Author Archives: sanderdy

Deaggregation of multi-hazard risks, losses, and connectivity: An application to the joint earthquake-tsunami hazard at Seaside, OR

Research Question

The Pacific Northwest is subject to the rupture of the Cascadia Subduction Zone (CSZ) which will result in an earthquake and nearfield tsunami. Low-lying communities along the coast (such as Seaside, OR) are susceptible to infrastructure damage from both earthquake ground shaking and tsunami inundation. Given resource constraints (budget, personnel, time, etc.), it is not feasible for city planners and resource managers to expect to mitigate all possible damages resulting from an earthquake/tsunami event; however, it is possible to optimize resources in order to minimize the expected damages. Consequently, a first step toward resource optimization is a thorough understanding of the risks posed by the CSZ.

For this project, I investigated how the spatial pattern of tax-lot damage and connectivity to critical infrastructure following a multi-hazard earthquake/tsunami in Seaside, OR can provide insight into possible hazard mitigation measures. The damages and connectivity were deaggregated by both hazard (earthquake vs. tsunami) as well as intensity of the event. An understanding of the deaggregated hazard provides insight into vulnerable areas within Seaside.

Description of Dataset

My dataset consists of maps of the built environment (or infrastructure) and hazard maps (earthquake ground shaking and tsunami inundation) for Seaside, OR. The infrastructure is composed of buildings, an electric power network, a transportation network, and a water supply network (Figure 1). The earthquake and tsunami hazard maps were previously generated through numerical modeling (Park, Cox, Alam, & Barbosa, 2017). Due to computational limitations, tsunami inundation is modeled using a “bare-earth” model indicating that no infrastructure is included.

In addition to the infrastructure and hazard maps, a suite of probabilistic damage codes were utilized. These damage codes implement Monte-Carlo methods to evaluate the expected damages, economic losses, and connectivity associated with earthquake/tsunami hazards.

Figure 1: Infrastructure at Seaside (Kameshwar et al., n.d.)

 

Hypotheses

The project was divided into three exercises. The questions posed by each exercise, and hypotheses are outlined below:

  1. What is the spatial pattern of economic losses resulting from a joint earthquake/tsunami event? How does each hazard contribute to this spatial pattern?
    1. Earthquakes and tsunamis pose different hazards to the built environment. The former results in strong ground shaking whereas the latter results in inundation. Tsunami inundation is much more spatially variable due to limiting characteristics such as ground elevation and friction losses. Conversely, earthquake ground shaking is not limited by elevation or friction and is subsequently not as spatially variable within a geographic region (especially at scales the size of Seaside). Because of these differences in hazardous conditions, I expect the spatial pattern of tsunami losses to be concentrated along the coast, whereas the spatial pattern of economic losses will be dispersed around Seaside.

 

  1. How does the spatial pattern of economic losses relate to the spatial pattern of tsunami momentum flux?
    1. Because there is significantly more spatial variability in the tsunami hazard compared to the earthquake hazard, I wanted to investigate how this spatial pattern relates to the spatial pattern of tsunami economic losses. Economic losses are driven by the intensity of the hazard, therefore, I expect a significant correlation between economic losses and tsunami momentum flux (a measure of both inundation depth and velocity).

 

  1. How vulnerable is Seaside’s networked infrastructure to a joint earthquake/tsunami event?
    1. While economic losses to infrastructure can play a role in hazard mitigation planning, it should not serve as the only driver. Mitigation planning should also (and perhaps more importantly) consider resident accessibility to critical services and facilities immediately following a disaster. Given the importance of resident accessibility, I wanted to perform a connectivity analysis of networked infrastructure (electricity, transportation, and water) following a joint earthquake/tsunami event. The networked infrastructure in Figure 1 shows that failure of a few key links could severely limit large populations of Seaside (g. the network is not highly “parallel”, but exhibits some “series” features). For example, failure of the pipes leading to the water treatment plant (Figure 1-d) would result in complete disconnectivity of water to Seaside. Given the structure of the networks at Seaside, I expect sharp increases in the disconnectivity if some of the key links fail.

