sew19.bib

@Article{Bivand2018,
author="Bivand, Roger S. and Wong, David W. S.",
title="Comparing implementations of global and local indicators of spatial association",
journal="TEST",
year="2018",
month="Sep",
day="01",
volume="27",
number="3",
pages="716--748",
abstract="Functions to calculate measures of spatial association, especially measures of spatial autocorrelation, have been made available in many software applications. Measures may be global, applying to the whole data set under consideration, or local, applying to each observation in the data set. Methods of statistical inference may also be provided, but these will, like the measures themselves, depend on the support of the observations, chosen assumptions, and the way in which spatial association is represented; spatial weights are often used as a representational technique. In addition, assumptions may be made about the underlying mean model, and about error distributions. Different software implementations may choose to expose these choices to the analyst, but the sets of choices available may vary between these implementations, as may default settings. This comparison will consider the implementations of global Moran's I, Getis--Ord G and Geary's C, local {\$}{\$}I{\_}i{\$}{\$}Iiand {\$}{\$}G{\_}i{\$}{\$}Gi, available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages.",
issn="1863-8260",
doi="10.1007/s11749-018-0599-x",
url="https://doi.org/10.1007/s11749-018-0599-x"
}
@article{bivand+piras:15,
   author = {Roger S. Bivand and Gianfranco Piras},
   title = {Comparing Implementations of Estimation Methods for Spatial Econometrics},
   journal = {Journal of Statistical Software},
   volume = {63},
   number = {1},
   year = {2015},
   issn = {1548-7660},
   pages = {1--36},
   doi = {10.18637/jss.v063.i18},
}
@article{bivandetal13,
author = {Bivand, Roger S. and Hauke, Jan and Kossowski, Tomasz},
title = {Computing the {J}acobian in {G}aussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods},
journal = {Geographical Analysis},
volume = {45},
number = {2},
pages = {150--179},
year = {2013},
doi = {10.1111/gean.12008},
url = {https://doi.org/10.1111/gean.12008}
}
@article{bivandetal17a,
title = "A comparison of estimation methods for multilevel models of spatially structured data ",
journal = "Spatial Statistics ",
year = "2017",
url = "https://doi.org/10.1016/j.spasta.2017.01.002",
author = "Roger S. Bivand and Zhe Sha and Liv Osland and Ingrid Sandvig Thorsen",
}
@book{geocomp,
  title = { Geocomputation with {R} },
  author = { Robin Lovelace and Jakub Nowosad and Jannes Muenchow},
  publisher = { Chapman and Hall/CRC },
  year = { 2019 },
  url = { https://geocompr.robinlovelace.net/ },
  isbn = {9781138304512}
}
@Article{unitsrj,
    author = {Edzer Pebesma and Thomas Mailund and James Hiebert},
    title = {Measurement Units in {R}},
    journal = {The R Journal},
    year = {2016},
    volume = {8},
    number = {2},
    pages = {486--494},
    month = {december},
	doi = {10.32614/RJ-2016-061}
}
@article{RJ-2018-009,
  author = {Edzer Pebesma},
  title = {{Simple Features for R: Standardized Support for Spatial
          Vector Data}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-009/index.html},
  pages = {439--446},
  volume = {10},
  number = {1}
}
@article{RJ-2018-075,
  author = {Iñaki Ucar and Edzer Pebesma and Arturo Azcorra},
  title = {{Measurement Errors in R}},
  year = {2018},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2018/RJ-2018-075/index.html},
  pages = {549--557},
  volume = {10},
  number = {2}
}
@manual{pebesma+bivand:20,
author = {Edzer Pebesma and Roger S. Bivand},
title = {Spatial data science},
year = {2020},
url = {https://deploy-preview-20--keen-swartz-3146c4.netlify.com/}
}
@article{ord+getis12,
   author = {Ord, J. K. and Getis, A.},
   title = {Local spatial heteroscedasticity ({LOSH})},
   journal = {Annals of Regional Science},
   volume = {48},
   number = {2},
   year = {2012},
   pages = {529--539}
}
@article{xuetal14,
   author = {Xu, M. and Mei, C. L. and Yan, N.},
   title = {A note on the null distribution of the local spatial heteroscedasticity ({LOSH}) statistic},
   journal = {Annals of Regional Science},
   volume = {52},
   number = {3},
   year = {2014},
   pages = {697--710}
}
@article{ord+getis:01,
Author = {Ord, J. K. and Getis, A.},
Title = {Testing for local spatial autocorrelation in the presence of global
   autocorrelation},
Journal = {Journal of Regional Science},
Year = {2001},
Volume = {41},
Number = {3},
Pages = {411-432}
}
@book{cliff+ord:73,
   author = {Cliff, A. D. and Ord, J. K.},
   title = {Spatial Autocorrelation},
   year = {1973},
   publisher = {Pion},
   address = {London}
}
@book{cliff+ord:81,
   author = {Cliff, A. D. and Ord, J. K.},
   title = {Spatial Processes},
   year = {1981},
   publisher = {Pion},
   address = {London}
}
@article{tiefelsdorf:02,
   author = {Tiefelsdorf, M.},
   title = {The Saddlepoint approximation of {Moran}'s {I} 
     and local {Moran}'s ${I}_i$ reference distributions and their numerical
     evaluation},
   journal = {Geographical Analysis},
   volume = {34},
   pages = {187--206},
   year = {2002},
}
@article{bivandetal:09,
   author = {Roger S. Bivand and Werner M\"{u}ller and Marcus Reder},
   title = {Power Calculations for Global and Local {M}oran's {$I$}},
   journal = {Computational Statistics and Data Analysis},
   year = {2009},
   volume = {53},
   pages = {2859-2872}
}
@Book{a88,
author = {L. Anselin},
title = {Spatial econometrics: methods and models},
publisher = {Kluwer Academic Publishers},
year = {1988}
}
@article{bivand17,
	author = {Bivand, Roger S.},
	title = {Revisiting the {B}oston data set --- Changing the units of observation affects estimated willingness to pay for clean air},
	journal = {REGION},
	volume = {4},
	number = {1},
	year = {2017},
	issn = {2409-5370},
	pages = {109--127},
	doi = {10.18335/region.v4i1.107},
	url = {http://openjournals.wu.ac.at/ojs/index.php/region/article/view/107}
}
@book{WallerGotway:2004,
   author    = {Lance A. Waller and Carol A. Gotway},
   title     = {Applied Spatial Statistics for Public Health Data},
   year      = {2004},
   publisher = {John Wiley \& Sons},
   address = {Hoboken, NJ}
}
@book{lesage+pace:09,
author = {James P. {LeSage} and Kelley R. Pace},
title = {Introduction to Spatial Econometrics},
year = {2009},
publisher = {CRC Press},
address = {Boca Raton, FL}
}

