geocompr.bib

@misc{_map_1993,
  title = {Map Projections},
  year = {1993},
  publisher = {US Geological Survey},
  doi = {10.3133/70047422}
}

@book{abelson_structure_1996,
  title = {Structure and Interpretation of Computer Programs},
  author = {Abelson, Harold and Sussman, Gerald Jay and Sussman, Julie},
  year = {1996},
  series = {The {{MIT}} Electrical Engineering and Computer Science Series},
  edition = {Second},
  publisher = {MIT Press},
  address = {Cambridge, Massachusetts},
  isbn = {0-262-01153-0},
  lccn = {QA76.6 .A255 1985},
  keywords = {Computer programming,LISP (Computer program language),nosource}
}

@article{adams_seeded_1994,
  title = {Seeded Region Growing},
  author = {Adams, R. and Bischof, L.},
  year = {1994},
  month = jun,
  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
  volume = {16},
  number = {6},
  pages = {641--647},
  issn = {01628828},
  doi = {10.1109/34.295913},
  urldate = {2022-09-23},
  abstract = {We present here a new algorithm for segmentation of intensity images which is robust, rapid, and free of tuning parameters. The method, however, requires the input of a number of seeds, either individual pixels or regions, which will control the formation of regions into which the image will be segmented. In this correspondence, we present the algorithm, discuss briefly its properties, and suggest two ways in which it can be employed, namely, by using manual seed selection or by automated procedures.},
  langid = {english}
}

@book{akima_akima_2016,
  title = {Akima: {{Interpolation}} of {{Irregularly}} and {{Regularly Spaced Data}}},
  author = {Akima, Hiroshi and Gebhardt, Albrecht},
  year = {2016},
  keywords = {nosource}
}

@article{alessandretti_multimodal_2022,
  title = {Multimodal Urban Mobility and Multilayer Transport Networks},
  author = {Alessandretti, Laura and Natera Orozco, Luis Guillermo and Battiston, Federico and Saberi, Meead and Szell, Michael},
  year = {2022},
  month = jul,
  journal = {Environment and Planning B: Urban Analytics and City Science},
  pages = {23998083221108190},
  publisher = {SAGE Publications Ltd STM},
  issn = {2399-8083},
  doi = {10.1177/23998083221108190},
  urldate = {2022-07-20},
  abstract = {Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modeling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open-source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.},
  langid = {english},
  keywords = {complex systems,human mobility,multilayer networks,science of cities,transport networks,urban data science}
}

@article{appel_gdalcubes_2019,
  title = {On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library},
  author = {Appel, Marius and Pebesma, Edzer},
  year = {2019},
  journal = {Data},
  volume = {4},
  number = {3},
  doi = {10.3390/data4030092},
  article-number = {92}
}

@book{baddeley_spatial_2015,
  ids = {baddeley_spatial_2015-1},
  title = {Spatial Point Patterns: Methodology and Applications with {{R}}},
  author = {Baddeley, Adrian and Rubak, Ege and Turner, Rolf},
  year = {2015},
  publisher = {CRC Press},
  keywords = {nosource}
}

@article{baddeley_spatstat_2005,
  title = {Spatstat: An {{R}} Package for Analyzing Spatial Point Patterns},
  author = {Baddeley, Adrian and Turner, Rolf},
  year = {2005},
  journal = {Journal of statistical software},
  volume = {12},
  number = {6},
  pages = {1--42},
  doi = {10/gf29tr},
  keywords = {conditional intensity,edge corrections,exploratory data analysis,generalised,hood,inhomogeneous point patterns,Linear Models,marked point patterns,maximum pseudolikeli-,nosource,spatial clustering}
}

@book{becker_mlr3_2022,
  title = {Mlr3 Book},
  author = {Becker, M. and Binder, M. and Bischl, B. and Lang, M. and Pfisterer, F. and Reich, N.G. and Richter, J. and Schratz, P. and Sonabend, R.},
  year = {2022}
}

@book{bellos_alex_2011,
  title = {Alex's {{Adventures}} in {{Numberland}}},
  author = {Bellos, Alex},
  year = {2011},
  month = apr,
  publisher = {Bloomsbury Paperbacks},
  address = {London},
  abstract = {The world of maths can seem mind-boggling, irrelevant and, let's face it, boring. This groundbreaking book reclaims maths from the geeks. Mathematical ideas underpin just about everything in our lives: from the surprising geometry of the 50p piece to how probability can help you win in any casino. In search of weird and wonderful mathematical phenomena, Alex Bellos travels across the globe and meets the world's fastest mental calculators in Germany and a startlingly numerate chimpanzee in Japan. Packed with fascinating, eye-opening anecdotes, Alex's Adventures in Numberland is an exhilarating cocktail of history, reportage and mathematical proofs that will leave you awestruck.},
  isbn = {978-1-4088-0959-4},
  langid = {english}
}

@book{berg_computational_2008,
  title = {Computational {{Geometry}}: {{Algorithms}} and {{Applications}}},
  shorttitle = {Computational {{Geometry}}},
  author = {de Berg, Mark and Cheong, Otfried and van Kreveld, Marc and Overmars, Mark},
  year = {2008},
  month = mar,
  publisher = {Springer Science \& Business Media},
  abstract = {Computational geometry emerged from the field of algorithms design and analysis in the late 1970s. It has grown into a recognized discipline with its own journals, conferences, and a large community of active researchers. The success of the ?eld as a research discipline can on the one hand be explained from the beauty of the problems studied and the solutions obtained, and, on the other hand, by the many application domains---computer graphics, geographic information systems (GIS), robotics, and others---in which geometric algorithms play a fundamental role. For many geometric problems the early algorithmic solutions were either slow or dif?cult to understand and implement. In recent years a number of new algorithmic techniques have been developed that improved and simpli?ed many of the previous approaches. In this textbook we have tried to make these modern algorithmic solutions accessible to a large audience. The book has been written as a textbook for a course in computational geometry, but it can also be used for self-study.},
  googlebooks = {tkyG8W2163YC},
  isbn = {978-3-540-77973-5},
  langid = {english},
  keywords = {Computers / Computer Graphics,Computers / Computer Science,Computers / Data Processing,Computers / Databases / General,Computers / Information Technology,Computers / Programming / Algorithms,Mathematics / Discrete Mathematics,Mathematics / Geometry / General,Science / Earth Sciences / General,Technology & Engineering / General}
}

@article{bischl_mlr:_2016,
  title = {Mlr: {{Machine Learning}} in {{R}}},
  author = {Bischl, Bernd and Lang, Michel and Kotthoff, Lars and Schiffner, Julia and Richter, Jakob and Studerus, Erich and Casalicchio, Giuseppe and Jones, Zachary M.},
  year = {2016},
  journal = {Journal of Machine Learning Research},
  volume = {17},
  number = {170},
  pages = {1--5},
  keywords = {No DOI found,nosource}
}

@book{bivand_applied_2013,
  ids = {bivand_applied_2013a},
  title = {Applied Spatial Data Analysis with {{R}}},
  author = {Bivand, Roger and Pebesma, Edzer and {G{\'o}mez-Rubio}, Virgilio},
  year = {2013},
  volume = {747248717},
  publisher = {Springer},
  googlebooks = {v0eIU9ObJXgC},
  keywords = {Mathematics / Probability & Statistics / General,Medical / Biostatistics,Medical / General,Science / Earth Sciences / Geography,Science / Environmental Science,Technology & Engineering / Environmental / General}
}

@article{bivand_comparing_2015,
  title = {Comparing {{Implementations}} of {{Estimation Methods}} for {{Spatial Econometrics}}},
  author = {Bivand, Roger and Piras, Gianfranco},
  year = {2015},
  journal = {Journal of Statistical Software},
  volume = {63},
  number = {18},
  pages = {1--36},
  doi = {10/cqxj},
  keywords = {nosource}
}

@article{bivand_implementing_2000,
  title = {Implementing Functions for Spatial Statistical Analysis Using the Language},
  author = {Bivand, Roger and Gebhardt, Albrecht},
  year = {2000},
  journal = {Journal of Geographical Systems},
  volume = {2},
  number = {3},
  pages = {307--317},
  doi = {10.1007/PL00011460},
  urldate = {2017-07-12},
  keywords = {nosource}
}

@book{bivand_maptools_2017,
  title = {Maptools: {{Tools}} for {{Reading}} and {{Handling Spatial Objects}}},
  author = {Bivand, Roger and {Lewin-Koh}, Nicholas},
  year = {2017},
  keywords = {nosource}
}

@article{bivand_more_2001,
  title = {More on {{Spatial Data Analysis}}},
  author = {Bivand, Roger},
  year = {2001},
  journal = {R News},
  volume = {1},
  number = {3},
  pages = {13--17},
  keywords = {No DOI found,nosource}
}

@inproceedings{bivand_open_2000,
  title = {Open Source Geocomputation: Using the {{R}} Data Analysis Language Integrated with {{GRASS GIS}} and {{PostgreSQL}} Data Base Systems},
  booktitle = {Proceedings of the 5th {{International Conference}} on {{GeoComputation}}},
  author = {Bivand, Roger and Neteler, Markus},
  editor = {Neteler, Markus and Bivand, Roger S.},
  year = {2000},
  keywords = {No DOI found,nosource}
}

@article{bivand_progress_2021,
  title = {Progress in the {{R}} Ecosystem for Representing and Handling Spatial Data},
  author = {Bivand, Roger},
  year = {2021},
  month = oct,
  journal = {Journal of Geographical Systems},
  volume = {23},
  number = {4},
  pages = {515--546},
  issn = {1435-5949},
  doi = {10/ghnwg3},
  urldate = {2021-12-17},
  abstract = {Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307--317, 2000. https://doi.org/10.1007/PL00011460) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend.},
  langid = {english}
}

@book{bivand_rgrass7_2016,
  title = {Rgrass7: {{Interface Between GRASS}} 7 {{Geographical Information System}} and {{R}}},
  author = {Bivand, Roger},
  year = {2016},
  keywords = {nosource}
}

@book{bivand_spdep_2017,
  title = {Spdep: {{Spatial Dependence}}: {{Weighting Schemes}}, {{Statistics}} and {{Models}}},
  author = {Bivand, Roger},
  year = {2017},
  keywords = {nosource}
}

@book{bivand_spgrass6_2016,
  title = {Spgrass6: {{Interface}} between {{GRASS}} 6 and {{R}}},
  author = {Bivand, Roger},
  year = {2016},
  keywords = {nosource}
}

@article{bivand_using_2000,
  title = {Using the {{R}} Statistical Data Analysis Language on {{GRASS}} 5.0 {{GIS}} Database Files},
  author = {Bivand, Roger},
  year = {2000},
  journal = {Computers \& Geosciences},
  volume = {26},
  number = {9},
  pages = {1043--1052},
  doi = {10.1016/S0098-3004(00)00057-1},
  urldate = {2017-07-11},
  keywords = {nosource}
}

@book{blangiardo_spatial_2015,
  title = {Spatial and {{Spatio-temporal Bayesian Models}} with {{R-INLA}}},
  shorttitle = {Spatial and {{Spatio-temporal Bayesian Models}} with {{R-INLA}}},
  author = {Blangiardo, Marta and Cameletti, Michela},
  year = {2015},
  month = apr,
  publisher = {John Wiley \& Sons, Ltd},
  address = {Chichester, UK},
  doi = {10.1002/9781118950203},
  urldate = {2018-02-07},
  isbn = {978-1-118-95020-3 978-1-118-32655-8},
  langid = {english},
  keywords = {nosource}
}

@incollection{bohner_image_2006,
  title = {Image Segmentation Using Representativeness Analysis and Region Growing},
  booktitle = {{{SAGA}} - {{Analysis}} and {{Modelling Applications}}},
  author = {B{\"o}hner, J{\"u}rgen and Selige, Thomas and Ringeler, Andre},
  editor = {{B{\"o}hner, J{\"u}rgen} and {McCloy, K.R.} and {Strobl, J.}},
  year = {2006},
  pages = {10},
  publisher = {Goettinger Geographische Abhandlungen},
  address = {Goettingen},
  abstract = {Image segmentation is a crucial task in the emerging field of object oriented image analysis. This paper contributes to the ongoing debate by presenting a segmentation procedure currently implemented in SAGA. Key feature at the core of the segmentation procedure is the representativeness analysis, performed for each pixel using geostatistical (semi-variogram) analysis measures. The representativeness layer supports conventional region growing algorithm with necessary start seeds, brake of criterions, and additional opportunities for fast performing initial image segmentation. The segmentation procedure aims to create spatially discrete object primitives and homogenous regions from remotely sensed images as the basic entities for further image classification procedures and thematic mapping applications. In a comprehensive evaluation study comparing eCognition, RHSEG and SAGA segmentation procedures, the SAGA approach was tested as robust and fast. SAGA performed at high quality a detailed segmentation of the actual landscape pattern represented by the remotely sensed imagery.},
  langid = {english}
}

@incollection{bohner_spatial_2006,
  title = {Spatial Prediction of Soil Attributes Using Terrain Analysis and Climate Regionalisation},
  booktitle = {{{SAGA}} - {{Analysis}} and {{Modelling Applications}}},
  author = {B{\"o}hner, J{\"u}rgen and Selige, Thomas},
  editor = {B{\"o}hner, J and {McCloy, K.R.} and {Strobl, J.}},
  year = {2006},
  pages = {19},
  publisher = {Goettinger Geographische Abhandlungen},
  address = {Goettingen},
  abstract = {A method of predicting spatial soil parameters is proposed and tested. The method uses a digital terrain model (DTM) of the area and regionalised climate data to derive the soil regionalised variables that form the basis of the prediction. The method was tested using 94 soil profile samples in the Quaternary stratum of the Schatterbach test site, a 2387 ha investigation area in the Bavarian Tertiary Hills (Germany). The approach is based on the assumption that the shape of the landscape and the late Quaternary climate history determines slope development and soil forming processes. To develop the method, a suite of terrain- indices and complex process parameters was derived from DTM and climate data. Step-wise linear regression was then used to identify which of these terrain indices and process parameters were most useful in predicting the required soil attributes. Testing of the approach showed that 88.1\% of the variance was explained by a combination of the sediment transport, mass balance and solifluction parameters, providing a sound basis for the prediction of soil parameters in hilly terrain.},
  langid = {english}
}

@article{bondaruk_assessing_2020,
  title = {Assessing the State of the Art in {{Discrete Global Grid Systems}}: {{OGC}} Criteria and Present Functionality},
  shorttitle = {Assessing the State of the Art in {{Discrete Global Grid Systems}}},
  author = {Bondaruk, Ben and Roberts, Steven A. and Robertson, Colin},
  year = {2020},
  month = mar,
  journal = {Geomatica},
  volume = {74},
  number = {1},
  pages = {9--30},
  publisher = {NRC Research Press},
  issn = {1195-1036},
  doi = {10.1139/geomat-2019-0015},
  urldate = {2021-08-12}
}

@book{borcard_numerical_2011,
  title = {Numerical Ecology with {{R}}},
  author = {Borcard, Daniel and Gillet, Fran{\c c}ois and Legendre, Pierre},
  year = {2011},
  series = {Use {{R}}!},
  publisher = {Springer},
  address = {New York},
  isbn = {978-1-4419-7975-9},
  lccn = {QH541.15.S72 B67 2011},
  keywords = {Data processing,Ecology,nosource,R (Computer program language),Statistical methods},
  annotation = {OCLC: ocn690089213}
}

@article{borland_rainbow_2007,
  title = {Rainbow Color Map (Still) Considered Harmful},
  author = {Borland, David and Taylor II, Russell M},
  year = {2007},
  journal = {IEEE computer graphics and applications},
  volume = {27},
  number = {2},
  publisher = {IEEE},
  doi = {10.1109/MCG.2007.323435},
  keywords = {nosource}
}

@article{breiman_random_2001,
  title = {Random {{Forests}}},
  author = {Breiman, Leo},
  year = {2001},
  month = oct,
  journal = {Machine Learning},
  volume = {45},
  number = {1},
  pages = {5--32},
  issn = {1573-0565},
  doi = {10/d8zjwq},
  keywords = {nosource}
}

@book{brenning_arcgis_2012,
  title = {{{ArcGIS Geoprocessing}} in {{R}} via {{Python}}},
  author = {Brenning, Alexander},
  year = {2012},
  keywords = {nosource}
}

