Big Data Analytics In The Geo-Spatial Domain
Creators: name: Romulo Goncalves
name: Ivanova, Milena
Publisher: name: Zenodo
Year: 2015

description: Big data collections in many Scientific domains have inherently rich spatial and geospatial features. Spatial location is among the core aspects of data in Earth observation sciences,astronomy,and seismology to name a few. The NLeSC project aims at developing software technology and tools for building of generic geo-spatial analysis systems with focus on three dimensional (3D) city models. The project has three major partners, VU Geographic Information Systems (GIS) group / Geodan represented by Prof. Henk Scholten, CWI Database group / MonetDB solutions represented by Prof. Martin Kersten and TUDelft Geo-information for Crisis Management group represented by Prof. Sisi Zlatanova.

Digital 3D city models play a crucial role in research of urban phenomena; they form the basis for e.g. flow simulations (e.g. wind streams, water runoff and heat island effects), urban planning, analysis of underground formations, etc. Urban scenes consist of large collections of complex objects which have rich semantic properties, such as materials, colors, etc. Modeling and storing these properties indicating the relationships between them is best handled in a relational database.

Database management systems (DBMSs) are a well-established solution when it comes to archiving, filtering, analysis, and correlation of large data collections. Ability to perform analysis near data is one of the key requirements identified by the 4th Paradigm to handle the data deluge. A single spatial DBMS offers functionality for geo-spatial modeling and management of semantic properties in one place, thus avoiding the need for multiple software tools associated with high volume data transfer and format transformations.

The provision of spatial and geo-spatial features in database systems needs to be extended and brought to maturity to fulfill the requirements of real-world scientific applications. A class of DBMSs, called column-stores, have proven efficiency for analytic applications on extremely large data sets. Column stores have become the de-facto standard for managing large data warehouses. Although column stores have a proven track record in business analytics, their pros- and cons- for GIS applications are not yet well understood.

Such core technology extensions for geo-spatial analytics are beneficial for public as well as private sector. Municipalities and spatial data providers can both extend and enhance their offerings of data products and services. Several municipalities have expressed an interest in using the technology in their urban projects for management and analysis of geo-spatial data collections, and the port of Rotterdam will be used as a driving application during the design and implementation.

A Port of Rotterdam’s 3D GIS has been built to aid various multi-stakeholder construction projects where new structures are built in, on top of and around the existing port (underground)infrastructure. Extending and modifying the port is challenging as it is home to many different companies that often cover extensive areas and manage vast infrastructures such roads, pipes and cables. The port thus requires a 3D GIS that is able to store all harbor assets and analyze existing assets with future interventions and detect conflicts. The 3D GIS currently being built is aimed at collecting data from different sources and formats and converting it to a common format to enable 3D operations and analyses such as 3D intersections.

The development relies as much as possible on existing open-source tools and libraries, and be distributed through an open-source product to maximize the take-up in education and science. It will be offered as an associated NLeSC technology.