@article{pace+lesage:08,
author = {Pace, RK and {LeSage}, JP},
title = {A Spatial {H}ausman Test},
journal = {Economics Letters},
year = {2008},
volume = {101},
pages = {282--284}
}
@Article{burridge:81,
  author={P. Burridge},
  title={Testing for a common factor in a spatial autoregression model},
  journal={Environment and Planning A},
  year={1981},
  volume={13},
  pages={795--800}
}

@article{fingleton:99,
Author = {Fingleton, B.},
Title = {{Spurious spatial regression: Some Monte Carlo results with a spatial
   unit root and spatial cointegration}},
Journal = {{Journal of Regional Science}},
Year = {1999},
Volume = {9},
Pages = {1--19},
}
@book{mcmillen:13,
author={Mc{M}illen, D. P.},
year={2013},
title={Quantile Regression for Spatial Data},
publisher={Springer-Verlag},
address={Heidelberg}
}
@article {halleck-vega+elhorst:15,
author = {Halleck Vega, Solmaria and Elhorst, J. Paul},
title = {The {SLX} model},
journal = {Journal of Regional Science},
volume = {55},
number = {3},
issn = {1467-9787},
url = {https://doi.org/10.1111/jors.12188},
doi = {10.1111/jors.12188},
pages = {339--363},
year = {2015},
}
@article{mur+angulo:06,
author = { Jesús   Mur  and  Ana   Angulo },
title = {The Spatial Durbin Model and the Common Factor Tests},
journal = {Spatial Economic Analysis},
volume = {1},
number = {2},
pages = {207-226},
year  = {2006},
publisher = {Routledge},
doi = {10.1080/17421770601009841},
URL = {https://doi.org/10.1080/17421770601009841},
eprint = {https://doi.org/10.1080/17421770601009841}
}

@article{goulardetal:17,
author = {Michel Goulard and Thibault Laurent and Christine Thomas-Agnan},
title = {About predictions in spatial autoregressive models: optimal and almost optimal strategies},
journal = {Spatial Economic Analysis},
volume = {12},
number = {2-3},
pages = {304-325},
year  = {2017},
publisher = {Routledge},
doi = {10.1080/17421772.2017.1300679},
URL = {https://doi.org/10.1080/17421772.2017.1300679},
eprint = {https://doi.org/10.1080/17421772.2017.1300679}
}
@article{RJ-2013-013,
  author = {Stefan Wilhelm and Miguel Godinho de Matos},
  title = {{Estimating Spatial Probit Models in R}},
  year = {2013},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2013-013},
  url = {https://doi.org/10.32614/RJ-2013-013},
  pages = {130--143},
  volume = {5},
  number = {1}
}

