An analysis and visualization of weather data in a website.
The specific weather data is used to analyze how weather changes as we get closer to the equator.
All website components must created. Tools used: HTML, Bootstrap, CSS, Jupyter Notebook, csv files.
[image: landing page]
1. Data
The data is pulled from the OpenWeatherMap API to include 500 cities from around the globe, their coordinates, data on cloudiness, humidity, temperatures, and wind speed at their locations.
2. Visualization
Plotting of weather data in four graphs:
-Humidity
-Maximum Temperature
-Cloudiness
-Wind Speed
(see below visualization example: Humidity)
3. Analysis
Based on the four graphs, what conclusions can be drawn for the effects of all four factors on the weather as one moves closer to the equator?
How do weather factors change at locations as they are closer to the equator:
a) Maximum Temperature: Positive correlation (temperatures become significantly warmer as one approaches equator)
b) Humidity: No measurable correlation (level of humidity is distributed evenly across latitudes)
c) Cloudiness: No measurable correlation (data is scattered at all levels of latitude)
d) Wind Speed: No measurable correlation (graph suggests no higher concentration of higher wind speed around the equator)