THE
GEOVISUALIZATION VIEW
Another
key aspect of a GIS is the ability to create and work with intelligent maps and
other views of geographic information. Interactive and printed maps, 3D scenes
and globes, summary charts and tables, time-based views, and schematic views of
network relationships are examples of how GIS users interact with geographic
information. Maps provide a powerful metaphor to define and standardize how
people use and interact with their geographic datasets. Interactive maps
provide the main user interface for most GIS applications. Users can point to locations and discover new relationships,
perform editing and analysis, and effectively present results using geographic
views such as maps and globe
GIS
users pan and zoom interactive maps, where map layers apply symbols based on a
set of attributes and perform query and analysis operations through the map layers.
For example, parcels can be shaded with colors based on their zoning types, or
the size of point symbols for oil wells can be specified based on production
levels. A GIS user can point to a geographic object in an interactive map to
get information about the object. Stores of a certain type can be found within
a specified distance of schools, or the wetland areas within 500 meters of selected
roads can be identified. In addition, GIS users can edit data and feature
representations through interactive maps.
It’s
through an interactive map that GIS users perform most common GIS tasks from
simple to advanced. It’s the main ‘business form’ in a GIS that enables access
to geographic information for an organization. Developers often embed maps in
custom applications, and many users publish Web maps on the Internet for
focused GIS use. In addition to maps, other interactive views, such as temporal,
globe, and schematic drawings, are used as views into GIS databases.
THE
GEOPROCESSING VIEW
Geographic
datasets can represent raw measurements—for example, satellite imagery information
interpreted and compiled by analysts—for example, roads, buildings, and
soil types—or information derived from
other data sourcesusing analysis and modeling algorithms. Geoprocessing refers to the tools and
processes used to generate derived datasets.
A GIS
includes a rich set of tools to work with and process geographic information.
This collection of tools is used to operate on the GIS information objects,
such as the datasets, attribute fields, and cartographic elements for printed
maps. Together, these comprehensive tools and the data objects on which they operate
form the basis of a rich geoprocessing framework.
THE
GEOPROCESSING VIEW
Data
+ Tool = New Data
GIS
tools are the building blocks for assembling multistep operations. A tool
applies an operation to existing data to
derive new data. The geoprocessing framework in a GIS is used to string
together a series of these operations, enabling users to automate work flows,
program analytical models, and build recurring procedures. Stringing a sequence
of operations together forms a process model and is used to automate and record
numerous geoprocessing tasks in the GIS. The building and application of such
procedures is referred o as geoprocessing
A complete GIS contains generic information and a
rich set of GIS operators to work with the information.
Geoprocessing
in action
Geoprocessing
is used in virtually all phases of a GIS for data automation and compilation,
data management, analysis and modeling, and advanced cartography. Geoprocessing
is used to model how data flows from one structure to another to perform many
common GIS tasks—for example, to import data from numerous formats, integrate
that data into the GIS, perform a number of standard-quality validation checks
against the imported data, as well as perform powerful analysis and modeling.
The ability to automate and repeat such work flows is a key capability in a
GIS. It is applied widely in numerous GIS applications and scenarios.
One
mechanism used to build geoprocessing work flows is
to
execute a number of tools in a specific sequence.
.
Data
compilation
Data
compilation procedures are automated using geoprocessing to ensure data quality
and integrity and to perform repetitive quality assurance/quality control (QA/ QC)
tasks. Automating these work flows using geoprocessing helps to share and
communicate the series
of
procedures, perform batch processing flows, and document these key processes
for derived data.
Analysis
and modeling
Geoprocessing
is the key framework for modeling and analysis. Some common modeling
applications include:
•
Models for suitability and capability, prediction, and
assessment
of alternative scenarios
•
Integration of external models
•
Model sharing
Data
management
Managing
GIS data flows is critical in all GIS applications. GIS users apply
geoprocessing functions to move data in and out of databases; publish data in
many formats, such as in Geographic Markup Language (GML) profiles; join adjacent
datasets; update GIS database schemas; and perform batch processes on their GIS
databases.
Seamless
geospatial computing
Geospatial information is crucial to bridge the knowledge gap.
Unfortunately, existing geographic information systems (GIS) lack the
flexibility needed to efficiently address this challenge. The answer is
‘Seamless Geospatial Computing’.
In a Seamless Geospatial Computing
environment, geospatial data sources, geospatial services, and visualization seamlessly
integrate to enhance and support enterprise business processes. Seamless
Geospatial Computing will allow organizations to geospatially enable existing
business processes and quickly create new enhanced applications.
