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:
- Building locations (represented as tax lots)
- 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:
- Buildings: expected damage and economic losses to buildings.
- Electric power system: a connectivity analysis of each building to the electric substation. There is one electric substation within Seaside.
- Transportation system: a connectivity analysis of each building to critical infrastructure. Critical infrastructure at Seaside consists of two fire stations and one hospital.
- 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
- Arc-info: novice
- 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.
- R: novice
- Image processing: novice
- Other relevant software: I’m proficient in QGIS.
Dylan, very good start. A few things to work on: 1) Research question lacks a mechanism. What factors of buildings and or their locations and/or connectivity might influence their susceptibility and the amount of damage, and why? 3) Hypotheses. Lots of hypotheses are possible. I suggest that you try rephrasing to emphasize the spatial patterns, for example “I expect that spatial patterns of damage and death from an earthquake in Seaside will be related to spatial patterns of building types (height, density of people), while spatial patterns of tsunami damage will also be related to distance from the coast (=distance to the tsunami evacuation sites) because (mechanisms – what are these? taller buildings more likely to collapse? There are some really big hotels right on the beach. Or smaller buildings because they are more likelyl to be completely inundated in a tsunami?)” 4). For Ex. 1 try conducting spatial interpolation of the tax lot or building information to create a map of zones of high vs. low potential damage. For Ex. 2, maybe conduct some kind of network analysis – what would this look like? I noticed that the stunami evacuation signs in Seaside state that people are to walk to the hills east of town, not drive, so evacuation may not be too dependent on routings. What other network processes are you itnerested in?