Analyzing NYC Climate Projections: Extreme Events and Sea Level Rise

Introduction

This vignette demonstrates how to use the nyc_events_sealevel function to explore projected extreme climate events and sea level rise for New York City.

The dataset provides projections under different climate scenarios, including: - Number of heatwaves per year - Cooling and heating degree days - Projected sea level rise

Researchers, city planners, and policymakers can use this information to understand future climate risks, prepare for extreme weather events, and plan adaptation strategies.

Load the Package

library(nycOpenData)
library(dplyr)
library(ggplot2)
library(knitr)

Retrive a Sample of Data

sample_data <- nyc_events_sealevel(limit = 10)
sample_data

## # A tibble: 10 × 16
##    period           sea_lelel_rise number_of_days_year_…¹ number_of_days_year_…²
##    <chr>            <chr>          <chr>                  <chr>                 
##  1 Baseline (1981-… n/a            69                     17                    
##  2 2030s (10th Per… 6 in           85                     27                    
##  3 2030s (25th Per… 7 in           85                     27                    
##  4 2030s (75th Per… 11 in          99                     46                    
##  5 2030s (90th Per… 13 in          104                    54                    
##  6 2050s (10th Per… 12 in          91                     32                    
##  7 2050s (25th Per… 14 in          99                     38                    
##  8 2050s (75th Per… 19 in          100                    62                    
##  9 2050s (90th Per… 23 in          121                    69                    
## 10 2080s (10th Per… 21 in          104                    46                    
## # ℹ abbreviated names: ¹​number_of_days_year_with, ²​number_of_days_year_with_1
## # ℹ 12 more variables: number_of_days_year_with_2 <chr>,
## #   number_of_days_year_with_3 <chr>, number_of_heatwaves_year <chr>,
## #   average_lenth_of_heat_waves <chr>, number_of_days_year_with_4 <chr>,
## #   number_of_days_year_with_5 <chr>, cooling_degree_days <chr>,
## #   number_of_days_year_with_6 <chr>, heating_degree_days <chr>,
## #   number_of_days_year_with_7 <chr>, number_of_days_year_with_8 <chr>, …

This code retrieves 10 rows of data from NYC Open Data endpoint for extreme events and sea level rise projections.

Summarize Key Metrics

summary_table <- sample_data %>%
  select(period, number_of_heatwaves_year, cooling_degree_days, heating_degree_days) %>%
  head(10)
summary_table

## # A tibble: 10 × 4
##    period         number_of_heatwaves_…¹ cooling_degree_days heating_degree_days
##    <chr>          <chr>                  <chr>               <chr>              
##  1 Baseline (198… 2                      1156                4659               
##  2 2030s (10th P… 3                      1397                3589               
##  3 2030s (25th P… 3                      1471                3766               
##  4 2030s (75th P… 6                      1757                4049               
##  5 2030s (90th P… 7                      1903                4240               
##  6 2050s (10th P… 4                      1568                3102               
##  7 2050s (25th P… 5                      1713                3384               
##  8 2050s (75th P… 8                      2124                3754               
##  9 2050s (90th P… 9                      2335                3996               
## 10 2080s (10th P… 6                      1817                2298               
## # ℹ abbreviated name: ¹​number_of_heatwaves_year

• Period=climate period (e.g., “Baseline”, “2030s”)
• number_of_heatwaves_year=projected heatwaves per year
• cooling_degree_days/heating_degree_days=metrics of temperature extremes

This table gives a quick overview of projected extreme events for different scenarios.

Visualization

ggplot(sample_data, aes(x = period, y = as.numeric(number_of_heatwaves_year))) +
  geom_col() +
  labs(
    title = "Projected Number of Heatwaves by Scenario",
    x = "Climate Scenario",
    y = "Number of Heatwaves"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

This plot shows how the number of heatwaves is projected to change across scenarios. It helps visualize future climate risks at glance.