To the answer the questions above I collected data from over 500 different cities around the world using API calls. Using latitudes, longitudes and the OpenWeatherMap API, I was able to retrieve temperature, humidity, cloudiness, and wind speed. Once I have retrieved the dependent variables from the API, I then plotted each of them against the latitude using Matplotlib scatter plots. I then split the data into northern hemisphere and southern hemisphere to see if there is a difference in correlation between the north and the south. ***All of the visualizations and the data can be found in the output_data directory.
- In the Northern Hemisphere there is a high negative correlation between latitude and max temperature. The further north from the equator you go the lower the temperature becomes. The correlation coefficient is roughly -.84 which is close to -1 (a perfect negative relationship).
- The scatter plot with the least amount of correlation is the "Northern Hemisphere - Wind Speed (mph) vs. Latitude" with a correlation coefficient of roughly .11. All these comparisons did not have much correlation besides the correlation between the max temperature and latitude.
- The only linear regression model that fits the data somewhat well is the "Northern Hemisphere - Max Temp vs. Latitude Linear Regression model". The R-squared returned a number around .73 which is close to 1 and shows that it tracks the movement of the data quite well. The same comparison for the southern hemisphere did not give that great of results but I believe it is because there was less data used in the modeling and correlation analysis.
I filtered the data from the previous dataset using what I believe to be the ideal weather conditions for vacation. Using Google APIs, I then retrieved the nearest hotel in the cities that I retrieved from the filtered data set. I then created info boxes for the hotel information and plotted the result on a heatmap. Below you can see the results.
