This column is sponsored by ESRI
A well thought out spatial data model is critical to get the most out of geographic information systems (GISs), because it dictates how spatial data are stored and represented within the database, and the rules for how the data can be analyzed and manipulated. In addition to different data models to represent vector or raster data, the data model is also the means to create a common set of attributes, rules and workflows for specific application areas.
Determining your spatial data model is a foundational step, and increasingly this is accomplished collaboratively with a set of like-minded peers in the industry at large. It helps to look outside of your organization for assistance here, because standardized spatial data models will greatly enhance interoperability among many users and will ensure that you get the most from your system.
Models within the Model
The term model can get quite confusing, given its use in a number of different contexts. The word model crops up in the spatial domain when referring to how data is described and stored in the database or how a drawing represents reality in 3D. There are also many different models within a spatial data model that parse different elements of the data and how it can be used within a system.
A conceptual or semantic model describes the elements of significance for a specific purpose, including attribute characteristics and relationships between attributes. The logical model represents business requirements with definitions and examples that prioritize importance and how elements relate to each other. The physical model describes how the logical model is represented in a database, with tables, columns, rules, storage procedures, etc.
Key to Integration
Spatial data models can apply to industries or to specific problems or purposes. There are standard data models for such communities as agriculture, forestry, geology, local government, etc. There are data models for such data types as addresses, land parcels, base maps and hydrolology. And there are data models for specific analysis functions such as carbon footprint calculations and biodiversity.
The data model facilitates integration of data to and from other systems and collaboration with other users. Coming to consensus on a data model greatly enhances the utility of the model because it then meshes with other representations in other organizations. The commonality of the model assists advanced application, because analysis tools and workflows can also be standardized across domains leading to easier and more reliable analysis.
A good foundational spatial data model is important, but that doesn’t mean that the model needs to be static. The spatial data model can be extended for additional purposes down the road.
Coincident to our tackling this week’s question, ESRI announced the Building Interior Space Data Model. This announcement is illustrative of the importance of a data model for the extension of GIS functionality to new application areas. Specifically, this data model creates a means to ingest information about the interior of buildings within a GIS, and opens up a whole new area of application for facility information management. The data model was painstakingly crafted to ingest CAD and BIM data, to work at multiple scales, and to integrate with other systems.
How spatial data is modeled in the database has huge implications for the sustainability of your system, the effectiveness of the system, and your return on investment.
Read what Jeff Thurston has to say on this subject here.
READING
The GIS Spatial Data Model, GIS@UW
Download ESRI Data Models
ESRI Building Interior Space Data Model (BISDM)
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As mentioned in the article, Data Models do have/need application specific flavors. But we need to think of a base/foundation spatial footprint model which exhibits the fundamental characteristics of the spatial entity. Different application oriented data models can be constructed on the top of this foundation model. In this respect we can say that the foundation data model is a fine topographic data model which describes man made and natural spatial features in the ‘purest form’. The resolution of this model needs to be highest considering the application areas and money available for compiling this data model. As far as possible this model should exhibit all the fundamental characteristics of the described spatial entities ( Roads are connected, River flow from higher to lower elevation, etc.)
Different methods are used by modelling systems to inject behaviors, properties and relationships into the model. This will get more intelligent as the technology grows . But the cumulative effort in creating a model should be preserved. i.e. the locational information/representation (coordinates) of the spatial entities need to be persevered in the appropriate resolution with basic geometric attributes, topological relationships and also a temporal element. This model will be the base/foundation for any technical data model, and should have the capability to derive representations at different lower levels (generalization etc.).
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