@inproceedings{brenning_spatial_2012,
  title = {Spatial Cross-Validation and Bootstrap for the Assessment of Prediction Rules in Remote Sensing: {{The R}} Package Sperrorest},
  shorttitle = {Spatial Cross-Validation and Bootstrap for the Assessment of Prediction Rules in Remote Sensing},
  author = {Brenning, Alexander},
  year = {2012},
  month = jul,
  pages = {5372--5375},
  publisher = {IEEE},
  doi = {10/gf238w},
  urldate = {2017-11-24},
  isbn = {978-1-4673-1159-5 978-1-4673-1160-1 978-1-4673-1158-8},
  keywords = {nosource}
}

@book{brewer_designing_2015,
  title = {Designing {{Better Maps}}: {{A Guide}} for {{GIS Users}}},
  shorttitle = {Designing {{Better Maps}}},
  author = {Brewer, Cynthia A.},
  year = {2015},
  month = dec,
  edition = {Second},
  publisher = {Esri Press},
  address = {Redlands, California},
  isbn = {978-1-58948-440-5},
  langid = {english}
}

@techreport{bristol_city_council_deprivation_2015,
  title = {Deprivation in {{Bristol}} 2015},
  author = {{Bristol City Council}},
  year = {2015},
  institution = {Bristol City Council},
  keywords = {nosource}
}

@book{brunsdon_introduction_2015,
  title = {An {{Introduction}} to {{R}} for {{Spatial Analysis}} and {{Mapping}}},
  author = {Brunsdon, Chris and Comber, Lex},
  year = {2015},
  month = feb,
  publisher = {SAGE Publications Ltd},
  address = {Los Angeles},
  abstract = {"In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical 'how to' guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses." - Richard Harris, Professor of Quantitative Social Science, University of Bristol  R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and `non-geography' students and researchers interested in spatial analysis and mapping.   This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality.   Brunsdon and Comber take readers from `zero to hero' in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes:      Example data and commands for exploring it     Scripts and coding to exemplify specific functionality     Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends      Self-contained exercises for students to work through     Embedded code within the descriptive text.   ~This is a definitive 'how to' that takes students - of any discipline - from coding to actual applications and uses of R.},
  isbn = {978-1-4462-7295-4},
  langid = {english}
}

@article{brus_sampling_2018,
  title = {Sampling for Digital Soil Mapping: {{A}} Tutorial Supported by {{R}} Scripts},
  shorttitle = {Sampling for Digital Soil Mapping},
  author = {Brus, D. J.},
  year = {2018},
  month = aug,
  journal = {Geoderma},
  issn = {0016-7061},
  doi = {10/gf34fk},
  urldate = {2018-09-11},
  abstract = {In the past decade, substantial progress has been made in model-based optimization of sampling designs for mapping. This paper is an update of the overview of sampling designs for mapping presented by de Gruijter et al. (2006). For model-based estimation of values at unobserved points (mapping), probability sampling is not required, which opens up the possibility of optimized non-probability sampling. Non-probability sampling designs for mapping are regular grid sampling, spatial coverage sampling, k-means sampling, conditioned Latin hypercube sampling, response surface sampling, Kennard-Stone sampling and model-based sampling. In model-based sampling a preliminary model of the spatial variation of the soil variable of interest is used for optimizing the sample size and or the spatial coordinates of the sampling locations. Kriging requires knowledge of the variogram. Sampling designs for variogram estimation are nested sampling, independent random sampling of pairs of points, and model-based designs in which either the uncertainty about the variogram parameters, or the uncertainty about the kriging variance is minimized. Various minimization criteria have been proposed for designing a single sample that is suitable both for estimating the variogram and for mapping. For map validation, additional probability sampling is recommended, so that unbiased estimates of map quality indices and their standard errors can be obtained. For all sampling designs, R scripts are available in the supplement. Further research is recommended on sampling designs for mapping with machine learning techniques, designs that are robust against deviations of modeling assumptions, designs tailored at mapping multiple soil variables of interest and soil classes or fuzzy memberships, and probability sampling designs that are efficient both for design-based estimation of populations means and for model-based mapping.},
  keywords = {K-means sampling,Kriging,Latin hypercube sampling,Model-based sampling,nosource,Spatial coverage sampling,Spatial simulated annealing,Variogram}
}

@book{brzustowicz_data_2017,
  title = {Data Science with {{Java}}: [Practical Methods for Scientists and Engineers]},
  shorttitle = {Data Science with {{Java}}},
  author = {Brzustowicz, Michael R.},
  year = {2017},
  edition = {First},
  publisher = {O{\textasciiacute}Reilly},
  address = {Beijing Boston Farnham},
  isbn = {978-1-4919-3411-1},
  langid = {english},
  keywords = {Data Mining,Data mining Software,Datenanalyse,Java,Java (Computer program language),nosource},
  annotation = {OCLC: 993428657}
}

@article{bucklin_rpostgis_2018,
  title = {Rpostgis: {{Linking R}} with a {{PostGIS Spatial Database}}},
  author = {Bucklin, David and Basille, Mathieu},
  year = {2018},
  journal = {The R Journal},
  doi = {10/c7fc},
  keywords = {nosource}
}

@book{burrough_principles_2015,
  title = {Principles of Geographical Information Systems},
  author = {Burrough, P. A. and McDonnell, Rachael and Lloyd, Christopher D.},
  year = {2015},
  edition = {Third},
  publisher = {Oxford University Press},
  address = {Oxford, New York},
  isbn = {978-0-19-874284-5},
  lccn = {G70.212 .B87 2015},
  keywords = {Geographic information systems,nosource},
  annotation = {OCLC: ocn915100245}
}

@article{calenge_package_2006,
  title = {The Package Adehabitat for the {{R}} Software: Tool for the Analysis of Space and Habitat Use by Animals},
  author = {Calenge, C.},
  year = {2006},
  journal = {Ecological Modelling},
  volume = {197},
  pages = {1035},
  doi = {10.1016/j.ecolmodel.2006.03.017},
  keywords = {nosource}
}

@article{cawley_overfitting_2010,
  title = {On Over-Fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation},
  author = {Cawley, Gavin C. and Talbot, Nicola LC},
  year = {2010},
  journal = {Journal of Machine Learning Research},
  volume = {11},
  number = {Jul},
  pages = {2079--2107},
  keywords = {No DOI found,nosource}
}

@book{chambers_extending_2016,
  title = {Extending {{R}}},
  author = {Chambers, John M.},
  year = {2016},
  month = jun,
  publisher = {CRC Press},
  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.},
  googlebooks = {kxxjDAAAQBAJ},
  isbn = {978-1-4987-7572-4},
  langid = {english},
  keywords = {Business & Economics / Statistics,Mathematics / Probability & Statistics / General}
}

@incollection{cheshire_spatial_2015,
  title = {Spatial Data Visualisation with {{R}}},
  booktitle = {Geocomputation},
  author = {Cheshire, James and Lovelace, Robin},
  editor = {Brunsdon, Chris and Singleton, Alex},
  year = {2015},
  pages = {1--14},
  publisher = {SAGE Publications},
  keywords = {nosource}
}

@article{clementini_comparison_1995,
  title = {A Comparison of Methods for Representing Topological Relationships},
  author = {Clementini, Eliseo and Di Felice, Paolino},
  year = {1995},
  month = may,
  journal = {Information Sciences - Applications},
  volume = {3},
  number = {3},
  pages = {149--178},
  issn = {1069-0115},
  doi = {10/ddtnhx},
  urldate = {2021-11-13},
  abstract = {In the field of spatial information systems, a primary need is to develop a sound theory of topological relationships between spatial objects. A category of formal methods for representing topological relationships is based on point-set theory. In this paper, a high level calculus-based method is compared with such point-set methods. It is shown that the calculus-based method is able to distinguish among finer topological configurations than most of the point-set methods. The advantages of the calculus-based method are the direct use in a calculus-based spatial query language and the capability of representing topological relationships among a significant set of spatial objects by means of only five relationship names and two boundary operators.},
  langid = {english}
}

@article{conrad_system_2015,
  title = {System for {{Automated Geoscientific Analyses}} ({{SAGA}}) v. 2.1.4},
  author = {Conrad, O. and Bechtel, B. and Bock, M. and Dietrich, H. and Fischer, E. and Gerlitz, L. and Wehberg, J. and Wichmann, V. and B{\"o}hner, J.},
  year = {2015},
  month = jul,
  journal = {Geosci. Model Dev.},
  volume = {8},
  number = {7},
  pages = {1991--2007},
  issn = {1991-9603},
  doi = {10.5194/gmd-8-1991-2015},
  urldate = {2017-06-12},
  abstract = {The System for Automated Geoscientific Analyses (SAGA) is an open source geographic information system (GIS), mainly licensed under the GNU General Public License. Since its first release in 2004, SAGA has rapidly developed from a specialized tool for digital terrain analysis to a comprehensive and globally established GIS platform for scientific analysis and modeling. SAGA is coded in C++ in an object oriented design and runs under several operating systems including Windows and Linux. Key functional features of the modular software architecture comprise an application programming interface for the development and implementation of new geoscientific methods, a user friendly graphical user interface with many visualization options, a command line interpreter, and interfaces to interpreted languages like R and Python. The current version 2.1.4 offers more than 600 tools, which are implemented in dynamically loadable libraries or shared objects and represent the broad scopes of SAGA in numerous fields of geoscientific endeavor and beyond. In this paper, we inform about the system's architecture, functionality, and its current state of development and implementation. Furthermore, we highlight the wide spectrum of scientific applications of SAGA in a review of published studies, with special emphasis on the core application areas digital terrain analysis, geomorphology, soil science, climatology and meteorology, as well as remote sensing.}
}

@manual{cooley_sfheaders_2020,
  type = {Manual},
  title = {Sfheaders: {{Converts}} between r Objects and Simple Feature Objects},
  author = {Cooley, David},
  year = {2020}
}

@article{coombes_efficient_1986,
  title = {An {{Efficient Algorithm}} to {{Generate Official Statistical Reporting Areas}}: {{The Case}} of the 1984 {{Travel-to-Work Areas Revision}} in {{Britain}}},
  shorttitle = {An {{Efficient Algorithm}} to {{Generate Official Statistical Reporting Areas}}},
  author = {Coombes, M. G. and Green, A. E. and Openshaw, S.},
  year = {1986},
  month = oct,
  journal = {The Journal of the Operational Research Society},
  volume = {37},
  number = {10},
  eprint = {2582282},
  eprinttype = {jstor},
  pages = {943},
  issn = {01605682},
  doi = {10/b58h3x},
  urldate = {2017-12-18},
  keywords = {nosource}
}

@article{coppock_history_1991,
  title = {The History of {{GIS}}},
  author = {Coppock, J Terry and Rhind, David W},
  year = {1991},
  journal = {Geographical Information Systems: Principles and Applications, vol. 1.},
  volume = {1},
  number = {1},
  pages = {21--43},
  abstract = {Coppock, J. T., and Rhind, D. W. 1991. The History of GIS. In Geographical Information Systems: Principles and Applications, vol. 1, ed. D. J. Maguire, M. F. Goodchild, and D. W. Rhind, pp. 21-43. New York: John Wiley and Sons.},
  keywords = {History of GIS,No DOI found,nosource}
}

@book{dieck_algebraic_2008,
  title = {Algebraic Topology},
  author = {tom Dieck, Tammo},
  year = {2008},
  series = {{{EMS}} Textbooks in Mathematics},
  publisher = {European Mathematical Society},
  address = {Z{\"u}rich},
  isbn = {978-3-03719-048-7},
  lccn = {QA612 .D53 2008},
  keywords = {Algebraic topology,Homology theory,Homotopy theory},
  annotation = {OCLC: ocn261176011}
}

@book{diggle_modelbased_2007,
  title = {Model-Based Geostatistics},
  author = {Diggle, Peter and Ribeiro, Paulo Justiniano},
  year = {2007},
  publisher = {Springer},
  keywords = {nosource}
}

@incollection{dillon_lomas_2003,
  title = {The {{Lomas}} Formations of Coastal {{Peru}}: {{Composition}} and Biogeographic History},
  booktitle = {El {{Ni{\~n}o}} in {{Peru}}: {{Biology}} and Culture over 10,000 Years},
  author = {Dillon, M. O. and Nakazawa, M. and Leiva, S. G.},
  editor = {Haas, J. and Dillon, M. O.},
  year = {2003},
  pages = {1--9},
  publisher = {Field Museum of Natural History},
  address = {Chicago},
  keywords = {nosource}
}

@book{dorman_learning_2014,
  title = {Learning {{R}} for {{Geospatial Analysis}}},
  author = {Dorman, Michael},
  year = {2014},
  publisher = {Packt Publishing Ltd},
  keywords = {nosource}
}

@article{douglas_algorithms_1973,
  title = {Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or Its Caricature},
  author = {Douglas, David H and Peucker, Thomas K},
  year = {1973},
  journal = {Cartographica: The International Journal for Geographic Information and Geovisualization},
  volume = {10},
  number = {2},
  pages = {112--122},
  doi = {10/bjwv52},
  keywords = {nosource}
}

@manual{dunnington_ggspatial_2021,
  type = {Manual},
  title = {Ggspatial: {{Spatial}} Data Framework for Ggplot2},
  author = {Dunnington, Dewey},
  year = {2021}
}

@article{eddelbuettel_extending_2018,
  title = {Extending {{R}} with {{C}}++: {{A Brief Introduction}} to {{Rcpp}}},
  shorttitle = {Extending {{R}} with {{C}}++},
  author = {Eddelbuettel, Dirk and Balamuta, James Joseph},
  year = {2018},
  month = jan,
  journal = {The American Statistician},
  volume = {72},
  number = {1},
  pages = {28--36},
  issn = {0003-1305},
  doi = {10/gdg3fb},
  urldate = {2018-10-01},
  abstract = {R has always provided an application programming interface (API) for extensions. Based on the C language, it uses a number of macros and other low-level constructs to exchange data structures between the R process and any dynamically loaded component modules authors added to it. With the introduction of the Rcpp package, and its later refinements, this process has become considerably easier yet also more robust. By now, Rcpp has become the most popular extension mechanism for R. This article introduces Rcpp, and illustrates with several examples how the Rcpp Attributes mechanism in particular eases the transition of objects between R and C++ code. Supplementary materials for this article are available online.},
  keywords = {nosource}
}

@inproceedings{egenhofer_mathematical_1990,
  title = {A Mathematical Framework for the Definition of Topological Relations},
  booktitle = {Proc. the Fourth International Symposium on Spatial Data Handing},
  author = {Egenhofer, Max and Herring, John},
  year = {1990},
  pages = {803--813},
  keywords = {No DOI found}
}

@article{essd-11-647-2019,
  title = {{{ICGEM}} -- 15 Years of Successful Collection and Distribution of Global Gravitational Models, Associated Services, and Future Plans},
  author = {Ince, E. S. and Barthelmes, F. and Rei{\ss}land, S. and Elger, K. and F{\"o}rste, C. and Flechtner, F. and Schuh, H.},
  year = {2019},
  journal = {Earth System Science Data},
  volume = {11},
  number = {2},
  pages = {647--674},
  doi = {10/gg5tzm}
}

@article{galletti_land_2016,
  title = {Land Changes and Their Drivers in the Cloud Forest and Coastal Zone of {{Dhofar}}, {{Oman}}, between 1988 and 2013},
  author = {Galletti, Christopher S. and Turner, Billie L. and Myint, Soe W.},
  year = {2016},
  journal = {Regional Environmental Change},
  volume = {16},
  number = {7},
  pages = {2141--2153},
  issn = {1436-3798, 1436-378X},
  doi = {10/gkb5bm},
  urldate = {2018-10-17},
  langid = {english},
  keywords = {nosource}
}

@book{garrard_geoprocessing_2016,
  title = {Geoprocessing with {{Python}}},
  author = {Garrard, Chris},
  year = {2016},
  publisher = {Manning Publications},
  address = {Shelter Island, NY},
  isbn = {978-1-61729-214-9},
  lccn = {GA102.4.E4 G37 2016},
  keywords = {Cartography,Computer programs,Data processing,Geospatial data,nosource,Python (Computer program language)},
  annotation = {OCLC: ocn915498655}
}

@book{gelfand_handbook_2010,
  title = {Handbook of Spatial Statistics},
  author = {Gelfand, Alan E and Diggle, Peter and Guttorp, Peter and Fuentes, Montserrat},
  year = {2010},
  publisher = {CRC press},
  isbn = {1-4200-7288-9},
  keywords = {nosource}
}

@book{gillespie_efficient_2016,
  title = {Efficient {{R Programming}}: {{A Practical Guide}} to {{Smarter Programming}}},
  author = {Gillespie, Colin and Lovelace, Robin},
  year = {2016},
  publisher = {O'Reilly Media},
  isbn = {978-1-4919-5078-4},
  keywords = {nosource}
}