@article{MARTINETTI201730,
title = "Approximate likelihood estimation of spatial probit models",
journal = "Regional Science and Urban Economics",
volume = "64",
pages = "30 - 45",
year = "2017",
issn = "0166-0462",
doi = "https://doi.org/10.1016/j.regsciurbeco.2017.02.002",
url = "http://www.sciencedirect.com/science/article/pii/S0166046217300546",
author = "Davide Martinetti and Ghislain Geniaux",
keywords = "Spatial probit, Multivariate normal probabilities, Spatial auto-regressive model, Spatial error model, Spatial discrete choice models",
abstract = "A new estimation method for spatial binary probit models is presented. Both spatial auto-regressive (SAR) and spatial error (SEM) models are considered. The proposed estimator relies on the approximation of the likelihood function, that follows a multivariate normal distribution which parameters depend on the spatial structure of the observations. The approximation is inspired by the univariate conditioning procedure proposed by Mendell and Elston, with some modifications to improve accuracy and speed. Very accurate parameter estimations have been achieved in reasonable time for simulated data samples with as much as one million observations. The lessons learned in the Monte Carlo experiment have been applied to a case study on urban sprawl over more than forty thousands plots in Southern France."
}

@Article{wagner+zeileis:19,
  title = {Heterogeneity and Spatial Dependence of Regional Growth in the {EU}: A Recursive Partitioning Approach},
  author = {Martin Wagner and Achim Zeileis},
  journal = {German Economic Review},
  year = {2019},
  volume = {20},
  number = {1},
  pages = {67--82},
  doi = {10.1111/geer.12146},
}

@article{GRIFFITH2015119,
title = "Implementing Approximations to Extreme Eigenvalues and Eigenvalues of Irregular Surface Partitionings for Use in SAR and CAR Models",
journal = "Procedia Environmental Sciences",
volume = "26",
pages = "119 - 122",
year = "2015",
note = "Spatial Statistics conference 2015",
issn = "1878-0296",
doi = "https://doi.org/10.1016/j.proenv.2015.05.013",
url = "http://www.sciencedirect.com/science/article/pii/S1878029615001802",
author = "Daniel Griffith and Roger S. Bivand and Yongwan Chun",
keywords = "Extreme eigenvalues, Spatial regression, Rayleigh quotient",
abstract = "Good approximations of eigenvalues exist for the regular square and hexagonal tessellations. To complement this situation, spatial scientists need good approximations of eigenvalues for irregular tessellations. Starting from known or approximated extreme eigenvalues, the remaining eigenvalues may be in turn approximated. One reason spatial scientists are interested in eigenvalues is because they are needed to calculate the Jacobian term in the autonormal probability model. If eigenvalues are not needed for model fitting, good approximations are needed to give the interval within which the spatial parameter will lie."
}

@article{GOMEZRUBIO2015116,
title = "A New Latent Class to Fit Spatial Econometrics Models with Integrated Nested Laplace Approximations",
journal = "Procedia Environmental Sciences",
volume = "27",
pages = "116 - 118",
year = "2015",
note = "Spatial Statistics conference 2015",
issn = "1878-0296",
doi = "https://doi.org/10.1016/j.proenv.2015.07.119",
url = "http://www.sciencedirect.com/science/article/pii/S187802961500331X",
author = "Virgilio Gómez-Rubio and Roger S. Bivand and Håvard Rue",
keywords = "Integrated Nested Laplace Approximation, Spatial econometrics",
abstract = "The new slm latent model for estimating spatial econometrics models using INLA has recently been introduced. It will be described briefly and its use will be demonstrated in the accompanying poster."
}

@Article{dong15,
author = {G. Dong and R. Harris},
title = {Spatial autorgressive models for geographically hierarchical data structures},
journal = {Geographical Analysis},
year = {2015},
volume = {47},
number = {2},
pages = {173-191}
}


@Article{dongetal15,
author = {G. Dong and R. Harris and K. Jones and J. Yu},
title = {Multilevel modeling with spatial interaction effects with application to an emerging land market in Beijing, China},
journal = {{PLOS} One},
year = {2015},
volume = {10},
number = {6},
doi = {doi:10.1371/journal.pone.0130761}
}



