Final Project

Research Goal

The primary objective of this study is to assess the vulnerability of New Orleans, Louisiana, to flooding events, with a specific focus on the following aspects:

1. Evaluating the City’s Susceptibility: Analyze the geographical, environmental, and infrastructural factors that contribute to flood risks in New Orleans.
   
2. Assessment of Current Anti-Flooding Systems: Examine the effectiveness of existing flood mitigation systems, focused majorly on the drainage networks and the distribution of grocery stores, to determine their adequacy in handling extreme weather events.
   
3. Understanding Socioeconomic Influences: Investigate how socioeconomic disparities, such as income levels, housing locations or population density, and accessibility, influence the city’s flood resilience.
   
4. Preparing for Future Flooding Events: Provide actionable insights and recommendations to help residents, policymakers, and urban planners prepare for and mitigate the impacts of future flooding.
   

The accompanying first map illustrates the location of Orleans Parish within Louisiana. The second map set the spatial context for this research. Both images are captured from the official website of the City of New Orleans.

Location of NOLA

Boundary and Landmass of NOLA

Background

Hurricane Katrina in 2005 was one of the most devastating natural disasters in New Orleans’ history. Beyond the destruction caused by the flooding, it exposed the city’s vulnerabilities to recurring environmental hazards that continue to pose significant threats to its infrastructure, environment, and communities. The following table highlights the natural hazards with the highest probability of recurrence in New Orleans, as identified by the Hazard Mitigation Plan of the City of New Orleans (https://ready.nola.gov/hazard-mitigation/hazards/summary/). The annual recurrence probabilities for each hazard were calculated using data from the National Oceanic and Atmospheric Administration’s (NCDC/NCEI) database. While this dataset has certain limitations, it represents the most reliable resource currently available.

natural hazard probability

Based on the data in the table, many high-probability hazards are related to flooding, leading us to conclude that it is essential to focus on flooding and its underlying causes, impacts, and mitigation strategies.

Flooding varies in type and severity, influenced by a combination of natural and anthropogenic factors. Natural variables include precipitation, topography, vegetation, soil texture, and seasonality, which collectively shape the occurrence and impact of flood events. Additionally, human activities, referred to as anthropogenic factors, further complicate flooding risks. Urbanization and changes in land use disrupt natural drainage patterns, while flood-control structures, such as drainage pumps and levees, are intended to mitigate flooding. (Source: https://ready.nola.gov/hazard-mitigation/hazards/flooding/)

Levee System

Elevation and Levee

The above two images, sourced from Wikimedia (https://commons.wikimedia.org/w/index.php?curid=40910094) and US Army Corps of Engineers (https://www.mvn.usace.army.mil/Missions/HSDRRS/), present the current levee system construction in New Orleans and its cross-section graph with the elevations in feet relative to the sea level. Although the coastal levee system has successfully mitigated coastal erosion and flooding from the Mississippi River and Lake Pontchartrain, significant vulnerabilities remain due to the other factors and require further attention to these flooding-related risks.

Precipitation Analysis

Stormwater or rainfall is one of the main reasons that causes flooding in the city. It is necessary to observe the pattern from the region’s precipitation for a flooding research.

Data The precipitation data was sourced from NOAA-NCEI (https://www.ncei.noaa.gov/cdo-web/search). It consists of daily records from multiple weather stations in the city of New Orleans for the past 10 years (12/1/2014 - 12/1/2024).

Methodology The monthly average precipitation was calculated for individual weather stations (with several obvious outliers/wrong data filtered out), followed by aggregating the values across all stations to derive an overall monthly trend. The data was further visualized to observe patterns and identify wetter periods.

Results In the visualization of monthly average precipitation by station (first figure), we see that while there were some minor deviations, the pattern is consistent across all stations where their reported data is very similar. This makes sense because they are all in a small regional area. Hence, we chose to aggregate the data. When aggregated across stations, the monthly precipitation trend (second figure) demonstrated clear seasonal patterns. A noticeable spike in precipitation occurred during the summer months, with July being the month with the highest precipitation on average, indicating this as the wettest season. You may use the month to hover over each data point in this interactive plot to see the calculated average value with it’s corresponding month in the tooltip. This observation was further confirmed by having the 10-year average calculated for each month and displayed the months with the highest average precipitation value. This result indicates that this time of the year for New Orleans is the most susceptible to flooding.

