My Geographic Map of Florida COVID-19 Cases by ZIP Code

Introduction

For this mapping exercise, I chose to visualize Florida COVID-19 data from 2020, specifically examining the distribution of cases across ZIP codes throughout the state. This dataset provides a comprehensive view of how the pandemic affected different geographic regions of Florida during the early stages of the outbreak.

The map reveals fascinating patterns about population density, urban centers, and the geographic spread of COVID-19 cases across the Sunshine State.

Dataset and Process

Data Source: Florida COVID-19 data by ZIP Code(2020).

.https://drive.google.com/file/d/1fNyh8DCZAkJuXGEi0XuQGjKpR5lsGPEK/view

Challenges Faced

My biggest hurdle was Tableau Public repeatedly crashing. Since starting this project on September 1st, the app consistently froze and closed whenever I dragged fields to create the map. After multiple restarts and attempts, it finally worked properly.

The dataset used ZIP codes rather than street addresses, but this actually worked better for visualizing COVID patterns across geographic regions.

The Geographic Visualization

The map displays 1,005 ZIP codes across Florida, with each point color-coded to represent the intensity of COVID-19 cases. The darker blue dots indicate areas with higher case counts, while lighter dots represent areas with fewer cases.


Would point size or color scales help?

Yes. Adding point size variation would create dual-encoding - making high-case areas both darker AND larger. A red color scale might show differences more clearly than blue.

Could adding text labels or tool tips guide the viewer?

Definitely. City labels for Miami, Orlando, Tampa, and Jacksonville would provide geographic context. Tooltips showing ZIP code and exact case counts would make the data more accessible.

How does proximity or similarity group patterns?

Nearby ZIP codes show similar case levels, creating visible urban clusters. The color coding groups areas by intensity, revealing Florida's population corridors and metropolitan boundaries through natural clustering patterns.


Location: 5PXX5963+WX

Comments

Post a Comment

Popular posts from this blog

R Programming Journal - Omar Hamad

Image
  RStudio Installation Experience Yesterday, I switched from Windows to Mac, which was new for me. Installing R and RStudio on macOS  It was simple, but I had to change some security settings in my Mac to allow the software to run.  I downloaded the correct files from the official sites, installed them, and opened RStudio successfully.  What is an R Vector? An R vector is a basic data structure that stores a sequence of elements all of the same type, such as  numbers, words, or logical values. It’s a core part of R because it allows you to organize and work with  data efficiently. Vectors are the foundation for many operations in R, making data manipulation and  analysis faster and easier. Vectors are essential in R because they enable you to perform operations on entire collections of data at  once, rather than handling each element individually. This makes your code more concise and efficient.  Understanding vectors is crucial because most ...
Read more

Assignment 6

Code: # Task 1: Matrix Addition & Subtraction A = matrix(c(2, 0, 1, 3), ncol = 2) B = matrix(c(5, 2, 4, -1), ncol = 2) cat("Matrix A:\n") print(A) cat("\nMatrix B:\n") print(B) A_plus_B = A + B cat("\nA + B:\n") print(A_plus_B) A_minus_B = A - B cat("\nA - B:\n") print(A_minus_B) # Task 2: Create a Diagonal Matrix D = diag(c(4, 1, 2, 3)) cat("\nDiagonal Matrix D:\n") print(D) # Task 3: Construct a Custom 5x5 Matrix first_col = c(3, 2, 2, 2, 2) remaining_block = rbind(   c(1, 1, 1, 1),   diag(3, 4) ) custom_matrix = cbind(first_col, remaining_block) cat("\nCustom 5x5 Matrix:\n") print(custom_matrix) Output: > # Task 1: Matrix Addition & Subtraction > A = matrix(c(2, 0, 1, 3), ncol = 2) > B = matrix(c(5, 2, 4, -1), ncol = 2) > cat("Matrix A:\n") Matrix A: > print(A)      [,1] [,2] [1,]    2    1 [2,]    0    3 > cat("\nMatrix B:\n") Matrix B: > print(B)      [,1] [,2] [1,]   ...
Read more

Generic Functions in R

What I Did   This assignment helped me learn how R's generic functions work and the difference between S3 and S4 objects. I used the mtcars dataset to see how functions change behavior based on what kind of object they're working with.   Loading the Dataset   First, I loaded mtcars data(" mtcars ") head( mtcars ) str( mtcars )   This dataset has 11 columns with things like miles per gallon, horsepower, Etc . How Generic Functions Work   I tested some basic R functions to see how they adapt to different objects:   summary( mtcars ) plot( mtcars )   lm_model = lm (mpg ~ wt + hp, data = mtcars ) summary( lm_model )   What I noticed: when you run summary( ) on a data frame, you get min/max/mean values. But when you run it on a linear model, you get totally different outputs, like coefficients and p-values. Same function name, different behavior!   Creating S3 Objects   S3 is the simpler object system. Here's how I made one:   s3...
Read more