@manual{giraud_mapsf_2021,
  type = {Manual},
  title = {Mapsf: {{Thematic}} Cartography},
  author = {Giraud, Timoth{\'e}e},
  year = {2021}
}

@article{goetz_evaluating_2015,
  title = {Evaluating Machine Learning and Statistical Prediction Techniques for Landslide Susceptibility Modeling},
  author = {Goetz, J.N. and Brenning, A. and Petschko, H. and Leopold, P.},
  year = {2015},
  month = aug,
  journal = {Computers \& Geosciences},
  volume = {81},
  pages = {1--11},
  issn = {00983004},
  doi = {10/f7hcgp},
  urldate = {2017-11-24},
  langid = {english},
  keywords = {nosource}
}

@book{gomez-rubio_bayesian_2020,
  title = {Bayesian Inference with {{INLA}}},
  author = {{G{\'o}mez-Rubio}, Virgilio},
  year = {2020},
  publisher = {CRC Press}
}

@article{goncalves_segoptim_2019,
  title = {{{SegOptim}}---{{A}} New {{R}} Package for Optimizing Object-Based Image Analyses of High-Spatial Resolution Remotely-Sensed Data},
  author = {Gon{\c c}alves, Jo{\~a}o and P{\^o}{\c c}as, Isabel and Marcos, Bruno and M{\"u}cher, C.A. and Honrado, Jo{\~a}o P.},
  year = {2019},
  month = apr,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {76},
  pages = {218--230},
  issn = {15698432},
  doi = {10.1016/j.jag.2018.11.011},
  urldate = {2022-10-06},
  abstract = {Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods.},
  langid = {english}
}

@book{goovaerts_geostatistics_1997,
  title = {Geostatistics for Natural Resources Evaluation},
  author = {Goovaerts, Pierre},
  year = {1997},
  series = {Applied Geostatistics Series},
  publisher = {Oxford University Press},
  address = {New York},
  isbn = {978-0-19-511538-3},
  lccn = {QE33.2.M3 G66 1997},
  keywords = {Geology,nosource,Statistical methods}
}

@article{graser_processing_2015,
  title = {Processing: {{A Python Framework}} for the {{Seamless Integration}} of {{Geoprocessing Tools}} in {{QGIS}}},
  author = {Graser, Anita and Olaya, Victor},
  year = {2015},
  doi = {10/f76d7c},
  urldate = {2017-06-12},
  abstract = {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.},
  keywords = {nosource}
}

@book{grolemund_r_2016,
  title = {R for {{Data Science}}},
  author = {Grolemund, Garrett and Wickham, Hadley},
  year = {2016},
  month = jul,
  publisher = {O'Reilly Media},
  isbn = {978-1-4919-1039-9},
  langid = {english}
}

@article{harris_more_2017,
  title = {More Bark than Bytes? {{Reflections}} on 21+ Years of Geocomputation},
  shorttitle = {More Bark than Bytes?},
  author = {Harris, Richard and O'Sullivan, David and Gahegan, Mark and Charlton, Martin and Comber, Lex and Longley, Paul and Brunsdon, Chris and Malleson, Nick and Heppenstall, Alison and Singleton, Alex and {Arribas-Bel}, Daniel and Evans, Andy},
  year = {2017},
  month = jul,
  journal = {Environment and Planning B: Urban Analytics and City Science},
  doi = {10/ggr3jb},
  urldate = {2017-07-10},
  abstract = {This year marks the 21st anniversary of the International GeoComputation Conference Series. To celebrate the occasion, Environment and Planning B invited some members of the geocomputational community to reflect on its achievements, some of the unrealised potential, and to identify some of the on-going challenges.},
  langid = {english},
  keywords = {nosource}
}

@book{hengl_practical_2007,
  title = {A Practical Guide to Geostatistical Mapping of Environmental Variables},
  author = {Hengl, Tomislav},
  year = {2007},
  publisher = {Publications Office},
  address = {Luxembourg},
  abstract = {Geostatistical mapping can be defined as analytical production of maps by using field observations, auxiliary information and a computer program that calculates values at locations of interest. Today, increasingly the heart of a mapping project is, in fact, the computer program that implements some (geo)statistical algorithm to a given point data set. Purpose of this guide is to assist you in producing quality maps by using fully-operational tools, without a need for serious additional investments. It will first introduce you the to the basic principles of geostatistical mapping and regression-kriging, as the key prediction technique, then it will guide you through four software packages: ILWIS GIS, R+gstat, SAGA GIS and Google Earth, which will be used to prepare the data, run analysis and make final layouts. These materials have been used for the five-days advanced training course "Hands-on-geostatistics: merging GIS and spatial statistics", that is regularly organized by the author and collaborators. Visit the course website to obtain a copy of the datasets used in this exercise. [R{\'e}sum{\'e} de l'auteur].},
  isbn = {978-92-79-06904-8},
  langid = {english},
  keywords = {nosource},
  annotation = {OCLC: 758643236}
}

@article{hengl_random_2018,
  title = {Random Forest as a Generic Framework for Predictive Modeling of Spatial and Spatio-Temporal Variables},
  author = {Hengl, Tomislav and Nussbaum, Madlene and Wright, Marvin N. and Heuvelink, Gerard B.M. and Gr{\"a}ler, Benedikt},
  year = {2018},
  month = aug,
  journal = {PeerJ},
  volume = {6},
  pages = {e5518},
  issn = {2167-8359},
  doi = {10/gd66jm},
  urldate = {2018-09-25},
  langid = {english},
  keywords = {nosource}
}

@dataset{hengl_t_2021_5774954,
  title = {Global {{MODIS-based}} Snow Cover Monthly Long-Term (2000-2012) at 500 m, and Aggregated Monthly Values (2000-2020) at 1 Km},
  author = {Hengl, T.},
  year = {2021},
  month = dec,
  publisher = {Zenodo},
  doi = {10.5281/zenodo.5774954},
  version = {v1.0}
}

@article{hesselbarth_opensource_2021,
  title = {Open-Source Tools in {{R}} for Landscape Ecology},
  author = {Hesselbarth, Maximillian H.K. and Nowosad, Jakub and Signer, Johannes and Graham, Laura J.},
  year = {2021},
  month = jun,
  volume = {6},
  number = {3},
  pages = {97--111},
  publisher = {{Springer Science and Business Media LLC}},
  doi = {10/gnckbj}
}

@article{hickman_transitions_2011,
  title = {Transitions to Low Carbon Transport Futures: Strategic Conversations from {{London}} and {{Delhi}}},
  shorttitle = {Transitions to Low Carbon Transport Futures},
  author = {Hickman, Robin and Ashiru, Olu and Banister, David},
  year = {2011},
  month = nov,
  journal = {Journal of Transport Geography},
  series = {Special Section on {{Alternative Travel}} Futures},
  volume = {19},
  number = {6},
  pages = {1553--1562},
  issn = {0966-6923},
  doi = {10/cwxs9s},
  urldate = {2016-05-14},
  abstract = {Climate change is a global problem and across the world there are major difficulties being experienced in reducing carbon dioxide (CO2) emissions. The transport sector in particular is finding it difficult to reduce CO2 emissions. This paper reports on two studies carried out by the authors in London (UK) and Delhi (India). It considers the common objectives for transport CO2 reduction, but the very different contexts and baselines, potentials for change, and some possible synergies. Different packages of measures are selected and scenarios developed for each context which are consistent with contraction and convergence objectives. CO2 reduction potentials are modelled and quantified by package and scenario. London is considering deep reductions on current transport CO2 emission levels; Delhi is seeking to break the huge projected rise in transport CO2 emissions. The scale of policy intervention required to achieve these goals is huge and there is certainly little public discussion of the magnitude of the changes required. The paper argues for a `strategic conversation' at the city level, using scenario analysis, to discuss the priorities for intervention in delivering low carbon transport futures. A greater focus is required in developing participatory approaches to decision making, alongside network investments, urban planning, low emission vehicles and wider initiatives. Aspirations towards equitable target emissions may assist in setting sufficiently demanding targets. Only then is a wider awareness and ownership of potential carbon efficient transport futures likely to take place.},
  keywords = {City planning,CO2,Delhi,London,Sustainable,Transport}
}

@book{hijmans_geosphere_2016,
  title = {Geosphere: {{Spherical Trigonometry}}},
  author = {Hijmans, Robert J.},
  year = {2016},
  keywords = {nosource}
}

@manual{hijmans_terra_2021,
  type = {Manual},
  title = {Terra: {{Spatial}} Data Analysis},
  author = {Hijmans, Robert J.},
  year = {2021}
}

@book{hollander_transport_2016,
  title = {Transport {{Modelling}} for a {{Complete Beginner}}},
  author = {Hollander, Yaron},
  year = {2016},
  month = dec,
  publisher = {CTthink!},
  abstract = {Finally! A book about transport modelling which doesn't require any previous knowledge. "Transport modelling for a complete beginner" explains the basics of transport modelling in a simple language, with lots of silly drawings, and without using any mathematics. Click here to watch a 3-minute introductory video (or search for the book name on YouTube if the link doesn't show). ~ This book is aimed at transport planners, town planners, students in transport-related courses, policy advisors, economists, project managers, property developers, investors, politicians, journalists, and anyone else who wants to understand the process of making decisions on transport infrastructure. It is suitable for readers in any country.~ ~ The book is split into two parts. The first part is about the principles of transport modelling. This part talks about travel demand, transport networks, zones, trip matrices, the value of time, trip generation, mode split, destination choice, model calibration -- lots of scary words that need explaining in order to understand the role of models in the assessment of transport projects. All modes of transport are covered: cars, buses, trains, trucks, taxis, walking, cycling and others. Hot air balloons may be the only transport mode that is hardly mentioned.~ ~ The second part of the book covers more strategic issues. It talks about the culture of transport modelling, including the management of transport modelling work, the way model outputs are communicated, and the professional environment where this is done. This part of the book also contains an honest discussion of common modelling practices which should be recommended and others which should not.~ ~ ``Transport modelling for a complete beginner'' will help you ensure that anything you do with a transport model remains fair, effective and based on real evidence.},
  isbn = {978-0-9956624-1-4},
  langid = {english}
}

@book{horni_multi-agent_2016,
  title = {The {{Multi-Agent Transport Simulation MATSim}}},
  author = {Horni, Andreas and Nagel, Kai and Axhausen, Kay W.},
  year = {2016},
  month = aug,
  publisher = {Ubiquity Press},
  urldate = {2017-12-29},
  abstract = {The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status.},
  isbn = {978-1-909188-77-8 978-1-909188-75-4 978-1-909188-78-5 978-1-909188-76-1},
  langid = {english},
  keywords = {nosource}
}

@inproceedings{hornik_approaches_2003,
  title = {Approaches to {{Classes}} for {{Spatial Data}} in {{R}}},
  booktitle = {Proceedings of {{DSC}}},
  author = {Bivand, Roger},
  editor = {Hornik, Kurt and Leisch, Friedrich and Zeileis, Achim},
  year = {2003},
  urldate = {2017-06-27},
  keywords = {No DOI found,nosource}
}

@article{huang_geospark_2017,
  title = {{{GeoSpark SQL}}: {{An Effective Framework Enabling Spatial Queries}} on {{Spark}}},
  shorttitle = {{{GeoSpark SQL}}},
  author = {Huang, Zhou and Chen, Yiran and Wan, Lin and Peng, Xia},
  year = {2017},
  month = sep,
  journal = {ISPRS International Journal of Geo-Information},
  volume = {6},
  number = {9},
  pages = {285},
  issn = {2220-9964},
  doi = {10/gcnq5h},
  urldate = {2018-06-29},
  langid = {english},
  keywords = {nosource}
}

@article{huff_probabilistic_1963,
  title = {A {{Probabilistic Analysis}} of {{Shopping Center Trade Areas}}},
  author = {Huff, David L.},
  year = {1963},
  journal = {Land Economics},
  volume = {39},
  number = {1},
  eprint = {3144521},
  eprinttype = {jstor},
  pages = {81--90},
  issn = {0023-7639},
  doi = {10/b69ptc},
  urldate = {2017-11-06},
  keywords = {nosource}
}

@book{hunziker_velox:_2017,
  title = {Velox: {{Fast Raster Manipulation}} and {{Extraction}}},
  author = {Hunziker, Philipp},
  year = {2017},
  keywords = {nosource}
}

@article{jafari_investigation_2015,
  title = {Investigation of {{Centroid Connector Placement}} for {{Advanced Traffic Assignment Models}} with {{Added Network Detail}}},
  author = {Jafari, Ehsan and Gemar, Mason D. and Juri, Natalia Ruiz and Duthie, Jennifer},
  year = {2015},
  month = jun,
  journal = {Transportation Research Record: Journal of the Transportation Research Board},
  volume = {2498},
  pages = {19--26},
  issn = {0361-1981},
  doi = {10/gkb5nj},
  urldate = {2018-01-01},
  langid = {english}
}

@book{james_introduction_2013,
  title = {An Introduction to Statistical Learning: With Applications in {{R}}},
  shorttitle = {An Introduction to Statistical Learning},
  editor = {James, Gareth and Witten, Daniela and Hastie, Trevor and Tibshirani, Robert},
  year = {2013},
  series = {Springer Texts in Statistics},
  number = {103},
  publisher = {Springer},
  address = {New York},
  isbn = {978-1-4614-7137-0},
  lccn = {QA276 .I585 2013},
  keywords = {Mathematical models,Mathematical statistics,nosource,R (Computer program language),Statistics},
  annotation = {OCLC: ocn828488009}
}

@article{jasiewicz_geomorphons_2013,
  title = {Geomorphons --- a Pattern Recognition Approach to Classification and Mapping of Landforms},
  author = {Jasiewicz, Jaros{\l}aw and Stepinski, Tomasz F.},
  year = {2013},
  month = jan,
  journal = {Geomorphology},
  volume = {182},
  pages = {147--156},
  issn = {0169555X},
  doi = {10.1016/j.geomorph.2012.11.005},
  urldate = {2020-06-29},
  abstract = {We introduce a novel method for classification and mapping of landform elements from a DEM based on the principle of pattern recognition rather than differential geometry. At the core of the method is the concept of geomorphon (geomorphologic phonotypes) --- a simple ternary pattern that serves as an archetype of a particular terrain morphology. A finite number of 498 geomorphons constitute a comprehensive and exhaustive set of all possible morphological terrain types including standard elements of landscape, as well as unfamiliar forms rarely found in natural terrestrial surfaces. A single scan of a DEM assigns an appropriate geomorphon to every cell in the raster using a procedure that self-adapts to identify the most suitable spatial scale at each location. As a result, the method classifies landform elements at a range of different spatial scales with unprecedented computational efficiency. A general purpose geomorphometric map --- an interpreted map of topography --- is obtained by generalizing allgeomorphons to a small number of the most common landform elements. Due to the robustness and high computational efficiency of the method high resolution geomorphometric maps having continental and even global extents can be generated from giga-cell DEMs. Such maps are a valuable new resource for both manual and automated geomorphometric analyses. In order to demonstrate a practical application of this new method, a 30 m cell-1 geomorphometric map of the entire country of Poland is generated and the features and potential usage of this map are briefly discussed. The computer implementation of the method is outlined. The code is available in the public domain.},
  langid = {english}
}

@incollection{jenny_guide_2017,
  title = {A Guide to Selecting Map Projections for World and Hemisphere Maps},
  booktitle = {Choosing a {{Map Projection}}},
  author = {Jenny, Bernhard and {\v S}avri{\v c}, Bojan and Arnold, Nicholas D and Marston, Brooke E and Preppernau, Charles A},
  editor = {Lapaine, Miljenko and Usery, Lynn},
  year = {2017},
  pages = {213--228},
  publisher = {Springer},
  keywords = {nosource}
}

@book{jr_geor_2016,
  title = {{{geoR}}: {{Analysis}} of {{Geostatistical Data}}},
  author = {Jr, Paulo J. Ribeiro and Diggle, Peter J.},
  year = {2016},
  keywords = {nosource}
}

@article{kahle_ggmap_2013,
  title = {Ggmap: {{Spatial Visualization}} with Ggplot2},
  author = {Kahle, D and Wickham, Hadley},
  year = {2013},
  journal = {The R Journal},
  volume = {5},
  pages = {144--161},
  doi = {10.32614/RJ-2013-014},
  keywords = {nosource}
}

