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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. [ABSTRACT FROM AUTHOR]

Many of today's world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given. [ABSTRACT FROM AUTHOR]

The collection, visualization, and analysis of movement data is at the forefront of geographic information science research. Movement data are generally collected by recording an object's spatial location (e.g.,XYcoordinates) at discrete time intervals. Methods for extracting useful information, for example space–time patterns, from these increasingly large and detailed datasets have lagged behind the technology for generating them. In this article we review existing quantitative methods for analyzing movement data. The objective of this article is to provide a synthesis of the existing literature on quantitative analysis of movement data while identifying those techniques that have merit with novel datasets. Seven classes of methods are identified: (1) time geography, (2) path descriptors, (3) similarity indices, (4) pattern and cluster methods, (5) individual–group dynamics, (6) spatial field methods, and (7) spatial range methods. Challenges routinely faced in quantitative analysis of movement data include difficulties with handling space and time attributes together, representing time in GIS, and using classical statistical testing procedures with space–time movement data. Areas for future research include investigating equivalent distance comparisons in space and time, measuring interactions between moving objects, developing predictive frameworks for movement data, integrating movement data with existing geographic layers, and incorporating theory from time geography into movement models. In conclusion, quantitative analysis of movement data is an active research area with tremendous opportunity for new developments and methods. [ABSTRACT FROM AUTHOR]