Difference between revisions of "Ivan-big-data"

From Earlham CS Department
Jump to navigation Jump to search
(Develop queries to explore your ideas in the data)
Line 11: Line 11:
 
* http://data.un.org/Data.aspx?q=education&d=UNESCO&f=series%3aXGOVEXP
 
* http://data.un.org/Data.aspx?q=education&d=UNESCO&f=series%3aXGOVEXP
 
* http://data.un.org/Data.aspx?d=UNODC&f=tableCode%3a1
 
* http://data.un.org/Data.aspx?d=UNODC&f=tableCode%3a1
 +
 +
==Metadata==
 +
 +
*Homicide - rate per 100,000 population
 +
-- Data for 195 countries
 +
-- Min: 0.373944299393987
 +
-- Max: 60.8707260176513
 +
 +
*Education_cost - expenditure on education as % total government expenditure
 +
-- Data for 170 countries
 +
-- Min: 10.13355
 +
-- Max: 53.99897
 +
 +
*Abortion - Abortions per 1,000 women (woman 15-44)
 +
-- Data for 61 countries
 +
-- Min: 0.1
 +
-- Max: 53.7
 +
 +
*Total Population - both sexes combined (thousands)
 +
-- Data for 263 countries
 +
-- Min: 0.146
 +
-- Max: 6895889.018
 +
 +
*GDP - at current prices - US dollars
 +
-- Data for 209 countries
 +
-- Min: 150.617985349753
 +
-- Max: 186174.902651821
 +
  
 
==Data cleaning and pre-processing==
 
==Data cleaning and pre-processing==

Revision as of 19:03, 4 December 2011

  • Project title: Relationship between Homicide, Education, Abortion, HIV Incidence, Population and GDP for countries around the globe
  • Project data set: United Nations DB (UNdata)
Project Tasks

Identifying and downloading the target data set

Data sets can be founded here:

Metadata

  • Homicide - rate per 100,000 population

-- Data for 195 countries -- Min: 0.373944299393987 -- Max: 60.8707260176513

  • Education_cost - expenditure on education as % total government expenditure

-- Data for 170 countries -- Min: 10.13355 -- Max: 53.99897

  • Abortion - Abortions per 1,000 women (woman 15-44)

-- Data for 61 countries -- Min: 0.1 -- Max: 53.7

  • Total Population - both sexes combined (thousands)

-- Data for 263 countries -- Min: 0.146 -- Max: 6895889.018

  • GDP - at current prices - US dollars

-- Data for 209 countries -- Min: 150.617985349753 -- Max: 186174.902651821


Data cleaning and pre-processing

The first obstacle I faced with cleaning and pre-processing was inconsistency in countries naming. For example name China in education and name People's Republic of China in homicide... So when I did full join of country columns I realized that not all of them are in one line (things that are supposed to be in one line). So I changed names and made it unique through all 6 data sets.   

Load the data into your Postgres instance

Data-sets I downloaded were in CSV files.

  • Here is an example for inserting data-set homicide into my PQSL:

drop table homicide;
create TABLE homicide (COUNTRY varchar primary key, YEAR int, RATE float);
COPY homicide FROM '/home/postgres/HOMICIDE.csv' DELIMITER ';' CSV;

Develop queries to explore your ideas in the data

  • Query to connect all data sets with country as a unique key and to see the overlap (Use full join to see all):

select * from (((gdp inner join education_cost ON (gdp.country = education_cost.country))
inner join population ON (gdp.country = population.country))
inner join homicide ON (gdp.country = homicide.country))
inner join abortion ON (gdp.country = abortion.country)

  • This is what I used to export result from PSQL to CSV:

Copy (some_query) to '/home/postgres/something.csv' with CSV;

Develop and document the model function you are exploring in the data

Develop a visualization to show the model/patterns in the data

Tech Details
  • Node: as2
  • Path to storage space: /scratch/big-data/ivan
Results
  • The visualization(s)
  • The story