LH Blog

Lliana Hwang Dataset

By Lliana Hwang

January 30, 2019

For the dataset project, I was interested in researching housing conditions in Pittsburgh because of horror stories I’ve heard about old housing from the Industrial Revolution and frozen pipes or rat problems. One of my professors once shared the important of good housing conditions because while he was cleaning his new house, an entire layer of black dust fell on him when he lifted a ceiling panel. Most of my friends living in apartments or rent houses complain frozen pipes always take forever to fix that they have to use friends’ showers and amenities, living spaces are usually dirty and landlords don’t clean the space well, and rats are often an issue with renting houses. It’s really common to hear students complain about the horrible treatment from their landlords and how they would conspire together to raise rent together. 

For my initial idea, I was interested in the issue of old houses in poor condition being rented for high/increasing prices and compare their worth. I would have to research datasets showing increasing rent, causes for the increase, student housing, old housing location, poor housing location, etc. In the end, there were too many factors that needed to be considered because the starting idea wasn’t very concrete and I wasn’t sure how to make the visual representations of the datasets that I found. It was starting to get confusing navigating and organizing all the data, so I decided to break it down to common housing problems by looking at housing service request dataset.


Now that I’m looking at housing service emergency types, non-emergency is the highest category and has the largest gap, so I made two bar graphs with and without, so viewers have a clearer idea of how far apart the other categories are. I also included tables of the dataset for viewers to get the exact total. I didn’t want to combine it with the bar graph because the height difference of each bar is sufficient enough to eyeball the changes. The tables serve as reference for exact measurements. I did consider of editing the bar graphs with Photoshop and add icons, but I felt it might end up making the visuals confusing since some categories were better explained with words, like ‘Community Environment’ and ‘Complaint Housing.’ 


Since the non-emergency category has way too large of a gap from the rest (1000s vs 100s), I wanted to see what it was made up and broke it down into request types: community, environment, complaint housing, West Nile, and rodent. I didn’t include the other categories with request types because they’re mostly under ‘Complaint Housing.’ One issue with the datasets is I would’ve liked more details of each category from the “Housing Community Environment Data Description and Dictionary,” especially for ‘non-emergency’, but I made do with what I could.


Coding the website wasn’t too difficult because I’m going for a simple design of organizing each section of text with a supporting visual and Bootstrap made it possible to make a quick and simple structure.