## # A tibble: 1,074 × 3
## # Groups:   STATION [18]
##    STATION     YearMonth avg_prcp
##    <chr>       <chr>        <dbl>
##  1 US1LAJF0005 2020-05     0.66  
##  2 US1LAJF0005 2020-06     0.377 
##  3 US1LAJF0005 2020-07     0.467 
##  4 US1LAJF0005 2020-08     0.317 
##  5 US1LAJF0005 2020-09     0.14  
##  6 US1LAJF0005 2020-10     0.137 
##  7 US1LAJF0005 2020-11     0.0229
##  8 US1LAJF0005 2020-12     0.116 
##  9 US1LAJF0005 2021-01     0.0872
## 10 US1LAJF0005 2021-02     0.179 
## # ℹ 1,064 more rows

## # A tibble: 121 × 2
##    YearMonth avg_prcp
##    <chr>        <dbl>
##  1 2014-12     0.120 
##  2 2015-01     0.164 
##  3 2015-02     0.0694
##  4 2015-03     0.162 
##  5 2015-04     0.416 
##  6 2015-05     0.184 
##  7 2015-06     0.117 
##  8 2015-07     0.180 
##  9 2015-08     0.0930
## 10 2015-09     0.156 
## # ℹ 111 more rows

## # A tibble: 3 × 2
##   Month avg_prcp
##   <dbl>    <dbl>
## 1     7    0.290
## 2     6    0.260
## 3     8    0.256

Soil Texture Analysis

Certain soil types would be more vulnerable to floodings, especially those caused by rainfalls, so we will analyze this environmental factor as well.

Data The soil data was from ArcGIS Online USA Soils Map Units (https://www.arcgis.com/home/item.html?id=06e5fd61bdb6453fb16534c676e1c9b9) to examine the types and characteristics of soils in New Orleans. It has shapefiles for all major soil types along with their spatial distribution across the region.

Methodology Since this shapefile of the soil data have a very high resolution, and R was not very good at displaying that amount of data in the map, it was processed and visualized using ArcGIS Pro to identify areas of New Orleans with specific soil types. The original data was clipped to the extent of the New Orleans boundary. By referring to the literature review on soil type characteristics (https://www.nrcs.usda.gov/conservation-basics/natural-resource-concerns/soil, https://www.nature.com/articles/s43247-021-00198-4#Sec2, https://rangelandsgateway.org/topics/rangeland-ecology/twelve-soil-orders), we assigned colors to represent the soil types’ ability to handle flooding, ranging from dark green for the least capable to light green for the most capable.

Results The spatial map clearly shows that all portions of New Orleans are dominated by the four soil types: Histosols, Vertisols, Inceptisols and Entisols. During the literature review, we see that Histosols is the most flood-prone type of soil due to its high organic content and poor drainage capabilities, which would lead to surface flooding. Vertisols, which is very rich in clay and exhibiting low infiltration rates, would prevent effective water absorption during heavy rainfall events. Inceptisols and Entisols are primarily located in floodplain regions, which would naturally accumulate water and further amplifying flooding risk. These demonstrate that the soil composition in New Orleans significantly contributes to its flood vulnerability, and all regions in the city are highly susceptible to flooding if there is heavy rainfall.

Soil Type

Census Demographics Analysis

It is essential to consider social and human influences in a project like this. Let us explore the population and socioeconomic data to better analyze the community’s vulnerability to floodings.

Data The 2020 Census data was from Census API. It consists the data of total population, race, median age, median income, and car ownership for the city of New Orleans.

Methodology From the data gathered with the Cenesus API, we calculated the population density and dominant racial group for each census tract. Then, We plotted the census demographics and analyzed the individual maps. After getting some initial results, we proceeded to perform the geospatial overlay analysis by overlaying two or three variables together.

# We used the following code to calculate population density
nola_data <- nola_data %>%
  mutate(
    Area_km2 = as.numeric(st_area(geometry)) / 1e6, # Convert area to square kilometers
    Population_Density = total_populationE / Area_km2
  )

