A density-based heuristic for household detection in college communities through Big Data analysis

Abstract

In the age of big data, the wealth of information offers unprecedented opportunities to glean valuable insights into human behavior and activities. This study focuses on leveraging data collected from mobile applications used by students at a local college to identify their home locations and other shared points of interest. Through this research, we aim to enhance understanding of mobility patterns within student communities, providing valuable information for decision-making in transportation planning and mobility-related issues in surrounding areas. This paper introduces a heuristic based on density-related clustering to detect home locations from real-time big data collected by a mobile application. The results demonstrate satisfactory precision, with potential for further improvement as additional data is acquired, thus offering insights into potential future applications and services.