@article{suesse:18,
title = "Marginal maximum likelihood estimation of SAR models with missing data",
journal = "Computational Statistics & Data Analysis",
volume = "120",
pages = "98 - 110",
year = "2018",
issn = "0167-9473",
doi = "https://doi.org/10.1016/j.csda.2017.11.004",
url = "http://www.sciencedirect.com/science/article/pii/S0167947317302396",
author = "Thomas Suesse",
keywords = "SAR model, EM algorithm, Marginal likelihood, Missing data, Maximum likelihood",
abstract = "Maximum likelihood (ML) estimation of simultaneous autocorrelation models is well known. Under the presence of missing data, estimation is not straightforward, due to the implied dependence of all units. The EM algorithm is the standard approach to accomplish ML estimation in this case. An alternative approach is considered, the method of maximising the marginal likelihood. At first glance the method is computationally complex due to inversion of large matrices that are of the same size as the complete data, but these can be avoided, leading to an algorithm that is usually much faster than the EM algorithm and without typical EM convergence issues. Another approximate method is also proposed that serves as an alternative, for example when the contiguity matrix is dense. The methods are illustrated using a well known data set on house prices with 25,357 units."
}
@article{ord:75,
author = {Ord, J. K.},
title = {{Estimation Methods for Models of Spatial Interaction}},
journal = {Journal of the American Statistical Association},
year = {1975},
volume = {70},
number = {349},
pages = {120--126}
}

@article{bivand:84,
   author = {Roger S. Bivand},
   title = {{Regression Modeling with Spatial Dependence: an Application of Some Class Selection and Estimation Methods}},
   journal = {Geographical Analysis},
   volume = {16},
   pages = {25--37},
   year = {1984}
}

@article{bivand:02,
   author = {Roger S. Bivand},
   title = {Spatial Econometrics Functions in {R}: Classes and Methods},
   journal = {Journal of Geographical Systems},
   volume = {4},
   pages = {405--421},
   year = {2002}
}

@article{bivand:12,
   author = {Roger S. Bivand},
   title = {{After 'Raising the Bar': Applied Maximum Likelihood Estimation of Families of Models in Spatial Econometrics}},
   journal = {Estad\'{i}stica Espa\~{n}ola},
   volume = {54},
   pages = {71--88},
   year = {2012}
}
@article{elhorst:10,
   author = {J. Paul Elhorst},
   title = {Applied Spatial Econometrics: Raising the Bar},
   journal = {Spatial Economic Analysis},
   volume = {5},
   pages = {9--28},
   year = {2010}
}
@book{kaluznyetal:98,
author = {Kaluzny, S.P. and Vega, S.C. and Cardoso, T.P. and Shelly, A.A.},
title = {{S+SpatialStats}},
year = {1998},
publisher = {Springer},
address = {New York, NY}
}
@article{GRIFFITH2015119,
title = "Implementing Approximations to Extreme Eigenvalues and Eigenvalues of Irregular Surface Partitionings for Use in SAR and CAR Models",
journal = "Procedia Environmental Sciences",
volume = "26",
pages = "119 - 122",
year = "2015",
note = "Spatial Statistics conference 2015",
issn = "1878-0296",
doi = "https://doi.org/10.1016/j.proenv.2015.05.013",
url = "http://www.sciencedirect.com/science/article/pii/S1878029615001802",
author = "Daniel Griffith and Roger S. Bivand and Yongwan Chun",
keywords = "Extreme eigenvalues, Spatial regression, Rayleigh quotient",
abstract = "Good approximations of eigenvalues exist for the regular square and hexagonal tessellations. To complement this situation, spatial scientists need good approximations of eigenvalues for irregular tessellations. Starting from known or approximated extreme eigenvalues, the remaining eigenvalues may be in turn approximated. One reason spatial scientists are interested in eigenvalues is because they are needed to calculate the Jacobian term in the autonormal probability model. If eigenvalues are not needed for model fitting, good approximations are needed to give the interval within which the spatial parameter will lie."
}
@article{pace+barry:97a,
Author = {Pace, R. K. and Barry, R. P.},
Title = {{Fast CARs}},
Journal = {{Journal of Statistical Computation and Simulation}},
Year = {1997},
Volume = {{59}},
Number = {{2}},
Pages = {{123-145}},
Abstract = {{This paper develops methods for quickly computing maximum likelihood
   conditional autoregressions (CARs). By using sparse matrix methods,
   reorganizing the sum-of-squared errors function to avoid unnecessary
   calculations, and precomputing a set of determinants, simulations of
   large CARs become possible. As an illustration of the power of these
   approaches, a simulation of 250 CARs of 2,905 observations can take
   fewer than three minutes on a personal computer, despite the necessity
   of evaluating 100 determinants of 2,905 by 2,905 matrices. The
   computation of each estimate via examining the profile likelihood
   sampled at 100 points avoids problems of local optima. Simulating
   estimates avoids other problems associated with the traditional
   information matrix approach to inference.}},
Unique-ID = {{ISI:A1997YF73500002}},
}