Approaches/Methods

  1. Spatial pattern of economic losses:
    1. To evaluate the spatial pattern of economic losses, I created real market value and economic loss heatmaps. Economic loss heatmaps provide insight into densely populated regions within Seaside that could potentially benefit from a single mitigation option. Heatmaps were generated by using the kernel density estimation tool within the QGIS processing toolbox.
  2. Relationship between economic losses and tsunami momentum flux:
    1. Performing a geographically weighted regression (GWR) provided insight into the relationship between economic losses and tsunami momentum flux. Here, the percent of economic loss depended on the tsunami momentum flux. The GWR was performed using the python spatial analysis library PySal.
  3. Connectivity of networked infrastructure:
    1. Connectivity analyses between tax lots and critical infrastructure provided insight into the probability of tax lots becoming disconnected from critical infrastructure. Additionally, maps showing the probability of link failure were generated to isolate vulnerable links within the networks. The connectivity analysis was performed using the python network analysis package python-igraph.

Results

Spatial pattern of economic losses

Heatmaps showing the spatial pattern of economic losses deaggregated by both hazard and intensity are shown in Figure 2. It can be seen that a hot spot of damages is located at the central business district (CBD), and appears for low magnitude earthquake events. The tsunami damages are more evenly distributed along the coast, relative to the earthquake damages. Figure 3 shows the total economic risks for of Seaside deaggregated by hazard and intensity. Here, risk is defined as the economic losses multiplied by the annual probability of occurrence (the inverse of the return year). Quantifying the economic losses by risk allows for the isolation of events that are both likely to occur and produce significant damages. It can be seen in Figure 3 that the 250 to 1000-year events pose the highest economic risks. If economic losses are a priority, using Figures 2 and 3, a city planner could identify regions of buildings within Seaside that are vulnerable to earthquake damage. Subsequently these regions would benefit the most from mitigation options.

Figure 2: Deaggregated heatmaps of economic losses

Figure 3: Deaggregated economic risks for all of Seaside

Relationship between economic losses and tsunami momentum flux

The relationship between percent of economic losses and tsunami momentum flux was measured by performing a geographically weighted regression (GWR). A key parameter of the GWR is the bandwidth, which describes the area under which the regression is performed. A bandwidth of 200 was initially used (see to exercise 2). The bandwidth has been further optimized by comparing the resulting r2 values of multiple GWRs (Figure 4).  It can be seen that a bandwidth of 75 results in the largest r2 value, and was subsequently used for further analysis. Bandwidths below this value resulted in errors in the GWR code.

The results from the GWR with an updated bandwidth are shown in Figure 5. It can be seen that the slope is small near to the coast with hotspots of larger values located further inland. Conversely, the intercept is large near to the coast, and decreases as further inland. The intercept can be explained by the large momentum flux near to the coast and decreases as the tsunami propagates landward.

Interestingly, some of the slope values are less than 0 indicating that the damages decrease as momentum flux increases. This is likely explained by the heterogeneity of building types within Seaside. For example, concrete buildings are more resistant to tsunami damage than wood buildings. Consequently, a concrete building that experiences a large momentum flux may result in less damage than a wood building that experiences a small momentum flux. To validate this, the buildings in Seaside were deaggregated by their characteristics and a linear regression was performed (Figure 6). The buildings can be classified according to the construction material (wood vs. concrete), year built, and number of floors. Here, W1 corresponds to one-story wood building; W2 corresponds to two-story wood building; and C1 corresponds to concrete buildings with moment a frame. The concrete moment frame can be further divided into less than or greater than 4 stories. It can be seen in Figure 6 that all regression slopes are positive with relatively high r2 values. Considering the most extreme case, it can be seen that if a W1 building built before 1979 is next to a C1 building built after 2003, the resulting regression slope would likely be negative.

Figure 4: GWR bandwidth vs. r^2. Used for bandwidth selection.

Figure 5: Slope and intercept results from GWR analysis

Figure 6: Linear regression deaggregated by building characteristics

Connectivity of networked infrastructure

The probability of each tax lot being connected to critical facilities was evaluated by performing a connectivity analysis using the EPN, transportation, and water networks (see networks in Figure 1). The total fraction of tax lots disconnected from critical infrastructure are shown in Figure 7 (left-hand side). Similar to economic risks, the fraction of disconnection was multiplied by the probability of occurrence to determine the disconnectivity risk. It can be seen that for the 1000-year event, nearly all of Seaside becomes disconnected. Furthermore, the 250- to 500-year events pose the highest risk across the three networked infrastructures.

