File: references.bib

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@article{Anselin1996b,
author = {Anselin, Luc and Smirnov, Oleg},
title = {Efficient algorithms for constructing proper higher order spatial lag operators},
journal = {Journal of Regional Science},

volume = {36},
number = {1},
pages = {67-89},
doi = {10.1111/j.1467-9787.1996.tb01101.x},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9787.1996.tb01101.x},
abstract = {ABSTRACT. This paper extends the work of Blommestein and Koper (1992)–BK–on the construction of higher-order spatial lag operators without redundant and circular paths. For the case most relevant in spatial econometrics and spatial statistics, i.e., when contiguity between two observations (locations) is defined in a simple binary fashion, some deficiencies of the BK algorithms are outlined, corrected and an improvement suggested. In addition, three new algorithms are introduced and compared in terms of performance for a number of empirical contiguity structures. Particular attention is paid to a graph theoretic perspective on spatial lag operators and to the most efficient data structures for the storage and manipulation of spatial lags. The new forward iterative algorithm which uses a list form rather than a matrix to store the spatial lag information is shown to be several orders of magnitude faster than the BK solution. This allows the computation of proper higher-order spatial lags “on the fly” for even moderately large data sets such as 3,111 contiguous U. S. counties, which is not practical with the other algorithms.},
year = {1996}
}




@Article{pysal2007,
  author={Rey, Sergio J. and Anselin, Luc},
  title={{PySAL: A Python Library of Spatial Analytical Methods}},
  journal={The Review of Regional Studies},
  year=2007,
  volume={37},
  number={1},
  pages={5-27},
  keywords={Open Source; Software; Spatial}
}


@Article{Watts1998,
  author={Watts, D.J. and S.H. Strogatz},
  year={1998},
  title={Collective dynamics of 'small-world' networks},
  journal={Nature},
  volume={393},
  pages={440-442},
  keywords={networks}
}