@article{pace+barry:97b,
Author = {Pace, R. K. and Barry, R. P.},
Title = {{Quick computation of spatial autoregressive estimators}},
Journal = {{Geographics Analysis}},
Year = {1997},
Volume = {{29}},
Number = {{3}},
Pages = {{232-247}},
Month = {{JUL}},
Abstract = {{Spatial estimators usually require the manipulation of n(2) relations
   among n observations and use operations such as determinants,
   eigenvalues, and inverses whose operation counts grow at a rate
   proportional to n(3). This paper provides ways to quickly compute
   estimates when the dependent variable follows a spatial autoregressive
   process, which by appropriate specification of the independent
   variables can subsume the case when the errors follow a spatial
   autoregressive process. Since only nearby observations fend to affect a
   given observation, most observations have no effect and hence the
   spatial weight matrix becomes sparse. By exploiting sparsity and
   rearranging computations, one can compute estimates at low cost. As a
   demonstration of the efficacy of these techniques, the paper provides a
   Monte Carlo study whereby 3,107 observation regressions require only
   0.1 seconds each when using Matlab on a 200 Mhz Pentium Pro personal
   computer. In addition, the paper illustrates these techniques by
   examining voting behavior across U.S. counties in the 1980 presidential
   election.}},
Unique-ID = {{ISI:A1997XH96900004}},
}
@article{pace+barry:97c,
Author = {Pace, R. K. and Barry, R. P.},
Title = {{Sparse spatial autoregressions}},
Journal = {{Statistics \& Probability Letters}},
Year = {1997},
Volume = {{33}},
Number = {{3}},
Pages = {{291-297}},
Month = {{MAY 5}},
Abstract = {{Given local spatial error dependence, one can construct sparse spatial
   weight matrices. As an illustration of the power of such sparse
   structures, we computed a simultaneous autoregression using 20 640
   observations in under 19 min despite needing to compute a 20 640 by 20
   640 determinant 10 times.}},
Unique-ID = {{ISI:A1997XB17200010}},
}

@article{pace+barry:97d,
Author = {Pace, R. K. and Barry, R. P.},
Title = {{Fast spatial estimation}},
Journal = {{Applied Economics Letters}},
Year = {1997},
Volume = {{4}},
Number = {{5}},
Pages = {{337-341}},
Month = {{MAY}},
Abstract = {{Spatial estimators usually provide lower prediction errors than their
   aspatial counterparts. However, most of the standard techniques require
   a large number of operations. Fortunately, for a given observation only
   a relatively small number of nearby observations typically exhibit
   correlated errors. This means that most of the elements of the n by n
   spatial matrices are zero. The use of sparse matrix techniques can
   dramatically lower storage requirements and reduce execution times. In
   addition, adopting a first differencing model allows the use of GLS
   which avoids the necessity of evaluating an n by n determinant. This
   also greatly reduces computational costs.}},
Unique-ID = {{ISI:A1997XA40200015}},
}
@Article{alam-ronnegard-shen:2015,
  author       = {Moudud Alam and Lars R\"{o}nneg{\aa}rd and Xia Shen}, 
  title        = {Fitting Conditional and Simultaneous Autoregressive Spatial Models in hglm}, 
  journal      = {The {R} Journal},
  year         = 2015,
  volume       = 7,
  number       = 2,
  pages        = {5--18},
  month        = dec,
  url          = {https://doi.org/10.32614/RJ-2015-017}
}
@Book{wood:17,
    title = {Generalized Additive Models: An Introduction with R},
    year = {2017},
    author = {S.N Wood},
    edition = {2},
    publisher = {Chapman and Hall/CRC},
}
@Article{umlaufetal:15,
    title = {Structured Additive Regression Models: An {R} Interface to
      {BayesX}},
    author = {Nikolaus Umlauf and Daniel Adler and Thomas Kneib and
      Stefan Lang and Achim Zeileis},
    journal = {Journal of Statistical Software},
    year = {2015},
    volume = {63},
    number = {21},
    pages = {1--46},
    url = {http://www.jstatsoft.org/v63/i21/},
}
@article{martin05,
author = {R. J. Martin},
title = {Some Fast Methods for Fitting Some One-parameter Spatial Models},
year = {2005},
journal = {Journal of Mathematics and Statistics},
volume = {1},
pages = {326-336},
url = {https://www.thescipub.com/pdf/10.3844/jmssp.2005.326.336}
}