Kelly Banh and Stephanie Andrade
Introduction
On average California gets about 21 inches of rain annually (Los Angeles Almanac). This rain fall is unpredictable and these heavy storm years often result in general flooding that creates treacherous conditions for people and animals. In 1976 San Diego was hit by an unpredictable Category 1 hurricane, Hurricane Kathleen. This brought a record amount of destruction including damage to various transportation routes, freeways, homes, and railways (Academic). This hurricane made vulnerable infrastructure come to light.
Since California rain fall is so sporadic, much of its current flood infrastructure in certain communities is poor and is unable to sufficiently protect communities from floods. This leaves many of these communities in danger of falling trees, stormwater pollution, damage to coastal structures caused by high coastal surf, and a number of additional health risks (Chabria et al). Moreso, these rain fall events create dangerous debris flow in previous wildfire areas causing dangerous conditions for those who have been already been impacted by wildfires (FEMA). We want to analyze the disproportionate impacts of flooding in low income communities in San Diego. Our goal is to produce visual analysis showing vulnerable locations in the county in an effort to urge city officials to prioritize the safety of these communities.
Flood data is vital because it helps pinpoint the communities that are most vulnerable to displacement. Typically, these include minority groups living closer to flood prone areas with little flood protection. Flood data allows for an in depth analysis of where infrastructure can be improved by determining what areas experience frequent flooding. This is beneficial for cities because it allows them to adapt to climate change allowing them to better protect those that reside there. In a recent article by The San Diego Union Tribune San Diego flood insurance costs are on the rise. These costs come from FEMA new Risk Rating 2.0 implementation making insurance costs more equitable. Unfortunately, raising insurance costs does not necessarily reduce flood risks (Smolen). Flood protection is vital since climate change will continue to exacerbate weather conditions and create unpredictable rain fall events. It is important for residents safety; reducing injuries and devasting property damage.
Spatial Scope
This project will be focused in San Diego, specifically, at the county level. Southern California is an arid environment that is prone to drought. Unfortunately, this also means that California is subject to unforeseen rain conditions. This leaves many communities ill prepared for disasters.
By focusing our data analysis in the county of San Diego we will be able to focus on one Southern California city that borders the coast and is subject to dangerous storm conditions. We will attempt to identify any areas that are prone to flooding such as Logan Heights and Mission Valley. Areas like these also tend to be located close to bodies of water that rise during the rainy seasons and overflow into unprotected communities.
Description of Data Sources
Our data analysis is compromised of 2 main datasets listed below. One is from the San Diego data website, SanGIS. Our second data source looks at Census data. Ultimately, our goal was to find overlaps in socioeconomic status of communities and their proximity to flood hazards. We used additional datasets and analyzed those as well, however these are the primary sets that were analyzed first.
Source 1: SanGIS Drain Conveyance Data
Using this source, I will be able to access information on municipal storm sewer system, flood control, and road culverts in San Diego, San Diego County, and Unincorporated San Diego County. The information is available for download with a user account as a pdf or shapefile. Specifically, we would like to look at San Diego city data that looks at drain conveyance and prevalence in certain areas.
Source 2: Census Reporter Median Income Median household income is important to our analysis. This allows us to see income distribution within Census tracts in San Diego County. This will be a geojson file which is useful in plotting and organizing our data on Python. We hope to eventually bring both of these datasets together to create a visualization describing the social aspect of flood prone areas.
What do we intend to find?
Our intention in this analysis is to view the impacts of flooding in low income communities in San Diego. We want to create a visualizations of showing the specific areas that experience heavy floods to see if the movement of water motivates the movement of people. Further questions we have are: Do people relocate or stay in these areas? Does choosing to stay actually cost people more in the term if repairs are constantly needed?
We want to analyze flooding and articulate its impact on people of different socioeconomic status via mapping to emphasize the importance of having flood protection in place for at risk areas.
Conclusion
This research will provide visualization of what can be improved in these areas and where funding could be placed to prevent community members from being displaced because of natural disasters. Climate change is the main driver of various environmental issues in the world today. It produces unpredictable conditions and can displace communities. It is important to prioritize the areas that are most vulnerable and create infrastructure that can prepare these communities for natural disasters.