Understanding our world requires us to model and interpret the spatial relationships between human, social and physical geography. We live in a connected world of spatial and non-spatial relationships, and it is essential that we be conscious of these relationships, as this helps us to grasp the impact of our decisions on our daily lives.
Because we live in a world where human, social and physical geography is always changing, we need a way to dynamically understand our changing world. A change requires us to assess the effect that event has had on the geography. Many times, this involves simulating, predicting, preventing and even influencing positive change.
Spatial modeling tools are a prerequisite to dynamically modeling and understanding our changing earth. Spatial modeling is the process of using spatial operators on a variety of geospatial datasets (for example, imagery, terrain and features) to create an information product. This involves using a set of procedures that simulates real-world conditions within a geospatial application, using the spatial relationships of geographic features and their attributes. From these created models, you can perform spatial analysis to derive information from data, such as distributions or patterns, using the spatial context of the problem. Actionable decisions require real information – not just data – and spatial modeling is key in driving these decisions.
Today, many spatial modeling environments are independent and static. Historically, spatial modeling has been either raster or vector focused, thereby catering to a philosophy that the earth can only be modeled with raster data or with feature data. Limiting the understanding of our world to a single geospatial genre limits the possibility of what we can do and what we can understand. Imagery, vector, point cloud, radar, video and hyperspectral data are great sources of data that, when fused, can help us foster a clear understanding of the world.
In addition, these historically segmented approaches are slow, requiring you to fully create the end-result file before any analysis can be performed and information extracted. Depending on the total data size, this can take anywhere from 10 minutes to 10 hours. For each iteration of work, users create and run a spatial model, generate new files on disk, perform analysis and then repeat the process until they are able to achieve the final result. A static approach is both time consuming and ultimately inaccurate if your goal is to create a holistic picture of a model. In addition, current approaches require a tremendous amount of programming, which can be a hindrance to users not trained in a proprietary software’s programming language. Spatial modeling requires an iterative approach to modeling geography.
At the Hexagon 2012 conference, we showcased a preview of the Intergraph Geospatial 2013 release, highlighting ERDAS IMAGINE®’s next-generation spatial modeling environment. This new capability is presented with a dynamically connected and integrated graphical modeling and viewing framework, providing on-the-fly modeling capabilities that present the result as changes are made. This approach enables users to truly “taste” their results as they are creating their model, and tweak them as needed without having to start over and begin a new iteration of work. This revolutionary approach provides a unique environment for the creation of solutions, combining ERDAS IMAGINE’s image processing capabilities with the powerful vector analysis functionality of GeoMedia®.
Ultimately, this technology changes the game for the GIS industry, transforming spatial modeling from static to dynamic, with an intuitive interface, driven by the ability to drag and drop components instead of relying on expert, time-intensive programming methods. The fusion of modeling, analysis and visualization enables Hexagon to deliver a Dynamic GIS to help us understand our ever-evolving world.
Already a trailblazer in making spatial modeling more useable, ERDAS IMAGINE’s prior capabilities enabled users to graphically create models of complex raster imagery processing workflows. Those with less domain knowledge could easily execute the models to obtain the same accurate, reliable results as more expert users. By reducing training time and helping automate complicated workflows, the ability to process imagery using automated spatial models has become a quintessential feature for many users.
The new spatial modeling framework expands this proven technology. By providing full extensibility and true interoperability with other systems, users can now integrate data types from the different geospatial genres and construct complete geoprocessing workflows. Users can also enhance existing solutions with the spatial modeling technology, allowing you to add new types and operations, providing full extensibility.
Building upon Hexagon’s vision of a Dynamic GIS, our new spatial modeler takes core platform components, integrates these into a key product to support customizable workflows and support an organization’s total solution – providing a means for delivering real-time actionable information.