{"id":36339,"date":"2023-03-21T11:26:09","date_gmt":"2023-03-21T15:26:09","guid":{"rendered":"https:\/\/studentwork.prattsi.org\/infovis\/?p=36339"},"modified":"2023-03-21T18:50:06","modified_gmt":"2023-03-21T22:50:06","slug":"a-visualisation-of-housing-prices-in-california","status":"publish","type":"post","link":"https:\/\/studentwork.prattsi.org\/infovis\/visualization\/a-visualisation-of-housing-prices-in-california\/","title":{"rendered":"A visualisation of Housing Prices in California"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

This visualisation is an exploration of the housing prices in the state of California. The dataset gives an insight into household income, housing price , age of residents and location of the properties. The entire dataset is 20,000+ entries and is a fairly tidy dataset. The idea with this visualisation was to figure proximity to ocean which are so-called premium properties and plot it against median-income and median-resident age. I also wanted to explore property prices as a function ocean proximity and affordability as suggested by median income.<\/p>\n\n\n\n

Dataset<\/strong><\/p>\n\n\n\n

https:\/\/www.kaggle.com\/datasets\/camnugent\/california-housing-prices<\/a><\/p>\n\n\n\n

The dataset was from an open-data platform made available by Kaggle. The datasheet was a fairly tidy one and didn\u2019t require much cleaning.<\/p>\n\n\n\n

The data covers parameters like.<\/p>\n\n\n\n

1. longitude<\/p>\n\n\n\n

2.latitude<\/p>\n\n\n\n

3.housing_median_age<\/p>\n\n\n\n

4.total_rooms<\/p>\n\n\n\n

5.total_bedrooms<\/p>\n\n\n\n

6.population<\/p>\n\n\n\n

7.households<\/p>\n\n\n\n

8.median_income<\/p>\n\n\n\n

9.median_house_value<\/p>\n\n\n\n

10.ocean_proximity<\/p>\n\n\n\n

I further used Open Refine to check for tidyness of dataset<\/p>\n\n\n\n

Tools -used<\/strong><\/p>\n\n\n\n

Tools that were used to make this visualisation were Excel, Open Refine and R. The report was made and published using WordPress.<\/p>\n\n\n\n

Process<\/strong><\/p>\n\n\n\n

The making of this visualisation required tools like OpenRefine, excel and R. Each tool served a separate purpose<\/p>\n\n\n\n

Research<\/em><\/p>\n\n\n\n

The first phase of my process involved researching for Open-data sources that could provide this data. This also involved using excel to manually go through and understand the data and then using Open Refine to do a check on any further cleaning that may be required.<\/p>\n\n\n\n

Open-Refine- Data Cleaning<\/em><\/p>\n\n\n\n

I used Open Refine to clean the data. The data was fairly clean, hence I just exported it as a csv<\/p>\n\n\n\n

R- Data Representation<\/em><\/p>\n\n\n\n

The visualisations were done in R. I imported the tidyverse<\/em> library and then used the read_csv<\/em> to import my cleaned csv. I also added a few columns like housing price\/1000 to denote the numbers in a $100K multiples format.I also used the mutate command and generated another column to view median income in million $ multiple format<\/p>\n\n\n\n

Visualisations & Observations<\/em><\/p>\n\n\n\n

1. Geometric Point: I initially used geometric points to plot median household age vs median income and used ocean proximity of the property as an aesthetic value to understand what type of housing a certain segment of society was able to afford. This turned out to be very scattered, hence I used a logarithmic scale to tone down the data points.<\/p>\n\n\n\n

\"\"
Median income in millions for California vs median household age with colour representing proximity of house to the ocean<\/figcaption><\/figure>\n\n\n\n

https:\/\/rpubs.com\/snehganjoo\/1018160<\/a><\/p>\n\n\n\n

2. Column Data – The above data was difficult to read hence I plotted the same using a column chart which made understanding that data easier. I also used a column chart to plot ocean proximity vs median income.<\/p>\n\n\n\n

\"\"
Median income in millions for California vs median household age with colour representing proximity of house to the ocean<\/figcaption><\/figure>\n\n\n\n

https:\/\/rpubs.com\/snehganjoo\/1018155<\/a><\/p>\n\n\n\n

https:\/\/rpubs.com\/snehganjoo\/1018158<\/a><\/p>\n\n\n\n

3. Box Plot – I finally used a box-plot to plot distribution of median income vs housing cost and used the ocean proximity to reinforce premium nature of property.<\/p>\n\n\n\n

\"\"
Median income vs median house value for houses in California with property proximity to the ocean<\/figcaption><\/figure>\n\n\n\n

https:\/\/rpubs.com\/snehganjoo\/1018157<\/a><\/p>\n\n\n\n

Reflection and Critique<\/strong><\/p>\n\n\n\n

Limitations<\/em><\/p>\n\n\n\n

R has a learning curve, hence I felt limited in terms of knowledge to perform complex if, else functions. I would have also liked to plot geographic visualisation of these properties as per longitude and latitude data mentioned in the data sheet as a factor of housing cost but was limited by my knowledge of R to plot this.<\/p>\n\n\n\n

Positives<\/em><\/p>\n\n\n\n

R is an interesting tool to visualise data and can lead to lot of deep insights through analysis and visualisations. It has a bit of a learning curve but it\u2019s ability to perform complex operations and render visualisations can be quite powerful.<\/p>\n\n\n\n

Peer Critique and changes<\/em><\/p>\n\n\n\n

My final chart had some issues with the scale and was difficult to understand, I tried to use an introduced column but that didn\u2019t work hence I ended up performing a divide operation in the ggplot to fix the scale.<\/p>\n\n\n\n

Bibliography<\/strong><\/p>\n\n\n\n

California Housing Prices<\/em>. (n.d.). Retrieved March 21, 2023, from https:\/\/www.kaggle.com\/datasets\/camnugent\/california-housing-prices<\/a><\/p>\n\n\n\n

RPubs – Week 7<\/em>. (n.d.). Retrieved March 21, 2023, from https:\/\/rpubs.com\/jladams\/week_7<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

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