GIS-based NNA and Kernel Density Analysis for Identifying Distribution of Restaurant's Popularity Index in Bandung

Abstract

With the advancement of information and communication technology, geographic information systems (GIS) also grow. The existence of GIS allows problems to be solved as much as possible by paying attention to the surrounding space. GIS applications have been widely applied in everyday life including in the culinary field. The existence of GIS in the culinary field can make it easier to find location information where a restaurant is located and find out how the restaurant's popularity index is. This research focuses on using NNA and KDA to analyze distribution patterns formed from each classification of restaurant popularity index in Bandung and the density of the restaurant point. Restaurant data containing restaurant names, restaurant addresses, restaurant types, food types, and restaurant popularity indexes were obtained from Zomato using Zomato's Application Programming Interface (API). The result of this research are spatial distribution pattern of the high, medium, and low popularity restaurants in Bandung City showing the same characteristics, clustering and has a large density in several sub-districts.