figure 7.**Spatial distributions of the building counts for the SARA typologies obtained from the preliminary downscaled model. The colour scale reflects the material type (green = wooden; orange = masonry; blue = reinforced concrete).

**Figure 8.**Building footprints for a certain area in Valparaíso (© OpenStreetMap contributors 2021), distributed under the Open Data Commons Open Database License (ODbL) v1.0. Map data © Google Earth 2020. Figure reprinted from [50].

**Figure 9.**Spatial distributions of the building counts for the subcategories in the range A–F obtained from the probabilistic model in Valparaíso.

**Figure 10.**Spatial distributions of the building counts for the SARA typologies obtained from the probabilistic model in Valparaíso. The colour scale was selected in terms of the material type (green = wooden; orange = masonry; blue = reinforced concrete).

**Figure 11.**Comparison of the building counts per SARA typology for the three exposure models considered for Valparaíso, showing the initial commune-based model with merged classes, the preliminary model (simple downscaling), and the Bayesian model.

**Figure 15.**Normalised loss for the earthquake scenario in Valparaíso for the three exposure models (in each subplot) and 27 ground motion assemblages using the set of uncorrelated ground motions with Vs

_{30}(Topogr.) with the Abrahams 2015 GMPE as a benchmark.

**Figure 16.**Similar information to that shown in Figure 15, showing the normalised loss differences between each exposure model.

**Table 1.**SARA building classes proposed for the study area along with short descriptions. Average number of dwellings (Dwel./bdg), nighttime residents (Res./bdg), and replacement cost (Repl. Cost (USD)) are reported as in [67] and in https://sara.openquake.org/ (accessed date: 21 December 2021). Average footprint area (Ft./bdg. m

^{2}) values are derived from the construction quality categories per dwelling as suggested in [67] as well as the mean range of storeys and dwellings per class (see Table A1). A new typology in the range A–F is proposed in terms of the similarities of their footprint areas.

Typologies | Description | Dwel/bdg. | Ft/bdg. (m^{2}) | Res./ bdg. | Repl. Cost (USD) | New Type |
---|---|---|---|---|---|---|

ER + ETR/H:1,2 | Rammed earth, 1–2 stories | 1.25 | 78.79 | 4 | 43,750 | A |

MUR + ADO/H:1,2 | Unreinforced masonry with adobe blocks, 1–2 stories | 1.25 | 66.84 | 4 | 43,750 | |

MUR + STDRE/H:1,2 | Unreinforced masonry, dressed stone, 1–3 stories | 1.25 | 65.32 | 5 | 43,750 | |

W + WS/H:1,2 | Solid wood, between 1–2 stories | 1.25 | 80.00 | 4 | 108,000 | |

MCF/DNO/H:1,3 | Confined masonry (non-ductile), 1–3 stories | 4 | 46.67 | 5 | 94,500 | B |

MUR/H:1,3 | Unreinforced masonry, 1–3 stories | 1.5 | 70.00 | 6 | 52,500 | |

W + WLI/H:1,3 | Light wood members, 1–3 stories | 1.5 | 80.00 | 5 | 108,000 | |

CR/LWAL/DNO/H:1,3 | Reinforced concrete wall system (non-ductile), 1–3 stories | 4 | 160.00 | 14 | 288,000 | C |

CR/LWAL/DUC/H:1,3 | Reinforced concrete wall system (ductile), 1–3 stories | 4 | 140.00 | 15 | 336,000 | |

MCF/DUC/H:1,3 | Confined masonry (ductile), 1–3 stories | 4 | 160.00 | 5 | 288,000 | |

CR + PC/LWAL/H:1,3 | Precast reinforced concrete wall system, 1–3 stories | 5 | 160.00 | 18 | 360,000 | D |

MR/DNO/H:1,3 | Reinforced masonry (non-ductile), 1–3 stories | 5 | 160.00 | 18 | 360,000 | |

MR/DUC/H:1,3 | Reinforced masonry (ductile), 1–3 stories | 5 | 140.00 | 18 | 360,000 | |

CR/LWAL/DNO/H:4,7 | Reinforced concrete wall system (non-ductile), 4–7 stories | 15 | 240.00 | 54 | 1,080,000 | E |

CR/LWAL/DUC/H:4,7 | Reinforced concrete wall system (ductile), 4–7 stories | 15 | 210.00 | 54 | 1,260,000 | |

CR/LWAL/DUC/H:8,19 | Reinforced concrete wall system (ductile), 8–19 stories | 48 | 218.15 | 173 | 4,032,000 | F |

**Table 2.**Average footprint areas and heights derived for the six subcategories in the range A–F for Viña del Mar and Valparaíso.

