# Merging Spatio-Temporal Objects and Business Processes: Land Reform Process Case Study
###### Abstract  
Although significant progress has been achieved in the development of spatio-temporal models that capture real-world phenomena, more interaction between phenomenon-based and business-based modeling approaches is still required. This represents a much-needed step forward in balancing the management and scientific aspects of many urban applications. The main goal of this paper is to provide an integrated modeling framework that combines MADS (Modeling of Application Data with Spatio-temporal Features), a specific conceptual spatiotemporal model that successfully represents geographical data, with BPMN (Business Process Model Notation), which models and determines how business processes interact with geographic objects. The integration of MADS and BPMN guarantees clarity, extensibility, and autonomy between data and management operations, as it portrays spatio-temporal abstractions on a conceptual level. This integration offers a unique opportunity to accurately represent, understand, and explain the rationale behind the changes occurring in space and time. The approach has been implemented within a PostgreSQL/PostGIS database management system. A case study implementation shows the practical applicability and potential of this modeling approach. Geographic Information System; spatio-temporal data modeling; conceptual modeling; business process modeling; logical modeling +
Footnote †: journal: Medical Informatics  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
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of GIS. This methodology should start with analyzing and modeling the case study and extend to data manipulation and visualization tasks. Most GIS predominantly rely on logical models for representation data with spatio-temporal features. This practice can be insufficient when addressing complex case studies [4]. These complex scenarios require conceptual models offering a clearer and more comprehensive data representation, particularly when data needs to be shared and comprehended across different organizations. To this end, specific conceptual modeling approaches for spatio-temporal data have been developed and are valuable for describing the complexity of a specific application of interest as the concepts behind them are easy to understand and apply for non-database specialists [5]. They are well-suited for representing geographical objects within a specific geographical area, such as a city, and excel in showing the spatial and/or temporal characteristics of various elements like cadasters, buildings, roads, rivers, and lakes. Furthermore, they capture topological relationships, including for example, \"inclusion\" relationships between buildings and cadasters. Nonetheless, these conceptual models fall short when tasked with describing the intricate data management processes within an organization, particularly when data management tasks take precedence. Consider, for instance, the management of road traffic in this city as an illustrative example. This process involves identifying critical road sections, analyzing traffic congestion during specific events, and formulating evacuation plans for emergency situations. Although this process comprises interconnected events closely related to the physical entities modeled (such as roads, intersections, and bridges), an accurate representation of it cannot be effectively achieved using the conceptual models for data alone. Indeed, it is important to seamlessly incorporate both the representation of real-world objects and management concerns into the realm of conceptual modeling [4]. However, the convergence of these two modeling approaches into a unified and comprehensive spatio-temporal modeling framework remains a challenge. Consequently, our research is steadfastly dedicated to overcoming this challenge by crafting a geographical modeling framework that harmoniously accommodates both perspectives within a complementary framework.