SPATIAL DATA LAYERS - VERTICAL DATA ORGANIZATION
In most GIS software data is organized in themes as data layers. This approach allows data to be input as separate themes and overlaid based on analysis requirements. This can conceptualized as vertical layering the characteristics of the earth's surface. The overlay concept is so natural to cartographers and natural resource specialists that it has been built into the design of most CAD vector systems as well. The overlay/layer approach used in CAD systems is used to separate major classes of spatial features. This concept is also used to logically order data in most GIS software. The terminology may differ between GIS software, but the approach is the same. A variety of terms are used to define data layers in commercial GIS software. These include themes, coverages, layers, levels, objects, and feature classes. Data layer and theme are the most common and the least proprietary to any particular GIS software and accordingly, as used throughout the book.
In any GIS project a variety of data layers will be required. These must be identified before the project is started and a priority given to the input or digitizing of the spatial data layers. This is mandatory, as often one data layer contains features that are coincident with another, e.g. lakes can be used to define polygons within the forest inventory data layer. Data layers are commonly defined based on the needs of the user and the availability of data. They are completely user definable.
When considering the physical requirements of the GIS software it is important to understand that two types of data are required for each layer, attribute and spatial data. Commonly, data layers are input into the GIS one layer at a time. As well, often a data layer is completely loaded, e.g. graphic conversion, editing, topological building, attribute conversion, linking, and verification, before the next data layer is started. Because there are several steps involved in completely loading a data layer it can become very confusing if many layers are loaded at once.
The proper identification of layers prior to starting data input is critical. The identification of data layers is often achieved through a user needs analysis. The user needs analysis performs several functions including:
|identifying the users;|
educating users with respect to GIS needs;
identifying information products;
identifying data requirements for information products;
priorizing data requirements and products; and
determining GIS functional requirements.
Often a user needs assessment will include a review of existing operations, e.g. sometimes called a situational assessment, and a cost-benefit analysis. The cost-benefit process is well established in conventional data processing and serves as the mechanism to justify acquisition of hardware and software. It defines and compares costs against potential benefits. Most institutions will require this step before a GIS acquisition can be undertaken.
Most GIS projects integrate data layers to create derived themes or layers that represent the result of some calculation or geographic model, e.g. forest merchantability, land use suitability, etc. Derived data layers are completely dependant on the aim of the project.
Each data layer would be input individually and topologically integrated to create combined data layers. Based on the data model, e.g. vector or raster, and the topological structure, selected data analysis functions could be undertaken. It is important to note that in vector based GIS software the topological structure defined can only be traversed by means of unique labels to every feature.
Spatial Indexing - Horizontal Data Organization
The proprietary organization of data layers in a horizontal fashion within a GIS is known as spatial indexing. Spatial indexing is the method utilized by the software to store and retrieve spatial data. A variety of different strategies exist for speeding up the spatial feature retrieval process within a GIS software product. Most involve the partitioning of the geographic area into manageable subsets or tiles. These tiles are then indexed mathematically, e.g. by quadtrees, by R (rectangle) trees, to allow for quick searching and retrieval when querying is initiated by a user. Spatial indexing is analygous to the definition of map sheets, except that specific indexing techniques are used to access data across map sheet (tile) boundaries. This is done simply to improve query performance for large data sets that span multiple map sheets, and to ensure data integrity across map sheet boundaries.
The method and process of spatial indexing is usually transparent to the user. However, it becomes very important especially when large data sets are utilized. The notion of spatial indexing has become increasingly important in the design of GIS software over the last few years, as larger scale applications have been initiated using GIS technology. Users have found that often the response time in querying very large data sets is unacceptably slow. GIS software vendors have responded by developing sophisticated algorithms to index and retrieve spatial data. It is important to note that raster systems, by the nature of their data structure, do not typically require a spatial indexing method. The raster approach imposes regular, readily addressable partitions on the data universe intrinsically with its data structure. Accordingly, spatial indexing is usually not required. However, the more sophisticated vector GIS does require a method to quickly retrieve spatial objects.
The horizontal indexing of spatial data within GIS software involves several issues. These concern the extent of the spatial indexing approach. They include:
|the use of a librarian subsystem to organize data for users;|
|the requirement for a formal definition of layers;|
|the need for feature coding within themes or layers; and|
|requirements to maintain data integrity through transaction control, e.g. the locking of selected spatial tiles (or features) when editing is being undertaken by a permitted user.|
While all these issues need not be satisfied for spatial indexing to occur, they are important aspects users should consider when evaluating GIS software.
While the spatial indexing method is usually not the selling point of any GIS, users should consider these requirements, especially if very large data sets, e.g. 10,000 + polygons, are to be the norm in their applications, and a vector data model is to be employed.