Geospatial

Find, download, convert and read Open Street Map data extracts obtained from several providers.

Bespoke images of OpenStreetMap (OSM) data and data visualisation using OSM objects.

Geocode with the OpenCage API, either from place name to longitude and latitude (forward geocoding) or from longitude and latitude to the name and address of a location (reverse geocoding), see https://opencagedata.com.

1. Cano, J., Rodríguez, A., Simpson, H., Tabah, E. N., Gómez, J. F., & Pullan, R. L. (2018). Modelling the spatial distribution of aquatic insects (Order Hemiptera) potentially involved in the transmission of Mycobacterium ulcerans in Africa. Parasites & Vectors, 11(1). http://doi.org/10.1186/s13071-018-3066-3
2. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
3. Deribe, K., Simpson, H., Pullan, R. L., Bosco, M. J., Wanji, S., Weaver, N. D., … Cano, J. (2020). Predicting the Environmental Suitability and Population at Risk of Podoconiosis in Africa. https://doi.org/10.1101/2020.03.04.977827

Provides neutral landscape models (doi:10.1007/BF02275262, http://sci-hub.tw/10.1007/bf02275262).
Neutral landscape models range from “hard” neutral models (completely random distributed), to “soft” neutral models (definable spatial characteristics) and generate landscape patterns that are independent of ecological processes. Thus, these patterns can be used as null models in landscape ecology. nlmr combines a large number of algorithms from other published software for simulating neutral landscapes. The simulation results are obtained in a geospatial data format (raster* objects from the raster package) and can, therefore, be used in any sort of raster data operation that is performed with standard observation data.

1. Langhammer, M., Thober, J., Lange, M., Frank, K., & Grimm, V. (2019). Agricultural landscape generators for simulation models: A review of existing solutions and an outline of future directions. Ecological Modelling, 393, 135–151. https://doi.org/10.1016/j.ecolmodel.2018.12.010
2. Fletcher, R., & Fortin, M.-J. (2018). Land-Cover Pattern and Change. Spatial Ecology and Conservation Modeling, 55–100. https://doi.org/10.1007/978-3-030-01989-1_3
3. Harris, M. (2019). KLRfome - Kernel Logistic Regression on Focal Mean Embeddings. Journal of Open Source Software, 4(35), 722. https://doi.org/10.21105/joss.00722
4. Etherington, T., & Omondiagbe, O. (2019). virtualNicheR: generating virtual fundamental and realised niches for use in virtual ecology experiments. Journal of Open Source Software, 4(41), 1661. https://doi.org/10.21105/joss.01661
5. Betts, M. G., Wolf, C., Pfeifer, M., Banks-Leite, C., Arroyo-Rodríguez, V., Ribeiro, D. B., … Ewers, R. M. (2019). Extinction filters mediate the global effects of habitat fragmentation on animals. Science, 366(6470), 1236–1239. https://doi.org/10.1126/science.aax9387
6. Scherer, C., Radchuk, V., Franz, M., Thulke, H., Lange, M., Grimm, V., & Kramer‐Schadt, S. (2020). Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes. Oikos. https://doi.org/10.1111/oik.07002
7. Silva, I., Crane, M., Marshall, B. M., & Strine, C. T. (2020). Revisiting reptile home ranges: moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. https://doi.org/10.1101/2020.02.10.941278
8. Dupont, G., Royle, J. A., Nawaz, M. A., & Sutherland, C. (2020). Optimal sampling design for spatial capture-recapture. https://doi.org/10.1101/2020.04.16.045740
9. Kuempel, C. D., Frazier, M., Nash, K. L., Jacobsen, N. S., Williams, D. R., Blanchard, J. L., … Halpern, B. S. (2020). Integrating Life Cycle and Impact Assessments to Map Food’s Cumulative Environmental Footprint. One Earth, 3(1), 65–78. https://doi.org/10.1016/j.oneear.2020.06.014
10. Silva, I., Crane, M., Marshall, B. M., & Strine, C. T. (2020). Reptiles on the wrong track? Moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. Movement Ecology, 8(1). https://doi.org/10.1186/s40462-020-00229-3
11. Braziunas, K. H., Seidl, R., Rammer, W., & Turner, M. G. (2020). Can we manage a future with more fire? Effectiveness of defensible space treatment depends on housing amount and configuration. Landscape Ecology, 36(2), 309–330. https://doi.org/10.1007/s10980-020-01162-x

Download and import of OpenStreetMap (OSM) data as sf or sp objects. OSM data are extracted from the Overpass web server (https://overpass-api.de/) and processed with very fast C++ routines for return to R.