@article{kaiser_algorithms_1993,
  title = {Algorithms for Computing Centroids},
  author = {Kaiser, M.J. and Morin, T.L.},
  year = {1993},
  month = feb,
  journal = {Computers \& Operations Research},
  volume = {20},
  number = {2},
  pages = {151--165},
  issn = {03050548},
  doi = {10/dvxsr3},
  urldate = {2018-07-10},
  abstract = {Algorithms are given for the computation of centroids of discrete, polygonal, and continuous convex regions in the plane. These include the zero-dimensional center-of-gravity for discrete systems, and the area, perimeter, and curvature centroids for both discrete and continuous regions. The zero-dimensional inter-of-gravity is motivated through analytic, arithmetic, and geometric fo{\textasciitilde}ulations, and is an integral part of the computations of the area, perimeter, and curvature centroids. Several remarks are made that connect the computation of the centoid points to optimization theory and their practical application in various fields. The complexity of each algorithm is aho examined.},
  langid = {english},
  keywords = {nosource}
}

@article{karatzoglou_kernlab_2004,
  title = {Kernlab - {{An S4}}  {{Package}} for {{Kernel Methods}} in {{R}}},
  author = {Karatzoglou, Alexandros and Smola, Alex and Hornik, Kurt and Zeileis, Achim},
  year = {2004},
  journal = {Journal of Statistical Software},
  volume = {11},
  number = {9},
  issn = {1548-7660},
  doi = {10/gdq9pc},
  urldate = {2018-03-28},
  langid = {english},
  keywords = {nosource}
}

@article{knuth_computer_1974,
  title = {Computer {{Programming As}} an {{Art}}},
  author = {Knuth, Donald E.},
  year = {1974},
  month = dec,
  journal = {Commun. ACM},
  volume = {17},
  number = {12},
  pages = {667--673},
  issn = {0001-0782},
  doi = {10/fhrtw3},
  urldate = {2018-07-11},
  abstract = {When Communications of the ACM began publication in 1959, the members of ACM's Editorial Board made the following remark as they described the purposes of ACM's periodicals [2]: ``If computer programming is to become an important part of computer research and development, a transition of programming from an art to a disciplined science must be effected.'' Such a goal has been a continually recurring theme during the ensuing years; for example, we read in 1970 of the ``first steps toward transforming the art of programming into a science'' [26]. Meanwhile we have actually succeeded in making our discipline a science, and in a remarkably simple way: merely by deciding to call it ``computer science.''},
  keywords = {nosource}
}

@book{krainski_advanced_2018,
  title = {Advanced {{Spatial Modeling}} with {{Stochastic Partial Differential Equations Using R}} and {{INLA}}},
  author = {Krainski, Elias and G{\'o}mez Rubio, Virgilio and Bakka, Haakon and Lenzi, Amanda and {Castro-Camilo}, Daniela and Simpson, Daniel and Lindgren, Finn and Rue, H{\aa}vard},
  year = {2018},
  month = sep,
  abstract = {Book on spatial and spatio-temporal modeling with SPDEs and INLA. R code and free Gitbook version here: http://www.r-inla.org/spde-book .},
  isbn = {978-1-138-36985-6}
}

@article{krug_clearing_2010,
  title = {Clearing of Invasive Alien Plants under Different Budget Scenarios: Using a Simulation Model to Test Efficiency},
  shorttitle = {Clearing of Invasive Alien Plants under Different Budget Scenarios},
  author = {Krug, Rainer M. and {Roura-Pascual}, N{\'u}ria and Richardson, David M.},
  year = {2010},
  journal = {Biological invasions},
  volume = {12},
  number = {12},
  pages = {4099--4112},
  doi = {10/fn3bmr},
  urldate = {2017-08-24},
  keywords = {nosource}
}

@book{kuhn_applied_2013,
  title = {Applied Predictive Modeling},
  author = {Kuhn, Max and Johnson, Kjell},
  year = {2013},
  publisher = {Springer},
  address = {New York},
  isbn = {978-1-4614-6848-6},
  lccn = {QA276 .K79 2013},
  keywords = {Mathematical models,Mathematical statistics,nosource,Prediction theory},
  annotation = {OCLC: ocn827083441}
}

@manual{lahn_openeo_2021,
  type = {Manual},
  title = {Openeo: {{Client}} Interface for '{{openEO}}' Servers},
  author = {Lahn, Florian},
  year = {2021}
}

@book{lamigueiro_displaying_2014,
  title = {Displaying Time Series, Spatial, and Space-Time Data with {{R}}},
  author = {Lamigueiro, Oscar},
  year = {2014},
  publisher = {CRC Press},
  keywords = {nosource}
}

@book{lamigueiro_displaying_2018,
  title = {Displaying {{Time Series}}, {{Spatial}}, and {{Space-Time Data}} with {{R}}},
  author = {Lamigueiro, Oscar Perpinan},
  year = {2018},
  month = aug,
  edition = {Second},
  publisher = {{Chapman and Hall/CRC}},
  address = {Boca Raton},
  abstract = {Focusing on the exploration of data with visual methods, Displaying Time Series, Spatial, and Space-Time Data with R, Second Edition, presents methods and R code for producing high-quality static graphics, interactive visualizations, and animations of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code.The book illustrates how to display a dataset starting with an easy and direct approach, and progressively adds improvements that involve more complexity. Each of the three parts of the book is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics.The first edition of this book was mainly focused on static graphics. Four years later, recent developments in the "htmlwidgets" family of packages are covered in this second edition with many new interactive graphics. In addition, the "ggplot2" approach is now used in most of the spatial graphics, thanks to the new "sf" package. Finally, code has been cleaned and improved, and data has been updated.Features{$\bullet$} Offers detailed information on producing high-quality graphics, interactive visualizations, and animations{$\bullet$} Uses real data from meteorological, climate, economic, social science, energy, engineering, environmental, and epidemiological research in many practical examples{$\bullet$} Shows how to improve graphics based on visualization theory{$\bullet$} Provides the graphics, data, and R code on the author's website, enabling you to practice with the methods and modify the code to suit your own needs.},
  isbn = {978-1-138-08998-3},
  langid = {english}
}

@article{landa_new_2008,
  title = {New {{GUI}} for {{GRASS GIS}} Based on {{wxPython}}},
  author = {Landa, Martin},
  year = {2008},
  journal = {Departament of Geodesy and Cartography},
  pages = {1--17},
  keywords = {No DOI found,nosource}
}

@article{landau_targets_2021,
  title = {The Targets {{R}} Package: A Dynamic {{Make-like}} Function-Oriented Pipeline Toolkit for Reproducibility and High-Performance Computing},
  author = {Landau, William Michael},
  year = {2021},
  journal = {Journal of Open Source Software},
  volume = {6},
  number = {57},
  pages = {2959},
  doi = {10.21105/joss.02959}
}

@article{lang_mlr3_2019,
  title = {Mlr3: {{A}} Modern Object-Oriented Machine Learning Framework in {{R}}},
  shorttitle = {Mlr3},
  author = {Lang, Michel and Binder, Martin and Richter, Jakob and Schratz, Patrick and Pfisterer, Florian and Coors, Stefan and Au, Quay and Casalicchio, Giuseppe and Kotthoff, Lars and Bischl, Bernd},
  year = {2019},
  journal = {Journal of Open Source Software},
  volume = {4},
  number = {44},
  pages = {1903},
  doi = {10.21105/joss.01903}
}

@article{lefkowitz_identification_1975,
  title = {Identification of Adenylate Cyclase-Coupled Beta-Adrenergic Receptors with Radiolabeled Beta-Adrenergic Antagonists},
  author = {Lefkowitz, R. J.},
  year = {1975},
  month = sep,
  journal = {Biochemical Pharmacology},
  volume = {24},
  number = {18},
  pages = {1651--1658},
  issn = {0006-2952},
  langid = {english},
  pmid = {11},
  keywords = {Adenylyl Cyclases,Adrenergic beta-Antagonists,Alprenolol,Animals,Anura,Binding Sites,Catecholamines,Cattle,Cell Membrane,Eels,Erythrocytes,Guinea Pigs,In Vitro Techniques,Isoproterenol,Kinetics,nosource,Propranolol,Receptors Adrenergic,Stereoisomerism,Tritium}
}

@book{liu_essential_2009,
  title = {Essential Image Processing and {{GIS}} for Remote Sensing},
  author = {Liu, Jian-Guo and Mason, Philippa J.},
  year = {2009},
  publisher = {Wiley-Blackwell},
  address = {Chichester, West Sussex, UK, Hoboken, NJ},
  isbn = {978-0-470-51032-2 978-0-470-51031-5},
  lccn = {G70.4 .L583 2009},
  keywords = {Earth (Planet),Geographic information systems,Image processing,nosource,Remote sensing,Surface Remote sensing}
}

@book{livingstone_geographical_1992,
  title = {The {{Geographical Tradition}}: {{Episodes}} in the {{History}} of a {{Contested Enterprise}}},
  shorttitle = {The {{Geographical Tradition}}},
  author = {Livingstone, David N.},
  year = {1992},
  month = dec,
  publisher = {John Wiley \& Sons Ltd},
  address = {Oxford, UK ; Cambridge, USA},
  abstract = {The Geographical Tradition  presents the history of an essentially contested tradition. By examining a series of key episodes in geography{$\prime$}s history since 1400, Livingstone argues that the messy contingencies of history are to be preferred to the manufactured idealizations of the standard chronicles. Throughout, the development of geographical thought and practice is portrayed against the background of the broader social and intellectual contexts of the times. Among the topics investigated are geography during the Age of Reconnaissance, the Scientific Revolution and The Englightenment; subsequently geography{$\prime$}s relationships with Darwinism, imperialism, regionalism, and quantification are elaborated.},
  isbn = {978-0-631-18586-4},
  langid = {english}
}

@article{loecher_rgooglemaps_2015,
  title = {{{RgoogleMaps}} and Loa: {{Unleashing R Graphics Power}} on {{Map Tiles}}},
  shorttitle = {{{RgoogleMaps}} and Loa},
  author = {Loecher, Markus and Ropkins, Karl},
  year = {2015},
  month = feb,
  journal = {Journal of Statistical Software},
  volume = {63},
  pages = {1--18},
  issn = {1548-7660},
  doi = {10/gfgwng},
  urldate = {2021-11-01},
  abstract = {The RgoogleMaps package provides (1) an R interface to query the Google and the OpenStreetMap servers for static maps in the form of PNGs, and (2) enables the user to overlay plots on those maps within R. The loa package provides dedicated panel functions to integrate RgoogleMaps within the lattice plotting environment. In addition to solving the generic task of plotting on a map background in R, we introduce several specific algorithms to detect and visualize spatio-temporal clusters. This task can often be reduced to detecting over-densities in space relative to a background density. The relative density estimation is framed as a binary classification problem. An integrated hotspot visualizer is presented which allows the efficient identification and visualization of clusters in one environment. Competing clustering methods such as the scan statistic and the density scan offer higher detection power at a much larger computational cost. Such clustering methods can then be extended using the lattice trellis framework to provide further insight into the relationship between clusters and potentially influential parameters. While there are other options for such map `mashups' we believe that the integration of RgoogleMaps and lattice using loa can in certain circumstances be advantageous, e.g., by providing a highly intuitive working environment for multivariate analysis and flexible testbed for the rapid development of novel data visualizations.},
  copyright = {Copyright (c) 2013 Markus Loecher, Karl Ropkins},
  langid = {english}
}

@article{loidl_spatial_2016,
  ids = {loidl_spatial_2016a},
  title = {Spatial Patterns and Temporal Dynamics of Urban Bicycle Crashes---{{A}} Case Study from {{Salzburg}} ({{Austria}})},
  author = {Loidl, Martin and Traun, Christoph and Wallentin, Gudrun},
  year = {2016},
  month = apr,
  journal = {Journal of Transport Geography},
  volume = {52},
  number = {Supplement C},
  pages = {38--50},
  issn = {0966-6923},
  doi = {10/f8qrzb},
  urldate = {2017-10-18},
  abstract = {Most bicycle crash analyses are designed as explanatory studies. They aim to identify contributing risk factors and calculate risk rates based on -- most of the time -- highly aggregated statistical data. In contrast to such explanatory study designs, the presented study follows an exploratory approach, focusing on the absolute number of crashes. The aim is to reveal and describe patterns and dynamics of urban bicycle crashes on various spatial scale levels and temporal resolutions through a multi-stage workflow. Spatial units are delineated in the network space and serve as initial units of aggregation. In order to facilitate comparisons among regions and quantify temporal dynamics, a reference value of crash frequency is simulated for each unit of the respective spatial scale level and temporal resolution. For the presented case study, over 3000 geo-coded bicycle crashes in the city of Salzburg (Austria) were analyzed. The data set covers 10years and comprises all bicycle crashes reported by the police. Distinct spatial and temporal patterns with clusters, seasonal variations, and regional particularities could be revealed. These insights are indicators for urban dynamics in the transport system and allow for further, targeted in-depth analyses and subsequent counter measures. Moreover, the results prove the applicability of the proposed multi-stage workflow and demonstrate the added value of analyses of small aggregates on various scale levels, down to single crashes, and temporal resolutions.},
  keywords = {Bicycle crashes,Exploratory analysis,Spatial and temporal dynamics}
}

@book{longley_geocomputation_1998,
  title = {Geocomputation: {{A Primer}}},
  shorttitle = {Geocomputation},
  editor = {Longley, Paul and Brooks, Sue M. and McDonnell, Rachael and MacMillan, Bill},
  year = {1998},
  month = oct,
  publisher = {Wiley},
  address = {Chichester, Eng. ; New York},
  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.},
  isbn = {978-0-471-98576-1},
  langid = {english}
}

@book{longley_geographic_2015,
  title = {Geographic Information Science \& Systems},
  author = {Longley, Paul},
  year = {2015},
  edition = {Fourth edition},
  publisher = {Wiley},
  address = {Hoboken, NJ},
  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"--},
  isbn = {978-1-118-67695-0},
  lccn = {G70.212 .L658 2015},
  keywords = {Geographic information systems,nosource,Technology & Engineering / Remote Sensing & Geographic Information Systems}
}

@techreport{lott_geographic_2015,
  title = {Geographic Information-{{Well-known}} Text Representation of Coordinate Reference Systems},
  author = {Lott, Roger},
  year = {2015},
  institution = {Open Geospatial Consortium}
}

@book{lovelace_geocomputation_2019,
  title = {Geocomputation with {{R}}},
  author = {Lovelace, Robin and Nowosad, Jakub and Muenchow, Jannes},
  year = {2019},
  publisher = {CRC Press},
  urldate = {2017-10-05},
  abstract = {Book on geographic data with R.},
  isbn = {1-138-30451-4}
}

@article{lovelace_jittering_2022b,
  title = {Jittering: {{A Computationally Efficient Method}} for {{Generating Realistic Route Networks}} from {{Origin-Destination Data}}},
  shorttitle = {Jittering},
  author = {Lovelace, Robin and F{\'e}lix, Rosa and Carlino, Dustin},
  year = {2022},
  month = apr,
  journal = {Findings},
  pages = {33873},
  publisher = {Findings Press},
  doi = {10.32866/001c.33873},
  urldate = {2022-05-05},
  abstract = {Origin-destination (OD) datasets are often represented as `desire lines' between zone centroids. This paper presents a `jittering' approach to pre-processing and conversion of OD data into geographic desire lines that (1) samples unique origin and destination locations for each OD pair, and (2) splits `large' OD pairs into `sub-OD' pairs. Reproducible findings, based on the open source \_odjitter\_ Rust crate, show that route networks generated from jittered desire lines are more geographically diffuse than route networks generated by `unjittered' data. We conclude that the approach is a computationally efficient and flexible way to simulate transport patterns, particularly relevant for modelling active modes. Further work is needed to validate the approach and to find optimal settings for sampling and disaggregation.},
  copyright = {Creative Commons Attribution-ShareAlike 4.0 International Licence (CC-BY-SA)},
  langid = {english}
}