The network performance can also be characterized spatially by creating maps indicating the probability of link failure and tax lot disconnection. An example is shown in Figure 8 for the tsunami damage resulting from a 500-year event. This case was selected to view spatially because it drives the risk associated with the transportation network. Figure 8 shows that tax lots west of the Necanicum River have a high probability of becoming disconnected. Viewing the link failure map, it becomes clear that the disconnection is not driven by bridge failure, but rather from road washout.

Figure 7: Disconnection results: (left) fraction of tax lots disconnected from critical infrastructure and (right) disconnection risk

Figure 8: Probability of (left) link failure and (right) tax-lot disconnection

Significance

The ability to spatially isolate vulnerable infrastructure within a community serves as a first step in optimizing resource management. Being able to identify highly vulnerable areas, ensures that resources are not spent in areas that are already relatively resilient to the earthquake/tsunami hazard. The risk results from this analysis show that Seaside is particularly vulnerable to the 250- to 1000-year rupture of the CSZ. There is a large concentration of economic value located at the city center of seaside. If resource managers and city planners place an emphasis on reducing economic losses, then they should focus on mitigation measures to reduce damages within this region. However, more important than economic losses is resident and tourist accessibility to critical facilities following a natural disaster. Network mitigation measures should focus on reducing the damages associated with the 250- to 500-year events.

The results from this analysis are not comprehensive and future work should include:

  1. Spatial analysis of mitigation measures – Additional damage modeling should be performed with various mitigation measures in place. For example, the effect on transportation connectivity resulting from earthquake retrofitting of bridges can be incorporated into the damage model. Similar spatial maps as those produced in this project could be created in order to determine the spatial effect that mitigation measures have.
  2. Incorporate population in analyses – Climate change and natural disasters tend to have a disproportionate effect on populations within a geographic region. Future work should include spatially mapping socio-economic vulnerability indices to identify vulnerable populations within Seaside.
  3. Perform additional GWRs – It was shown that the building classification lead to counterintuitive results in the GWR. Additional GWRs could be performed by only considering similar building types with a geographic region.

Learning

Throughout this term, I learned how to both use new software as well as apply new statistical methods.

  1. Software –
    1. Python – Prior to this course, I was familiar with both QGIS and python; however, I had not used the two together. A personal goal was to learn how to perform geospatial analysis in python. This was accomplished by performing the GWR using the python library I additionally learned how to read and modify shapefiles in python using the geospatial library geopandas.
    2. QGIS processing toolbox – I learned how to use multiple tools within the QGIS processing toolbox (g. heatmap interpolation and hotspot analysis).
  2. Statistics –
    1. Hot spot analysis – Although I did not use hotspot analysis for my project, I learned about this method and did a test case for exercise 1. The hotspot analysis was used to isolate areas within Seaside that resulted in disproportionate damages relative to the surrounding area.
    2. Geographically weighted regression – Geographically weighted regression was performed to evaluate the relationship between tsunami momentum flux and percent damage.

As a significant portion of my research will be spatially-oriented, the tools and skills I learned during this term will be beneficial for future work. Furthermore, this course introduced additional geo-spatial statistic methods that were not implemented for this project but could be relevant for additional work.

References

Kameshwar, S., Farokhnia, K., Park, H., Alam, M. S., Cox, D., Barbosa, A., & van de Lindt, J. (n.d.). Probabilistic decision-support framework for community resilience: Incorporating multi-hazards, infrastructure interdepencies, and target objectives in a Bayesian network. Reliability Engineering and System Safety.

Park, H., Cox, D. T., Alam, M. S., & Barbosa, A. R. (2017). Probabilistic Seismic and Tsunami Hazard Analysis Conditioned on a Megathrust Rupture of the Cascadia Subduction Zone. Frontiers in Built Environment, 3(June), 1–19. https://doi.org/10.3389/fbuil.2017.00032

 

Estimates of connectivity to critical infrastructure in Seaside, OR following a rupture of the Cascadia Subduction Zone

Question

For exercise 3, I evaluated the connectivity of each building within Seaside, OR, to critical infrastructure following a rupture of the Cascadia Subduction Zone. The probability of connectivity for each building was determined using networks and considered the following:

  • Electric Power Network (EPN): probability that each building has electricity.
  • Transportation: probability that each building can reach the hospital or fire stations via the road network.
  • Water Supply Network: probability that each building has access to running water.