1. Hawker, L., Rougier, J., Neal, J., Bates, P., Archer, L., & Yamazaki, D. (2018). Implications of Simulating Global Digital Elevation Models for Flood Inundation Studies. Water Resources Research. https://doi.org/10.1029/2018wr023279
2. Briz-Redón, Á. (2019). SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes, 5. https://doi.org/10.3897/rio.5.e33521
3. Morelle, K., Jezek, M., Licoppe, A., & Podgorski, T. (2019). Deathbed choice by ASF‐infected wild boar can help find carcasses. Transboundary and Emerging Diseases. https://doi.org/10.1111/tbed.13267
4. Lara-Lizardi, F., Hoyos-Padilla, M., Hearn, A., Klimley, A. P., Galván-Magaña, F., Arauz, R., … Ketchum, J. T. (2020). Shark movements in the Revillagigedo Archipelago and connectivity with the Eastern Tropical Pacific. https://doi.org/10.1101/2020.03.02.972844
5. Borgoni, R., Gilardi, A., & Zappa, D. (2020). Assessing the Risk of Car Crashes in Road Networks. Social Indicators Research. https://doi.org/10.1007/s11205-020-02295-x
6. Dunnett, S., Sorichetta, A., Taylor, G., & Eigenbrod, F. (2020). Harmonised global datasets of wind and solar farm locations and power. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0469-8
7. Baddeley, A., Nair, G., Rakshit, S., McSwiggan, G., & Davies, T. M. (2020). Analysing point patterns on networks — A review. Spatial Statistics, 100435. https://doi.org/10.1016/j.spasta.2020.100435
8. Cervigni, E., Renton, M., Haslam McKenzie, F., Hickling, S., & Olaru, D. (2020). Describing and mapping diversity and accessibility of the urban food environment with open data and tools. Applied Geography, 125, 102352. doi:10.1016/j.apgeog.2020.102352
9. Padgham, M., Lovelace, R., Salmon, M., & Rudis, B. (2017). osmdata. The Journal of Open Source Software, 2(14), 305. https://doi.org/10.21105/joss.00305

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2020) doi:10.1111/2041-210X.13215). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks In addition, the randomizations function provides data-stream randomization methods suitable for GPS data.

1. Robitaille, A. L., Webber, Q. M. R., & Vander Wal, E. (2018). Conducting social network analysis with animal telemetry data: applications and methods using spatsoc. https://doi.org/10.1101/447284
2. Webber, Q. M. R., & Vander Wal, E. (2019). Trends and perspectives on the use of animal social network analysis in behavioural ecology: a bibliometric approach. Animal Behaviour, 149, 77–87. https://doi.org/10.1016/j.anbehav.2019.01.010
3. Peignier, M., Webber, Q. M. R., Koen, E. L., Laforge, M. P., Robitaille, A. L., & Vander Wal, E. (2019). Space use and social association in a gregarious ungulate: Testing the conspecific attraction and resource dispersion hypotheses. Ecology and Evolution. https://doi.org/10.1002/ece3.5071
4. Gilbertson, M. L. J., White, L. A., & Craft, M. E. (2020). Trade‐offs with telemetry‐derived contact networks for infectious disease studies in wildlife. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13355
5. Robitaille, A. L., Webber, Q. M. R., Turner, J. W., & Wal Eric, V. (2020). The problem and promise of scale in multilayer animal social networks. Current Zoology. https://doi.org/10.1093/cz/zoaa052

Setup and connect to OpenTripPlanner (OTP) http://www.opentripplanner.org/. OTP is an open source platform for multi-modal and multi-agency journey planning written in Java. The package allows you to manage a local version or connect to remote OTP server to find walking, cycling, driving, or transit routes. This package has been peer-reviewed by rOpenSci (v. 0.2.0.0).

