This project focuses on exploring the U.S. National Oceanic and Atmospheric Administration (NOAA) storm database, which provides detailed information on significant storms and weather events across the United States. The database records the location and timing of these events, as well as their impact in terms of fatalities, injuries, and property damage. Severe weather events, such as storms, can lead to major public health and economic challenges for communities and local governments. Many of these events result in significant loss of life, injury, and damage to property, making it crucial to mitigate these impacts as much as possible. The objective of this analysis is to investigate the NOAA Storm Database and answer key questions about severe weather events: Which types of events are most harmful to population health across the United States? Which types of events have the most significant economic consequences nationwide? Data was analyzed using R, with a focus on identifying the types of events that are most harmful to public health and those that result in the greatest economic losses. Graphs and tables were used to illustrate the findings

Nestor Flores background

A collection of four maps; from left to right, row-wise: 1) Cameron County ZCTAs, cropped to include -- and be bounded by -- the U.S. Census Tracts for Cameron County 2) Adjusted Population Estimates for fractionated U.S. Census Tracts arbitrarily divided by ZCTAs (adjusted population estimates sum to 421,110; original 2022 population estimates equal 421,854 -- 99.8% of county reflected) 3) The estimated population estimate by ZCTA based on the adjusted population estimates generated for Map 2. 4) The original population estimates as presented by the ACS-5 2022 population estimates (table B01001) .. **Be sure to adjust the map's usability by playing with the layers! ** ** the gray icon of stacked squares! ** To use: use the cursor to click and select the fragments in each map. Use the hovering red circle in each other map section to compare and contrast as desired. Thanks! .. library("easypackages") libraries(c("tidyverse", "tmap", "sf", "tidycensus", "tigris", "readxl")) cam_pop_tracts<- get_acs("tract", table = "B01001", state = "TX", county = "Cameron", year= 2022, survey = "acs5", geometry = TRUE) national_zctas<- zctas() crosswalk_2022 <- read_excel("C:/Users/daniel.pinon/Downloads/zip_tract_122022.xlsx") // note: you'll need to download the crosswalk file here: // https://www.huduser.gov/portal/datasets/usps_crosswalk.html cam_ZCTAs_22<- left_join(crosswalk_2022 %>% filter(USPS_ZIP_PREF_STATE == "TX") %>% filter(grepl("48061", .$TRACT)), national_zctas, by=c("ZIP"="ZCTA5CE20")) unique_intersected_zcta_tract_areas<- st_intersection(st_as_sf(cam_pop_tracts %>% filter(variable == "B01001_001")), st_as_sf(left_join(crosswalk_2022 %>% filter(USPS_ZIP_PREF_STATE == "TX") %>% filter(grepl("48061", .$TRACT)), national_zctas, by=c("ZIP"="ZCTA5CE20")) , crs = st_crs(cam_pop_tracts %>% filter(variable == "B01001_001")))) %>% dplyr::select(ZIP, GEOID, NAME, estimate, geometry) %>% cbind(.,st_area(.)) %>% unique(.) cam_pop_ests_across_ZCTA<- st_as_sf(cam_pop_tracts %>% filter(variable == "B01001_001")) %>% dplyr::select(GEOID, NAME, estimate, geometry) %>% cbind(.,st_area(.)) %>% st_drop_geometry(.) %>% left_join(., as.data.frame(unique_intersected_zcta_tract_areas), by=c("GEOID"="GEOID")) %>% unique(.) %>% dplyr::rename(., orig_area=4, intersected_area=8) %>% drop_na(.) %>% mutate(diff = intersected_area/orig_area, new_est = as.numeric(diff*estimate.x), new_est = round(new_est)) %>% group_by(ZIP) %>% mutate(zip_pop = sum(new_est)) %>% st_as_sf(.) tmap_mode("view") cam_zips_maps<- tm_shape(cam_pop_ests_across_ZCTA %>% dplyr::rename(., "New Population Estimate"=11, "New Zip Population Estimate"=12, "Original Population Estimate"=3) %>% .[,-1]) + tm_polygons(col=c("ZIP","New Population Estimate", "New Zip Population Estimate", "Original Population Estimate"), alpha=0.7) + tm_facets(nrow=2, sync=TRUE)

Using Temperature datasets analyze the comparison with histogram, scatterplot and boxplot

data prep hw