refs-geostat.bib

@Book{longley_geographic_2015a,
  location = {{Hoboken, NJ}},
  title = {Geographic Information Science \& Systems},
  edition = {Fourth edition},
  isbn = {978-1-118-67695-0},
  abstract = {{"}Effective use of today's powerful GIS technology requires an understanding of the science of problem-solving that underpins it. Since the first edition published over a decade ago, this book has led the way, with its focus on the scientific principles that support GIS usage. It has also provided thorough, upto- date coverage of GIS procedures, techniques and public policy applications. This unique combination of science, technology and practical problem solving has made this book a best-seller across a broad spectrum of disciplines. This fully updated 4th edition continues to deliver on these strengths{"}--},
  pagetotal = {477},
  publisher = {{Wiley}},
  date = {2015},
  keywords = {Geographic information systems,TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems},
  author = {Paul Longley and Michael Goodchild and David Maguire and David Rhind},
}

@Article{bivand_implementing_2000,
  title = {Implementing Functions for Spatial Statistical Analysis Using the Language},
  volume = {2},
  url = {http://www.springerlink.com/index/CJRPUMB78JUYH54W.pdf},
  number = {3},
  journaltitle = {Journal of Geographical Systems},
  urldate = {2017-07-12},
  date = {2000},
  pages = {307--317},
  author = {Roger Bivand and Albrecht Gebhardt},
}

@InProceedings{hornik_approaches_2003,
  title = {Approaches to {{Classes}} for {{Spatial Data}} in {{R}}},
  url = {https://www.r-project.org/nosvn/conferences/DSC-2003/Proceedings/Bivand.pdf},
  booktitle = {Proceedings of {{DSC}}},
  urldate = {2017-06-27},
  date = {2003},
  author = {Roger Bivand},
  editor = {Kurt Hornik and Friedrich Leisch and Achim Zeileis},
}

@Book{bivand_rgdal_2018,
  title = {Rgdal: {{Bindings}} for the '{{Geospatial}}' {{Data Abstraction Library}}},
  url = {https://CRAN.R-project.org/package=rgdal},
  date = {2018},
  author = {Roger Bivand and Tim Keitt and Barry Rowlingson},
  note = {R package version 1.2-18},
}

@Article{bivand_using_2000,
  title = {Using the {{R}} Statistical Data Analysis Language on {{GRASS}} 5.0 {{GIS}} Database Files},
  volume = {26},
  url = {http://www.sciencedirect.com/science/article/pii/S0098300400000571},
  number = {9},
  journaltitle = {Computers \& Geosciences},
  urldate = {2017-07-11},
  date = {2000},
  pages = {1043--1052},
  author = {Roger S. Bivand},
}

@Book{hijmans_raster_2017,
  title = {Raster: {{Geographic Data Analysis}} and {{Modeling}}},
  url = {https://CRAN.R-project.org/package=raster},
  date = {2017},
  author = {Robert J. Hijmans},
  note = {R package version 2.6-7},
}

@Book{pebesma_sf_2018,
  title = {Sf: {{Simple Features}} for {{R}}},
  url = {https://CRAN.R-project.org/package=sf},
  date = {2018},
  author = {Edzer Pebesma},
  note = {R package version 0.6-2},
}

@Book{pebesma_stars_2018,
  title = {Stars: {{Scalable}}, {{Spatiotemporal Tidy Arrays}} for {{R}}},
  url = {https://github.com/r-spatial/stars/},
  date = {2018},
  author = {Edzer Pebesma},
  note = {R package version 0.1-1},
}

@Book{bivand_applied_2013,
  langid = {english},
  location = {{New York}},
  title = {Applied Spatial Data Analysis with {{R}}},
  edition = {2nd ed.},
  isbn = {978-1-4614-7617-7},
  pagetotal = {405},
  publisher = {{Springer}},
  date = {2013-06-21},
  author = {Roger S. Bivand and Edzer Pebesma and Virgilio Gomez-Rubio},
}

@Book{bivand_rgeos_2017,
  title = {Rgeos: {{Interface}} to {{Geometry Engine}} - {{Open Source}} ('{{GEOS}}')},
  url = {https://CRAN.R-project.org/package=rgeos},
  date = {2017},
  author = {Roger Bivand and Colin Rundel},
  note = {R package version 0.3-26},
}