The connectivity analysis was deaggregated by hazard as well as the intensity of the event.

Tool and approach

For this exercise, I used: (1) a probabilistic earthquake/tsunami damage model to evaluate the functionality of linkages; (2) the network analysis package python-igraph to evaluate the connectivity of each tax lot to critical infrastructure; and (3) QGIS for spatial visualization of the results.

Description of steps

Networks were created to represent the connectivity of the three infrastructure components (Figure 1). A network consists of nodes connected to each other through edges. When edges are removed, a connectivity analysis can be performed to determine whether there is any path from one node to any other specific node. A disconnection in the network results in two (or more) separate networks.

Here, the earthquake and tsunami hazards cause damages to edges which are removed from the network if deemed nonfunctional. A connectivity analysis between each tax lot and critical infrastructure was performed, and each tax lot was triggered with a binary yes/no for connectivity. A Monte-Carlo approach with 10,000 simulations was implemented to determine the probability of each tax lot being connected to critical infrastructure. The resulting probabilities were then added as attributes to GIS shapefiles in order to evaluate the spatial distribution of connectivity.

Figure 1: GIS and representative networks for each infrastructure component

 

Description of results

Characteristics of the network can be described by a degree distribution. In a network, the degree of a node is the number of immediate connections that the node has to other nodes (e.g. a node connected to 3 other nodes has a degree of 3). A histogram of the degrees can be generated to describe the overall distribution of the entire network. The degree distribution for the three infrastructure components are shown in Figure 2. It can be seen that in the EPN network, most nodes are connected to two other nodes. This is likewise apparent in the network of Figure 1, as the EPN network appears more “linear” compared to the transportation and water networks. The transportation and water networks exhibit similar characteristics to each other in that the majority of nodes have a degree of three.

Figure 2: Degree distribution for each infrastructure component

Using the results from the Monte-Carlo network analysis, maps were created to show the spatial variability of connectivity. The connectivity was deaggregated by both hazard and intensity of the event, as deaggregation provides an avenue for smart mitigation planning. Although similar maps were produced for all three networked infrastructure, for brevity, only the spatial distribution of the transportation network is shown (Figure 3). The maps show the probability of each tax lot becoming disconnected from the fire stations (2 in Seaside) and hospital (1 in Seaside) via the road network. It can be observed that the tsunami hazard results in significant damage to the transportation system relative to the earthquake hazard. The result of bridge failures caused by the tsunami can be observed for intensities larger than the 500-year event. The region west of the Necanicum River becomes completely disconnected from the fire stations and hospital which are located east of the river.

In addition to the spatial deaggregation, the aggregated results provide a comprehensive overview of the connectivity. Figure 4 shows the average fraction of tax lots disconnected from critical infrastructure across all of Seaside. The three networked infrastructure systems approach complete disconnectivity for hazard intensities larger than the 1000-year event. The transportation and water networks are dominated by the tsunami for the higher magnitude events; whereas the EPN see’s significant damage from both the tsunami and earthquake. Consequently, if resource managers are planning for high magnitude events, they should invest in tsunami damage mitigation measures.

Figure 3: Spatial distribution of connectivity to fire station and hospitals

Figure 4: Fraction of tax lots connected to critical infrastructure

 

Critique

Network analysis provides a means to evaluate the connectivity of tax lots to critical infrastructure, and incorporating probabilistic methods accounts for uncertainties as opposed to a deterministic approach. While this type of analysis can be useful to determine overall connectivity, it does not account for limitations and additional stresses in the “flow” of the network. For example, damage to the transportation network would result in additional travel times to the fire stations and hospital. In order to provide a more comprehensive analysis of the impact to networked infrastructure, both connectivity and flow should be considered.

