Core Data: Generate UTCI Temp dataframe Across Time (days) and Space (ERA5 geo-unit of data)

[FanWangEcon]

Following our chat on Monday, May 29th, we will generate UTCI dataframe across time and space to allow for inequality analysis. The dataframe will be in this format:

Temperature over Time and Location Dataframe

• name: df_era5_utci_china_2020 for 2020 data, the same file structure, with df_era5_utci_china_2010 for 2010 data
• size: N by M
  • N is the number of all units of observed geo-squares (i.e., the 0.25 km by 0.25 km square units of observation)
  • M is the number of days in the year
• value in cell: average UTCI level at the $n^{th}$ geo-square on $m^{th}$ day
  • Average: It seems like the UTCI downloaded has a single number for each cell, so we don't need to average? If this is not the case, we average for each day.

A illustration of the desired output table is shown in the following Table "PM10 exposure across locations and time" from Section 1.3.1 from the Time, Location, Population, and Environmental Exposures file.

Key objective:

• [x] Develop Code that is able to transform downloaded UTCI data to the specified structure here.
• [x] Generalize the code as function so that is can be used for any country/world and at any time frame.
• [x] Generate df_era5_utci_china_2020 dataframe
• [x] Generate df_era5_utci_china_2010 dataframe

[FanWangEcon]

Following existing structure, Generate UTCI files in all potential available census years

• [ ] 1980
• [ ] 1990
• [ ] 2000
• 2010, completed
• 2020, completed.