Weather and Housing in North America
Exploring the Relationship between Weather and Housing Conditions in 2012
By [source]
About this dataset
This comprehensive dataset explores the relationship between housing and weather conditions across North America in 2012. Through a range of climate variables such as temperature, wind speed, humidity, pressure and visibility it provides unique insights into the weather-influenced environment of numerous regions. The interrelated nature of housing parameters such as longitude, latitude, median income, median house value and ocean proximity further enhances our understanding of how distinct climates play an integral part in area real estate valuations. Analyzing these two data sets offers a wealth of knowledge when it comes to understanding what factors can dictate the value and comfort level offered by residential areas throughout North America
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How to use the dataset
This dataset offers plenty of insights into the effects of weather and housing on North American regions. To explore these relationships, you can perform data analysis on the variables provided.
First, start by examining descriptive statistics (i.e., mean, median, mode). This can help show you the general trend and distribution of each variable in this dataset. For example, what is the most common temperature in a given region? What is the average wind speed? How does this vary across different regions? By looking at descriptive statistics, you can get an initial idea of how various weather conditions and housing attributes interact with one another.
Next, explore correlations between variables. Are certain weather variables correlated with specific housing attributes? Is there a link between wind speeds and median house value? Or between humidity and ocean proximity? Analyzing correlations allows for deeper insights into how different aspects may influence one another for a given region or area. These correlations may also inform broader patterns that are present across multiple North American regions or countries.
Finally, use visualizations to further investigate this relationship between climate and housing attributes in North America in 2012. Graphs allow you visualize trends like seasonal variations or long-term changes over time more easily so they are useful when interpreting large amounts of data quickly while providing larger context beyond what numbers alone can tell us about relationships between different aspects within this dataset
Research Ideas
- Analyzing the effect of climate change on housing markets across North America. By looking at temperature and weather trends in combination with housing values, researchers can better understand how climate change may be impacting certain regions differently than others.
- Investigating the relationship between median income, house values and ocean proximity in coastal areas. Understanding how ocean proximity plays into housing prices may help inform real estate investment decisions and urban planning initiatives related to coastal development.
- Utilizing differences in weather patterns across different climates to determine optimal seasonal rental prices for property owners. By analyzing changes in temperature, wind speed, humidity, pressure and visibility from season to season an investor could gain valuable insights into seasonal market trends to maximize their profits from rentals or Airbnb listings over time
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: Weather.csv
| Column name |
Description |
| Date/Time |
Date and time of the observation. (Date/Time) |
| Temp_C |
Temperature in Celsius. (Numeric) |
| Dew Point Temp_C |
Dew point temperature in Celsius. (Numeric) |
| Rel Hum_% |
Relative humidity in percent. (Numeric) |
| Wind Speed_km/h |
Wind speed in kilometers per hour. (Numeric) |
| Visibility_km |
Visibility in kilometers. (Numeric) |
| Press_kPa |
Atmospheric pressure in kilopascals. (Numeric) |
| Weather |
Weather conditions. (Text) |
File: Housing.csv
| Column name |
Description |
| longitude |
The longitude of the location. (Numeric) |
| latitude |
The latitude of the location. (Numeric) |
| housing_median_age |
The median age of the housing in the area. (Numeric) |
| total_rooms |
The total number of rooms in the area. (Numeric) |
| total_bedrooms |
The total number of bedrooms in the area. (Numeric) |
| population |
The population of the area. (Numeric) |
| households |
The number of households in the area. (Numeric) |
| median_income |
The median income of the area. (Numeric) |
| median_house_value |
The median house value of the area. (Numeric) |
| ocean_proximity |
The proximity of the area to the ocean. (Categorical) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .