Foundations of Geographic Information Systems Final Project
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Week3-SpatialDataModelingFA19GIS51031.pptx
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Foundations of Geographic Information Science
GIS 5103
Fall 2019 - Week 3
Spatial Data Modeling
By the end of Week Three you should be able to:
Provide a definition of a ‘spatial data model’
Explain how spatial entities are used to create a data model
Distinguish between rasters and vectors
Describe a spatial data structure
Explain what topology is
List the advantages and disadvantages of different types of spatial models
File Types
Files in a GIS may be binary data files. They can be read by computers (a program or a hardware understands in advance exactly how it is formatted) but not people.
All executable programs and most numeric data files are stored in binary files.
Or they can be ASCII text. ASCII files can be read by humans.
Binary is faster to read and smaller.
Raster and Vector Data
A file must have a mental model of how the physical data represent a geographic feature.
GIS traditionally use either raster or vector models to store data.
Rasters represent phenomenon distributed continuously over a large area.
Vectors are object based (point, line, polygon).
Raster Data Example
Satellite data has a RASTER data structure (GRID
CELL), composed of matrix of pixels;
Each pixel records the “radiation intensity” measured by the remote sensing instrument called its radiometric resolution.
The intensity of radiation is represented by a data number (DN), that is, the “brightness value”.
One grid cell (pixel) has one attribute that represents its data number/ brightness value (even if 0); a cell also has a spatial resolution, given as the cell size in ground units.
Grid Structure of Raster Data, p.81-82
Vector Data Example
Spatial feature files that are object (vector) based:
Have identifiable boundaries or spatial extent;
Relevant to an intended application;
They have “ATTRIBUTES” – descriptive characteristics of a feature.
For example, a tax parcel can have a rectangular shape and be represented by a polygon, that is the feature’s geometry. The owner name, parcel id, property value, etc. would be considered the
polygon’s attributes.
Attribute data are stored in tables comprised of a matrix of numbers and values
and are stored in rows and columns, like a spreadsheet, e.g. attribute tables.
OBJECTS may be:
Exact, e.g., man-made objects; or
Inexact (fuzzy), e.g., soil types and forest stands etc.
Recall: spatial objects represented by vector files may be represented either as
points, lines or polygons (areas) [slides 9-11, class 2].
Vectors vs Rasters:
Vectors can store information about topology (relational space, p. 51-52).
GIS Data File Formats
Most GIS systems can import different data formats, or use utility programs to convert them;
Data formats can be industry standard or commonly accepted standard;
Example vector formats: DLG (Digital Line Graph) and TIGER (Topologically Integrated Geographic Encoding and Referencing system), shp. (ESRI)
Personal, File, and Enterprise GeoDatabase (ESRI);
Most raster formats are digital image formats, e.g. Tiff (Tagged Image File
Format) or DEMs (Digital Elevation Models).
Data Models
Once you have determined which features you would like to map or create data
layers for you next have to decide how you wish to portray those features
and also consider the following:
How the geographic features are to be represented for each theme (for example, as points, lines, polygons, or rasters) along with their tabular attributes
How the data will be organized into datasets, such as feature classes, attributes, raster datasets, and so forth
What additional spatial and database elements will be needed for integrity rules, for implementing rich GIS behavior (such as topologies, networks, and raster catalogs), and defining spatial and attribute relationships between datasets.
Representation
Each GIS database design begins with a decision as to what the geographic representations will be for each dataset. Individual geographic entities can be represented as:
Feature classes (sets of points, lines, and polygons)
Imagery and rasters (aerial photography, grid datasets)
Continuous surfaces that can be represented using features (such as contours), rasters (digital elevation models [DEM]), or triangulated irregular networks (TINs) using terrain datasets
Attribute tables for descriptive data – these tables can be stand-alone tables that contain information related to a feature in a feature class – such as an inspection, an activity (catch basin cleaning, tree maintenance, etc.)
Data Themes
Geographic representations are organized in a series of data themes (sometimes referred to as thematic layers). A key concept in a GIS is one of data layers, or themes. A data theme is a collection of common geographic elements such as a road network, a collection of parcel boundaries, soil types, an elevation surface, satellite imagery for a certain date, well locations, and so on.
