geosnap.bib

@article{Cortes2019b,
  title = {Pysal/Segregation: {{Segregation Analysis}}, {{Inference}}, and {{Decomposition}}},
  author = {Cortes, Renan Xavier and Knaap, Elijah and Rey, Sergio and Kang, Wei and Stephens, Philip and Wolf, Levi John and Härkönen, Antti and Gaboardi, James and Arribas-Bel, Dani},
  date = {2019-07-19},
  doi = {10.5281/ZENODO.3343074},
  url = {https://zenodo.org/record/3343074},
  urldate = {2019-11-02}
}

@article{Cortes2020,
  title = {An Open-Source Framework for Non-Spatial and Spatial Segregation Measures: The {{PySAL}} Segregation Module},
  author = {Cortes, Renan Xavier and Rey, Sergio and Knaap, Elijah and Wolf, Levi John},
  date = {2020-04-23},
  journaltitle = {Journal of Computational Social Science},
  volume = {3},
  number = {1},
  pages = {135--166},
  publisher = {{Springer Singapore}},
  issn = {2432-2717},
  doi = {10.1007/s42001-019-00059-3},
  url = {http://link.springer.com/10.1007/s42001-019-00059-3},
  isbn = {0123456789},
  keywords = {com,gmail,open-source,Open-source,pysal,PySAL,renan xavier cortes,renanxcortes,segregation,Segregation,spatial analysis,Spatial analysis},
  annotation = {7 citations (Crossref) [2022-08-11]}
}

@article{Feng2021,
  title = {Spopt: A Python Package for Solving Spatial Optimization Problems in {{PySAL}}},
  author = {Feng, Xin and Barcelos, Germano and Gaboardi, James D and Wei, Ran and Wolf, Levi J. and Zhao, Qunshan and Rey, Sergio J. and Knaap, Elijah and Wei, Ran and Wolf, Levi J. and Zhao, Qunshan and Rey, Sergio J.},
  date = {2021-06-14},
  journaltitle = {Journal of Open Source Software},
  volume = {7},
  number = {74},
  pages = {1--7},
  publisher = {{Open Journals}},
  issn = {2475-9066},
  doi = {10.21105/joss.03330},
  url = {https://joss.theoj.org/papers/10.21105/joss.03330},
  annotation = {0 citations (Crossref) [2022-08-11]}
}

@software{Kang-giddy2019,
  title = {Pysal/Giddy: Giddy 2.2.1},
  author = {Kang, Wei and Rey, Sergio and Stephens, Philip and Malizia, Nicholas and Wolf, Levi John and Lumnitz, Stefanie and Gaboardi, James and {jlaura} and Schmidt, Charles and Knaap, Eli and Eschbacher, Andy},
  date = {2019-07-26},
  doi = {10.5281/ZENODO.3351744},
  url = {https://github.com/pysal/giddy},
  urldate = {2019-08-22},
  keywords = {software}
}

@misc{Knaap2020b,
  title = {Pysal/Tobler},
  author = {Knaap, Eli and Cortes, Renan Xavier and Rey, Sergio and Gaboardi, James and Frontiera, Patty},
  date = {2020-09-23},
  doi = {10.5281/zenodo.3386576},
  url = {https://zenodo.org/record/3386576},
  urldate = {2020-12-10}
}

@book{rey_open_2015-1,
  title = {Open Geospatial Analytics with \{\vphantom\}{{PySAL}}\vphantom\{\}},
  author = {Rey, S J and Anselin, L and Li, X and Pahle, R and Laura, J and Li, W and Koschinsky, J},
  date = {2015-02}
}

@incollection{rey_python_2014,
  title = {Python \{\vphantom\}{{Spatial}}\vphantom\{\} \{\vphantom\}{{Analysis}}\vphantom\{\} \{\vphantom\}{{Library}}\vphantom\{\} (\{\vphantom\}{{PySAL}}\vphantom\{\}): \{\vphantom\}{{An}}\vphantom\{\} Update and Illustration},
  booktitle = {Geocomputation},
  author = {Rey, Sergio J},
  editor = {Brunsdon, C and SIngleton, A},
  date = {2014},
  publisher = {{Sage}},
  location = {{London}}
}

