Module 4. Assignment: Time Series Visualization with Tableau Public

-



I choose a few things that connect safety outcomes to the city: Passenger Injuries, Total Injuries, People Waiting or Leaving Injuries, Primary UZA Population, Primary UZA LAC Code, and Organization Type. I chose them because they enable me compare not just the number of occurrences but also the size of the population and the kind of organization that reported them. This makes it easier for me to understand the facts.

My illustration shows how the number of injuries fluctuates from month to month and how they are different for different types of companies. Public organizations tend to have more injuries than corporations or institutions, which shows where safety measures are most needed. The summer is when injuries happen the most, which might be because more people utilize public transit then. More people living in cities (higher UZA values) typically equals more events, although not always in a straight line. This might suggest that the rules for reporting or safety are different. The breakdown of "waiting/leaving" injuries indicates that safety problems aren't only with cars; they're also with stations and stops. 









I choose a few things that connect safety outcomes to the city: Passenger Injuries, Total Injuries, People Waiting or Leaving Injuries, Primary UZA Population, Primary UZA LAC Code, and Organization Type. I chose them because they enable me compare not just the number of occurrences but also the size of the population and the kind of organization that reported them. This makes it easier for me to understand the facts.

Patterns and insights that originate from the picture:
My illustration shows how the number of injuries fluctuates from month to month and how they are different for different types of companies. Public organizations tend to have more injuries than corporations or institutions, which shows where safety measures are most needed. The summer is when injuries happen the most, which might be because more people utilize public transit then. More people living in cities (higher UZA values) typically equals more events, although not always in a straight line. This might suggest that the rules for reporting or safety are different. The breakdown of "waiting/leaving" injuries indicates that safety problems aren't only with cars; they're also with stations and stops. This image depicts both how bad transportation safety concerns are and how bad they are.




 

Comments

Post a Comment

Popular posts from this blog

LIS 4317 Final Project – Diamonds Analysis

-
Image
Diamonds constitute one of the most extensively examined datasets in R due to their numerous quality attributes that affect valuation. For this undertaking, I employed the diamonds dataset from the ggplot2 package to investigate the relationship between carat (weight), cut quality, and clarity in relation to diamond prices. This dataset provided an opportunity to apply and demonstrate proficiency in distribution analysis, comparative evaluation, and multivariate visualization methods acquired during LIS 4317. Overview of the Dataset The diamonds dataset comprises 53,940 observations and ten variables, thereby satisfying the project's criterion of a minimum of fifty rows and five to twenty variables. Key variables encompass: Cost – Price of diamond in United States dollars Carat – the mass of the diamond Reduce — reduction in quality levels (Fair, Good, Very Good, Premium, Ideal) Color – the process of color grading Clarity – Evaluation of transparency and lucidity I ...
Read more

Visualizing Distributions in R

-
Image
  The goal for this week was to make a distribution visualization in R and think about how well it worked. I used the usual mtcars dataset that comes with R for this purpose. This multidimensional figure makes it evident that there is a negative correlation: as the weight of the automobile goes up, the MPG goes down. The grid also reveals that automobiles with 4, 6, and 8 cylinders are grouped together. This design follows the advice of Stephen Few and Nathan Yau, who say that tiny multiples with aligned axes are better for straightforward comparison than a single, overloaded chart. I completely agree with Few's criticism that conventional ways of visualizing data, including layering graphs, might hide the underlying structure of a dataset. This way of breaking the facts into a grid makes the tale more clearer and more honest. R-code # Load the ggplot2 library library(ggplot2) # Create a density plot ggplot(mtcars, aes(x = mpg)) + geom_density(fill = "skyblue", ...
Read more

Module 5. Part-to-Whole and Ranking Analysis

-
Image
  I used a dataset that is split into groups (T, S, R, Q, and Other) for this lesson so I could see how each part fits in with the whole. It was easy for me to choose because the numbers make it clear how the different groups are different. There are three different stories in the donut charts: The total numbers are shown in X.1. "Other" is much bigger than the others. In terms of rank, the average position shows that "Other" is way ahead of the others, with the others being close behind. Time shows how long each group takes, and we can see that one group takes most of the time. Visuals that show the relationship between parts and wholes are a quick way to show who has the biggest share. They look good on a blog and are easy to read. But it's harder to see the little changes between the slices, so a bar chart would be better some of the time. In general, I like how this chart makes the imbalance clear while also letting me know the boundaries when I...
Read more