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geocompr.bib
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@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},
publisher = {R package},
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 = {Applied {{Machine Learning Using}} Mlr3 in \{\vphantom\}{{R}}\vphantom\{\}},
editor = {Bischl, Bernd and Sonabend, R. and Kotthoff, Lars and Lang, Michel},
year = {2024},
publisher = {CRC Press}
}
@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}
}
@book{bischl_applied_2024,
title = {Applied {{Machine Learning Using}} Mlr3 in {{R}}},
author = {Bischl, Bernd and Sonabend, Raphael and Kotthoff, Lars and Lang, Michel},
year = {2024},
month = jan,
publisher = {CRC Press},
abstract = {mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components.Features: In-depth coverage of the mlr3 ecosystem for users and developers Explanation and illustration of basic and advanced machine learning concepts Ready to use code samples that can be adapted by the user for their application Convenient and expressive machine learning pipelining enabling advanced modelling Coverage of topics that are often ignored in other machine learning books The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning.},
googlebooks = {5wrsEAAAQBAJ},
isbn = {978-1-00-383057-3},
langid = {english},
keywords = {Computers / Artificial Intelligence / General,Computers / Data Science / Machine Learning,Computers / Mathematical & Statistical Software,Mathematics / Probability & Statistics / General,Technology & Engineering / Automation,Technology & Engineering / Environmental / 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},
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},
publisher = {R package},
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},
publisher = {R package},
keywords = {nosource}
}
@book{bivand_spdep_2017,
title = {Spdep: {{Spatial Dependence}}: {{Weighting Schemes}}, {{Statistics}} and {{Models}}},
author = {Bivand, Roger},
year = {2017},
publisher = {R package},
keywords = {nosource}
}
@book{bivand_spgrass6_2016,
title = {Spgrass6: {{Interface}} between {{GRASS}} 6 and {{R}}},
author = {Bivand, Roger},
year = {2016},
publisher = {R package},
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 = {{{RPyGeo}}: {{ArcGIS Geoprocessing}} in {{R}} via {{Python}}},
author = {Brenning, Alexander},
year = {2012},
publisher = {R package},
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.}
}
@book{cooley_sfheaders_2020,
title = {Sfheaders: {{Converts}} between {{R}} Objects and Simple Feature Objects},
author = {Cooley, David},
year = {2020},
publisher = {R package}
}
@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}
}
@book{dunnington_ggspatial_2021,
title = {{{ggspatial}}: Spatial Data Framework for {{ggplot2}}},
author = {Dunnington, Dewey},
year = {2021},
publisher = {R package}
}
@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}
}
@book{giraud_mapsf_2021,
title = {Mapsf: {{Thematic}} Cartography},
author = {Giraud, Timoth{\'e}e},
year = {2021},
publisher = {R package}
}
@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}
}
@article{gold_outsidein_1996,
title = {Outside-in: An Alternative Approach to Forest Map Digitizing},
shorttitle = {Outside-In},
author = {Gold, C. M. and Nantel, J. and Yang, W.},
year = {1996},
month = apr,
journal = {International Journal of Geographical Information Science},
publisher = {Taylor \& Francis Group},
doi = {10.1080/02693799608902080},
urldate = {2024-05-04},
abstract = {Abstract. This paper examines the problem of polygon digitizing, and suggests an inversion of the traditional approach for polygons of the environmental type, where each individual polygon, rather ...},
copyright = {Copyright Taylor and Francis Group, LLC},
langid = {english},
annotation = {21 citations (Crossref) [2024-05-04]}
}
@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},
volume = {4},
number = {4},
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},
publisher = {R package},
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}
}