The concept of a thematic layer was one of the early notions in GIS. Practitioners thought about how the geographic information in maps could be partitioned into logical information layers—as more than a random collection of individual objects (such as a road, a bridge, a hill, a house, a peninsula). These early GIS users organized information in thematic layers that described the distribution of a phenomenon and how it should be portrayed across a geographic extent. These layers also provided a protocol (capture rules) for collecting the representations (as feature sets, raster layers, attribute tables, and so on).
In GIS, thematic layers are one of the main organizing principles for GIS database design.
Each GIS will contain multiple themes for a common geographic area. The collection of themes acts as layers in a stack. Each theme can be managed as an information set independent of other themes. Each has its own representations (points, lines, polygons, surfaces, rasters, and so on). Because the various independent themes are spatially referenced, they overlay one another and can be combined in a common map display. Plus, GIS analysis operations, such as overlay, can fuse information between themes.
GIS datasets are collections of representations for a data
theme
Geographic data collections can be represented as feature classes and raster-based datasets in a GIS database.
Many themes are represented by a single collection of homogeneous features such as a feature class of soil type polygons and a point feature class of well locations. Other themes, such as a transportation framework, are represented by multiple datasets (such as a set of spatially related feature classes for streets, intersections, bridges, highway ramps, and so on).
Raster datasets are used to represent continuous surfaces, such as elevation, slope, and aspect, as well as to hold satellite imagery, aerial photography, and other gridded datasets (such as land cover and vegetation types).
GIS datasets are collections of representations for a data
theme – Cont.
Both the intended use and existing data sources influence spatial representations in a GIS. When designing a GIS database, users have a set of applications in mind. They understand what questions will be asked of the GIS. Defining these uses helps to determine the content specification for each theme and how each is to be represented geographically. For example, there are numerous alternatives for representing surface elevation: as contour lines and spot height locations (such as hilltops, peaks), as a continuous terrain surface (a TIN), or as shaded relief. Any or all of these may be relevant for each particular GIS database design. The intended uses of the data will help to determine which of these representations will be required.
Frequently, the geographic representations will be predetermined to some degree by the available data sources for the theme. If a preexisting data source was collected at a particular scale and representation, it will often be necessary to adapt your design to use it.
Individual GIS datasets often are collected in concert with other data
layers
While each GIS dataset can be used independently of other GIS data, it is often quite important to collect datasets in concert with other information layers so that the fundamental spatial behavior and spatial relationships are maintained and consistent between the related GIS data layers. Here are a few examples that help to illustrate this concept:
The spatial relationships between elevation, landform, soil type, slope, vegetation, surficial geology, and other terrain properties are typically compiled in unison to characterize environmental resource units. Understanding the science behind these spatial relationships helps to build a consistent, logical database where features from each data layer are consistent with each other.
Topographic basemap information is compiled in an integrated manner. Hydrography, transportation, structures, administrative boundaries, and other topographic map layers are compiled in unison. These cartographic representations in the map display are built in an integrated manner to communicate clearly and accurately and draw attention to key map locations.
Individual GIS datasets often are collected in concert with other data
layers – Cont.
In each of these cases, a data model defines a collection of related data themes that fit into an overall information framework. Each framework is essentially a collection of related data themes that are best captured in unison with each other. The data capture guidelines follow sound scientific principles about their spatial behavior and relationships. Each theme plays an important part in the holistic characterization of a particular landscape. For example:
Terrain landscape. Topographic maps, elevation, drainage network, transportation network, map features, cross-country movement, and so forth
Urban landscape. Buildings, critical infrastructure, and so forth
Imagery landscape. Satellite and aerial, local, regional, and national assets, and so forth
Human landscape. Demographics (population characteristics), cultural centers, citizens, administrative districts and zones and so forth
Workforce landscape. Mobile workforce tracking, service centers, traffic
conditions, warehouses, and so forth
Sensor landscape. Camera locations, devices, and so forth
Operations and plans landscape. Zones of control, planned movements,
response, and so forth
This concept of collecting integrated data themes in unison is one of the key design principles used in each of the ArcGIS Data Models.
Topology
Topology is a collection of rules that, coupled with a set of editing tools and techniques, enables the geodatabase to more accurately model geometric relationships. ArcGIS implements topology through a set of rules that define how features may share a geographic space and a set of editing tools that work with features that share geometry in an integrated fashion. A topology is stored in a geodatabase as one or more relationships that define how the features in one or more feature classes share geometry. The features participating in a topology are still simple feature classes—rather than modifying the definition of the feature class, a topology serves as a description of how the features can be spatially related.