Typology | Average Height (m) | In Viña del Mar | In Valparaíso | ||
---|---|---|---|---|---|

Average Footprint Area (m^{2}) | Proportion | Average Footprint Area (m^{2}) | Proportion | ||

A | 3.75 | 67.5 | 21% | 73 | 38% |

B | 4.5 | 71 | 59% | 66 | 50% |

C | 6 | 153 | 11% | 153 | 7% |

D | 7.5 | 115 | 5% | 153 | 3% |

E | 15 | 225 | 3% | 225 | 1% |

F | 36 | 280 | 1% | 280 | 1% |

**Table 3.**Comparison between the counts and frequencies for each building typology of the three considered exposure models.

Initial Model (Commune-Based with Merged Classes, Section 3.2.1) | Freq. (%) | Preliminary Model (Simple Downscaling, Section 3.2.2) | Freq. (%) | Probabilistic Model (Bayesian Dowsncaling, Section 3.2.4) | Freq. (%) | |
---|---|---|---|---|---|---|

W-WLI-H1-3 | 21,631 | 27.29 | 23,374 | 26.22 | 33,695 | 24.97 |

MCF-DNO-H1-3 | 20,617 | 26.01 | 23,146 | 25.97 | 31,244 | 23.15 |

W-WS-H1-2 | 9610 | 12.12 | 10,619 | 11.91 | 14,835 | 10.99 |

MUR-ADO-H1-2 | 8793 | 11.09 | 9013 | 10.11 | 14,298 | 10.59 |

MUR-H1-3 | 4723 | 5.96 | 5494 | 6.16 | 7128 | 5.28 |

MCF-DUC-H1-3 | 2712 | 3.42 | 3216 | 3.61 | 4111 | 3.05 |

CR-LWAL-DNO-H1-3 | 2537 | 3.2 | 3006 | 3.37 | 3854 | 2.86 |

ER-ETR-H1-2 | 2335 | 2.95 | 2542 | 2.85 | 3801 | 2.82 |

MR-DNO-H1-3 | 2274 | 2.87 | 2728 | 3.06 | 3449 | 2.56 |

MR-DUC-H1-3 | 924 | 1.17 | 1222 | 1.37 | 1407 | 1.04 |

CR-LWAL-DUC-H1-3 | 865 | 1.09 | 1166 | 1.31 | 1311 | 0.97 |

CR-LWAL-DNO-H4-7 | 775 | 0.98 | 1065 | 1.19 | 8293 | 6.14 |

CR-LWAL-DUC-H4-7 | 502 | 0.63 | 792 | 0.89 | 4980 | 3.69 |

MUR-STDRE-H1-2 | 452 | 0.57 | 723 | 0.81 | 689 | 0.51 |

CR-PC-LWAL-H1-3 | 388 | 0.49 | 633 | 0.71 | 603 | 0.45 |

CR-LWAL-DUC-H8-19 | 129 | 0.16 | 398 | 0.45 | 1265 | 0.94 |

∑ total | 79,267 | 100 | 89,137 | 100 | 134,963 | 100 |

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Gómez Zapata, J.C.; Zafrir, R.; Pittore, M.; Merino, Y.
Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions. *ISPRS Int. J. Geo-Inf.* **2022**, *11*, 113.
https://doi.org/10.3390/ijgi11020113

**AMA Style**

Gómez Zapata JC, Zafrir R, Pittore M, Merino Y.
Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions. *ISPRS International Journal of Geo-Information*. 2022; 11(2):113.
https://doi.org/10.3390/ijgi11020113

**Chicago/Turabian Style**

Gómez Zapata, Juan Camilo, Raquel Zafrir, Massimiliano Pittore, and Yvonne Merino.
2022. "Towards a Sensitivity Analysis in Seismic Risk with Probabilistic Building Exposure Models: An Application in Valparaíso, Chile Using Ancillary Open-Source Data and Parametric Ground Motions" *ISPRS International Journal of Geo-Information* 11, no. 2: 113.
https://doi.org/10.3390/ijgi11020113