@article{lovelace_open_2021,
  ids = {lovelace_open_2021a},
  title = {Open Source Tools for Geographic Analysis in Transport Planning},
  author = {Lovelace, Robin},
  year = {2021},
  month = jan,
  journal = {Journal of Geographical Systems},
  publisher = {{Springer Science and Business Media LLC}},
  issn = {1435-5949},
  doi = {10/ghtnrp},
  urldate = {2021-01-17},
  abstract = {Geographic analysis has long supported transport plans that are appropriate to local contexts. Many incumbent `tools of the trade' are proprietary and were developed to support growth in motor traffic, limiting their utility for transport planners who have been tasked with twenty-first century objectives such as enabling citizen participation, reducing pollution, and increasing levels of physical activity by getting more people walking and cycling. Geographic techniques---such as route analysis, network editing, localised impact assessment and interactive map visualisation---have great potential to support modern transport planning priorities. The aim of this paper is to explore emerging open source tools for geographic analysis in transport planning, with reference to the literature and a review of open source tools that are already being used. A key finding is that a growing number of options exist, challenging the current landscape of proprietary tools. These can be classified as command-line interface, graphical user interface or web-based user interface tools and by the framework in which they were implemented, with numerous tools released as R, Python and JavaScript packages, and QGIS plugins. The review found a diverse and rapidly evolving `ecosystem' tools, with 25 tools that were designed for geographic analysis to support transport planning outlined in terms of their popularity and functionality based on online documentation. They ranged in size from single-purpose tools such as the QGIS plugin AwaP to sophisticated stand-alone multi-modal traffic simulation software such as MATSim, SUMO and Veins. Building on their ability to re-use the most effective components from other open source projects, developers of open source transport planning tools can avoid `reinventing the wheel' and focus on innovation, the `gamified' A/B Street https://github.com/dabreegster/abstreet/\#abstreetsimulation software, based on OpenStreetMap, a case in point. The paper, the source code of which can be found at https://github.com/robinlovelace/open-gat, concludes that, although many of the tools reviewed are still evolving and further research is needed to understand their relative strengths and barriers to uptake, open source tools for geographic analysis in transport planning already hold great potential to help generate the strategic visions of change and evidence that is needed by transport planners in the twenty-first century.},
  langid = {english}
}

@article{lovelace_propensity_2017,
  title = {The {{Propensity}} to {{Cycle Tool}}: {{An}} Open Source Online System for Sustainable Transport Planning},
  shorttitle = {The {{Propensity}} to {{Cycle Tool}}},
  author = {Lovelace, Robin and Goodman, Anna and Aldred, Rachel and Berkoff, Nikolai and Abbas, Ali and Woodcock, James},
  year = {2017},
  month = jan,
  journal = {Journal of Transport and Land Use},
  volume = {10},
  number = {1},
  issn = {1938-7849},
  doi = {10/gfgzf7},
  urldate = {2017-06-01},
  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.},
  copyright = {Copyright (c) 2016 Robin Lovelace, Anna Goodman, Rachel Aldred, Nikolai Berkoff, Ali Abbas, James Woodcock},
  langid = {english},
  keywords = {Cycling,modelling,Participatory,Planning}
}

@book{lovelace_spatial_2016,
  title = {Spatial Microsimulation with {{R}}},
  author = {Lovelace, Robin and Dumont, Morgane},
  year = {2016},
  publisher = {CRC Press},
  keywords = {nosource}
}

@book{majure_sgeostat_2016,
  title = {Sgeostat: {{An Object-Oriented Framework}} for {{Geostatistical Modeling}} in {{S}}+},
  author = {Majure, James J. and Gebhardt, Albrecht},
  year = {2016},
  keywords = {nosource}
}

@book{maling_coordinate_1992,
  title = {Coordinate Systems and Map Projections},
  author = {Maling, D. H.},
  year = {1992},
  edition = {Second},
  publisher = {Pergamon Press},
  address = {Oxford ; New York},
  isbn = {978-0-08-037234-1},
  lccn = {GA110 .M32 1992},
  keywords = {Grids (Cartography),Map projection,nosource}
}

@book{mccune_analysis_2002,
  title = {Analysis of Ecological Communities},
  author = {McCune, Bruce and Grace, James B. and Urban, Dean L.},
  year = {2002},
  edition = {Second},
  publisher = {MjM Software Design},
  address = {Gleneden Beach, OR},
  isbn = {978-0-9721290-0-8},
  langid = {english},
  keywords = {nosource},
  annotation = {OCLC: 846056595}
}

@article{meulemans_small_2017,
  title = {Small {{Multiples}} with {{Gaps}}},
  author = {Meulemans, Wouter and Dykes, Jason and Slingsby, Aidan and Turkay, Cagatay and Wood, Jo},
  year = {2017},
  month = jan,
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  volume = {23},
  number = {1},
  pages = {381--390},
  issn = {1077-2626},
  doi = {10/f92gd5},
  urldate = {2018-09-02},
  abstract = {Small multiples enable comparison by providing different views of a single data set in a dense and aligned manner. A common frame defines each view, which varies based upon values of a conditioning variable. An increasingly popular use of this technique is to project two-dimensional locations into a gridded space (e.g. grid maps), using the underlying distribution both as the conditioning variable and to determine the grid layout. Using whitespace in this layout has the potential to carry information, especially in a geographic context. Yet, the effects of doing so on the spatial properties of the original units are not understood. We explore the design space offered by such small multiples with gaps. We do so by constructing a comprehensive suite of metrics that capture properties of the layout used to arrange the small multiples for comparison (e.g. compactness and alignment) and the preservation of the original data (e.g. distance, topology and shape). We study these metrics in geographic data sets with varying properties and numbers of gaps. We use simulated annealing to optimize for each metric and measure the effects on the others. To explore these effects systematically, we take a new approach, developing a system to visualize this design space using a set of interactive matrices. We find that adding small amounts of whitespace to small multiple arrays improves some of the characteristics of 2D layouts, such as shape, distance and direction. This comes at the cost of other metrics, such as the retention of topology. Effects vary according to the input maps, with degree of variation in size of input regions found to be a factor. Optima exist for particular metrics in many cases, but at different amounts of whitespace for different maps. We suggest multiple metrics be used in optimized layouts, finding topology to be a primary factor in existing manually-crafted solutions, followed by a trade-off between shape and displacement. But the rich range of possible optimized layouts leads us to challenge single-solution thinking; we suggest to consider alternative optimized layouts for small multiples with gaps. Key to our work is the systematic, quantified and visual approach to exploring design spaces when facing a trade-off between many competing criteria---an approach likely to be of value to the analysis of other design spaces.},
  langid = {english},
  keywords = {nosource}
}

@article{meyer_improving_2018,
  title = {Improving Performance of Spatio-Temporal Machine Learning Models Using Forward Feature Selection and Target-Oriented Validation},
  author = {Meyer, Hanna and Reudenbach, Christoph and Hengl, Tomislav and Katurji, Marwan and Nauss, Thomas},
  year = {2018},
  month = mar,
  journal = {Environmental Modelling \& Software},
  volume = {101},
  pages = {1--9},
  issn = {13648152},
  doi = {10/gc2tsg},
  urldate = {2018-04-18},
  langid = {english},
  keywords = {nosource}
}

@article{miller_tobler_2004,
  title = {Tobler's First Law and Spatial Analysis},
  author = {Miller, Harvey J.},
  year = {2004},
  journal = {Annals of the Association of American Geographers},
  volume = {94},
  number = {2},
  doi = {10/dh39xr},
  abstract = {Discusses Tobler's First Law of (TFL) Geography, that everything is related to everything else, but near things are more related than distant things. Relatonships between two geographic entities; TFL as the core of spatial autocorrelation statistics; Quantitative techniques for analyzing correlation relative to distance or connectivity relationships.},
  keywords = {nosource}
}

@book{moraga_geospatial_2019,
  title = {Geospatial Health Data: {{Modeling}} and Visualization with {{R-INLA}} and Shiny},
  shorttitle = {Geospatial Health Data},
  author = {Moraga, Paula},
  year = {2019},
  publisher = {CRC Press}
}

@book{moraga_spatial_2023,
  title = {Spatial {{Statistics}} for {{Data Science}}: {{Theory}} and {{Practice}} with {{R}}},
  shorttitle = {Spatial {{Statistics}} for {{Data Science}}},
  author = {Moraga, Paula},
  year = {2023},
  month = dec,
  edition = {1st edition},
  publisher = {{Chapman and Hall/CRC}},
  address = {Boca Raton, FL},
  abstract = {Spatial data is crucial to improve decision-making in a wide range of fields including environment, health, ecology, urban planning, economy, and society. Spatial Statistics for Data Science: Theory and Practice with R describes statistical methods, modeling approaches, and visualization techniques to analyze spatial data using R. The book provides a comprehensive overview of the varying types of spatial data, and detailed explanations of the theoretical concepts of spatial statistics, alongside fully reproducible examples which demonstrate how to simulate, describe, and analyze spatial data in various applications. Combining theory and practice, the book includes real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses. The book utilizes publicly available data and offers clear explanations of the R code for importing, manipulating, analyzing, and visualizing data, as well as the interpretation of the results. This ensures contents are easily accessible and fully reproducible for students, researchers, and practitioners.Key Features:Describes R packages for retrieval, manipulation, and visualization of spatial data.Offers a comprehensive overview of spatial statistical methods including spatial autocorrelation, clustering, spatial interpolation, model-based geostatistics, and spatial point processes.Provides detailed explanations on how to fit and interpret Bayesian spatial models using the integrated nested Laplace approximation (INLA) and stochastic partial differential equation (SPDE) approaches.},
  isbn = {978-1-03-263351-0},
  langid = {english}
}

@article{moreno-monroy_public_2017,
  title = {Public Transport and School Location Impacts on Educational Inequalities: {{Insights}} from {{S{\~a}o Paulo}}},
  shorttitle = {Public Transport and School Location Impacts on Educational Inequalities},
  author = {{Moreno-Monroy}, Ana I. and Lovelace, Robin and Ramos, Frederico R.},
  year = {2017},
  month = sep,
  journal = {Journal of Transport Geography},
  issn = {0966-6923},
  doi = {10/gdkhrz},
  urldate = {2017-10-15},
  abstract = {In many large Latin American urban areas such as the S{\~a}o Paulo Metropolitan Region (SPMR), growing social and economic inequalities are embedded through high spatial inequality in the provision of state schools and affordable public transport to these schools. This paper sheds light on the transport-education inequality nexus with reference to school accessibility by public transport in the SPMR. To assess school accessibility, we develop an accessibility index which combines information on the spatial distribution of adolescents, the location of existing schools, and the public transport provision serving the school catchment area into a single measure. The index is used to measure school accessibility locally across 633 areas within the SPMR. We use the index to simulate the impact of a policy aiming at increasing the centralisation of public secondary education provision, and find that it negatively affects public transport accessibility for students with the lowest levels of accessibility. These results illustrate how existing inequalities can be amplified by variable accessibility to schools across income groups and geographical space. The research suggests that educational inequality impacts of school agglomeration policies should be considered before centralisation takes place.},
  keywords = {Accessibility,Inequality,Latin America,nosource,Public transport,Schools}
}

@article{morgan_opentripplanner_2019,
  title = {{{OpenTripPlanner}} for {{R}}},
  author = {Morgan, Malcolm and Young, Marcus and Lovelace, Robin and Hama, Layik},
  year = {2019},
  month = dec,
  journal = {Journal of Open Source Software},
  volume = {4},
  number = {44},
  pages = {1926},
  issn = {2475-9066},
  doi = {10/gkb5nh},
  urldate = {2020-01-29},
  abstract = {Morgan et al., (2019). OpenTripPlanner for R. Journal of Open Source Software, 4(44), 1926, https://doi.org/10.21105/joss.01926},
  langid = {english},
  annotation = {ZSCC: 0000000}
}

@article{morgan_travel_2020,
  ids = {morgan_travel_2020a},
  title = {Travel Flow Aggregation: Nationally Scalable Methods for Interactive and Online Visualisation of Transport Behaviour at the Road Network Level},
  author = {Morgan, Malcolm and Lovelace, Robin},
  year = {2020},
  journal = {Environment \& Planning B: Planning \& Design},
  publisher = {SAGE PublicationsSage UK: London, England},
  doi = {10/gh6gb5},
  copyright = {CC0 1.0 Universal Public Domain Dedication}
}

@manual{morganwall_rayshader_2021,
  type = {Manual},
  title = {Rayshader: {{Create}} Maps and Visualize Data in {{2D}} and {{3D}}},
  author = {{Morgan-Wall}, Tyler},
  year = {2021}
}

@article{muenchow_geomorphic_2012,
  title = {Geomorphic Process Rates of Landslides along a Humidity Gradient in the Tropical {{Andes}}},
  author = {Muenchow, Jannes and Brenning, Alexander and Richter, Michael},
  year = {2012},
  month = feb,
  journal = {Geomorphology},
  volume = {139--140},
  pages = {271--284},
  issn = {0169555X},
  doi = {10/dp554q},
  urldate = {2017-06-23},
  langid = {english},
  keywords = {nosource}
}

@article{muenchow_predictive_2013,
  title = {Predictive Mapping of Species Richness and Plant Species' Distributions of a {{Peruvian}} Fog Oasis along an Altitudinal Gradient},
  author = {Muenchow, Jannes and Br{\"a}uning, Achim and Rodr{\'i}guez, Eric Frank and {von Wehrden}, Henrik},
  year = {2013},
  month = sep,
  journal = {Biotropica},
  volume = {45},
  number = {5},
  pages = {557--566},
  issn = {1744-7429},
  doi = {10/f49j56},
  urldate = {2017-08-28},
  abstract = {Tropical arid to semi-arid ecosystems are nearly as diverse as more humid forests and occupy large parts of the tropics. In comparison, however, they are vastly understudied. For instance, fog precipitation alone supports a unique vegetation formation, locally termed lomas, on coastal mountains in the Peruvian desert. To effectively protect these highly endemic and threatened ecosystems, we must increase our understanding of their diversity patterns in relation to environmental factors. Consequently, we recorded all vascular species from 100 random 4~{\texttimes}~4~m plots on the fog-exposed southern slope of the mountain Mong{\'o}n. We used topographic and remotely sensed covariates in statistical models to generate spatial predictions of alpha diversity and plant species' distribution probabilities. Altitude was the most important predictor in all models and may represent fog moisture levels. Other significant covariates in the models most likely refer also to water availability but on a finer spatial scale. Additionally, model-based clustering revealed five altitudinal vegetation zones. This study contributes to a better spatial understanding of the biodiversity and spatial arrangement of vegetation belts of the largely unknown but highly unique lomas formations. Furthermore, mapping species richness and plant species' distributions could support a long-needed lomas strategic conservation scheme.},
  langid = {english},
  keywords = {biodiversity conservation,climatic gradient,El Nino Southern Oscillation (ENSO),La Nina,lomas,nosource,species distribution models,species richness model,tropical plant diversity}
}

@article{muenchow_review_2018,
  title = {A Review of Ecological Gradient Research in the {{Tropics}}: Identifying Research Gaps, Future Directions, and Conservation Priorities},
  shorttitle = {A Review of Ecological Gradient Research in the {{Tropics}}},
  author = {Muenchow, Jannes and Dieker, Petra and Kluge, J{\"u}rgen and Kessler, Michael and {von Wehrden}, Henrik},
  year = {2018},
  journal = {Biodiversity and Conservation},
  volume = {27},
  number = {2},
  pages = {273--285},
  issn = {0960-3115, 1572-9710},
  doi = {10/gcthf9},
  urldate = {2017-11-23},
  langid = {english},
  keywords = {nosource}
}

@article{muenchow_rqgis:_2017,
  title = {{{RQGIS}}: {{Integrating R}} with {{QGIS}} for Statistical Geocomputing},
  author = {Muenchow, Jannes and Schratz, Patrick and Brenning, Alexander},
  year = {2017},
  journal = {The R Journal},
  volume = {9},
  number = {2},
  pages = {409--428},
  doi = {10/gf3d48},
  keywords = {nosource}
}

@article{muenchow_soil_2013,
  title = {Soil Texture and Altitude, Respectively, Largely Determine the Floristic Gradient of the Most Diverse Fog Oasis in the {{Peruvian}} Desert},
  author = {Muenchow, Jannes and Hauenstein, Simon and Br{\"a}uning, Achim and B{\"a}umler, Rupert and Rodr{\'i}guez, Eric Frank and {von Wehrden}, Henrik},
  year = {2013},
  month = sep,
  journal = {Journal of Tropical Ecology},
  volume = {29},
  number = {05},
  pages = {427--438},
  issn = {0266-4674, 1469-7831},
  doi = {10/f5b5v7},
  urldate = {2017-09-21},
  langid = {english},
  keywords = {nosource}
}

