Find a Living Place with the Help of Data Visualization Techniques

Author: Swaminathan Ramesh Sashi

1. Introduction

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. Analysis

2.1 Software used

Tableau 9.2, Excel, and R.

2.2 Dataset

The dataset is built from multiple sources. These sources are excel sheets containing various bits of data that make up the problem set.

2.3 Approaches

In order to choose the right area to live in, recreation is an important consideration. Recreational facilities in Seattle are plotted on the map with its corresponding geo-location in Graph 1. The results depicts an even distribution of recreation areas on the map except the bay area, which has a much higher density of recreation facilities.

Graph 1: Recreation Zones

Another factor to consider is the traffic congestion present in the city. Graph 2 shows traffic congestion at different areas in the city and the magnitude is denoted by the size of the circles. It seems a prudent choice to avoid high congestion areas shown on the map. High congestion counts negatively in the consideration for possible areas of residence.


Graph 2: Traffic Congestion

Crime plays an important role in selecting areas to reside. Data for certain crimes are chosen and plotted on the map as shown in Graph 3. Different colors are used for different districts, and the size of circles represents the number of crime recordings. High crime rate counts negatively towards consideration of possible areas of residence.


Graph 3: Crime Density

New housing permits are hard to obtain in big cities and budget housing is a huge problem. I tried to see if permits were issued for construction and found that most of them were for houses between 250,000 to 500,000$ as shown in Graph4.

Graph 4: Housing permits

Educational institutions are found all over the city of Seattle as shown in the Graph 5. Thus this does not play a major role in choosing areas for living.


Graph 5: Educational Institutions

Places are also checked for connectivity and the major route for travel is shown in Graph 6. Living somewhere close to these lines of transportation is the best way to commute through the city.


Graph 6: Transport Lines

2.3 Results and Summary

Considering all the factors mentioned above, I found a place to live in as shown in the map below. This area had fairly good transportation facilities, housing and education facilities and had little crime.

3. Dataset

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9 thoughts on “Find a Living Place with the Help of Data Visualization Techniques

  1. SeungMin

    This is very interesting. Great job. I got a question: is there a correlation between housing prices and the area that is considered more “livable’? I am curious because the price of house or rent is also a big factor for how people choose the area to live in.

  2. Michael Baer

    I think this is a very useful application of statistical techniques to find an ideal place to live. However, I am curious how you went about weighting the different categories and combining the data to find an optimal location or whether you simply noticed patterns between the graphs. Additionally, I think adding price like SeungMin mentioned or adding housing availability could also be beneficial for making the decision.

  3. Ed Olson

    This is very useful when considering to live in an area, especially if it is new to you. In a class I am taking we learned about principal component analysis (PCA) that analyzes large amounts of data and variables and creates principal components that are the main differentiators of the data. I wonder if this could be applied to this case to find what factors are correlated with each other and help narrow down your choices quicker.

  4. David Sweetapple

    This is a very well done analysis. Going off of what Seung Min mentioned, I would be very interested to see how each of these variables are correlated. For example, I would expect crime density and the cost of housing permits to have some degree of negative correlation. Additionally, I think including a variable for population density could be helpful in making the decision of where to live.

    1. Wenjun Zhu

      I do agree with Bukola, job opportunity is also very important, and people could not live too far from where they work. And I have one more suggestion. When you deal with data about crimes, you drag both “district/sector” and “offense type” to “Color” on the Marks Card. But I think the different colors only represent different districts but not offense type, and the boundary and result are a bit confusing. Maybe you could set district color as ground color, then use different color to represent different types of crime, and size to represent sum of one certain crime.

  5. Tyler Parbs

    Great post! Very interesting use of data visualization in a personal, rather than business, application. From the perspective of software development, I wonder if your analysis could be implemented into a website such as or something similar to aid people considering relocating to a new area. Or, maybe this could be developed into an application that a company can offer their employees who are considering relocation within the company. Given that you had sufficient access to public datasets, the user could input a city of interest and a dashboard summarizing your analysis could help with their decision.

  6. Erik Pechnick

    This is a very cool way to see where to live in a city. Is there a way you could layer all of these factors and visualize the best place to live in the city? im thinking an area close to public transportation, schools, and a decent price would be optimal. It would be interesting to have a heat mapof the best areas.

  7. Eric Fleming

    This was interesting for me, especially as I’m starting to look for an apartment in a new city for next year. One comment that I had is that traffic congestion in an area that you live may not be a bad thing and could actually be a good thing. I say this because the dense downtown areas near the central business district tend to have the most congestion, but they also tend to be where the jobs are. Therefore, if you lived in this congested area you might be able to avoid the congestion because you are close enough to walk or bike to work or you have a short drive. If you live in an area of low congestion it’s likely that you still have to drive to/through the areas of congestion.
    Another comment is that I think in practice, deciding where to live is an interesting example of combining quantitative and qualitative factors. Some neighborhoods are just more fun, interesting, and beautiful than others, which is hard to quantify. But I bet there are some metrics that can track these factors such as density of small businesses and restaurants, the proportion of residents that are 18-30 years old, and the density of recreational facilities (which you included).

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