Seamless computing environment
Seamless Geospatial Data Access
Geospatial information is quickly growing to represent all information about a location coming from data models and realtime sensors. This information includes the geometry, attributes, status, and condition. Collecting and maintaining this information can be extremely expensive and often time consuming. Organizations need to maximize the return on investment (ROI) they have in the data by reducing delays in accessing the information and eliminating any loss of fidelity that can occur when exploiting geospatial information. Achieving ROI requires technology that can correctly access geospatial data regardless of where it resides, how it is stored, or what format it takes. Achieving seamless geospatial data access will be achieved by a combination of direct access to geospatial databases and Web Services. Management and maintenance of geospatial information typically involves data sets of tens of thousands and sometimes tens of millions of records. In addition, users must be able to lock or flag elements of the data in real time while maintenance is taking place. Direct database access is still seen as a
preferred method for the
management and maintenance of large enterprise geospatial databases. Intergraph
pioneered the concept of spatial data access to multiple sources with no
translation. This technology - Geographic Data Objects (GDO) - is embedded
across Intergraph's geospatial products. Intergraph is expanding data
connectivity by adding open data access Web Services. This will result in
strong data integration inside and outside the enterprise
Seamless Client Access
The complexity of most organizations requires a mix of desktop, Web, and mobile clients. Users can achieve significant savings on implementation and maintenance costs if these different client applications can access other business and spatial functionality without the need to replicate on different platforms. As technology evolves we will see greater levels of functional and transactional integration across the network.
Seamless Scalability
Organizations are living organisms. They grow, shrink, and restructure - often in unexpected ways. To protect their investments in geospatial information, technology, and applications, organizations need a technology environment that changes with them; one that can scale up or down - from one user to ten thousand and back to one. In the Seamless Geospatial Computing environment, Intergraph addresses a broad spectrum of geospatial data access and processing, including:
The complexity of most organizations requires a mix of desktop, Web, and mobile clients. Users can achieve significant savings on implementation and maintenance costs if these different client applications can access other business and spatial functionality without the need to replicate on different platforms. As technology evolves we will see greater levels of functional and transactional integration across the network.
Seamless Scalability
Organizations are living organisms. They grow, shrink, and restructure - often in unexpected ways. To protect their investments in geospatial information, technology, and applications, organizations need a technology environment that changes with them; one that can scale up or down - from one user to ten thousand and back to one. In the Seamless Geospatial Computing environment, Intergraph addresses a broad spectrum of geospatial data access and processing, including:
Single-person productivity and
field use on PDAs or tablet computers
Ten to hundreds of design
workstations that maintain data in a corporate data vault
Web products that handle
thousands of connected users
Business
Benefits of Seamless Geospatial Computing
Organizations
respond faster
– To changes in their business environments
– Ability to create quickly new business processes and apps.
– Common services reduce complexity and maintenance cost
Services and
Data Providers – increased ROI
– Increased potential markets
– Increased business flexibility
– Pre-built reusable services
Customer
services improvement
– Data and Services Availability regardless of existing IT
infrastructure
No need
for further IT infrastructure investment
Conclusion
Developments
in geospatial computing.
1. Geospatial Interoperability Reference Model (G.I.R.M.).
Interoperability means ("working together") among the
software systems that provide geospatial data, maps, services, and user
applications. Geospatial interoperability is based on shared agreements
governing essential geospatial concepts and their embodiment in communication
protocols, software interfaces, and data formats
2. Using Geospatial Information
in Sensor Networks .
3.Designs for Using Geospatial Data in Mobile Computing
Environments
Geospatial computing presents
significant challenges for many communities, and encompasses existing threads
of research such as data quality, lineage tracking, and geoprocessing workflow.
Advances will be accomplished through continuing work in these areas, as well
as attending to the emerging issues of improved semantics for spatial
operations, and the preservation and annotation of GIS and other spatial data.
References
1. M. F. Goodchild, "A
Spatial Analytical Perspective on Geographical Information Systems,"
International Journal of Geographical Information Systems, vol.
1, 1987, pp. 327-334.
2. http://modis.gsfc.nasa.gov
4 .www.google.com
5 .www.msnsearch.com
6. AN AGENT-BASED ARCHITECTURE
for DISTRIBUTED
IMAGERY and GEOSPATIAL COMPUTING by
James J. Nolan
7.An introduction to geographic
systems by kang-tsung chang
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