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 IV and AutoCarto (Vol. Volume XXXVIII Part 4). Orlando, Florida, USA....
There are three common spatial data models being used in GIS today: vector, raster, and triangulated irregular network (TIN). Avector data modeldefinesdiscrete objects using points, lines, and polygons, each composed of coordinates and attributes. Examples of discrete objects are fire hydrants, ro...
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...
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...
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 models The term “spatial modelling” refers to a particular form of disaggregation, in which an area is divided into a number (often a large number) of similar units: typically grid squares or polygons. The model may be linked to a GIS for data input and display. The transition ...
「GIS电子书」 Models in Spatial Analysis (Geographical Information Systems Series (ISTE-GIS))(PDF版本) 本书由Lena Sanders编写,于2007年4月1日由ISTE Publishing Company出版。它探讨了在空间数据处理中使用的数学模型和技术,以及如何将这些模型应用于地理信息系统(GIS)中。本书适合那些对GIS、空间分析和数学...
Combine industry-leading spatial analysis algorithms with open-source Python libraries to build precise spatial data science models. Reduce time spent managing dependencies across data science ecosystems, and increase cross-team collaboration and transparency. Ideate, iterate, and share workflows in a ...
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...
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 ...