@book{murrell_r_2016,
  title = {R {{Graphics}}},
  author = {Murrell, Paul},
  year = {2016},
  month = apr,
  edition = {Second},
  publisher = {CRC Press},
  abstract = {Extensively updated to reflect the evolution of statistics and computing, the second edition of the bestselling R Graphics comes complete with new packages and new examples. Paul Murrell, widely known as the leading expert on R graphics, has developed an in-depth resource that helps both neophyte and seasoned users master the intricacies of R graphics. New in the Second Edition   Updated information on the core graphics engine, the traditional graphics system, the grid graphics system, and the lattice package A new chapter on the ggplot2 package New chapters on applications and extensions of R Graphics, including geographic maps, dynamic and interactive graphics, and node-and-edge graphs   Organized into five parts, R Graphics covers both "traditional" and newer, R-specific graphics systems. The book reviews the graphics facilities of the R language and describes R's powerful grid graphics system. It then covers the graphics engine, which represents a common set of fundamental graphics facilities, and provides a series of brief overviews of the major areas of application for R graphics and the major extensions of R graphics.},
  isbn = {978-1-4398-3177-9},
  langid = {english},
  keywords = {Computers / Computer Graphics,Computers / General,Mathematics / Probability & Statistics / General}
}

@book{neteler_open_2008,
  title = {Open Source {{GIS}}: A {{GRASS GIS}} Approach},
  shorttitle = {Open Source {{GIS}}},
  author = {Neteler, Markus and Mitasova, Helena},
  year = {2008},
  edition = {Third},
  publisher = {Springer},
  address = {New York, NY},
  isbn = {978-0-387-35767-6 978-0-387-68574-8},
  langid = {english},
  keywords = {Analyse,Computerkartographie,Geographic information systems,Geoinformationssystem,GIS,GRASS,GRASS (Electronic computer system),Open source,Open source software,Programm,Programmierung,Raster,Software,Vektor,Visualisierung},
  annotation = {OCLC: 255568974}
}

@book{nolan_xml_2014,
  title = {{{XML}} and Web Technologies for Data Sciences with {{R}}},
  author = {Nolan, Deborah and Lang, Duncan Temple},
  year = {2014},
  series = {Use {{R}}!},
  publisher = {Springer},
  address = {New York, NY},
  abstract = {Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays. The XML and JSON data formats are widely used in Web services, regular Web pages and JavaScript code, and visualization formats such as SVG and KML for Google Earth and Google Maps. In addition, scientists use HTTP and other network protocols to scrape data from Web pages, access REST and SOAP Web Services, and interact with NoSQL databases and text search applications. This book provides a practical hands-on introduction to these technologies, including high-level functions the authors have developed for data scientists. It describes strategies and approaches for extracting data from HTML, XML, and JSON formats and how to programmatically access data from the Web. Along with these general skills, the authors illustrate several applications that are relevant to data scientists, such as reading and writing spreadsheet documents both locally and via GoogleDocs, creating interactive and dynamic visualizations, displaying spatial-temporal displays with Google Earth, and generating code from descriptions of data structures to read and write data. These topics demonstrate the rich possibilities and opportunities to do new things with these modern technologies. The book contains many examples and case-studies that readers can use directly and adapt to their own work},
  isbn = {978-1-4614-7900-0 978-1-4614-7899-7},
  langid = {english},
  keywords = {nosource},
  annotation = {OCLC: 841520665}
}

@article{nowosad_extended_2022,
  title = {Extended {{SLIC}} Superpixels Algorithm for Applications to Non-Imagery Geospatial Rasters},
  author = {Nowosad, Jakub and Stepinski, Tomasz F.},
  year = {2022},
  month = aug,
  journal = {International Journal of Applied Earth Observation and Geoinformation},
  volume = {112},
  pages = {102935},
  issn = {15698432},
  doi = {10.1016/j.jag.2022.102935},
  urldate = {2022-08-04},
  abstract = {Converting an image to a set of superpixels is a useful preprocessing step in many computer vision applications; it reduces the dimensionality of the data and removes noise. The most popular superpixels algorithm is the Simple Linear Iterative Clustering (SLIC). To use original SLIC with non-imagery data (for example, rasters of discrete probability distributions, time-series, or matrices describing local texture or pattern), the data needs to be converted to the false-color RGB image constructed from the first three principal components. Here we propose to extend the SLIC algorithm so it can work with non-imagery data structures without data reduction and conversion to the false-color image. The modification allows for using a data distance measure most appropriate to a particular data structure and for using a custom function for averaging values of clusters centers. Comparisons between the extended and original SLIC algorithms in three different mapping tasks are presented and discussed. The results show that the extended SLIC improves the accuracy of the final products in reverse proportion to the percentage of variability explained by the three-dimensional (RGB) approximation to multidimensional non-imagery data. Thus, the largest advantage of using the modified SLIC can be expected in applications to data that cannot be compressed to three dimensions without a significant departure from its original variability.},
  copyright = {Creative Commons Attribution 4.0 International License (CC-BY)},
  langid = {english}
}

@book{obe_postgis_2015,
  title = {{{PostGIS}} in Action},
  author = {Obe, Regina O. and Hsu, Leo S.},
  year = {2015},
  edition = {Second},
  publisher = {Manning},
  address = {Shelter Island, NY},
  abstract = {"PostGIS in Action, Second Edition teaches you to solve real-world goedata problems. It first gives you a background in vector-, raster-, and topology-based GIS and then quickly moves into analyzing, viewing, and mapping data. You'll learn how to optimize queries for maximum speed, simplify geometrics for greater efficiency, and create custom functions for your own applications. You'll also learn how to apply your existing GIS knowledge to PostGIS and integrate with other GIS tools. What's Inside: An introduction to spatial databases -- geometry, geography, raster, and topology spatial types, functions, and queries -- Applying PostGIS to real-world problems -- Extending PostGIS to web and desktop applications -- Updated for PostGIS 2.x and PostgreSQL 9.x"--Back cover},
  isbn = {978-1-61729-139-5},
  lccn = {G70.212 .O23 2015},
  keywords = {Database searching,Geographic information systems,nosource},
  annotation = {OCLC: ocn872985108}
}

@article{obrien_interactive_2016,
  title = {Interactive Mapping for Large, Open Demographic Data Sets Using Familiar Geographical Features},
  author = {O'Brien, Oliver and Cheshire, James},
  year = {2016},
  month = aug,
  journal = {Journal of Maps},
  volume = {12},
  number = {4},
  pages = {676--683},
  issn = {null},
  doi = {10/gkb5fm},
  urldate = {2017-05-22},
  abstract = {Ever-increasing numbers of large demographic data sets are becoming available. Many of these data sets are provided as open data, but are in basic repositories where it is incumbent on the user to generate their own visualisations and analysis in order to garner insights. In a bid to facilitate the use and exploration of such data sets, we have created a web mapping platform called DataShine. We link data from the 2011 Census for England and Wales with open geographical data to demonstrate the power and utility of creating a conventional map and combining it with a simple but flexible interface and a highly detailed demographic data set.},
  keywords = {census,Census,choropleth,DataShine,interactive,nosource,Open data,population,Population}
}

@misc{office_for_national_statistics_workplace_2014,
  title = {Workplace {{Zones}}: {{A}} New Geography for Workplace Statistics - {{Datasets}}},
  author = {{Office for National Statistics}},
  year = {2014},
  urldate = {2018-01-13},
  howpublished = {https://data.gov.uk/dataset/workplace-zones-a-new-geography-for-workplace-statistics3},
  keywords = {nosource}
}

@techreport{opengeospatialconsortium_wellknown_2019,
  type = {Implementation {{Standard}}},
  title = {Well-Known Text Representation of Coordinate Reference Systems},
  author = {{Open Geospatial Consortium}},
  year = {2019},
  number = {18-010r7},
  institution = {Open Geospatial Consortium},
  urldate = {2022-01-22},
  copyright = {Copyright {\copyright} 2019 Open Geospatial Consortium To obtain additional rights of use, visit http://www.opengeospatial.org/legal/.},
  langid = {english}
}

@book{openshaw_geocomputation_2000,
  title = {Geocomputation},
  editor = {Openshaw, Stan and Abrahart, Robert J.},
  year = {2000},
  month = may,
  publisher = {CRC Press},
  address = {London ; New York},
  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},
  isbn = {978-0-7484-0900-6},
  langid = {english}
}

@book{orourke_computational_1998,
  title = {Computational {{Geometry}} in {{C}}},
  author = {O'Rourke, Joseph},
  year = {1998},
  month = oct,
  edition = {Second},
  publisher = {Cambridge University Press},
  address = {Cambridge, UK, ; New York, NY, USA},
  abstract = {This is the newly revised and expanded edition of the popular introduction to the design and implementation of geometry algorithms arising in areas such as computer graphics, robotics, and engineering design. The second edition contains material on several new topics, such as randomized algorithms for polygon triangulation, planar point location, 3D convex hull construction, intersection algorithms for ray-segment and ray-triangle, and point-in-polyhedron. A new "Sources" chapter points to supplemental literature for readers needing more information on any topic. A novel aspect is the inclusion of working C code for many of the algorithms, with discussion of practical implementation issues. The self-contained treatment presumes only an elementary knowledge of mathematics, but reaches topics on the frontier of current research, making it a useful reference for practitioners at all levels. The code in this new edition is significantly improved from the first edition, and four new routines are included. Java versions for this new edition are also available. All code is accessible from the book's Web site (http://cs.smith.edu/{\textasciitilde}orourke/) or by anonymous ftp.},
  isbn = {978-0-521-64976-6},
  langid = {english}
}

@article{pebesma_classes_2005,
  title = {Classes and Methods for Spatial Data in {{R}}},
  author = {Pebesma, Edzer and Bivand, Roger},
  year = {2005},
  journal = {R news},
  volume = {5},
  number = {2},
  pages = {9--13},
  keywords = {No DOI found,nosource}
}

@article{pebesma_measurement_2016,
  title = {Measurement {{Units}} in {{R}}},
  author = {Pebesma, Edzer and Mailund, Thomas and Hiebert, James},
  year = {2016},
  month = dec,
  journal = {The R Journal},
  volume = {8},
  number = {2},
  pages = {486--494},
  doi = {10/gkb5pd},
  keywords = {nosource}
}

@article{pebesma_r_2012,
  title = {The {{R}} Software Environment in Reproducible Geoscientific Research},
  author = {Pebesma, Edzer and N{\"u}st, Daniel and Bivand, Roger},
  year = {2012},
  month = apr,
  journal = {Eos, Transactions American Geophysical Union},
  volume = {93},
  number = {16},
  pages = {163--163},
  issn = {2324-9250},
  doi = {10/gd8djc},
  urldate = {2017-10-25},
  abstract = {Reproducibility is an important aspect of scientific research, because the credibility of science is at stake when research is not reproducible. Like science, the development of good, reliable scientific software is a social process. A mature and growing community relies on the R software environment for carrying out geoscientific research. Here we describe why people use R and how it helps in communicating and reproducing research.},
  langid = {english},
  keywords = {0520 Data analysis: algorithms and implementation,0530 Data presentation and visualization,1694 Instruments and techniques,1819 Hydrology: Geographic Information Systems (GIS),1978 Software re-use,nosource,R project,reproducible research}
}

@article{pebesma_simple_2018,
  ids = {pebesma_simple_2018-1},
  title = {Simple Features for {{R}}: {{Standardized}} Support for Spatial Vector Data},
  author = {Pebesma, Edzer},
  year = {2018},
  journal = {The R Journal},
  doi = {10/gf2ztt},
  keywords = {nosource}
}

@book{pebesma_spatial_2022,
  title = {Spatial {{Data Science}} with Applications in {{R}}},
  author = {Pebesma, Edzer and Bivand, Roger},
  year = {2023}
}

@book{pebesma_spatial_2023,
  title = {Spatial {{Data Science}}: {{With Applications}} in {{R}}},
  shorttitle = {Spatial {{Data Science}}},
  author = {Pebesma, Edzer and Bivand, Roger},
  year = {2023},
  publisher = {CRC Press}
}

@manual{pebesma_stars_2021,
  type = {Manual},
  title = {Stars: {{Spatiotemporal}} Arrays, Raster and Vector Data Cubes},
  author = {Pebesma, Edzer},
  year = {2021}
}

@manual{pedersen_gganimate_2020,
  type = {Manual},
  title = {Gganimate: {{A}} Grammar of Animated Graphics},
  author = {Pedersen, Thomas Lin and Robinson, David},
  year = {2020}
}

@article{pereira_r5r_2021,
  title = {R5r: {{Rapid Realistic Routing}} on {{Multimodal Transport Networks}} with {{R}}{\textsuperscript{5}} in {{R}}},
  shorttitle = {R5r},
  author = {Pereira, Rafael H. M. and Saraiva, Marcus and Herszenhut, Daniel and Braga, Carlos Kaue Vieira and Conway, Matthew Wigginton},
  year = {2021},
  month = mar,
  journal = {Findings},
  pages = {21262},
  publisher = {Network Design Lab},
  doi = {10.32866/001c.21262},
  urldate = {2021-03-30},
  abstract = {Routing is a key step in transport planning and research. Nonetheless, researchers and practitioners often face challenges when performing this task due to long computation times and the cost of licensed software. R{\textasciicircum}5{\textasciicircum} is a multimodal transport network router that offers multiple routing features, such as calculating travel times over a time window and returning multiple itineraries for origin/destination pairs. This paper describes r5r, an open-source R package that leverages R{\textasciicircum}5{\textasciicircum} to efficiently compute travel time matrices and generate detailed itineraries between sets of origins and destinations at no expense using seamless parallel computing.},
  langid = {english}
}

@book{perpinan_rastervis_2016,
  title = {{{rasterVis}}},
  author = {Perpi{\~n}{\'a}n, Oscar and Hijmans, Robert},
  year = {2016},
  keywords = {nosource}
}

@article{pezanowski_senseplace3_2018,
  title = {{{SensePlace3}}: A Geovisual Framework to Analyze Place--Time--Attribute Information in Social Media},
  shorttitle = {{{SensePlace3}}},
  author = {Pezanowski, Scott and MacEachren, Alan M and Savelyev, Alexander and Robinson, Anthony C},
  year = {2018},
  month = sep,
  journal = {Cartography and Geographic Information Science},
  volume = {45},
  number = {5},
  pages = {420--437},
  issn = {1523-0406, 1545-0465},
  doi = {10/gc95n9},
  urldate = {2018-09-30},
  abstract = {SensePlace3 (SP3) is a geovisual analytics framework and web application that supports overview + detail analysis of social media, focusing on extracting meaningful information from the Twitterverse. SP3 leverages social media related to crisis events. It differs from most existing systems by enabling an analyst to obtain place-relevant information from tweets that have implicit as well as explicit geography. Specifically, SP3 includes not just the ability to utilize the explicit geography of geolocated tweets but also analyze implicit geography by recognizing and geolocating references in both tweet text, which indicates locations tweeted about, and in Twitter profiles, which indicates locations affiliated with users. Key features of SP3 reported here include flexible search and filtering capabilities to support information foraging; an ingest, processing, and indexing pipeline that produces near real-time access for big streaming data; and a novel strategy for implementing a web-based multi-view visual interface with dynamic linking of entities across views. The SP3 system architecture was designed to support crisis management applications, but its design flexibility makes it easily adaptable to other domains. We also report on a user study that provided input to SP3 interface design and suggests next steps for effective spatiotemporal analytics using social media sources.},
  langid = {english},
  keywords = {nosource}
}

@article{probst_hyperparameters_2018,
  title = {Hyperparameters and {{Tuning Strategies}} for {{Random Forest}}},
  author = {Probst, Philipp and Wright, Marvin and Boulesteix, Anne-Laure},
  year = {2018},
  month = apr,
  journal = {arXiv:1804.03515 [cs, stat]},
  eprint = {1804.03515},
  primaryclass = {cs, stat},
  urldate = {2018-08-02},
  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.},
  archiveprefix = {arxiv},
  keywords = {Computer Science - Machine Learning,No DOI found,nosource,Statistics - Machine Learning}
}

@article{qiu_development_2012,
  title = {The {{Development}} of an {{Areal Interpolation ArcGIS Extension}} and a {{Comparative Study}}},
  author = {Qiu, Fang and Zhang, Caiyun and Zhou, Yuhong},
  year = {2012},
  month = sep,
  journal = {GIScience \& Remote Sensing},
  volume = {49},
  number = {5},
  pages = {644--663},
  issn = {1548-1603},
  doi = {10/gkb5fn},
  urldate = {2017-08-07},
  keywords = {nosource}
}

@book{rcoreteam_introduction_2021,
  title = {An {{Introduction}} to {{R}}},
  author = {{R Core Team}},
  year = {2021},
  abstract = {An Introduction to R is based on the former `Notes on R', gives an introduction to the language and how to use R for doing statistical analysis and graphics.},
  keywords = {nosource}
}

