ThePySALEcosystem: Philosophy and Implementation

Abstract

PySAL is a library for geocomputation and spatial data science. Written in Python, the library has a long history of supporting novel scholarship and broadening methodological impacts far afield of academic work. Recently, many new techniques, methods of analyses, and development modes have been implemented, making the library much larger and more encompassing than that previously discussed in the literature. As such, we provide an introduction to the library as it stands now, as well as the scientific and conceptual underpinnings of its core set of components. Finally, we provide a prospective look at the library's future evolution.

TransBigData: A Python package for transportation spatio-temporal big data processing, analysis and visualization

“…PySAL (S. J. Rey et al, 2021;S. Rey & Anselin, 2007) is a family of packages that allows for advanced geospatial data science, which supports the development of high-level applications.…”

GeoDa, From the Desktop to an Ecosystem for Exploring Spatial Data

“…In contrast, the spatial regression component in GeoDa itself has remained essentially unchanged over time (except for some minor bug fixes). The development efforts pertaining to this functionality were moved to the spreg module of the PySAL Python library (Rey and Anselin 2007; Rey et al 2021), and separately released as GeoDaSpace, with a graphical user interface for Windows and MacOS (Anselin and Rey 2014). 3 The spreg module remains under active development, with a recent addition of functionality to handle spatial panel regression.…”

“…An important development in the last 20 years is the growing impact of the open source contributions. For spatial analysis, in particular, this is reflected in the large spatial ecosystem in R (Bivand 2006), as well as a growing component in Python, with geopandas (https://geopandas.org) and the PySAL library (Rey et al 2021).…”

R Packages for Analyzing Spatial Data: A Comparative Case Study with Areal Data

“…Over and beyond R packages, other open source software is also easily accessible for comparison, and GeoDa (Anselin, Li, and Koschinsky 2021) and PySAL (Rey et al 2021c) will be included in this selective review. Because Anselin, Li, and Koschinsky (2021) conduct a thorough set of comparative timing analyses, timings will just be noted here, with more attention paid to other differences, using the facility of open source software enabling checking the buck‐stops‐here documentation, the code.…”

“…Following data download, subsetting, and manipulation (described in an appendix), the moderately large data set with over 71,000 tracts but with fewer variables than the original for confidentiality reasons, will be explored for spatial autocorrelation using functions in the spdep package (Bivand 2022b), and spatial error models fitted with functions from the sphet (Piras 2021) and spatialreg (Bivand and Piras 2021) packages. In addition, exploratory spatial data analysis will be undertaken using the new rgeoda package (Li and Anselin 2021), and exploratory spatial data analysis and regression modeling reproduced using PySAL (Rey and Anselin 2007, 2010; Rey et al 2015, 2021c), as it was used in the original workflow. Taking advantage of the detailed collaboration that open source software enables, the discussion of results achieved using rgeoda takes up points also reported by Anselin, Li, and Koschinsky (2021) with regard to relative timings between GeoDa, PySAL, and spdep .…”