@Article{muenchow_rqgis_2017,
  title = {RQGIS: Integrating R with QGIS for Statistical Geocomputing},
  author = {Jannes Muenchow and Patrick Schratz and Alexander Brenning},
  journal = {The R Journal},
  year = {2017},
  volume = {9},
  issue = {2},
  pages = {409--428},
  note = {Accepted for publication on 2017-12-04},
  url = {https://rjournal.github.io/archive/2017/RJ-2017-067/RJ-2017-067.pdf},
}

@Article{graser_processing_2015,
  title = {Processing: {A} {Python} {Framework} for the {Seamless} {Integration} of {Geoprocessing} {Tools} in {QGIS}},
  volume = {4},
  issn = {2220-9964},
  shorttitle = {Processing},
  url = {http://www.mdpi.com/2220-9964/4/4/2219},
  doi = {10.3390/ijgi4042219},
  language = {en},
  number = {4},
  urldate = {2018-07-21},
  journal = {ISPRS International Journal of Geo-Information},
  author = {Anita Graser and Victor Olaya},
  month = {oct},
  year = {2015},
  pages = {2219--2245},
}

@Article{conrad_system_2015,
  title = {System for {Automated} {Geoscientific} {Analyses} ({SAGA}) v. 2.1.4},
  volume = {8},
  issn = {1991-9603},
  url = {http://www.geosci-model-dev.net/8/1991/2015/},
  doi = {10.5194/gmd-8-1991-2015},
  number = {7},
  urldate = {2017-06-12},
  journal = {Geosci. Model Dev.},
  author = {O. Conrad and B. Bechtel and M. Bock and H. Dietrich and E. Fischer and L. Gerlitz and J. Wehberg and V. Wichmann and J. B{\"o}hner},
  month = {jul},
  year = {2015},
  pages = {1991--2007},
}

@Book{lovelace_geocomputation_2018,
  title = {Geocomputation with {{R}}},
  series = {The R Series},
  publisher = {{CRC Press}},
  date = {2018},
  author = {Robin Lovelace and Jakub Nowosad and Jannes Muenchow},
}

@Article{schratz_performance_2018,
  archiveprefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1803.11266},
  primaryclass = {cs, stat},
  title = {Performance Evaluation and Hyperparameter Tuning of Statistical and Machine-Learning Models Using Spatial Data},
  url = {http://arxiv.org/abs/1803.11266},
  abstract = {Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several practical challenges in the field of ecological modeling related to unbiased performance estimation, optimization of algorithms using hyperparameter tuning and spatial autocorrelation. We address these issues in the comparison of several widely used machine-learning algorithms such as Boosted Regression Trees (BRT), k-Nearest Neighbor (WKNN), Random Forest (RF) and Support Vector Machine (SVM) to traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM). Different nested cross-validation methods including hyperparameter tuning methods are used to evaluate model performances with the aim to receive bias-reduced performance estimates. As a case study the spatial distribution of forest disease Diplodia sapinea in the Basque Country in Spain is investigated using common environmental variables such as temperature, precipitation, soil or lithology as predictors. Results show that GAM and RF (mean AUROC estimates 0.708 and 0.699) outperform all other methods in predictive accuracy. The effect of hyperparameter tuning saturates at around 50 iterations for this data set. The AUROC differences between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) performance estimates of the GAM and RF are 0.167 (24\%) and 0.213 (30\%), respectively. It is recommended to also use spatial partitioning for cross-validation hyperparameter tuning of spatial data.},
  urldate = {2018-06-18},
  date = {2018-03-29},
  keywords = {Computer Science - Learning,Statistics - Machine Learning,Statistics - Methodology},
  author = {Patrick Schratz and Jannes Muenchow and Eugenia Iturritxa and Jakob Richter and Alexander Brenning},
  file = {/home/jannes/Zotero/storage/V6WYYI7B/Schratz et al. - 2018 - Performance evaluation and hyperparameter tuning o.pdf;/home/jannes/Zotero/storage/6MWWLRUK/1803.html},
}