On the relationship between spatial variation of economic losses and tsunami momentum flux

Question

For Exercise 1, I evaluated the spatial pattern of economic losses resulting from a joint earthquake and tsunami event. I then deaggregated the losses by hazard (earthquake only, tsunami only, and combined) as well the intensity of the event.

For Exercise 2, I evaluated how the spatial pattern of economic losses resulting from a tsunami relates to the spatial pattern of tsunami momentum flux (a measure of velocity and inundation depth) by performing a geographically weighted regression (GWR). For this analysis, I only considered the tsunami because there is significant spatial variation in the hazard, whereas the spatial variation for the earthquake is minimal.

Tool and approach

I performed the GWR using the python library PySAL (Python Spatial Analysis Library). The independent variable was defined as the momentum flux, and the dependent variable defined as the percent loss of economic value.

Description of steps

The average losses at each building resulting from an earthquake/tsunami loss model were first converted to percent loss (loss divided by real market value), and added as an attribute to a GIS shapefile. The percent loss was used as opposed to the economic losses because each building has a different initial value. Consequently, the percent loss serves to normalize the economic losses across all buildings within Seaside. For this analysis, the results from the “1000-year” tsunami event were analyzed.

The GWR was then performed using PySAL with the momentum flux defined as the independent variable and the percent loss defined as the dependent variable. The GWR resulted in a slope and intercept at each tax lot, as well as a global r2 value. Two separate maps were generated wherein each tax lot was color coded based on values of the slope and intercept.

Description of results

The results from the GWR and a global regression are shown in Figures 1 and 2 respectively. A global r-squared value of 0.575 was obtained, indicating that the data is moderately correlated. In Figure 1, it can be seen that the intercept is larger near to the ocean, and decreases as the distance to the shore increases. This can be explained by the fact that the momentum flux is the largest near to the coast, and decreases as the tsunami propagates over the land.

Similar trends would be expected for the slope coefficients; however, it can be seen that along the coast the results are negative indicating that the economic losses decrease as the momentum flux increases. This can likely be explained by inconsistent building types within Seaside. For example, concrete buildings are able to better withstand the impact of a tsunami compared to their wood counterparts. Similarly, buildings of different heights (number of floors) have different damage properties. Consequently, because the building types are not consistent within Seaside, significant variations in the percent of loss within a small spatial region can occur (e.g. a wood building is located next to a concrete building). This would lead to a decrease in percent loss for a larger momentum flux.

Figure 1: Spatial variation of slope and intercept resulting from the GWR

Figure 2: Global regression and line of best fit

Critique

While the GWR does provide a means to evaluate correlation between two variables that are within the same geographical region, there are limitations for this particular application. The results showed negative slopes in some locations, which is likely caused by the large variation in the percent loss. To alleviate this, alternative statistical models could be developed using GWR that only consider similar building types. An example of a non-spatial regression for wood buildings with 2 and 3 floors can be seen in Figure 3. The improvement in r-squared values can be observed, and would likely translate to the GWR.

Figure 3: Example of global regression considering specific building types

 

Spatial variation of economic losses resulting from a joint earthquake/tsunami event: An application to Seaside, OR

Question

What is the spatial variability of economic value of buildings as well as losses resulting from a joint earthquake/tsunami event? How does this spatial variability relate to independent earthquake and tsunami events, as well as the intensity of the hazard?

The purpose of this analysis was to consider the spatial variability of initial economic value, as well as economic losses resulting from a joint earthquake/tsunami event. The losses were deaggregated by hazard (earthquake only, tsunami only, joint earthquake/tsunami), as well as intensity of the event (100-year, 250-year, etc.).

Tool and approach

Two methods were implemented to evaluate the spatial variability economic value and losses: (1) interpolation via kernel density, and (2) a hotspot analysis using the Getis-Ord Gi* statistic. The economic losses were determined using a probabilistic earthquake/tsunami damage and loss model. This model implements Monte-Carlo methods to estimate the expected economic losses following an earthquake/tsunami event. For this application, 10,000 simulations were ran, from which the average loss at each building was computed for earthquake only, tsunami only, and a joint earthquake/tsunami event.