@article{ripley_spatial_2001,
  title = {Spatial {{Statistics}} in {{R}}},
  author = {Ripley, Brian D},
  year = {2001},
  journal = {R News},
  volume = {1},
  number = {2},
  pages = {14--15},
  keywords = {No DOI found,nosource}
}

@book{rodrigue_geography_2013,
  title = {The {{Geography}} of {{Transport Systems}}},
  author = {Rodrigue, Jean-Paul and Comtois, Claude and Slack, Brian},
  year = {2013},
  month = jun,
  edition = {Third},
  publisher = {Routledge},
  address = {London, New York},
  isbn = {978-0-415-82254-1},
  langid = {english}
}

@article{Roussel2020,
  title = {{{lidR}}: {{An}} r Package for Analysis of Airborne Laser Scanning ({{ALS}}) Data},
  author = {Roussel, Jean-Romain and Auty, David and Coops, Nicholas C. and Tompalski, Piotr and Goodbody, Tristan R.H. and Meador, Andrew S{\'a}nchez and Bourdon, Jean-Fran{\c c}ois and {de Boissieu}, Florian and Achim, Alexis},
  year = {2020},
  month = dec,
  journal = {Remote Sensing of Environment},
  volume = {251},
  pages = {112061},
  publisher = {Elsevier BV},
  doi = {10/ghddxb}
}

@inproceedings{rowlingson_rasp:_2003,
  title = {Rasp: {{A Package}} for {{Spatial Statistics}}},
  booktitle = {Proceedings of the 3rd {{International Workshop}} on {{Distributed Statistical Computing}}},
  author = {Rowlingson, Barry and Baddeley, Adrian and Turner, Rolf and Diggle, Peter},
  editor = {Hornik, Kurt},
  year = {2003},
  editors = {Kurt Hornik and Friedrich Leisch and Achim Zeileis},
  keywords = {No DOI found,nosource}
}

@article{rowlingson_splancs_1993,
  title = {Splancs: {{Spatial}} Point Pattern Analysis Code in {{S-plus}}},
  shorttitle = {Splancs},
  author = {Rowlingson, B. S and Diggle, P. J},
  year = {1993},
  month = may,
  journal = {Computers \& Geosciences},
  volume = {19},
  number = {5},
  pages = {627--655},
  issn = {0098-3004},
  doi = {10/dvzffd},
  urldate = {2017-07-20},
  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.},
  keywords = {Epidemiology,Geographical Information Systems,Geomorphology,nosource,Software,Spatial statistics}
}

@book{rowlingson_splancs_2017,
  title = {Splancs: {{Spatial}} and {{Space-Time Point Pattern Analysis}}},
  author = {Rowlingson, Barry and Diggle, Peter},
  year = {2017},
  keywords = {nosource}
}

@article{rs13132428,
  title = {Satellite Image Time Series Analysis for Big Earth Observation Data},
  author = {Simoes, Rolf and Camara, Gilberto and Queiroz, Gilberto and Souza, Felipe and Andrade, Pedro R. and Santos, Lorena and Carvalho, Alexandre and Ferreira, Karine},
  year = {2021},
  journal = {Remote Sensing},
  volume = {13},
  number = {13},
  issn = {2072-4292},
  doi = {10.3390/rs13132428},
  abstract = {The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the world's fast moving agricultural frontiers for the year 2018.},
  article-number = {2428}
}

@article{savric_projection_2016,
  title = {Projection {{Wizard}} -- {{An Online Map Projection Selection Tool}}},
  author = {{\v S}avri{\v c}, Bojan and Jenny, Bernhard and Jenny, Helen},
  year = {2016},
  journal = {The Cartographic Journal},
  volume = {53},
  number = {2},
  pages = {177--185},
  doi = {10/ggsx6z},
  keywords = {nosource}
}

@article{schramm_openeo_2021,
  title = {The Openeo Api--Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities},
  author = {Schramm, Matthias and Pebesma, Edzer and Milenkovi{\'c}, Milutin and Foresta, Luca and Dries, Jeroen and Jacob, Alexander and Wagner, Wolfgang and Mohr, Matthias and Neteler, Markus and Kadunc, Miha and others},
  year = {2021},
  journal = {Remote Sensing},
  volume = {13},
  number = {6},
  pages = {1125},
  publisher = {Multidisciplinary Digital Publishing Institute},
  doi = {10.3390/rs13061125}
}

@article{schratz_hyperparameter_2019,
  title = {Hyperparameter Tuning and Performance Assessment of Statistical and Machine-Learning Algorithms Using Spatial Data},
  author = {Schratz, Patrick and Muenchow, Jannes and Iturritxa, Eugenia and Richter, Jakob and Brenning, Alexander},
  year = {2019},
  month = aug,
  journal = {Ecological Modelling},
  volume = {406},
  pages = {109--120},
  issn = {0304-3800},
  doi = {10.1016/j.ecolmodel.2019.06.002},
  urldate = {2022-02-23},
  abstract = {While the application of machine-learning algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages (such as R or Python), there are several practical challenges in the field of ecological modeling related to unbiased performance estimation. One is the influence of spatial autocorrelation in both hyperparameter tuning and performance estimation. Grouped cross-validation strategies have been proposed in recent years in environmental as well as medical contexts to reduce bias in predictive performance. In this study we show the effects of spatial autocorrelation on hyperparameter tuning and performance estimation by comparing several widely used machine-learning algorithms such as boosted regression trees (BRT), k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM) with traditional parametric algorithms such as logistic regression (GLM) and semi-parametric ones like generalized additive models (GAM) in terms of predictive performance. Spatial and non-spatial cross-validation methods were used to evaluate model performances aiming to obtain bias-reduced performance estimates. A detailed analysis on the sensitivity of hyperparameter tuning when using different resampling methods (spatial/non-spatial) was performed. As a case study the spatial distribution of forest disease (Diplodia sapinea) in the Basque Country (Spain) was investigated using common environmental variables such as temperature, precipitation, soil and lithology as predictors. Random Forest (mean Brier score estimate of 0.166) outperformed all other methods with regard to predictive accuracy. Though the sensitivity to hyperparameter tuning differed between the ML algorithms, there were in most cases no substantial differences between spatial and non-spatial partitioning for hyperparameter tuning. However, spatial hyperparameter tuning maintains consistency with spatial estimation of classifier performance and should be favored over non-spatial hyperparameter optimization. High performance differences (up to 47\%) between the bias-reduced (spatial cross-validation) and overoptimistic (non-spatial cross-validation) cross-validation settings showed the high need to account for the influence of spatial autocorrelation. Overoptimistic performance estimates may lead to false actions in ecological decision making based on biased model predictions.},
  langid = {english},
  keywords = {Hyperparameter tuning,Machine-learning,Spatial autocorrelation,Spatial cross-validation,Spatial modeling}
}

@article{schratz_mlr3spatiotempcv_2021,
  title = {Mlr3spatiotempcv: {{Spatiotemporal}} Resampling Methods for Machine Learning in {{R}}},
  shorttitle = {Mlr3spatiotempcv},
  author = {Schratz, Patrick and Becker, Marc and Lang, Michel and Brenning, Alexander},
  year = {2021},
  journal = {arXiv preprint arXiv:2110.12674},
  eprint = {2110.12674},
  archiveprefix = {arxiv},
  keywords = {No DOI found}
}

@article{schratz_performance_nodate,
  title = {Performance Evaluation and Hyperparameter Tuning of Statistical and Machine-Learning Models Using Spatial Data},
  author = {Schratz, Patrick and Muenchow, J. and Iturritxa, Eugenia and Richter, Jakob and Brenning, A.},
  year = {2018},
  keywords = {Computer Science - Machine Learning,No DOI found,nosource,Statistics - Machine Learning,Statistics - Methodology}
}

@article{shen_classification_2018,
  title = {Classification of Topological Relations between Spatial Objects in Two-Dimensional Space within the Dimensionally Extended 9-Intersection Model},
  author = {Shen, Jingwei and Chen, Min and Liu, Xintao},
  year = {2018},
  journal = {Transactions in GIS},
  volume = {22},
  number = {2},
  pages = {514--541},
  issn = {1467-9671},
  doi = {10/gnhcx9},
  urldate = {2021-11-13},
  abstract = {As an important topological relation model, the dimensionally extended 9-intersection model (DE-9IM) has been widely used as a basis for standards of queries in spatial databases. However, the negative conditions for the specification of the topological relations within the DE-9IM have not been studied. The specification of the topological relations is closely related to the definition of the spatial objects and the topological relation models. The interior, boundary, and exterior of the spatial objects, including the point, line, and region, are defined. Within the framework of the DE-9IM, 43 negative conditions are proposed to eliminate impossible topological relations. Configurations of region/region, region/line, line/line, region/point, line/point, and point/point relations are drawn. The mutual exclusion of the negative conditions is discussed, and the topological relations within the framework of 9IM and DE-9IM are compared. The results show that: (1) impossible topological relations between spatial objects can be eliminated by the application of 43 negative conditions; and (2) 12 relations between two regions, 31 relations between a region and a line, 47 relations between two lines, three relations between a region and a point, three relations between a line and a point, and two relations between two points can be distinguished by the DE-9IM.},
  langid = {english}
}

@book{sherman_desktop_2008,
  title = {Desktop {{GIS}}: {{Mapping}} the {{Planet}} with {{Open Source Tools}}},
  author = {Sherman, Gary},
  year = {2008},
  publisher = {Pragmatic Bookshelf},
  keywords = {nosource}
}

@inproceedings{simoes_rstac_2021,
  title = {Rstac: {{An R}} Package to Access Spatiotemporal Asset Catalog Satellite Imagery},
  booktitle = {2021 {{IEEE}} International Geoscience and Remote Sensing Symposium {{IGARSS}}},
  author = {Simoes, Rolf and Souza, Felipe and Zaglia, Matheus and Queiroz, Gilberto Ribeiro and Santos, Rafael and Ferreira, Karine},
  year = {2021},
  pages = {7674--7677},
  doi = {10.1109/IGARSS47720.2021.9553518}
}

@article{sorensen_calculation_2006,
  title = {On the Calculation of the Topographic Wetness Index: Evaluation of Different Methods Based on Field Observations},
  author = {S{\o}rensen, R and Zinko, U and Seibert, J},
  year = {2006},
  journal = {Hydrology and Earth System Sciences},
  pages = {13},
  doi = {10.5194/hess-10-101-2006},
  abstract = {The topographic wetness index (TWI, ln(a/tan{$\beta$})), which combines local upslope contributing area and slope, is commonly used to quantify topographic control on hydrological processes. Methods of computing this index differ primarily in the way the upslope contributing area is calculated. In this study we compared a number of calculation methods for TWI and evaluated them in terms of their correlation with the following measured variables: vascular plant species richness, soil pH, groundwater level, soil moisture, and a constructed wetness degree. The TWI was calculated by varying six parameters affecting the distribution of accumulated area among downslope cells and by varying the way the slope was calculated. All possible combinations of these parameters were calculated for two separate boreal forest sites in northern Sweden. We did not find a calculation method that performed best for all measured variables; rather the best methods seemed to be variable and site specific. However, we were able to identify some general characteristics of the best methods for different groups of measured variables. The results provide guiding principles for choosing the best method for estimating species richness, soil pH, groundwater level, and soil moisture by the TWI derived from digital elevation models.},
  langid = {english}
}

@book{spanier_algebraic_1995,
  title = {Algebraic Topology},
  author = {Spanier, Edwin Henry},
  year = {1995},
  edition = {1. corr. Springer ed},
  publisher = {Springer},
  address = {New York Berlin Barcelona Budapest},
  isbn = {978-0-387-94426-5 978-3-540-90646-9 978-0-387-90646-1}
}

@book{talbert_ancient_2014,
  title = {Ancient {{Perspectives}}: {{Maps}} and {{Their Place}} in {{Mesopotamia}}, {{Egypt}}, {{Greece}}, and {{Rome}}},
  shorttitle = {Ancient {{Perspectives}}},
  author = {Talbert, Richard J. A.},
  year = {2014},
  month = feb,
  publisher = {University of Chicago Press},
  abstract = {Ancient Perspectives encompasses a vast arc of space and time---Western Asia to North Africa and Europe from the third millennium BCE to the fifth century CE---to explore mapmaking and worldviews in the ancient civilizations of Mesopotamia, Egypt, Greece, and Rome. In each society, maps served as critical economic, political, and personal tools, but there was little consistency in how and why they were made. Much like today, maps in antiquity meant very different things to different people. Ancient Perspectives presents an ambitious, fresh overview of cartography and its uses. The seven chapters range from broad-based analyses of mapping in Mesopotamia and Egypt to a close focus on Ptolemy's ideas for drawing a world map based on the theories of his Greek predecessors at Alexandria. The remarkable accuracy of Mesopotamian city-plans is revealed, as is the creation of maps by Romans to support the proud claim that their emperor's rule was global in its reach. By probing the instruments and techniques of both Greek and Roman surveyors, one chapter seeks to uncover how their extraordinary planning of roads, aqueducts, and tunnels was achieved. Even though none of these civilizations devised the means to measure time or distance with precision, they still conceptualized their surroundings, natural and man-made, near and far, and felt the urge to record them by inventive means that this absorbing volume reinterprets and compares.},
  googlebooks = {srTbAgAAQBAJ},
  isbn = {978-0-226-78940-8},
  langid = {english},
  keywords = {History / Ancient / Egypt,History / Ancient / Greece,History / Ancient / Rome,History / Asia / Central Asia,History / General,Science / Earth Sciences / Geography,Technology & Engineering / Cartography}
}

@article{tallon_bristol_2007,
  title = {Bristol},
  author = {Tallon, Andrew R.},
  year = {2007},
  month = feb,
  journal = {Cities},
  volume = {24},
  number = {1},
  pages = {74--88},
  issn = {02642751},
  doi = {10/dmr8rv},
  urldate = {2018-01-03},
  langid = {english},
  keywords = {nosource}
}

@book{tennekes_elegant_2022,
  title = {Elegant and Informative Maps with Tmap},
  author = {Tennekes, Martijn and Nowosad, Jakub},
  year = {2022}
}

@article{tennekes_tmap_2018,
  title = {Tmap: {{Thematic Maps}} in {{R}}},
  author = {Tennekes, Martijn},
  year = {2018},
  journal = {Journal of Statistical Software, Articles},
  volume = {84},
  number = {6},
  pages = {1--39},
  issn = {1548-7660},
  doi = {10/gfdd6z},
  abstract = {Thematic maps show spatial distributions. The theme refers to the phenomena that is shown, which is often demographical, social, cultural, or economic. The best known thematic map type is the choropleth, in which regions are colored according to the distribution of a data variable. The R package tmap offers a coherent plotting system for thematic maps that is based on the layered grammar of graphics. Thematic maps are created by stacking layers, where per layer, data can be mapped to one or more aesthetics. It is also possible to generate small multiples. Thematic maps can be further embellished by configuring the map layout and by adding map attributes, such as a scale bar and a compass. Besides plotting thematic maps on the graphics device, they can also be made interactive as an HTML widget. In addition, the R package tmaptools contains several convenient functions for reading and processing spatial data.},
  keywords = {nosource,R,spatial data,thematic maps}
}

@article{theeconomist_autonomous_2016,
  title = {The Autonomous Car's Reality Check},
  author = {{The Economist}},
  year = {2016},
  journal = {The Economist},
  issn = {0013-0613},
  urldate = {2018-05-11},
  abstract = {Building highly detailed maps for robotic vehicles},
  keywords = {nosource}
}

@article{thiele_r_2014,
  title = {R {{Marries NetLogo}}: {{Introduction}} to the {{RNetLogo Package}}},
  author = {Thiele, J},
  year = {2014},
  journal = {Journal of Statistical Software},
  volume = {58},
  number = {2},
  pages = {1--41},
  doi = {10/ghfbck},
  keywords = {nosource}
}

@article{tobler_computer_1970,
  title = {A Computer Movie Simulating Urban Growth in the {{Detroit}} Region},
  author = {Tobler, Waldo R},
  year = {1970},
  journal = {Economic geography},
  pages = {234--240},
  issn = {0013-0095},
  doi = {10.2307/143141}
}

@article{tobler_smooth_1979,
  title = {Smooth {{Pycnophylactic Interpolation}} for {{Geographical Regions}}},
  author = {Tobler, Waldo R.},
  year = {1979},
  month = sep,
  journal = {Journal of the American Statistical Association},
  volume = {74},
  number = {367},
  pages = {519--530},
  issn = {0162-1459, 1537-274X},
  doi = {10/ghz78f},
  urldate = {2017-08-07},
  langid = {english},
  keywords = {nosource}
}