Why Topology?
Topology has long been a key GIS requirement for data management and integrity. In general, a topological data model manages spatial relationships by representing spatial objects (point, line, and area features) as an underlying graph of topological primitives—nodes, faces, and edges. These primitives, together with their relationships to one another and to the features whose boundaries they represent, are defined by representing the feature geometries in a planar graph of topological elements.
Why topology?
Topology is a collection of rules that, coupled with a set of editing tools and techniques, enables the geodatabase to more accurately model geometric relationships. ArcGIS implements topology through a set of rules that define how features may share a geographic space and a set of editing tools that work with features that share geometry in an integrated fashion. A topology is stored in a geodatabase as one or more relationships that define how the features in one or more feature classes share geometry. The features participating in a topology are still simple feature classes—rather than modifying the definition of the feature class, a topology serves as a description of how the features can be spatially related.
Why topology?
Topology is fundamentally used to ensure data quality of the spatial relationships and to aid in data compilation. Topology is also used for analyzing spatial relationships in many situations, such as dissolving the boundaries between adjacent polygons with the same attribute values or traversing a network of the elements in a topology graph.
Topology can also be used to model how the geometry from a number of feature classes can be integrated. Some refer to this as vertical integration of feature classes.
Ways that features share geometry within a topology
Features can share geometry within a topology. Here are some examples among adjacent
features:
Area features can share boundaries (polygon topology).
Line features can share endpoints (edge-node topology).
In addition, shared geometry can be managed between feature classes using a geodatabase topology. For example:
Line features can share segments with other line features.
Area features can be coincident with other area features. For example, parcels can
nest within blocks.
Line features can share endpoint vertices with other point features (node topology).
Point features can be coincident with line features (point events).
The next slide shows some commonly used topology rules found in ArcGIS.
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What are the advantages of topology?
The results are that topological primitives (nodes, edges, and faces) and their relationships to one another and their features can be efficiently discovered and assembled. This has several advantages:
Simple feature geometry storage is used for features. This storage model is open, efficient, and scales to large sizes and numbers of users.
This simple features data model is transactional and is multiuser. By contrast, the older topological storage models will not scale and have difficulties supporting multiple editor transactions and numerous other GIS data management workflows.
Geodatabase topologies fully support all the long transaction and versioning capabilities of the geodatabase. Geodatabase topologies need not be tiled, and many users can simultaneously edit the topological database—even their individual versions of the same features if necessary.
Feature classes can grow to any size (hundreds of millions of features) with very strong performance.
What are the advantages of topology?
Topology can be added this to an existing schema of spatially related feature classes. The alternative is that you must redefine and convert all your existing feature classes to new data schemas holding topological primitives.
There need only be one data model for geometry editing and data use, not two or more.
It is interoperable because all feature geometry storage adheres to simple features specifications from the Open Geospatial Consortium and ISO.
Data modeling is more natural because it is based on user features (such as parcels, streets, soil types, and watersheds) instead of topological primitives (such as nodes, edges, and faces). Users will begin to think about the integrity rules and behavior of their actual features instead of the integrity rules of the topological primitives. For example, how do parcels behave? This will enable stronger modeling for all kinds of geographic features. It will improve our thinking about streets, soils types, census units, watersheds, rail systems, geology, forest stands, land forms, physical features, and on and on.
What are the advantages of topology?
Geodatabase topologies provide the same information content as maintained topological implementations—either you store a topological line graph and discover the feature geometry (like coverages) or you store the feature geometry and discover the topological elements and relationships (like geodatabases).
In cases where users want to store the topological primitives, it is easy to create and post topologies and their relationships to tables for various analytic and interoperability purposes (such as users who want to post their features into an Oracle Spatial warehouse that stores tables of topological primitives).
At a pragmatic level, the ArcGIS topology implementation works. It scales to extremely large geodatabases and multiuser systems without loss of performance. It includes validation and editing tools for building and maintaining topologies in geodatabases. It includes rich and flexible data modeling tools that enable users to assemble practical, working systems on file systems, in any relational database, and on any number of schemas.