Creates geographic map tiles from geospatial map files or non-geographic map tiles from simple image files. This package provides a tile generator function for creating map tile sets for use with packages such as leaflet. In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images. These map tiles, which have a non-geographic, simple coordinate reference system (CRS), can also be used with leaflet when applying the simple CRS option. Map tiles can be created from an input file with any of the following extensions: tif, grd and nc for spatial maps and png, jpg and bmp for basic images. This package requires Python and the gdal library for Python. Windows users are recommended to install OSGeo4W (https://trac.osgeo.org/osgeo4w/) as an easy way to obtain the required gdal support for Python.

Parse geographic coordinates from various formats to decimal degree numeric values. Parse coordinates into their parts (degree, minutes, seconds); calculate hemisphere from coordinates; pull out individually degrees, minutes, or seconds; add and subtract degrees, minutes, and seconds. C++ code herein originally inspired from code written by Jeffrey D. Bogan, but then completely re-written.

Data aggregation via moving window or direct methods. Aggregate a fine-resolution raster to a grid. The moving window method smooths the surface using a specified function within a moving window of a specified size and shape prior to aggregation. The direct method simply aggregates to the grid using the specified function.

1. Robitaille, A. L., Webber, Q. M. R., Turner, J. W., & Wal Eric, V. (2020). The problem and promise of scale in multilayer animal social networks. Current Zoology. https://doi.org/10.1093/cz/zoaa052

Convert WKT to GeoJSON and GeoJSON to WKT. Functions included for converting between GeoJSON to WKT, creating both GeoJSON features, and non-features, creating WKT from R objects (e.g., lists, data.frames, vectors), and linting WKT.

Convert data to GeoJSON or TopoJSON from various R classes, including vectors, lists, data frames, shape files, and spatial classes. geojsonio does not aim to replace packages like sp, rgdal, rgeos, but rather aims to be a high level client to simplify conversions of data from and to GeoJSON and TopoJSON.

1. von Schmidt, A., Cyganski, R., & Heinrichs, M. 2019. Web-based Visualization of Daily Mobility Patterns in R. International Journal on Advances in Internet Technology, vol 12 (3 & 4). https://elib.dlr.de/133599/1/inttech_v12_n34_2019_2.pdf
2. Ranghetti, L., Boschetti, M., Nutini, F., & Busetto, L. (2020). “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data. Computers & Geosciences, 139, 104473. https://doi.org/10.1016/j.cageo.2020.104473
3. Shrestha, R. K., & Shrestha, R. (2020). Group segmentation and heterogeneity in the choice of cooking fuels in post-earthquake Nepal. arXiv preprint arXiv:2005.09616. https://arxiv.org/pdf/2005.09616.pdf

Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around https://postcodes.io.

Print maps, draw on them, scan them back in, and convert to spatial objects.

Tools for linting GeoJSON. Includes tools for interacting with the online tool http://geojsonlint.com, the Javascript library geojsonhint (https://www.npmjs.com/package/geojsonhint), and validating against a GeoJSON schema via the Javascript library (https://www.npmjs.com/package/is-my-json-valid). Some tools work locally while others require an internet connection.

Generate random positions (latitude/longitude), Well-known text (WKT) points or polygons, or GeoJSON points or polygons.

Utilities for visualizing species occurrence data. Includes functions to visualize occurrence data from spocc, rgbif, and other packages. Mapping options included for base R plots, ggplot2, leaflet and GitHub gists.

1. Fassio, G., Russini, V., Buge, B., Schiaparelli, S., Modica, M. V., Bouchet, P., & Oliverio, M. (2020). High cryptic diversity in the kleptoparasitic genus Hyalorisia Dall, 1889 (Littorinimorpha: Capulidae) with the description of nine new species from the Indo-West Pacific. Journal of Molluscan Studies, 86(4), 401–421. https://doi.org/10.1093/mollus/eyaa028

Email Address Validation.

Tools to get marine regions data from http://www.marineregions.org/. Includes tools to get region metadata, as well as data in GeoJSON format, as well as Shape files. Use cases include using data downstream to visualize geospatial data by marine region, mapping variation among different regions, and more.

Provides utility functions for some of the less-glamorous tasks involved in landscape analysis. It includes functions to coerce raster data to the common tibble format and vice versa, it helps with flexible reclassification tasks of raster data and it provides a function to merge multiple raster. Furthermore, landscapetools helps landscape scientists to visualize their data by providing optional themes and utility functions to plot single landscapes, rasterstacks, -bricks and lists of raster.

