Multipurpose temporal GIS model for cadastral data management

ABSTRACT

Past and current cadastral records are among the most valuable information that different countries need to solve land management and planning problems. However, many countries still face critical challenges in adopting modern temporal cadastral systems, including a sound integration of time constructs, efficient data integration and representation methods in the designed models. This research developed a new temporal GIS model to manage spatial and non-spatial temporal cadastral data, namely cadastral parcels, land-use and land-ownerships. Three-time dimensions defined by decision and valid and transaction times were formulated to qualify parcels data. A hybrid approach fusing on the Base State with Amendment and Space-Time Composite models is used to store significant parcel changes and their relationships in two interdependent sub-databases. We used administrative plot identifiers to associate with land use and ownership records, experiencing distinct temporal variations in the third sub-database within the same main repository. We experimented our model with data from Tanzania, and the results from queries demonstrate that the designed model can store all three temporal cadastral data and track their variations semantically and effectively. This model is very useful for storing cadastral parcels, reasons, events, and the transformed parcels’ values to improve decision-making processes.

KEYWORDS:

• Temporal GIS models
• temporal cadastral data changes
• semantical cadastral data modelling
• cadastre

Acknowledgements

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

Data and codes availability statement

The data and codes that support the findings of this study are available on figshare.com and can be accessed using the following link, https://doi.org/10.6084/m9.figshare.14188862

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work is partially supported by the projects funded by the National Natural Science Foundation of China (Grant Number: 41771410) and the Ministry of Education of China (Grant Number: Ministry of Education of Humanities and Social Science Project 19JZD023).

Notes on contributors

Joseph Mango

Joseph 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 modeling and remote sensing.

Christophe Claramunt

Christophe Claramunt is a professor in computer science and research chair of French Naval Academy Research Institute. Most of his research is oriented towards geographical information science and its applications to real world problems.

Jamila Ngondo

Jamila 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 modeling and remote sensing.

Di Zhang

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

Dong Xu

Dong 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.

EbruHusniye Colak

EbruHusniye Colak COLAK is a Professor and the University Lecturer at the Department of Geomatics Engineering at Karadeniz Technical University, Turkey. Her research interests include Geographic Information Systems, Health GIS and GIS History.

Xiang Li

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