postgis
Spatial Data Science with PostgreSQL: Geometries
Geometries are the glues that hold together geospatial data. They form an integral part of any spatial data processing. In this tutorial, I will go through some of the different types of geometries available in Postgis. We also touch on some of the most used functions with real-world data examples. In my last article, I explained how to install PostgreSQL and activate Postgis extensions.
Fast GeoSpatial Analysis in Python
This work is supported by Anaconda Inc., the Data Driven Discovery Initiative from the Moore Foundation, and NASA SBIR NNX16CG43P This work is a collaboration with Joris Van den Bossche. This blogpost builds on Joris's EuroSciPy talk (slides) on the same topic. You can also see Joris' blogpost on this same topic. Python's Geospatial stack is slow. Dask gives an additional 3-4x on a multi-core laptop.
Spatial database implementation of fuzzy region connection calculus for analysing the relationship of diseases
Davari, Somaye, Ghadiri, Nasser
Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of topological relations. In this paper, we propose a method for handling the topological relations in spatial databases based on fuzzy region connection calculus (RCC). The proposed method is implemented in PostGIS spatial database and evaluated in analyzing the relationship of diseases as an important application domain. We also used our fuzzy RCC implementation for fuzzification of the skyline operator in spatial databases. The results of the evaluation show that our method provides a more realistic view of spatial relationships and gives more flexibility to the data analyst to extract meaningful and accurate results in comparison with the existing methods.