@Article{probst_hyperparameters_2018,
  archiveprefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1804.03515},
  primaryclass = {cs, stat},
  title = {Hyperparameters and {{Tuning Strategies}} for {{Random Forest}}},
  url = {http://arxiv.org/abs/1804.03515},
  abstract = {The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples that a node must contain and the number of trees. In this paper, we first provide a literature review on the parameters' influence on the prediction performance and on variable importance measures, also considering interactions between hyperparameters. It is well known that in most cases RF works reasonably well with the default values of the hyperparameters specified in software packages. Nevertheless, tuning the hyperparameters can improve the performance of RF. In the second part of this paper, after a brief overview of tuning strategies we demonstrate the application of one of the most established tuning strategies, model-based optimization (MBO). To make it easier to use, we provide the tuneRanger R package that tunes RF with MBO automatically. In a benchmark study on several datasets, we compare the prediction performance and runtime of tuneRanger with other tuning implementations in R and RF with default hyperparameters.},
  urldate = {2018-08-02},
  date = {2018-04-10},
  keywords = {Computer Science - Machine Learning,Statistics - Machine Learning},
  author = {Philipp Probst and Marvin Wright and Anne-Laure Boulesteix},
}

@Collection{james_introduction_2013,
  location = {{New York}},
  title = {An Introduction to Statistical Learning: With Applications in {{R}}},
  isbn = {978-1-4614-7137-0},
  shorttitle = {An Introduction to Statistical Learning},
  pagetotal = {426},
  number = {103},
  series = {Springer texts in statistics},
  publisher = {{Springer}},
  date = {2013},
  keywords = {Mathematical models,Mathematical statistics,R (Computer program language),Statistics},
  editor = {Gareth James and Daniela Witten and Trevor Hastie and Robert Tibshirani},
  note = {OCLC: ocn828488009},
}

@Article{bischl_mlr:_2016,
  title = {Mlr: {{Machine Learning}} in {{R}}},
  volume = {17},
  url = {http://jmlr.org/papers/v17/15-066.html},
  number = {170},
  journaltitle = {Journal of Machine Learning Research},
  date = {2016},
  pages = {1-5},
  author = {Bernd Bischl and Michel Lang and Lars Kotthoff and Julia Schiffner and Jakob Richter and Erich Studerus and Giuseppe Casalicchio and Zachary M. Jones},
}

@Book{openshaw_geocomputation_2000,
  address = {London ; New York},
  edition = {1 edition},
  title = {Geocomputation},
  isbn = {978-0-7484-0900-6},
  abstract = {Geocomputation is essentially the follow-on revolution from Geographic Information Science and is expected to gather speed and momentum in the first decade of the 21st century. It comes into use once a GIS database has been set up, with a digital data library, and expanded and linked to a global geographical two or three dimensional co-ordinate system. It exploits developments in IT and new data gathering and earth observing technologies, and takes the notion of GIS beyond data and towards its analysis, modelling, and use in problem solving. This book provides pointers on how to harness these technologies in tandem and in the context of multiple different subjects and problem areas. It seeks to establish the principles and set the foundations for subsequent growth.L},
  language = {English},
  publisher = {{CRC Press}},
  editor = {Stan Openshaw and Robert J. Abrahart},
  month = {may},
  year = {2000},
}

@Book{chambers_extending_2016,
  title = {Extending {{R}}},
  isbn = {978-1-4987-7572-4},
  abstract = {Up-to-Date Guidance from One of the Foremost Members of the R Core Team  Written by John M. Chambers, the leading developer of the original S software, Extending R covers key concepts and techniques in R to support analysis and research projects. It presents the core ideas of R, provides programming guidance for projects of all scales, and introduces new, valuable techniques that extend R.  The book first describes the fundamental characteristics and background of R, giving readers a foundation for the remainder of the text. It next discusses topics relevant to programming with R, including the apparatus that supports extensions. The book then extends R's data structures through object-oriented programming, which is the key technique for coping with complexity. The book also incorporates a new structure for interfaces applicable to a variety of languages.  A reflection of what R is today, this guide explains how to design and organize extensions to R by correctly using objects, functions, and interfaces. It enables current and future users to add their own contributions and packages to R.},
  language = {en},
  publisher = {{CRC Press}},
  author = {John M. Chambers},
  month = {jun},
  year = {2016},
  keywords = {Mathematics / Probability \& Statistics / General,Business \& Economics / Statistics},
}

