RISeer: Inspecting the Status and Dynamics of Regional Industrial Structure via Visual Analytics

TL;DR

RISeer is an interactive visual analytics system that leverages interpretable machine learning to analyze and compare the evolution of regional industrial structures, aiding policymakers and researchers in understanding complex industrial dynamics.

Contribution

The paper introduces RISeer, a novel visualization tool combining interpretable machine learning and visual analytics to explore regional industrial structure dynamics.

Findings

Effective identification of industrial evolution patterns

Facilitates inter-regional comparison of industrial structures

Expert feedback confirms enhanced understanding of RIS dynamics

Abstract

Restructuring the regional industrial structure (RIS) has the potential to halt economic recession and achieve revitalization. Understanding the current status and dynamics of RIS will greatly assist in studying and evaluating the current industrial structure. Previous studies have focused on qualitative and quantitative research to rationalize RIS from a macroscopic perspective. Although recent studies have traced information at the industrial enterprise level to complement existing research from a micro perspective, the ambiguity of the underlying variables contributing to the industrial sector and its composition, the dynamic nature, and the large number of multivariant features of RIS records have obscured a deep and fine-grained understanding of RIS. To this end, we propose an interactive visualization system, RISeer, which is based on interpretable machine learning models and enhanced visualizations designed to identify the evolutionary patterns of the RIS and facilitate inter-regional inspection and comparison. Two case studies confirm the effectiveness of our approach, and feedback from experts indicates that RISeer helps them to gain a fine-grained understanding of the dynamics and evolution of the RIS.