Temporal GIS models for cadastral data management: the knowns, unknowns and future

Abstract

Numerous temporal GIS models for cadastral data management have been proposed, and to understand the state of their art, a study that critically assesses their designs is needed. This study reviewed 11 models and noted that except with earlier designs; most of the reviewed models could store temporal land parcels with their tracks of changes. However, they lack to maintain the semantics of their data, valid times and potential records of changes, and the alternative techniques to accelerate queries. Thus, a semantical and bi-temporal modelling framework is proposed. Future studies could use the framework and focus on implementation designs to obtain more robust models.

Keywords:

• Temporal GIS models
• Temporal cadastral data
• Semantical and bi-temporal modelling
• Conceptual and logical temporal cadastral framework
• Cadastral data management

Acknowledgements

The authors sincerely thank the editor and anonymous reviewers for their valuable comments and suggestions, which improved the quality of this article.

Additional information

Funding

This work is partially supported by the project funded by the National Natural Science Foundation of China [grant number 41771410] and the Philosophy and Social Sciences Research Key Project funded by the Ministry of Education of China [grant number 19JZD023].

Notes on contributors

J. Mango

J. Mango is a PhD student at School of Geographic Sciences of East China Normal University, Shanghai, and an assistant lecturer in the Department of Transportation and Geotechnical Engineering of the University of Dar es Salaam, Tanzania. His research interests include land surveying, temporal and spatiotemporal data modelling and remote sensing.

J. Ngondo

J. Ngondo is a PhD student of East China Normal University, Shanghai, and an assistant lecturer in the Department of Geography and Economics of the Dar es Salaam University College of Education, University of Dar es salaam, Tanzania. Her research interests include physical geography, spatial data modelling and remote sensing.

D. Xu

D. Xu is a PhD graduate from the School of Geographic Sciences of East China Normal University, Shanghai. His research interests include computational geometry, pedestrian simulation, reinforcement learning and deep learning.

D. Zhang

D. Zhang is a PhD student at School of Geographic Sciences of East China Normal University, Shanghai. His research interests include computational geometry, spatiotemporal big data analysis and deep learning.

X. Li

X. Li is a professor in the School of Geographic Sciences of East China Normal University. His research interests include spatiotemporal modelling, spatial optimisation, and spatial big data application. He has published more than 100 research articles in the above areas.