GeoPandas, Shapely, Fiona, PySAL, Pyliburo & Py4design

'GeoPandas is an open source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types. Geometric operations are performed by shapely. Geopandas further depends on fiona for file access and descartes and matplotlib for plotting.' to find out more...

Above passage via http://geopandas.org/

'Shapely is a Python package for set-theoretic analysis and manipulation of planar features using (via Python’s ctypes module) functions from the well known and widely deployed GEOS library. GEOS, a port of the Java Topology Suite (JTS), is the geometry engine of the PostGIS spatial extension for the PostgreSQL RDBMS. The designs of JTS and GEOS are largely guided by the Open Geospatial Consortium’s Simple Features Access Specification [1] and Shapely adheres mainly to the same set of standard classes and operations. Shapely is thereby deeply rooted in the conventions of the geographic information systems (GIS) world, but aspires to be equally useful to programmers working on non-conventional problems.' to find out more...

Source: https://shapely.readthedocs.io/en/stable/manual.html#spatial-analysis-methods

'Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely. Fiona is supported only on CPython versions 2.7 and 3.4+.' to find out more...

Source: https://fiona.readthedocs.io/en/latest/README.html#usage

'PySAL is an open source library of spatial analysis functions written in Python intended to support the development of high level applications. PySAL is open source under the BSD License.' to find out more...

Source: https://pysal.readthedocs.io/en/latest/

'Urban design optimisation is a powerful method for the exploration of multiple designs. In performing an urban design optimisation, we need to link and automatically execute multiple domain-specific applications, a technically complicated setup. Current solutions resolve the technical obstacle by embedding the applications within a single Computer-Aided Design (CAD) application to streamline the setup. The solution leverages the CAD application’s modelling workflow and capability to process the urban geometries for analyses. However, this solution is workflow specific; users either do not have access to optimisation algorithms or are restricted to the capabilities provided by a specific CAD application. For optimisation to be accessible to a wider community, we develop an open Python library, Pyliburo, to provide optimisation capability to all design workflows. Pyliburo aims to be easily integrated into a user’s existing design workflow to provide or enhance optimisation capability. To do so, Pyliburo emphasises interoperability, platform independence, ease of use, integration flexibility and extensibility.'  to find out more...

Source: http://www.ibpsa.org/proceedings/BS2017/BS2017_323.pdf

Python Library for Rapid Development of Design Workflows (Py4design)(Previously known as Pyliburo) to find out more...

Source: https://www.researchgate.net/project/Python-Library-for-Rapid-Development-of-Design-Workflows-Py4designPreviously-known-as-Pyliburo

Discussion and further reading:

• https://gis.stackexchange.com/questions/271733/geopandas-dissolve-overlapping-polygons
• https://gis.stackexchange.com/questions/197652/find-common-borders-of-features-and-get-their-attributes-using-fiona-and-shapely
• http://darribas.org/gds_scipy16/ https://www.ismll.uni-hildesheim.de/lehre/semSpatial-10s/script/2.pdf

YouTube:

• https://youtu.be/TY4QWnnd4jY (PySAL use cases on crime analysis on Geo space)