代写GEOG10001 Part 2代做留学生SQL 程序

GEOG10001

Part 2 (8 %)

Task outline:

Imagine you are an NGO trying to publicise changes in food security in Asia between 2012 and 2022.  This might be to suggest where past management/investment has worked to improve the situation, or it could help highlight those areas in need of aid.  

Write up to 300  (+/-10%)  words explaining three variables you think would be suitable for an NGO to use to publicise improvements or reductions in Asian countries food security from 2012 to 2022. In other words, justify why they are good for understanding food insecurity. The data is at the country spatial scale and the variables chosen should highlight those countries in the region that improved or decreased in food security from 2012 to 2022.  Use concepts from the lectures and tutorials to briefly justify the use of these variables. Please include references in APA format. Your reference list will not be counted in the word count. (8 %)

You could use https://www.ipcinfo.org/ipc-country-analysis/en to help understand what concerns have been identified in countries in Asia during the 2012 to 2022 time period.

You can also make use of the FAO mapping data https://www.fao.org/interactive/state-of-food-security-nutrition/2-1-1/en to help get a sense of what countries have declined in or increased in food security.  Please do not use these maps in your submission.

This is only 300 words, so does not require an introduction or conclusion. Try to use a similar amount of words addressing each variable/indicator.

To reference the FAO Food security spreadsheet please follow the directions here:

https://library.unimelb.edu.au/recite/referencing-styles/apa7#datasets

You can use the LMS weblink and the date of access of the spreadsheet in LMS.

Data requirements:

Instead of the 2017 FAO data used in the tutorial please use data from the FAO food security database Tutorial_7data_10042025.csv

Meta. data:

The data has been collated from https://www.fao.org/faostat/en/#data in the Asia region for the period between 2012 to 2022.  These data include averages over 3 year periods, so the 2012 data may be an average of 2011 to 2013 data for example. I have also added a column of difference data (diff) for each variable between time periods. You might want to think about what a significance difference is for each variable, and how you could represent this in a map. Information on the data, can be found on the fao website.  I have provided an xls file Tutorial_7dataINFO_10042025 with a header row that contains more details than the .csv file can contain to be able to import.

As we are joining data to the layer ‘world countries generalized’ the country names need to match.  The FAO_Country names are those that the FAO uses, and the Country names are those that match with ‘world countries generalized’.  There are three differences where Taiwan, Hong Kong and Macao are separated out in the FAO data.  These data will not be imported as separate country polygons do not exist for them in ‘world countries generalized’.  The mainland China will, therefore, erroneously apply for all these regions.