Spatial Data Management
Taming Spatial Data in Spatial Databases
Acknowledgment of Country
The content of this subject has been prepared and delivered on unceded Aboriginal land in Naarm (aka Melbourne). I pay my respects to the Wurundjeri People of the Kulin Nation on whose Country the Parkville campus of the University of Melbourne is located, as well as to all First Nations people and their Elders Past and Present.
Spatial sciences have an indisputable and tragic colonial history, where the charting of ‘blank spots’ in maps led to the killing, disposession and oppression of countless First Nations Peoples globally.
First Nations Peoples have also developed sophisticated ways to understand and manage Country, and work with Spatial Information, and this knowledge is essential for a just and equitable progress towards the righting of historical wrongs, and the achievement of a sustainable future (United Nations 2015) . I hope this subjects enablesyou to reflect on some of this history, and future, of spatial sciences.
Introduction
This subject will introduce you to spatial information management - the management of complex information that includes spatial information. It will introduce the fundamental theoretical concepts of spatio-temporal data modelling, representation, storage, and analysis (to a small extent). It will let you understand how spatial nfromation is handled by spatial databases in order to enable efficient, consistent analytical results.
This subject takes a particular approach to spatial data management - a database perspective. This allows to present all of the approaches in spatial data management from a unified perspective compatible with general knowledge in data management. It will provide you with all the skills and knowledge necessary to integrate spatial information into broader data projects.
Yet, all you will learn is exactly applicable to the more common work with plain GIS systems. Here, we will predominantly use GIS only as a client interface to spatial data, to visualise the data themselves, ro results of analyses. You hsould however be able to make the connection - what you instruct the database to do, can also be done by clicking around in a GIS graphical user interface ( without all the advantages of being backed by a database). If you make this connection, you will be able to quickly learn any new GIS system as the concepts will apply in the same way.
Approach
This subject is further structured along a small number of core messages:
- Spatial and spatio-temporal data have specific properties that must be explicitly modelled;
- These must be considered during the design and implementation of a spatial data project;
- These properties define how we store, query, and access the data
- These properties define how we manage database consistency;
- Loading data from common spatial datasets must consider these properties explicitly, too;
We will base the design of this course on the a number of theories. Importantly, the core theoretical concepts of spatial information Kuhn (2012) will be discussed.
We will dwell into:
- Modelling the spatial reality;
- Life cycle of (spatial) data management (from requirements analysis, through conceptual and logical design, to physical storage design);
- Data representations: vectors, rasters, networks, moving objects/trajectories, spatial reference systems
- Foundations of computational geometry, topological analysis, and network analysis for spatial data management;
- Efficient data access for analysis, maintenance of data consistency and data quality.
What you learn will become a foundation stone for your future growth as a spatial data manager, GIS analyst, and more.
We will be designing models, implementing them and interrogating the data thourgh a spatially-enabled Database Management System (DBMS). A DBMS is self-describing systems that allows to maintain important aspects of data consistency and integrity throughout the entire data lifecycle. We will maintain important linkages between records through explicit references, or via analysis of (spatial) properties.
Indeed, this ability to store and analyse the spatial location and extent of things is the core of this book. There are four things that make a DBMS a spatial DBMS:
- The ability to store spatial data types to capture the location and extent of things in the real world;
- The inclusion of spatial analysis methods to analyse the spatial properties and relationships of these things;
- The extension of the DBMS query language to enable to formulate spatial queries; and, optionally
- The inclusion of spatial access methods ( indexes) enabling to efficiently execute spatial queries.
Spatial is special
- Almost anything you can think of has a spatial component – stuff is somewhere;
- Many people forget that – this will be your career advantage!
- The spatial component of data reveals peculiar elements of their relationships
- Spatial problem solving requires people who understand databases AND can think spatially.
- Spatial data require special handling;
Spatial analytical applications
Geographic Information Science and Systems
- Spatial and spatio-temporal data have specific properties that must be explicitly modelled;
- These must be considered during the entire lifecycle of a spatial information system;
- These properties define how we represent, locate, query, and access the information;
- We build on the foundations of computational geometry, topological analysis, and network analysis
- These properties define how we manage data consistency;
- Loading data from common spatial datasets must consider these properties explicitly, too;
What do spatial databases bring
- The ability to store spatial data types to capture the location and extent of things in the real world;
- The inclusion of spatial analysis methods to analyse the spatial properties and relationships of these things;
- The extension of the DBMS query language to enable to formulate spatial queries; and, optionally
- The inclusion of spatial access methods ( indexes) enabling to efficiently execute spatial queries.