Project1

[Aislinging]

Project 1 [Explore Weather Trends]

Finish Date: Nov.18,2019

Tool: Tableau

Process:

SQL query:

SELECT *
FROM city_list

SELECT *
FROM city_data

SELECT *
FROM global_data

Export 3 data CSV files：

Temperature data around the world is an important subject. To measure temperature data, scientists combine measurements from the air and ocean surface.

In this project, I had analyzed local and global temperature data and compare the temperature trends where I lived to overall global temperature trends.

• Data visualization

  • Export 3 data CSV files: city_list, city_data, global_data

  • First,I chose the excel 2016 that bring my own computer for data analysis. It took me a lot of time,but it wasn't the data analysis what I wanted.

  • So I tried to use Google Spreadsheet suggested in the course tips without hesitation. Unexpectedly,the things didn't go well,whether it had a poor Internet connection or the details that come up during my slight.

  • Sad to say,I failed again. I tried to use Pycharm. Needless to say, I failed again. Because of my only Pycharm knowledge that forgotten. It was a disaster, I was so sad.

  • Finally, I chose the Tableau. Becuase it's easier for me and meets my visualization demands.

  1. Open the Tableau: choose 3 data CSV files;

  2. Drag the moving average of the selected city and global temperatures into the corresponding column or row.

     Filter:"year"( It’s good to synchronize the years of the two tables first.)

     Row(Measure)：avg_shenyang ,avg_global

     Moving average:

     Methods:

     1. Before using the Tableau, combine the data of two tables.

     2. The Tableau itself provides the measure of moving average.

     3. Excel - Data - Data analysis - Moving average.

     4. $$ =AVERAGE(B2:B4) $$

        

        

     > In statistics, a [moving average](https://en.wikipedia.org/wiki/Moving_average)(rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter.

     Rank："year"(the filter also has "year"). And for convenience sake, it's great to fill in time, whether before or after importing Tableau.

     Time Range: from 1750 to 2015.

     ​ The global temperature record is from 1750 to 2015.

     ​ The city of Shenyang‘s temperature record is from 1829 to 2013.

• My findings

  • Overall trend: the two temperature curves are similar. The world is getting hotter and hotter.

  • Changing with time, it's not hard to see the global temperature is higher than the city of Shenyang, which Shenyang‘s volatility has been so great.

  • For example,

  In 1833, the avg_global temperature is 7.70 degrees Celsius, the avg_shenyang's temperature is 6.20 degrees Celsius.

In 1862, the avg_global temperature is 7.79 degrees Celsius, the avg_shenyang's temperature is 6.08 degrees Celsius.

In 1986, the avg_global temperature is 8.73 degrees Celsius, the avg_shenyang's temperature is 7.19 degrees Celsius.

• Under the background of global warming, the amplitude of temperature increase is getting bigger and it has different features in different year. Although the temperature between global and city of Shenyang are similar, but there were exceptions.

  • For example,

  In 1852, the visualization show that the global of temperature is increasing. At the same time, Shenyang‘s temperature is the lowest in the historical record. But the coldest temperature of global is 6.94 degrees Celsius in 1812.

  Maybe I will use variance,bias,covariance.

  These are bound up with the reasons of global warming.

Environmental changes, volcanic activity,human factors, continental drift,solar radiation, ocean currents, and so on.

Correlation coefficient

​ R stands for linear relationship between two variables

​ Cov(X,Y)is the covariance of X and Y.

​ Var[X] is the variance of X.

​ Var[Y]is the variance of Y.

[Aislinging]

Project 1 [Explore Weather Trends] ZWEI

Finish Date: Nov. 18,2019

Modify Date: Nov. 20,2019

Tool: Tableau

Process:

SQL query:

SELECT *
FROM city_list

SELECT *
FROM city_data

SELECT *
FROM global_data

Export 3 data CSV files：

Temperature data around the world is an important subject. To measure temperature data, scientists combine measurements from the air and ocean surface.

In this project, I had analyzed local and global temperature data and compare the temperature trends where I lived to overall global temperature trends.

• Data visualization

  • Export 3 data CSV files: city_list, city_data, global_data

  • First,I chose the excel 2016 that bring my own computer for data analysis. It took me a lot of time,but it wasn't the data analysis what I wanted.

  • So I tried to use Google Spreadsheet suggested in the course tips without hesitation. Unexpectedly,the things didn't go well,whether it had a poor Internet connection or the details that come up during my slight.

  • Sad to say,I failed again. I tried to use Pycharm. Needless to say, I failed again. Because of my only Pycharm knowledge that forgotten. It was a disaster, I was so sad.

  • Finally, I chose the Tableau. Becuase it's easier for me and meets my visualization demands.

  1. Open the Tableau: choose 3 data CSV files;

  2. Drag the moving average of the selected city and global temperatures into the corresponding column or row.

     Filter:"year"( It’s good to synchronize the years of the two tables first.)

     Row(Measure)：avg_shenyang ,avg_global

     Moving average:

     Methods:

     1. Before using the Tableau, combine the data of two tables.

     2. The Tableau itself provides the measure of moving average.

     3. Excel - Data - Data analysis - Moving average.

     4. $$ =AVERAGE(B2:B11) $$

     

     

  > In statistics, a [moving average](https://en.wikipedia.org/wiki/Moving_average)(rolling average or running average) is a calculation to analyze data points by creating a series of averages of different subsets of the full data set. It is also called a moving mean (MM) or rolling mean and is a type of finite impulse response filter.

  Rank："year"(the filter also has "year"). And for convenience sake, it's great to fill in time, whether before or after importing Tableau.

  Time Range: from 1750 to 2015.

  ​ The global temperature record is from 1750 to 2015.

  ​ The city of Shenyang‘s temperature record is from 1829 to 2013.

• My findings

  1. Overall trend: the two temperature curves are similar. The world is getting hotter and hotter.

  2. Changing with time, it's not hard to see the global temperature is higher than the city of Shenyang, which Shenyang‘s volatility has been so great.

     For example,

     In 1829, the avg_global temperature is 8.11 degrees Celsius, the avg_shenyang's temperature is 11.80 degrees Celsius.

     In 1866, the avg_global temperature is 8.00 degrees Celsius, the avg_shenyang's temperature is 6.65 degrees Celsius.

     In 1992, the avg_global temperature is 8.93 degrees Celsius, the avg_shenyang's temperature is 7.93 degrees Celsius.

  3. Under the background of global warming, the amplitude of temperature increase is getting bigger and it has different features in different year. Although the temperature between global and city of Shenyang are similar, but there were exceptions.

     For example,

     In 1841, the visualization show that the global of temperature is increasing. At the same time, Shenyang‘s temperature is the lowest in the historical record. But the coldest temperature of global is 6.94 degrees Celsius in 1819.

  4. About the data source show that, the global temperature data records are earlier 79 years than Shenyang‘s.

  5. The Shenyang's temperature had plummeted from 1829 to 1841. And then began to rise.

  6. I had changed the rang of moving average from 2 to 10 years, my analysis of visualization was completely different. Maybe it's still not good yet. But Thank you very much.

     

  These are bound up with the reasons of global warming.

  Environmental changes, volcanic activity,human factors, continental drift,solar radiation, ocean currents, and so on.