Open Geospatial Analytics with PySAL

Rey, Sergio J.; Anselin, Luc; Li, Xun; Pahle, Robert; Laura, J.; Li, Wenwen; Koschinsky, Julia

doi:10.3390/ijgi4020815

Cited by 36 publications

(17 citation statements)

References 37 publications

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“…This threshold distance generally included 4–7 of a subregion’s nearest neighbors. The spatial weights matrix was row standardized and the MI were calculated using the PySAL library [30]. Likewise, we used Fisher’s Exact tests to evaluate differences in percent-positivity for each subregion as compared against the data pooled from all other subregions, and corrected for multiple testing using the Benjamini-Hochberg procedure.…”

Section: Methodsmentioning

confidence: 99%

Seasonal characteristics of influenza vary regionally across US

2019

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Given substantial regional differences in absolute humidity across the US and our understanding of the relationship between absolute humidity and influenza, we may expect important differences in regional seasonal influenza activity. Here, we assessed cross-seasonal influenza activity by comparing counts of positive influenza A and B rapid test results during the influenza season versus summer baseline periods for the 2016/2017 and 2017/2018 influenza years. Our analysis indicates significant regional patterns in cross-seasonal influenza activity, with relatively fewer influenza cases during the influenza season compared to summertime baseline periods in humid areas of the US, particularly in Florida and Hawaii. The cross-seasonal ratios vary from year-to-year and influenza type, but the geographic patterning of the ratios is relatively consistent. Mixed-effects regression models indicated absolute humidity during the influenza season was the strongest predictor of cross-seasonal influenza activity, suggesting a relationship between absolute humidity and cross-seasonal influenza activity. There was also evidence that absolute humidity during the summer plays a role, as well. This analysis suggests that spatial variation in seasonal absolute humidity levels may generate important regional differences in seasonal influenza activity and dynamics in the US.

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Section: Methodsmentioning

confidence: 99%

Seasonal characteristics of influenza vary regionally across US

2019

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“…Another open issue is the implementation of alternatives to storing intermediate results or temporary files in shapefiles in order to avoid the drawbacks of this format, particularly the truncation of attribute names. Current enhancement plans include a Google Summer of Code project to add multi-threading support to Processing [32], as well as the integration of the spatial analysis library PySAL [33], as mentioned in [34].…”

Section: Discussionmentioning

confidence: 99%

Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS

Gräser

Olaya

2015

IJGI

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Processing is an object-oriented Python framework for the popular open source Geographic Information System QGIS, which provides a seamless integration of geoprocessing tools from a variety of different software libraries. In this paper, we present the development history, software architecture and features of the Processing framework, which make it a versatile tool for the development of geoprocessing algorithms and workflows, as well as an efficient integration platform for algorithms from different sources. Using real-world application examples, we furthermore illustrate how the Processing architecture enables typical geoprocessing use cases in research and development, such as automating and documenting workflows, combining algorithms from different software libraries, as well as developing and integrating custom algorithms. Finally, we discuss how Processing can facilitate reproducible research and provide an outlook towards future development goals.

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“…are free and open-source (Anselin, Syabri, & Kho, 2006), as is Serge Rey's Python Spatial Analysis Library (PySAL: Rey, 2019;Rey et al, 2015). Paul Waddell's UrbanSim platform, a Python-based software library that supports urban land use and transportation modeling and simulation, is similarly free and open-source (Waddell, 2002).…”

Section: Advancing the Disciplinementioning

confidence: 99%

The right tools for the job: The case for spatial science tool‐building

Boeing

2020

Transactions in GIS

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Do I need to know the precise polygonal geometries of Los Angeles and the University of Southern California (USC) to assert that the latter is within the former? No. My mind contains no such precise geometric model of points and lines, yet I know that USC is in Los Angeles. When humans reason with the real world, they focus on its objects, relations, and processes-rather than starting with geometry-because these are the keys to

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Open Geospatial Analytics with PySAL

Cited by 36 publications

References 37 publications

Seasonal characteristics of influenza vary regionally across US

Seasonal characteristics of influenza vary regionally across US

Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS

The right tools for the job: The case for spatial science tool‐building

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