# This is used to calculated the race dominance in each census tract
nola_data <- nola_data %>%
  mutate(
    White_Proportion = race_whiteE / total_populationE,  # White
    Black_Proportion = race_blackE / total_populationE,  # Black
    Asian_Proportion = race_asianE / total_populationE,  # Asian
    Hispanic_Proportion = race_hispanicE / total_populationE, # Hispanic
    Dominant_Race = case_when(
      White_Proportion >= Black_Proportion & White_Proportion >= Asian_Proportion & White_Proportion >= Hispanic_Proportion ~ "White",
      Black_Proportion >= White_Proportion & Black_Proportion >= Asian_Proportion & Black_Proportion >= Hispanic_Proportion ~ "Black",
      Asian_Proportion >= White_Proportion & Asian_Proportion >= Black_Proportion & Asian_Proportion >= Hispanic_Proportion ~ "Asian",
      Hispanic_Proportion >= White_Proportion & Hispanic_Proportion >= Black_Proportion & Hispanic_Proportion >= Asian_Proportion ~ "Hispanic",
      TRUE ~ "Other" # Handles ties or missing data
    )
  )

Results From the individual maps (the following five maps), we conclude that:

1. Tracts with higher population or population density are particularly vulnerable to flooding due to the increased exposure of people and infrastructure. That is, we should pay special attentions to the dark areas in the first two maps. These areas often face compounded risks, as denser developments can exacerbate drainage issues and limit the effectiveness of flood mitigation strategies.
   

2. Tracts with lower median income, represented with the lighter green color in the map, are particularly vulnerable to flooding due to limited access to resources for recovery and resilience-building. Residents in these areas often face challenges such as inadequate infrastructure, poor housing conditions, and fewer financial means to implement mitigation measures or relocate from high-risk zones. Addressing flooding risks in lower-income tracts requires equitable strategies that prioritize resource allocation and community support to reduce disparities in flood resilience and recovery.
   

3. Observe the Northeast side or the north side of the city where the color is really dark, these tracts have a higher median age. That means they are likely to have more elderly residents, which can present unique challenges during flooding events. Elderly individuals may face reduced mobility, chronic health issues, and greater dependence on support systems, making evacuation or relocation more complex. These findings highlight the importance of tailored resource allocation and emergency planning to address the needs of these populations. Strategies such as ensuring accessible evacuation routes, providing medical facilities, and deploying special assistance teams can play a critical role in enhancing the safety and well-being of elderly residents in flood-prone areas.
   

4. For the map that reflects the tracts with dominant racial group, we may not be able to retrieve any useful information by itself. Then, it makes sense for us to perform the geospatial overlay analysis.
   

One aspect alone cannot account for the whole, so we concluded the following results from the overlay maps (the next four maps):

1. Examining the relationship between racial distribution and socioeconomic factors, such as income, highlights groups that may face heightened challenges in responding to and recovering from flooding. An overlap is observed between predominantly Black tracts in the racial distribution map and lower-income tracts. This suggests a need for targeted support for these especially vulnerable groups. Minority groups often experience systemic disadvantages, such as limited access to flood-resistant housing and fewer resources, which increases their vulnerability.

2. The overlay map of total population and population density can be very meaningful. While the southwest part of the map may not show a large total population in the previous map, it stands out with very high population density due to the division of the tracts. This may be ignored in analyses that focus solely on total population.
   

3. Another way to represent the overlay is having the color of polygon to represent both of the two variables. Shown in the map, special attention should be given to the darker purple and blue areas, as they represent medium to high population, low to average income regions. These areas are likely to face greater challenges during flooding events due to limited financial resources and higher population density, which can strain infrastructure and recovery efforts.
   

4. We may even have the overlay of three variables, saying the population density (represented by shades of blue polygons), median income (represented by the red gradient), and percentage of carless households (represented by bubble sizes). The city should pay special attention to regions with darker blue polygons and larger bubbles in lighter red when recovering from the flooding events, as these areas are likely to require targeted interventions to ensure accessible transportation and evacuation plans for vulnerable residents. From the map, we see that in the center of the city, several tracts exhibit high population density, low to moderate median income, and a significant percentage of carless households.
   

NOTE for this map: tmap_mode(“View”) would give warning: Legend for symbol sizes not available in view mode. Therefore, this map missed the size legend for carless household rate.

We also performed the overlay for the flooding map and census demographics maps. However, the resolution of the flooding data shapefile is too high for tmap view so it would crash the program. We tried to plot it the tmap plot mode or ggplot, but the returned maps are in very bad quality (refer to the following first figure). Therefore, we chose to conduct these overlays in the ArcGIS Pro.

This overlay of the median income map with the flooding map reveals the most economically vulnerable areas. The darker green areas, covered by pink overlay, highlights regions where residents are highly possible to be influenced by the flooding if it occurs in the city’s boundary, and they will face significant challenges in recovering from flood-related damages due to limited financial resources.

Bad Quality Flooding Overlay

Flooding and Income Overlay