Work

Usability Assessment Framework for Crowdsensing Data and the Implicit Spatiotemporal Information

Year: 2026

Type: article

Abstract: Crowdsensing data serves as a crucial resource for supporting spatiotemporal applications and services. However, its inherent heterogeneity and quality uncertainty present significant challenges for data usability assessment: the evaluation methods are difficult to standardize due to the diverse types of data; assessment dimensions are predominantly confined to internal quality attributes; and a comprehensive framework for data usability evaluation remains lacking. To address these challenges, this study proposes an innovative, multi-layered usability assessment framework applicable to six major categories of crowdsensing data: specialized spatial data, Internet of Things (IoT) sensing data, trajectory data, geographic semantic web, scientific literature, and web texts. Building upon a systematic review of existing research on data quality and usability, our framework conducts a comprehensive evaluation of data efficiency, effectiveness, and satisfaction from dual perspectives—data sources and content. We present a complete system comprising primary and secondary indicators and elaborate on their computation and aggregation methods. Indicator weights are determined through the Analytic Hierarchy Process (AHP) and expert consultations, with sensitivity analysis performed to validate the robustness of the framework. The practical applicability of the framework is demonstrated through a case study of constructing a spatiotemporal knowledge graph, where we assess all six types of data. The results indicate that the framework generates distinguishable usability scores and provides actionable insights for improvement. This framework offers a universal standard for selecting high-quality data in complex decision-making scenarios and facilitates the development of reliable spatiotemporal knowledge services.

Source: ISPRS International Journal of Geo-Information

DOI: 10.3390/ijgi15010029

Authors: Ying Chen, He Zhang, Jixian Zhang, Jing Shen, Yahang Li

Institutions: Shandong University of Science and Technology, Tongji University, National Quality Inspection and Testing Center for Surveying and Mapping Products, National Administration of Surveying, Mapping and Geoinformation of China

Language: English

Topic: Mobile Crowdsensing and Crowdsourcing

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,292