Description of steps

The average losses at each building resulting from the earthquake/tsunami loss model were added as an attribute to a GIS shapefile. Two methods to evaluate the spatial distribution were considered:

  1. Interpolation via kernel density: Spatial interpolation was performed using a kernel density estimate. A kernel with a size proportional to the value of the attribute under consideration (in this case economic value/loss) is placed at each data point. A map is then created by taking the sum of all kernels. The kernel radius and shape can vary to produce different results. In this analysis, a quartic kernel was utilized with a radius of 200 meters. The interpolation was performed using the built in interpolation feature in QGIS 3.
  2. Hotspot analysis using the Getis-Ord Gi* statistic: Hotspot analysis was performed using the Getis-Ord Gi* statistic. This statistic results in a p-value and z-score at each attribute, providing insight into whether the null-hypothesis can be rejected (in this case, spatial randomness). As such, features with a small p-value and a very large (or very small) z-score indicate that the null can be rejected (or that the data is not spatially random). Consequently, applied across an entire spatial dataset, the hotspot analysis identifies statistically significant clusters of high (or low) values. The hotspot analysis was performed using the available Hotspot Analysis plugin for QGIS 3.

Description of results

The results of the analysis are shown in Figure 1. The columns correspond to interpolation and hotspot analysis respectively. The first row shows the building values, whereas the second shows the economic losses resulting from a joint earthquake/tsunami event (2,500-year return period).

Areas of high economic value and losses can be easily observed from the interpolation analysis. Here, areas of red correspond to larger damages. Similarly, statistically significant clusters of large (and small) damages can be observed from the hotspot analysis. Again, red corresponds to a statistically significant hot spot (e.g. a cluster of large values and losses), whereas blue corresponds to a statistically significant cold spot (e.g. a cluster of small economic values and losses).

A large concentration of economic value is centrally located along the coast, and is due to the presence of resorts and condominium complexes. This area is observed from both the interpolation and hotspot analysis. Interestingly, more clusters are observed from the hotspot analysis as opposed to the interpolation. This could be explained by the scaling of the interpolation. In this case, the red regions correspond to a maximum value of $20M. If this value was reduced by half, more areas of high concentration would be observed.

The hotspot analysis provides insight into statistically significant clusters of high and low values, as opposed to single points of high values; however, when comparing interpolation and hotspot analysis, it should not be neglected that the results of the latter are visually more difficult to observe. This is due to the discrete nature of the Getis-Ord Gi* statistic (e.g. each point corresponds to a p-value and z-score, as opposed to the continuous surfaces of interpolation). This results in polygons that are shaded according to confidence levels.

Figure 1: Comparison of interpolation and hotspot analysis for both initial building value and economic losses

In addition to the initial value and economic losses resulting from the 2,500-year earthquake/tsunami event, interpolated maps were deaggregated based on hazard (earthquake only, tsunami only, combined) as well as intensity of the event (return years 100, 250, 500, 1,000, and 2,500). The results are shown in Figure 2, where each row corresponds to the hazard, and each column to the intensity of the event. Furthermore, the total economic losses to all buildings in Seaside were determined based on hazard and intensity (Figure 3).

Figure 2 shows that the economic losses are spatially consistent across Seaside for the 100-year event, and begin to exhibit spatial variability as the intensity increases. Losses begin to accumulate for the 250-year event near the center of Seaside, and it can be seen that the earthquake is the primary driving force. Similar trends are observed for the 500-year event. The 1000- and 2500-year events begin to see significant tsunami losses that are not as spatially concentrated as the earthquake losses, but are more evenly distributed along the coast. Figure 3 shows that the tsunami losses begin to dominate for the 1000-year event.

Figure 2: Earthquake and tsunami hazard deaggregated by hazard and intensity

Figure 3: Total earthquake and tsunami damages across Seaside, OR

Critique

Both the interpolation and hotspot analyses have limitations. As previously mentioned the hotspot analysis can be aesthetically challenging. Additionally, difficulties may arise in communicating the confidence levels to community planners and resource managers who may not have a statistical background.

Similarly, spatial interpolation via kernel density has its own limitations. As there are subjective options when performing the interpolation and viewing the results (e.g. radius, color scheme, and maximum values), the resulting maps could easily be deceiving. Figure 4 shows the same data but use of a different radius to define the kernel. It can be seen that the map on the right appears more severe than the map on the left. The practicality of a spatial interpolation map ultimately depends on the GIS analyst.

