Methodology and Motivation

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Top Highlights

• f a single item in a city has a high number of data classified as spam, and those data have a relatively small standard deviation from users who are not classified as spammers, it suggests that these data are probably misclassified, and the algorithm corrects the classification accordingly. These filters are crucial to ensure data accuracy and objectivity.

• 30 sophisticated filters to ensure data accuracy and integrity. The efficacy of each filter is enhanced as more inputs are included.

• he manually collected data from each source are entered twice yearly and given a weight that is three times higher than user-generated input to improve the reliability of the data.

• Numbeo performs both automatic and semi-automatic filters (algorithms) to reduce noise in the collected data. Numbeo restricts entries from specific IP addresses that are identified as spammers, including public proxies and Tor nodes.

• Methodology

• Numbeo's Cost of Living Index is calculated based on our estimated average expenses for a four-person family in a specific city

• Currently, the weights used are as follows:

• Gasoline (1 liter) | Transportation | 60 | 0

• Rice (white), (1kg) | Markets | 14 | 0 | | Eggs (regular) (12) | Markets | 20 | 0 | | Local Cheese (1kg) | Markets | 12 | 0 | | Chicken Fillets (1kg) | Markets | 15 | 0 | | Beef Round (1kg) (or Equivalent Back Leg Red Meat) | Markets | 15 | 0 | | Apples (1kg) | Markets | 31 | 0 | | Banana (1kg) | Markets | 25 | 0 | | Oranges (1kg) | Markets | 30 | 0 | | Tomato (1kg) | Markets | 22 | 0 | | Potato (1kg) | Markets | 24 | 0 | | Onion (1kg) | Markets | 10 | 0 | | Lettuce (1 head) | Markets | 18 | 0 | | Water (1.5 liter bottle) | Markets | 30 |

• Milk (regular), (1 liter) | Markets | 25 | 0 | | Loaf of Fresh White Bread (500g) | Markets | 31 | 0 |