The representation and management of evolving features in geospatial databases

Geographic features change over time, this change being the result of some kind of event or occurrence. It has been a research challenge to represent this data in a manner that reflects human perception. Most database systems used in geographic information systems (GIS) are relational, and change is either captured by exhaustively storing all versions of data, or updates replace previous versions. This stems from the inherent diffculty of modelling geographic objects in relational tables. This diffculty is compounded when the necessary time dimension is introduced to model how those objects evolve. There is little doubt that the object-oriented (OO) paradigm holds signi cant advantages over the relational model when it comes to modelling real-world entities and spatial data, and it is argued that this contention is particularly true when it comes to spatio-temporal data. This thesis describes an object-oriented approach to the design of a conceptual model for representing spatio-temporal geographic data, called the Feature Evolution Model (FEM), based on states and events. The model was used to implement a spatio-temporal database management system in Oracle Spatial, and an interface prototype is described that was used to evaluate the system by enabling querying and visualisation

Design and Implementation of an Object-Oriented Space-Time GIS Data Model

Geographic data are closely related to both spatial and temporal domains. Geographic information systems (GIS) can capture, manage, analyze, and display spatial data. However, they are not suitable for handling temporal data. Rapid developments of data collection and location-aware technologies stimulate the interests of obtaining useful information from the historical data. Researchers have been working to build various spatio-temporal data models to support spatio-temporal query. Nevertheless, the existing models exhibit weaknesses in various aspects. For instance, the snapshot model is plagued with data redundancy and the event-based spatio-temporal data model (ESTDM) is limited to raster dataset. This study reviews existing spatio-temporal data models in order to design an object-oriented space-time GIS data model that makes additional contributions to processing spatio-temporal data. A binary large object (BLOB) data type, labeled Space-Time BLOB, is added to ArcGIS geodatabase data model to store instantiated space-time objects. A Space-Time BLOB is associated with an array that contains the spatial and temporal information for an object at different time points and time intervals. This study also implements a space-time GIS prototype system, along with a set of spatio-temporal query functions, based on the proposed space-time GIS data model

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Standards-based sensor web for wide area monitoring of power systems

The balance of supply and demand of energy is the key factor in the stability of power systems. A small disturbance in the supply demand relationship, if not properly handled, can cascade into a major outage, costing millions of dollars to the stakeholders. Proper monitoring and exchange of critical information in real time is the only solution to prevent the instability in this vulnerable system. But, the disparity in the protocols used by power utilities and the lack of infrastructure for information exchange are proving to be hindrance to obtaining a reliable de-regularized power industry. In this thesis, an emerging Sensor Web Enablement (SWE) has been adapted for the wide area monitoring of power systems. SWE and CIM provide a solution to both problems; the heterogeneity of data and the lack of central repository of the data for proper action. The sensor data from utilities that are published in CIM were modeled thorough a SensorML and exposed via a Sensor Observation Service (SOS). This provides a standard method for discovering and accessing the sensor data between utilities and facilitates rapid response functionality to handle contingences

Historical collaborative geocoding

The latest developments in digital have provided large data sets that can increasingly easily be accessed and used. These data sets often contain indirect localisation information, such as historical addresses. Historical geocoding is the process of transforming the indirect localisation information to direct localisation that can be placed on a map, which enables spatial analysis and cross-referencing. Many efficient geocoders exist for current addresses, but they do not deal with the temporal aspect and are based on a strict hierarchy (..., city, street, house number) that is hard or impossible to use with historical data. Indeed historical data are full of uncertainties (temporal aspect, semantic aspect, spatial precision, confidence in historical source, ...) that can not be resolved, as there is no way to go back in time to check. We propose an open source, open data, extensible solution for geocoding that is based on the building of gazetteers composed of geohistorical objects extracted from historical topographical maps. Once the gazetteers are available, geocoding an historical address is a matter of finding the geohistorical object in the gazetteers that is the best match to the historical address. The matching criteriae are customisable and include several dimensions (fuzzy semantic, fuzzy temporal, scale, spatial precision ...). As the goal is to facilitate historical work, we also propose web-based user interfaces that help geocode (one address or batch mode) and display over current or historical topographical maps, so that they can be checked and collaboratively edited. The system is tested on Paris city for the 19-20th centuries, shows high returns rate and is fast enough to be used interactively.Comment: WORKING PAPE

An Analytics Platform for Integrating and Computing Spatio-Temporal Metrics

In large-scale context-aware applications, a central design concern is capturing, managing and acting upon location and context data. The ability to understand the collected data and define meaningful contextual events, based on one or more incoming (contextual) data streams, both for a single and multiple users, is hereby critical for applications to exhibit location- and context-aware behaviour. In this article, we describe a context-aware, data-intensive metrics platform —focusing primarily on its geospatial support—that allows exactly this: to define and execute metrics, which capture meaningful spatio-temporal and contextual events relevant for the application realm. The platform (1) supports metrics definition and execution; (2) provides facilities for real-time, in-application actions upon metrics execution results; (3) allows post-hoc analysis and visualisation of collected data and results. It hereby offers contextual and geospatial data management and analytics as a service, and allow context-aware application developers to focus on their core application logic. We explain the core platform and its ecosystem of supporting applications and tools, elaborate the most important conceptual features, and discuss implementation realised through a distributed, micro-service based cloud architecture. Finally, we highlight possible application fields, and present a real-world case study in the realm of psychological health