Seokho, L. J. L. (2010). Efficient Topological Data Models For Spatial Queries in 3D Gis. In Geospatial Data and Geovisualization: Environment, Security, and Society Special Joint Symposium of ISPRS Commission I
Open source PostGISadds spatial objects to the cross-platform PostgreSQL database. The three features that PostGIS delivers to PostgreSQL DBMS are spatial types, indexes, and functions. With support for different geometry types, the PostGIS spatial database allows querying and managing information abo...
The advantages of using an OO approach over traditional relational models are discussed as is how its implementation presents a practical solution to the problem of spatial data generalisation in GIS. Moreover, by using the Object Model and Dynamic Model of OMT, the relationships between different ...
ArcGIS Velocity is a software as a service offering for Internet of Things (IoT) analytics. Visualize and process real-time spatial data and better leverage your IoT data.
Spatial Data Models International Encyclopedia of Human Geography Reference work2009, International Encyclopedia of Human Geography L. Bian Explore book Introduction Spatial data models are a formal representation of space. Because the computing environment is finite and discrete, a geographic space must be...
摘要: State and event is a pair of basic conception in Temporal Geographical Information Systems (TGIS).Now most of spatio-temporal data models deal with both conceptions separately,and cannot combine the merits of both models.In this paper,a new spatial-temporal data model based on state-event...
Much of the effort in ArcGIS Pro has been put into a new interface for visualizing, editing data and performing analysis. TheArcGIS Pro – Reinventing Desktop GISblog post covers some of the capabilities of the new system, and anOverview of ArcGIS Proin the online Help is a good starting ...
Machine learning can be computationally intensive and often involves large and complex data. Advancements in data storage and parallel and distributed computing make solving problems related to both machine learning and GIS possible. The following capabilities and tools use machine learning and deep learni...
Geographically Weighted Regression (GWR)takes into account the geographic location of data points to model variable relationships. For example, GWR might model how deer prefer specific habitats. Traditional regression models assume that the relationship between dependent and independent variables is the same...
To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also ...