Global Land and Surface Temperature Trends Analysis

Assessing climate change year by year

By IBM Watson AI XPRIZE - Environment [source]

About this dataset

> This dataset from Kaggle contains global land and surface temperature data from major cities around the world. By relying on the raw temperature reports that form the foundation of their averaging system, researchers are able to accurately track climate change over time. With this dataset, we can observe monthly averages and create detailed gridded temperature fields to analyze localized data on a country-by-country basis. The information in this dataset has allowed us to gain a better understanding of our changing planet and how certain regions are being impacted more than others by climate change. With such insights, we can look towards developing better responses and strategies as our temperatures continue to increase over time

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How to use the dataset

> ## Introduction > This guide will show you how to use this dataset to explore global climate change trends over time. > > ## Exploring the Dataset > - Select one or more countries by using df[df['Country']=='countryname'] command in order to filter out any unnecessary information that is not related to those countries; > > - Use df.groupby('City')['AverageTemperature'] command in order to group all cities together with their respective average temperatures; > > - Compute basic summary statistics such as mean or median for each group with df['AverageTemperature'].{mean(),median()}, where {} can be replaced with mean or median according various statistic requirements; > > 4 .Plot a graph comparing these results from line plots or bar charts with pandas plot function such as df[column].plot(kind='line'/'bar'), etc., which can help visualize certain trends associated form these groups > > You can also use latitude/longitude coordinates provided alongwith every record further decompose records by location using folium library within python such as folium maps that provide visualization features & zoomable maps alongwith many other rendering options within them like mapping locations according different color shades & size based on different parameters given.. These are just some ways you could visualize your data! There are plenty more possibilities! >

Research Ideas

> - Analyzing temperature changes across different countries to identify regional climate trends and abnormalities. > - Investigating how global warming is affecting urban areas by looking at the average temperatures of major cities over time. > - Comparing historic average temperatures for a given region to current day average temperatures to quantify the magnitude of global warming in that region.

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > Data Source > >

License

> > > License: Dataset copyright by authors > - You are free to: > - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. > - Adapt - remix, transform, and build upon the material for any purpose, even commercially. > - You must: > - Give appropriate credit - Provide a link to the license, and indicate if changes were made. > - ShareAlike - You must distribute your contributions under the same license as the original. > - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: GlobalLandTemperaturesByCountry.csv

File: GlobalLandTemperaturesByMajorCity.csv

Acknowledgements

> If you use this dataset in your research, please credit the original authors. > If you use this dataset in your research, please credit IBM Watson AI XPRIZE - Environment.