This study aims to see the relationship between per capita income and the Gini ratio on the quality of life of 22 cities on the island of Sumatra as a reference. The data used in this study are secondary data and primary data. The secondary data used in this study are data on income per capita and the Gini ratio in 2020 sourced from BPS, and primary data obtained from SUSENAS for the calculation of Quality of Life. The analytical model used in this study is the Scatter plot model or the Klassen Typology model to determine the relationship between two variables, namely the division of Quadrant I, Quadrant II, Quadrant III, and Quadrant IV. The results of this study indicate that there are several cities that fall into different categories in each quadrant with the following information: Quadrant (1) cities that are included in the classification of per capita income and high Gini ratio and high quality of life; Quadrant (2) cities that have a low per capita income and Gini ratio but high quality of life; quadrant (3) cities located in quadrant three with low per capita income and Gini ratio and low quality of life; Quadrant (4) cities in quadrant 4 have a high per capita income and Gini ratio but low quality of life. The government should pay more attention to small cities, such as by building infrastructure in these cities so that they become cities that attract tourists to increase sources of regional income.

This study aims to see the relationship between per capita income and the Gini ratio on the quality of life of 22 cities on the island of Sumatra as a reference. The data used in this study are secondary data and primary data. The secondary data used in this study are data on income per capita and the Gini ratio in 2020 sourced from BPS, and primary data obtained from SUSENAS for the calculation of Quality of Life. The analytical model used in this study is the Scatter plot model or the Klassen Typology model to determine the relationship between two variables, namely the division of Quadrant I, Quadrant II, Quadrant III, and Quadrant IV. The results of this study indicate that there are several cities that fall into different categories in each quadrant with the following information: Quadrant (1) cities that are included in the classification of per capita income and high Gini ratio and high quality of life; Quadrant (2) cities that have a low per capita income and Gini ratio but high quality of life; quadrant (3) cities located in quadrant three with low per capita income and Gini ratio and low quality of life; Quadrant (4) cities in quadrant 4 have a high per capita income and Gini ratio but low quality of life. The government should pay more attention to small cities, such as by building infrastructure in these cities so that they become cities that attract tourists to increase sources of regional income