@incollection{Rey17_codeastext,
  title = {Code as {{Text}}: {{Open Source Lessons}} for {{Geospatial Research}} and {{Education}}},
  booktitle = {{{GeoComputational Analysis}} and {{Modeling}} of {{Regional Systems}}},
  author = {Rey, Sergio J},
  editor = {Thill, Jean-Claude and Dragicevic, Suzana},
  date = {2017},
  pages = {7--21},
  publisher = {{Springer International Publishing}},
  location = {{Cham}},
  doi = {10.1007/978-3-319-59511-5_2},
  url = {http://link.springer.com/10.1007/978-3-319-59511-5},
  abstract = {The contributed volume collects cutting-edge research in GeoComputational Analysis of Regional Systems. The contributions emphasize methodological innovations or substantive breakthroughs on many facets of the socio-economic and environmental reality of regional contexts. Preface; Contents; Part I General; GeoComputational Research on Regional Systems; References; Code as Text: Open Source Lessons for Geospatial Research and Education; Introduction; PySAL; Origins; Components; Lessons for Academic Open Source Developers; Lessons for Education; Lessons for Research; Conclusion; References; Considering Diversity in Spatial Decision Support Systems; Introduction; Kinds of Diversity; Diverse Solutions; Diverse Optimality; Diverse Toolboxes; Embracing Diversity; Encouraging and Maintaining Diverse Solutions; Hybrid Solution Toolboxes; Cooperative Methods ConclusionsReferences; Parallel Computing for Geocomputational Modeling; Introduction; Parallel Computing; Parallel Computing for Geocomputational Modeling; Spatial Statistics; Spatial Optimization; Spatial Simulation; Cartography and Geovisualization; Case Study; Agent-Based Spatial Simulation; Experiment; Conclusion; References; High-Performance GeoComputation with the Parallel Raster Processing Library; Introduction; High-Performance GeoComputation; Parallelizing Raster-Based Spatial Algorithms; Key Features of pRPL 2.0; Basic Components of pRPL; Flexible Execution of Transitions Multi-Layer ProcessingCentralized and Non-Centralized Focal Processing; Flexible Domain Decomposition; "Update-On-Change" and "edgesFirst" for Data Exchange; GDAL-based Centralized and Parallel I/O; Static and Dynamic Load-Balancing; Showcases and Performance Assessments; Parallel Spatial Analysis-Slope and Aspect Calculations; Parallel Spatio-Temporal Modeling-Cellular Automata; Conclusion; References; Part II Agent-based Systems and Microsimulations; `Can You Fix It?' Using Variance-Based Sensitivity Analysis to Reduce the Input Space of an Agent-Based Model of Land Use Change; Introduction Comprehensive Uncertainty and Sensitivity Analysis of Agent-Based Models of Land Use ChangeFramework; Variance-Based Sensitivity Analysis; Simple Example of Variance-Based SA; Sampling; ABM of Agricultural Land Conservation and Model Setup; Study Area; Data; Experiments and Outputs; Results of the Original ABM; Uncertainty Analysis; Sensitivity Analysis; Model Simplification and Discussion; Simplification-What for?; Suggestions for Best Practices to SA-Based Model Simplification; Conclusions; References; Agent-Based Modeling of Large-Scale Land Acquisition and Rural Household Dynamics IntroductionRural Systems and Large-Scale Land Acquisition; Extent of Large-Scale Land Acquisition; Implications of Large-Scale Land Acquisition; Prior Agent-Based Modeling on Traditional Societies in Rural Systems; Setting, Situation and Study Area; The OMOLAND Model; Model Description; Model Sequence; Policy Scenarios; Results; Scenario 1: Without Off-Farm Opportunities; Scenario 2: With Off-Farm Opportunities; Discussion and Conclusion; References; Spatial Agent-based Modeling to Explore Slum Formation Dynamics in Ahmedabad, India; Introduction; Modeling of Urban Systems},
  isbn = {978-3-319-59509-2}
}