@Article{rowlingson_splancs_1993,
  title = {Splancs: {{Spatial}} Point Pattern Analysis Code in {{S}}-Plus},
  volume = {19},
  issn = {0098-3004},
  shorttitle = {Splancs},
  doi = {10.1016/0098-3004(93)90099-Q},
  abstract = {In recent years, Geographical Information Systems have provided researchers in many fields with facilities for mapping and analyzing spatially referenced data. Commercial systems have excellent facilities for database handling and a range of spatial operations. However, none can claim to be a rich environment for statistical analysis of spatial data. We have made some powerful enhancements to the S-Plus system to produce a tool for display and analysis of spatial point pattern data. In this paper we give a brief introduction to the S-Plus system and a detailed description of the S-Plus enhancements. We then present three worked examples: two from geomorphology and one from epidemiology.},
  number = {5},
  journal = {Computers \& Geosciences},
  author = {B. S Rowlingson and P. J Diggle},
  month = {may},
  year = {1993},
  keywords = {Epidemiology,Software,Geographical Information Systems,Spatial statistics,Geomorphology},
  pages = {627-655},
}

@Book{longley_geocomputation_1998,
  address = {Chichester, Eng. ; New York},
  edition = {1 edition},
  title = {Geocomputation: {{A Primer}}},
  isbn = {978-0-471-98576-1},
  shorttitle = {Geocomputation},
  abstract = {Geocomputation A Primer edited by Paul A Longley Sue M Brooks Rachael McDonnell School of Geographical Sciences, University of Bristol, UK and Bill Macmillan School of Geography, University of Oxford, UK This book encompasses all that is new in geocomputation. It is also a primer - that is, a book which sets out the foundations and scope of this important emergent area from the same contemporary perspective. The catalyst to the emergence of geocomputation is the new and creative application of computers to devise and depict digital representations of the Earth's surface. The environment for geocomputation is provided by geographical information systems (GIS), yet geocomputation is much more than GIS. Geocomputation is a blend of research-led applications which emphasise process over form, dynamics over statics, and interaction over passive response. This book presents a timely blend of current research and practice, written by the leading figures in the field. It provides insights to a new and rapidly developing area, and identifies the key foundations to future developments. It should be read by all who seek to use geocomputational methods for solving real world problems.},
  language = {English},
  publisher = {{Wiley}},
  editor = {Paul A. Longley and Sue M. Brooks and Rachael McDonnell and Bill MacMillan},
  month = {oct},
  year = {1998},
}
@Article{steffen_trajectories_2018,
  title = {Trajectories of the {{Earth System}} in the {{Anthropocene}}},
  copyright = {Copyright \textcopyright{} 2018 the Author(s). Published by PNAS.. This open access article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1810141115},
  abstract = {We explore the risk that self-reinforcing feedbacks could push the Earth System toward a planetary threshold that, if crossed, could prevent stabilization of the climate at intermediate temperature rises and cause continued warming on a ``Hothouse Earth'' pathway even as human emissions are reduced. Crossing the threshold would lead to a much higher global average temperature than any interglacial in the past 1.2 million years and to sea levels significantly higher than at any time in the Holocene. We examine the evidence that such a threshold might exist and where it might be. If the threshold is crossed, the resulting trajectory would likely cause serious disruptions to ecosystems, society, and economies. Collective human action is required to steer the Earth System away from a potential threshold and stabilize it in a habitable interglacial-like state. Such action entails stewardship of the entire Earth System\textemdash{}biosphere, climate, and societies\textemdash{}and could include decarbonization of the global economy, enhancement of biosphere carbon sinks, behavioral changes, technological innovations, new governance arrangements, and transformed social values.},
  language = {en},
  journal = {Proceedings of the National Academy of Sciences},
  author = {Will Steffen and Johan Rockstr{\"o}m and Katherine Richardson and Timothy M. Lenton and Carl Folke and Diana Liverman and Colin P. Summerhayes and Anthony D. Barnosky and Sarah E. Cornell and Michel Crucifix and Jonathan F. Donges and Ingo Fetzer and Steven J. Lade and Marten Scheffer and Ricarda Winkelmann and Hans Joachim Schellnhuber},
  month = {aug},
  year = {2018},
  keywords = {Anthropocene,biosphere feedbacks,climate change,Earth System trajectories,tipping elements},
  pages = {201810141},
  pmid = {30082409},
}
@Article{clark_consequences_2016,
  title = {Consequences of Twenty-First-Century Policy for Multi-Millennial Climate and Sea-Level Change},
  issn = {1758-678X, 1758-6798},
  doi = {10.1038/nclimate2923},