Figure 4: Comparison of interpolation resulting from different kernel radii.

Deaggregation of infrastructure damages and functionality based on a joint earthquake/tsunami event: an application to Seaside, Oregon.

Research Question and Background

The Pacific Northwest is subject to a rupture of the Cascadia Subduction Zone (CSZ) which will consequently result in both an earthquake and tsunami. While all communities along the coast are vulnerable to the earthquake hazard (e.g. ground shaking), low lying communities are particularly vulnerable to both the earthquake as well as the subsequent tsunami. Completely mitigating all damage resulting from the joint earthquake/tsunami event is impossible, however, understanding the risks associated with each hazard individually can allow community planners and resource managers to isolate particularly vulnerable areas and infrastructure within the city.

The city of Seaside, Oregon is a low-lying community that is subject to both the earthquake and tsunami resulting from a rupture of the CSZ. The infrastructure at Seaside can be divided into four components: (1) buildings, (2) electric power system, (3) transportation system, and (4) water supply system. Similarly, the hazards can be viewed jointly (both earthquake and tsunami), as well as independently (just earthquake or tsunami).

Within this context, I’m particularly interested in looking at how the spatial pattern of infrastructure damage and functionality is related to individual earthquake and tsunami hazards via ground shaking and inundation respectively. Furthermore, I’m interested in looking at how these spatial patterns change as the intensity of the hazard increases.

Description of Dataset

The dataset I will be analyzing consists of two components: (1) spatial maps, and (2) infrastructure damage and functionality codes. Part of this analysis will be merging these two components to spatially view the infrastructure damage and functionality.

The spatial maps consist of:

  1. Building locations (represented as tax lots)
  2. Hazard maps: earthquake ground shaking and tsunami inundation hazard maps

The infrastructure damage and functionality codes implement Monte-Carlo methods to probabilistically define damages, losses, and connectivity. The four infrastructure codes consist of:

  1. Buildings: expected damage and economic losses to buildings.
  2. Electric power system: a connectivity analysis of each building to the electric substation. There is one electric substation within Seaside.
  3. Transportation system: a connectivity analysis of each building to critical infrastructure. Critical infrastructure at Seaside consists of two fire stations and one hospital.
  4. Water supply system: a connectivity analysis of each building to their respective pumping station. There are three water pumping stations within Seaside, and each building is assigned to a single pumping station.

Hypotheses

I hypothesize that the infrastructure damage is not spatially variable for the earthquake hazard, however it will be for the tsunami hazard (e.g. distance from coast). The relative damages due to tsunami will also increase as the intensity of the hazard increases.  That is, for small events, the damages will be dominated by earthquake, whereas for larger events, the damages will be dominated by the tsunami.

Approaches

While color-coordinating tax-lots based on economic losses provides a means to visualize damages throughout a study region, I am interested in learning about kernel density estimation and hot spot analysis to identify vulnerable regions (not just individual buildings). I am also interested in learning about different spatial network analysis methods, as only connectivity analyses within the infrastructure networks (electric, transportation, and water) have been considered so far.

Expected outcome

I’m hoping to produce maps showing how damages and economic losses relate to both joint hazards (earthquake and tsunami), as well as independent hazards (just earthquake or tsunami). I would also like to produce maps showing the connectivity of individual tax-lots to critical infrastructure. Furthermore, I would like to investigate visualizing both the economic losses and connectivity analysis through color-coordinating tax-lots, kernel density estimation and hot-spot analysis.

Significance

The ability to spatially isolate vulnerable areas will allow community planners and resource managers a means to better prepare mitigation plans. Deaggregating the damages and losses by infrastructure and hazard will isolate the relative importance of each, and can assist in mitigation measures. For example, identifying that the earthquake is the dominating force in producing building damages within a specific region, planners and resource managers can support retrofit options for homeowners within that region.

Level of preparation

  1. Arc-info: novice
  2. ModelBuilder and/or GIS programming in Python: Although I haven’t done GIS programming in Python, I am highly proficient in Python and am comfortable working with GIS data. Learning how to merge python and GIS should not be difficult.
  3. R: novice
  4. Image processing: novice
  5. Other relevant software: I’m proficient in QGIS.