@article{tomintz_geography_2008,
  title = {The Geography of Smoking in {{Leeds}}: Estimating Individual Smoking Rates and the Implications for the Location of Stop Smoking Services},
  author = {Tomintz, Melanie N M.N. and Clarke, Graham P and Rigby, Janette E J.E.},
  year = {2008},
  journal = {Area},
  volume = {40},
  number = {3},
  pages = {341--353},
  doi = {10/dn8x5b},
  keywords = {geography of smoking,health geography,location-allocation,microsimulation,modelling,nosource,stop smoking services}
}

@book{tomlin_geographic_1990,
  title = {Geographic Information Systems and Cartographic Modeling},
  author = {Tomlin, C. Dana},
  year = {1990},
  publisher = {Prentice Hall},
  address = {Englewood Cliffs, N.J},
  isbn = {978-0-13-350927-4},
  lccn = {G70.2 .T64 1990},
  keywords = {Cartography,Data processing,Geographic information systems,nosource}
}

@article{tomlin_map_1994,
  title = {Map Algebra: One Perspective},
  shorttitle = {Map Algebra},
  author = {Tomlin, C. Dana},
  year = {1994},
  journal = {Landscape and Urban Planning},
  volume = {30},
  number = {1-2},
  pages = {3--12},
  publisher = {Elsevier},
  doi = {10/dm2qm2}
}

@book{usgs_geological_2016,
  title = {U.{{S}}. {{Geological Survey}} ({{USGS}}) {{Earth Resources Observation}} and {{Science}} ({{EROS}}) {{Center}}},
  author = {{USGS}},
  year = {2016},
  keywords = {nosource}
}

@book{venables_modern_2002,
  title = {Modern {{Applied Statistics}} with {{S}}},
  author = {Venables, W. N. and Ripley, B. D.},
  year = {2002},
  edition = {Fourth},
  publisher = {Springer},
  address = {New York},
  keywords = {nosource}
}

@article{visvalingam_line_1993,
  title = {Line Generalisation by Repeated Elimination of Points},
  author = {Visvalingam, M. and Whyatt, J. D.},
  year = {1993},
  month = jun,
  journal = {The Cartographic Journal},
  volume = {30},
  number = {1},
  pages = {46--51},
  issn = {0008-7041, 1743-2774},
  doi = {10/fx74gh},
  urldate = {2018-01-03},
  langid = {english},
  keywords = {nosource}
}

@article{vonwehrden_pluralism_2009,
  title = {Pluralism and Diversity: Trends in the Use and Application of Ordination Methods 1990-2007},
  shorttitle = {Pluralism and Diversity},
  author = {{von Wehrden}, Henrik and Hanspach, Jan and Bruelheide, Helge and Wesche, Karsten},
  year = {2009},
  month = aug,
  journal = {Journal of Vegetation Science},
  volume = {20},
  number = {4},
  pages = {695--705},
  issn = {11009233, 16541103},
  doi = {10/ffp89h},
  urldate = {2018-07-25},
  langid = {english},
  keywords = {nosource}
}

@article{waldykowski_sustainable_2021,
  title = {Sustainable {{Urban Transport}}---{{Why}} a {{Fast Investment}} in a {{Complete Cycling Network Is Most Profitable}} for a {{City}}},
  author = {Wa{\l}dykowski, Piotr and Adamczyk, Joanna and Dorotkiewicz, Maciej},
  year = {2021},
  month = dec,
  journal = {Sustainability},
  volume = {14},
  pages = {119},
  doi = {10.3390/su14010119},
  abstract = {The development of safe cycling as a mode of transport is an important objective of the transformation towards sustainable mobility in European cities. A significant number of European cities are faced with the need to implement the assumptions of the European Green Deal, of which the promotion of sustainable urban transport is a part. The article presented a simulation of the Perfect Cycling City Model in real conditions that inspired the design of two scenarios for the fast development of bicycle routes in a key transport network area in Warsaw. Scenario 1 assumes building subsidiary bicycle routes and links between the main routes. In Scenario 2, the development of all optimal cycling links at the local level is assumed. An increase in cycling participation is expected in both scenarios. The comparison of projected costs of each scenario indicated that building a complete network of connections is more profitable in terms of increased cycling participation and could counter the dominance of private car use. For this to happen, measures encouraging individuals combined with improved safety and convenience of cycling around the city must be undertaken.}
}

@book{walker_analyzing_2022,
  title = {Analyzing {{US Census Data}}: {{Methods}}, {{Maps}}, and {{Models}} in {{R}}},
  author = {Walker, Kyle E.},
  year = {2022},
  publisher = {{Chapman and Hall/CRC}}
}

@article{wardrop_theoretical_1952,
  title = {Some Theoretical Aspects of Road Traffic Research},
  author = {Wardrop, J G},
  year = {1952},
  month = may,
  journal = {Proceedings of the Institution of Civil Engineers},
  volume = {1},
  number = {3},
  pages = {325--362},
  publisher = {ICE Publishing},
  doi = {10.1680/ipeds.1952.11259},
  urldate = {2023-11-29},
  keywords = {ALTERNATIVE,BEHAVIOUR,CAPACITY,DISTRIBUTION,FREQUENCY,GREENFORD,INTERSECTIONS,JOURNEYS,MIDDLESEX,OVERTAKING,QUEUES,RESEARCH,ROADS,ROUTES,SIGNALS,SPEED,THEORETICAL,TIME,TRAFFIC,UK,WESTERN AVENUE},
  annotation = {1619 citations (Crossref) [2023-11-29]}
}

@book{wegmann_remote_2016,
  title = {Remote Sensing and {{GIS}} for Ecologists: Using Open Source Software},
  shorttitle = {Remote Sensing and {{GIS}} for Ecologists},
  editor = {Wegmann, Martin and Leutner, Benjamin and Dech, Stefan},
  year = {2016},
  series = {Data in the Wild},
  publisher = {Pelagic Publishing},
  address = {Exeter},
  isbn = {978-1-78427-022-3 978-1-78427-023-0 978-1-78427-024-7 978-1-78427-025-4 978-1-78427-028-5},
  langid = {english},
  keywords = {nosource},
  annotation = {OCLC: 945979372}
}

@book{wickham_advanced_2019,
  title = {Advanced {{R}}, {{Second Edition}}},
  author = {Wickham, Hadley},
  year = {2019},
  month = may,
  publisher = {CRC Press},
  abstract = {Advanced R helps you understand how R works at a fundamental level. It is designed for R programmers who want to deepen their understanding of the language, and programmers experienced in other languages who want to understand what makes R different and special.   This book will teach you the foundations of R; three fundamental programming paradigms (functional, object-oriented, and metaprogramming); and powerful techniques for debugging and optimisingyour code.By reading this book, you will learn:    The difference between an object and its name, and why the distinction is important   The important vector data structures, how they fit together, and how you can pull them apart using subsetting    The fine details of functions and environments    The condition system, which powers messages, warnings, and errors   The powerful functional programming paradigm, which can replace many for loops   The three most important OO systems: S3, S4, and R6   The tidy eval toolkit for metaprogramming, which allows you to manipulate code and control evaluation   Effective debugging techniques that you can deploy, regardless of how your code is run   How to find and remove performance bottlenecks   The second edition is a comprehensive update:    New foundational chapters: "Names and values," "Control flow," and "Conditions"    comprehensive coverage of object oriented programming with chapters on S3, S4, R6, and how to choose between them   Much deeper coverage of metaprogramming, including the new tidy evaluation framework   use of new package like rlang (http://rlang.r-lib.org), which provides a clean interface to low-level operations, and purr (http://purrr.tidyverse.org/) for functional programming   Use of color in code chunks and figuresHadley Wickham is Chief Scientist at RStudio, an Adjunct Professor at Stanford University and the University of Auckland, and a member of the R Foundation. He is the lead developer of the tidyverse, a collection of R packages, including ggplot2 and dplyr, designed to support data science. He is also the author of R for Data Science (with Garrett Grolemund), R Packages, and ggplot2: Elegant Graphics for Data Analysis.},
  googlebooks = {JAOaDwAAQBAJ},
  isbn = {978-1-351-20129-2},
  langid = {english},
  keywords = {Mathematics / Probability & Statistics / General,Reference / General}
}

@book{wickham_ggplot2_2016,
  title = {Ggplot2: {{Elegant Graphics}} for {{Data Analysis}}},
  shorttitle = {Ggplot2},
  author = {Wickham, Hadley},
  year = {2016},
  month = jun,
  edition = {Second},
  publisher = {Springer},
  address = {New York, NY},
  abstract = {This new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: produce handsome, publication-quality plots with automatic legends created from the plot specification superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression save any ggplot2 plot (or part thereof) for later modification or reuse create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.},
  isbn = {978-3-319-24275-0},
  langid = {english}
}

@book{wickham_mastering_2021,
  title = {Mastering {{Shiny}}: {{Build Interactive Apps}}, {{Reports}}, and {{Dashboards Powered}} by {{R}}},
  shorttitle = {Mastering {{Shiny}}},
  author = {Wickham, Hadley},
  year = {2021},
  month = may,
  publisher = {O'Reilly Media},
  address = {Sebastopol, CA},
  abstract = {Master the Shiny web framework-and take your R skills to a whole new level. By letting you move beyond static reports, Shiny helps you create fully interactive web apps for data analyses. Users will be able to jump between datasets, explore different subsets or facets of the data, run models with parameter values of their choosing, customize visualizations, and much more. Hadley Wickham from RStudio shows data scientists, data analysts, statisticians, and scientific researchers with no knowledge of HTML, CSS, or JavaScript how to create rich web apps from R. This in-depth guide provides a learning path that you can follow with confidence, as you go from a Shiny beginner to an expert developer who can write large, complex apps that are maintainable and performant. Get started: Discover how the major pieces of a Shiny app fit together Put Shiny in action: Explore Shiny functionality with a focus on code samples, example apps, and useful techniques Master reactivity: Go deep into the theory and practice of reactive programming and examine reactive graph components Apply best practices: Examine useful techniques for making your Shiny apps work well in production},
  isbn = {978-1-4920-4738-4},
  langid = {english}
}

@article{wickham_tidy_2014,
  title = {Tidy {{Data}}},
  author = {Wickham, Hadley},
  year = {2014},
  journal = {Journal of Statistical Software},
  volume = {59},
  number = {10},
  issn = {1548-7660},
  doi = {10/gdm3p7},
  urldate = {2018-08-20},
  langid = {english},
  keywords = {nosource}
}

@article{wieland_market_2017,
  title = {Market {{Area Analysis}} for {{Retail}} and {{Service Locations}} with {{MCI}}},
  author = {Wieland, Thomas},
  year = {2017},
  journal = {The R Journal},
  volume = {9},
  number = {1},
  pages = {298--323},
  doi = {10/gkb5ft},
  keywords = {nosource}
}

@book{wilkinson_grammar_2005,
  title = {The Grammar of Graphics},
  author = {Wilkinson, Leland and Wills, Graham},
  year = {2005},
  publisher = {Springer Science+ Business Media},
  keywords = {nosource}
}

@book{wimberly_geographic_2023,
  title = {Geographic {{Data Science}} with {{R}}: {{Visualizing}} and {{Analyzing Environmental Change}}},
  shorttitle = {Geographic {{Data Science}} with {{R}}},
  author = {Wimberly, Michael C.},
  year = {2023},
  urldate = {2023-05-06},
  abstract = {A book example for a Chapman \& Hall book.}
}

@book{wise_gis_2001,
  title = {{{GIS}} Basics},
  author = {Wise, Stephen},
  year = {2001},
  publisher = {CRC Press},
  keywords = {nosource}
}

@book{wood_java_2002,
  title = {Java Programming for Spatial Sciences},
  author = {Wood, Jo},
  year = {2002},
  publisher = {Taylor \& Francis},
  address = {London ; New York},
  isbn = {978-0-415-26097-8 978-0-415-26098-5},
  lccn = {QA76.73.J38 W6615 2002},
  keywords = {Geographic information systems,Java (Computer program language),nosource}
}

@article{wright_ranger_2017,
  title = {Ranger: {{A Fast Implementation}} of {{Random Forests}} for {{High Dimensional Data}} in {{C}}++ and {{R}}},
  author = {Wright, Marvin N. and Ziegler, Andreas},
  year = {2017},
  journal = {Journal of Statistical Software},
  volume = {77},
  number = {1},
  pages = {1--17},
  doi = {10.18637/jss.v077.i01}
}

@book{wulf_invention_2015,
  title = {The Invention of Nature: {{Alexander}} von {{Humboldt}}'s New World},
  shorttitle = {The Invention of Nature},
  author = {Wulf, Andrea},
  year = {2015},
  publisher = {Alfred A. Knopf},
  address = {New York},
  isbn = {978-0-385-35066-2 978-0-345-80629-1},
  lccn = {Q143.H9 W85 2015},
  keywords = {Germany,Humboldt Alexander von,Naturalists,nosource,Scientists}
}

@book{xiao_gis_2016,
  title = {{{GIS Algorithms}}: {{Theory}} and {{Applications}} for {{Geographic Information Science}} \& {{Technology}}},
  shorttitle = {{{GIS Algorithms}}},
  author = {Xiao, Ningchuan},
  year = {2016},
  address = {London},
  doi = {10.4135/9781473921498},
  urldate = {2018-05-07},
  abstract = {Geographic information systems (GIS) have become increasingly important in helping us understand complex social, economic, and natural dynamics where spatial components play a key role. The critical algorithms used in GIS, however, are notoriously difficult to both teach and understand, in part due to the lack of a coherent representation. GIS Algorithms attempts to address this problem by combining rigorous formal language with example case studies and student exercises. Using Python code throughout, Xiao breaks the subject down into three fundamental areas: {$\quad\bullet\quad$}Geometric Algorithms {$\quad\bullet\quad$}Spatial Indexing {$\quad\bullet\quad$}Spatial Analysis and Modelling With its comprehensive coverage of the many algorithms involved, GIS Algorithms is a key new textbook in this complex and critical area of geography.},
  keywords = {nosource}
}

@book{xie_evolving_2011,
  title = {Evolving {{Transportation Networks}}},
  author = {Xie, Feng and Levinson, David},
  year = {2011},
  series = {Transportation {{Research}}, {{Economics}} and {{Policy}}},
  publisher = {Springer-Verlag},
  address = {New York},
  urldate = {2019-07-04},
  abstract = {Over the last two centuries, the development of modern transportation has significantly transformed human life. The main theme of this book is to understand the complexity of transportation development and model the process of network growth including its determining factors, which may be topological, morphological, temporal, technological, economic, managerial, social or political. Using multidimensional concepts and methods, the authors develop a holistic framework to represent network growth as an open and complex process with models that demonstrate in a scientific way how numerous independent decisions made by entities such as travelers, property owners, developers, and public jurisdictions could result in a coherent network of facilities on the ground. Models are proposed from innovative perspectives including self-organization, degeneration, and sequential connection to interpret the evolutionary growth of transportation networks in explicit consideration of independent economic and regulatory initiatives. Employing these models, the authors survey a series of topics ranging from network hierarchy and topology to first mover advantage. The authors demonstrate, with a wide spectrum of empirical and theoretical evidence, that network growth follows a path that is not only logical in retrospect, but also predictable and manageable from a planning perspective. In the larger scheme of innovative transportation planning, this book provides a re-consideration of conventional planning practice and sets the stage for further development on the theory and practice of the next-generation, evolutionary planning approach in transportation, making it of interest to scholars and practitioners alike in the field of transportation.},
  isbn = {978-1-4419-9803-3},
  langid = {english},
  keywords = {nosource}
}

@book{zuur_beginners_2017,
  title = {Beginner's Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with {{R-INLA}}},
  author = {Zuur, Alain F. and Ieno, Elena N. and Saveliev, Anatoly A. and Zuur, Alain F.},
  year = {2017},
  volume = {1},
  publisher = {Highland Statistics Ltd},
  address = {Newburgh, United Kingdom},
  isbn = {978-0-9571741-9-1},
  langid = {english},
  keywords = {nosource},
  annotation = {OCLC: 993615802}
}

@book{zuur_mixed_2009,
  title = {Mixed Effects Models and Extensions in Ecology with {{R}}},
  author = {Zuur, Alain and Ieno, Elena N. and Walker, Neil and Saveliev, Anatoly A. and Smith, Graham M.},
  year = {2009},
  series = {Statistics for {{Biology}} and {{Health}}},
  publisher = {Springer-Verlag},
  address = {New York},
  urldate = {2018-02-07},
  isbn = {978-0-387-87457-9},
  langid = {english},
  keywords = {nosource}
}