  journal = {Nature Climate Change},
  author = {Peter U. Clark and Jeremy D. Shakun and Shaun A. Marcott and Alan C. Mix and Michael Eby and Scott Kulp and Anders Levermann and Glenn A. Milne and Patrik L. Pfister and Benjamin D. Santer and Daniel P. Schrag and Susan Solomon and Thomas F. Stocker and Benjamin H. Strauss and Andrew J. Weaver and Ricarda Winkelmann and David Archer and Edouard Bard and Aaron Goldner and Kurt Lambeck and Raymond T. Pierrehumbert and Gian-Kasper Plattner},
  month = {feb},
  year = {2016},
}
@Article{pollard_potential_2015,
  title = {Potential {{Antarctic Ice Sheet}} Retreat Driven by Hydrofracturing and Ice Cliff Failure},
  volume = {412},
  issn = {0012-821X},
  doi = {10.1016/j.epsl.2014.12.035},
  abstract = {Geological data indicate that global mean sea level has fluctuated on 103 to 106 yr time scales during the last $\sim$25 million years, at times reaching 20 m or more above modern. If correct, this implies substantial variations in the size of the East Antarctic Ice Sheet (EAIS). However, most climate and ice sheet models have not been able to simulate significant EAIS retreat from continental size, given that atmospheric CO2 levels were relatively low throughout this period. Here, we use a continental ice sheet model to show that mechanisms based on recent observations and analysis have the potential to resolve this model\textendash{}data conflict. In response to atmospheric and ocean temperatures typical of past warm periods, floating ice shelves may be drastically reduced or removed completely by increased oceanic melting, and by hydrofracturing due to surface melt draining into crevasses. Ice at deep grounding lines may be weakened by hydrofracturing and reduced buttressing, and may fail structurally if stresses exceed the ice yield strength, producing rapid retreat. Incorporating these mechanisms in our ice-sheet model accelerates the expected collapse of the West Antarctic Ice Sheet to decadal time scales, and also causes retreat into major East Antarctic subglacial basins, producing $\sim$17 m global sea-level rise within a few thousand years. The mechanisms are highly parameterized and should be tested by further process studies. But if accurate, they offer one explanation for past sea-level high stands, and suggest that Antarctica may be more vulnerable to warm climates than in most previous studies.},
  journal = {Earth and Planetary Science Letters},
  author = {David Pollard and Robert M. DeConto and Richard B. Alley},
  month = {feb},
  year = {2015},
  keywords = {sea level,Antarctica,hydrofracture,ice cliff,ice sheet,subglacial basin},
  pages = {112-121},
}
@Article{lovelace_propensity_2017,
  title = {The {{Propensity}} to {{Cycle Tool}}: {{An}} Open Source Online System for Sustainable Transport Planning},
  volume = {10},
  copyright = {Copyright (c) 2016 Robin Lovelace, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, James Woodcock},
  issn = {1938-7849},
  shorttitle = {The {{Propensity}} to {{Cycle Tool}}},
  doi = {10.5198/jtlu.2016.862},
  abstract = {Getting people cycling is an increasingly common objective in transport planning institutions worldwide. A growing evidence base indicates that high quality infrastructure can boost local cycling rates. Yet for infrastructure and other cycling measures to be effective, it is important to intervene in the right places, such as along `desire lines' of high latent demand. This creates the need for tools and methods to help answer the question `where to build?'. Following a brief review of the policy and research context related to this question, this paper describes the design, features and potential applications of such a tool. The Propensity to Cycle Tool (PCT) is an online, interactive planning support system that was initially developed to explore and map cycling potential across England (see www.pct.bike). Based on origin-destination data it models cycling levels at area, desire line, route and route network levels, for current levels of cycling, and for scenario-based `cycling futures.' Four scenarios are presented, including `Go Dutch' and `Ebikes,' which explore what would happen if English people had the same propensity to cycle as Dutch people and the potential impact of electric cycles on cycling uptake. The cost effectiveness of investment depends not only on the number of additional trips cycled, but on wider impacts such as health and carbon benefits. The PCT reports these at area, desire line, and route level for each scenario. The PCT is open source, facilitating the creation of scenarios and deployment in new contexts. We conclude that the PCT illustrates the potential of online tools to inform transport decisions and raises the wider issue of how models should be used in transport planning.},
  language = {en},
  number = {1},
  journal = {Journal of Transport and Land Use},
  author = {Robin Lovelace and Anna Goodman and Rachel Aldred and Nikolai Berkoff and Ali Abbas and James Woodcock},
  month = {jan},
  year = {2017},
  keywords = {Planning,Cycling,modelling,Participatory},
}