When you move into a new city, do you wonder which area is the best for living? In this article, I use data visualization techniques to help a family find the best area to live in Seattle. Although other cities can also be analyzed in a similar way, Seattle is chosen as an example because of ready availability of public datasets.
2.1 Software used
Tableau 9.2, Excel, and R.
The dataset is built from multiple sources. These sources are excel sheets containing various bits of data that make up the problem set.… Read more
Data visualization is a useful tool to analyze exploratory data and display the results. This article attempts to explain a few data visualization exercises which aim to analyze a composite dataset consisting of 911-phone records as well as the subsequent police report filings.
2.1 Software used
Tableau 9.2 and Excel.
The dataset consists of the following variables derived from the website of UW Madison Police Department:
Incident number (a unique id), incident date, incident time, location, zip codes, significance, incident description, and incident type. The dataset includes data from the years 2012-2015.… Read more
We would like to introduce a panel discussion on Industrial Data Analytics Courses: The Need, Content and Expectations at the IIE annual conference that was held on May 30 – June 2 in Nashville, TN.
Three panelists joined this session, including Prof. Shiyu Zhou (University of Wisconsin-Madison), Prof. Satish Bukkapatnam (Texas A&M University), and Prof. Soundar Kumara (Penn State University). All panelists have been leading scholars in this field and have already offered at least one course related to industrial data analytics. The moderator of this session was Prof. Kaibo Liu (University of Wisconsin – Madison). The recorded video for this featured panels session is shown below:
The goal of this session is to enhance the education of industrial data analytics in Industrial Engineering departments by providing an interactive forum for the IE community to communicate on the course design and share information on the course materials.… Read more
This is the final part of Data Visualization and Analysis with R and Tableau Series, for the previous parts, please refer to part 1/3 and part 2/3 for details.
There are roughly 2 billion people around the world consuming alcoholic drinks. Among them, a great number of people are considered as “binge drinking”, which means consuming 5 or more drinks per single occasion for male and 4 or more drinks for female. Drinking alcohol has caused many problems and there are about 88,000 people died from alcohol-related causes annually. Moreover, $223.5 billion has been spent for alcohol misuse problems and 75% of the cost are related to binge drinking.… Read more
In the previous part, we introduced some data visualization techniques and showed the relationship of GDP, healthcare investment and life expectancy grouped by countries. In this article, we will focus on poverty and obesity, and analyze the dataset from United States Department of Agriculture. Specifically, by analyzing the dataset using data visualization techniques, we want to explore the following questions:
1. How does poverty rate vary within racial and ethnic groups?
2. Does the milk/soda price ratio influence obesity rate?
3. Are the obesity and diabetes rates higher in fast paced states like New York States than in agricultural states like Wisconsin?… Read more