@incollection{rey2010PySALPython,
  title = {{{PySAL}}: {{A Python Library}} of {{Spatial Analytical Methods}}},
  booktitle = {Handbook of {{Applied Spatial Analysis}}},
  author = {Rey, Sergio J. and Anselin, Luc},
  date = {2010},
  volume = {37},
  number = {2007},
  pages = {175--193},
  publisher = {{Springer Berlin Heidelberg}},
  location = {{Berlin, Heidelberg}},
  issn = {0048749X},
  doi = {10.1007/978-3-642-03647-7_11},
  url = {http://link.springer.com/chapter/10.1007/978-3-642-03647-7_11%5Cnhttp://books.google.com/books?hl=en&lr=&id=c0EP_6eYsjAC&oi=fnd&pg=PA174&dq=A+.+10+PySAL+:+A+Python+Library+of+Spatial+Analytical+Methods&ots=JBA9xoaH1S&sig=LsUiJlJvYljJg5y2d0jwVikYo9Q},
  abstract = {PySAL is an open source library for spatial analysis written in the object-oriented language Python. It is built upon shared functionality in two exploratory spatial data analysis packages GeoDA and STARSand is intended to leverage the shared development of these components. This paper presents an overview of the motivation behind and the design of PySAL, as well as suggestions for how the library can be used with other software projects. Empirical illustrations of several key components in a variety of spatial analytical problems are given, and plans for future development of PySAL are discussed.},
  isbn = {978-3-642-03646-0 978-3-642-03647-7},
  keywords = {and,anselin,bcs-0433132,c21,c88,esda,geoda,in part by national,jel classification,python,r15,rey,s research was supported,science foundation grants,science foundation grants bcs-0602581,spatial econometrics,stars}
}

@article{Rey2015,
  title = {Open {{Geospatial Analytics}} with {{PySAL}}},
  author = {Rey, Sergio and Anselin, Luc and Li, Xun and Pahle, Robert and Laura, Jason and Li, Wenwen and Koschinsky, Julia},
  date = {2015-05-13},
  journaltitle = {ISPRS International Journal of Geo-Information},
  volume = {4},
  number = {2},
  pages = {815--836},
  issn = {2220-9964},
  doi = {10.3390/ijgi4020815},
  url = {http://www.mdpi.com/2220-9964/4/2/815/},
  abstract = {This article reviews the range of delivery platforms that have been developed for the PySAL open source Python library for spatial analysis. This includes traditional desktop software (with a graphical user interface, command line or embedded in a computational notebook), open spatial analytics middleware, and web, cloud and distributed open geospatial analytics for decision support. A common thread throughout the discussion is the emphasis on openness, interoperability, and provenance management in a scientific workflow. The code base of the PySAL library provides the common computing framework underlying all delivery mechanisms.},
  keywords = {cybergis,cyberGIS,high performance computing,neighborhoods,open source software,spatial analysis,spatial decision support systems,spatial econometrics,spatial-analysis},
  annotation = {22 citations (Crossref) [2022-08-10]}
}

@article{rey2021PySALEcosystem,
  title = {The {{PySAL Ecosystem}}: {{Philosophy}} and {{Implementation}}},
  author = {Rey, Sergio J. and Anselin, Luc and Amaral, Pedro and Arribas-Bel, Dani and Cortes, Renan Xavier and Gaboardi, James David and Kang, Wei and Knaap, Elijah and Li, Ziqi and Lumnitz, Stefanie and Oshan, Taylor M. and Shao, Hu and Wolf, Levi John},
  date = {2021-06},
  journaltitle = {Geographical Analysis},
  pages = {gean.12276},
  issn = {0016-7363},
  doi = {10.1111/gean.12276},
  url = {https://onlinelibrary.wiley.com/doi/10.1111/gean.12276},
  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.},
  keywords = {geocomputation,Spatial analysis,spatial data science},
  annotation = {5 citations (Crossref) [2022-08-10]}
}