Geospatial Cyberinfrastructure for Regional Economic Growth

Abstract

Cyberinfrastructure (CI) has potential to assist economic activities that involve real-time data analytics. Important challenges include the integration of essential geospatial principles (such as spatial constraints in assessing events) with CI to offer a promising pathway for solving complex problems and improving just-in-time decision-making practices for economic success. As a new contribution to extend the effectiveness of CI, we propose a novel geospatial CI (GCI) that provides support for making immediate business decisions by conducting vehicular traffic data acquisition, analysis, and distribution. Important features of the proposed GCI include heuristic traffic data portals (DPs), real-time analytic engine (AE), Cloud-Fog-Mist computing, distribution mechanism (DM), and business model (BM). According to the preliminary results through MATLAB and Python simulation using synthetic workload, the proposed GCI assists increase profit up to 90% and 70% for a fast food restaurant and a gas station, respectively. The proposed GCI can be extended for sustaining regional economic growth through the adoption of emerging technologies such as Internet-of-Things (IoT).