Applying text mining for identifying future signals of land administration

Abstract

Companies and governmental agencies are increasingly seeking ways to explore emerging trends and issues that have the potential to shape up their future operational environments. This paper exploits text mining techniques for investigating future signals of the land administration sector. After a careful review of previous literature on the detection of future signals through text mining, we propose the use of topic models to enhance the interpretation of future signals. Findings of the study highlight the large spectrum of issues related to land interests and their recording, as nineteen future signal topics ranging from climate change mitigation and the use of satellite imagery for data collection to flexible standardization and participatory land consolidations are identified. Our analysis also shows that distinguishing weak signals from latent, well-known, and strong signals is challenging when using a predominantly automated process. Overall, this study summarizes the current discourses of the land administration domain and gives an indication of which topics are gaining momentum at present.