Exploratory Analysis Of Geo-Location Data

Abstract

Geography and regional human behavior may be more fully comprehended via the study of geo-locational data. A wealth of conveniences that make life easier in today's fast-paced, high-effort world. Many fields now rely heavily on geolocation and geographic information systems (GIS). Simply said, they may show geographical information and connect databases. This evaluates the effectiveness of an accommodation search in each given area as a way to demonstrate the value of geolocation. In this project, we apply K-Means Clustering to the geo-locational data we gathered from the Foursquare API (Application Programming Interface) URL (Uniform Resource Locator) in order to classify accommodations and determine which ones are best suited to a given set of coordinates. In this work, we use feature selection to identify location indicator words (LIWs) and test whether a smaller feature set improves geolocation precision.