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The goal of the pattern is to keep the in-memory representation and the persistent data store independent of each other and the data mapper itself. This is useful when one needs to model and enforce strict business processes on the data in the domain layer that do not map neatly to the persistent data store. [2]
Choropleth map showing estimated percent of the population below 150% poverty in the Contiguous United States by county, 2020 that uses the Jenks natural breaks classification. Jenks’ goal in developing this method was to create a map that was absolutely accurate, in terms of the representation of data's spatial attributes.
Object–relational mapping (ORM, O/RM, and O/R mapping tool) in computer science is a programming technique for converting data between a relational database and the memory (usually the heap) of an object-oriented programming language.
Entity Framework, included in .NET Framework 3.5 SP1 and above; iBATIS, free open source, maintained by ASF but now inactive. LINQ to SQL, included in .NET Framework 3.5; NHibernate, open source; nHydrate, open source; Quick Objects, free or commercial
LPGL 3.0 Cross-platform: Python: Documentation and tutorials fully available in ReadTheDocs: geoapps repository [24] The geoapps repository are open-source geoscientific applications in Python, including geophysical data processing, modelling, and inversion codes Mira Geoscience Ltd. MIT: Cross-platform: Python
Occupancy Grid Mapping refers to a family of computer algorithms in probabilistic robotics for mobile robots which address the problem of generating maps from noisy and uncertain sensor measurement data, with the assumption that the robot pose is known. Occupancy grids were first proposed by H. Moravec and A. Elfes in 1985.
Each node in the map space is associated with a "weight" vector, which is the position of the node in the input space. While nodes in the map space stay fixed, training consists in moving weight vectors toward the input data (reducing a distance metric such as Euclidean distance) without spoiling the topology induced from the map space. After ...
2005 DARPA Grand Challenge winner Stanley performed SLAM as part of its autonomous driving system. A map generated by a SLAM Robot. Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.