GIS and CAD Integration
Mark Altaweel discusses efforts to create a truly integrated system, where CAD components and software concepts work with GIS data.
Mark Altaweel discusses efforts to create a truly integrated system, where CAD components and software concepts work with GIS data.
In the world of personalized GIS, software being developed that can adapt to our behaviors and know what we need before having to request it or it can give us recommendations based on what tastes we have or have had in the past or based on others’ preferences.
Mapping and monitoring the human body in real time using GIS is one area of great opportunity for medical and healthcare professionals.
Multi-view GIS provides different perspectives of space and time for a given geographic area. There are many ways in which multiple views can be created using GIS software such as ringmaps and augmented reality.
Real-time collaborative GIS (RCGIS) enables users, from domain experts to common citizens, to collaborate on given issues and share data easily through a distributed framework.
For the last two decades, GIS technologies have increasingly been used to incorporate not only spatial relationships but also analyzing and visualizing space across time. Spatial-temporal GIS, or 4D GIS, has, in particular, become essential in areas where GIS is needed for predicting dimensions across time.
Relational databases present some limitations to scaling, preventing or limiting the applicability of big data and real-time data problems utilized within GIS. More and more GIS software companies and developers are adopting NoSQL formats where data retrieval is generally faster and easier to structure. NoSQL also facilitates analysis and integration within a variety of tools, which is why open source GIS has proven to be the most useful arena for NoSQL databases.
Within GIS, natural language processing can be utilized for spatial understanding of where events, places, or people may relate to a given phenomenon.
By integrating GIS with proportional hazard modeling, we are now beginning to see GIS increasing its analytical modeling repertoire for the sciences that leverage factors of spatial and time to better understand how emergence and evolution of given processes develops, even when uncertainty is persistent for areas of research.
The need to understand emergent phenomenon in a variety of fields has led to not only greater use of agent-based models (ABMs), but we are increasingly seeing tools that integrate GIS with ABMs.