1. Langhammer, M., Thober, J., Lange, M., Frank, K., & Grimm, V. (2019). Agricultural landscape generators for simulation models: A review of existing solutions and an outline of future directions. Ecological Modelling, 393, 135–151. https://doi.org/10.1016/j.ecolmodel.2018.12.010
2. Etherington, T., & Omondiagbe, O. (2019). virtualNicheR: generating virtual fundamental and realised niches for use in virtual ecology experiments. Journal of Open Source Software, 4(41), 1661. https://doi.org/10.21105/joss.01661
3. Betts, M. G., Wolf, C., Pfeifer, M., Banks-Leite, C., Arroyo-Rodríguez, V., Ribeiro, D. B., … Ewers, R. M. (2019). Extinction filters mediate the global effects of habitat fragmentation on animals. Science, 366(6470), 1236–1239. https://doi.org/10.1126/science.aax9387
4. Scherer, C., Radchuk, V., Franz, M., Thulke, H., Lange, M., Grimm, V., & Kramer‐Schadt, S. (2020). Moving infections: individual movement decisions drive disease persistence in spatially structured landscapes. Oikos. https://doi.org/10.1111/oik.07002
5. Silva, I., Crane, M., Marshall, B. M., & Strine, C. T. (2020). Revisiting reptile home ranges: moving beyond traditional estimators with dynamic Brownian Bridge Movement Models. https://doi.org/10.1101/2020.02.10.941278

Tidy tools for NetCDF data sources. Explore the contents of a NetCDF source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array().

Classes for GeoJSON to make working with GeoJSON easier. Includes S3 classes for GeoJSON classes with brief summary output, and a few methods such as extracting and adding bounding boxes, properties, and coordinate reference systems; working with newline delimited GeoJSON; linting through the geojsonlint package; and serializing to/from Geobuf binary GeoJSON format.

Utilities to generate bounding boxes from WKT (Well-Known Text) objects and R data types, validate WKT objects and convert object types from the sp package into WKT representations.

1. Bachman, S., Walker, B., Barrios, S., Copeland, A., & Moat, J. (2020). Rapid Least Concern: towards automating Red List assessments. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e47018

Vector map data from http://www.naturalearthdata.com/. Access functions are provided in the accompanying package rnaturalearth.

1. Rice, A., Šmarda, P., Novosolov, M., Drori, M., Glick, L., Sabath, N., … Mayrose, I. (2019). The global biogeography of polyploid plants. Nature Ecology & Evolution, 3(2), 265–273. https://doi.org/10.1038/s41559-018-0787-9
2. Bennie, J. A., De Cocker, K., Smith, J. J., & Wiesner, G. H. (2020). The epidemiology of muscle-strengthening exercise in Europe: A 28-country comparison including 280,605 adults. PLOS ONE, 15(11), e0242220. https://doi.org/10.1371/journal.pone.0242220

Facilitates mapping by making natural earth map data from http://www.naturalearthdata.com/ more easily available to R users.

1. Chapman, C. A., Omeja, P. A., Kalbitzer, U., Fan, P., & Lawes, M. J. (2018). Restoration Provides Hope for Faunal Recovery: Changes in Primate Abundance Over 45 Years in Kibale National Park, Uganda. Tropical Conservation Science, 11, 194008291878737. https://doi.org/10.1177/1940082918787376
2. Farache, F. H. A., Pereira, C. B., Koschnitzke, C., Barros, L. O., Souza, E. M. de C., Felício, D. T., … Pereira, R. A. S. (2018). The unknown followers: Discovery of a new species of Sycobia Walker (Hymenoptera: Epichrysomallinae) associated with Ficus benjamina L (Moraceae) in the Neotropical region. Journal of Hymenoptera Research. 67, 85–102. https://doi.org/10.3897/jhr.67.29733
3. Zizka, A., Silvestro, D., Andermann, T., Azevedo, J., Duarte Ritter, C., Edler, D., … Antonelli, A. (2019). CoordinateCleaner: standardized cleaning of occurrence records from biological collection databases. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13152
4. Atickem, A., Stenseth, N. C., Fashing, P. J., Nguyen, N., Chapman, C. A., Bekele, A., … Kalbitzer, U. (2019). Build science in Africa. Nature, 570(7761), 297–300. https://doi.org/10.1038/d41586-019-01885-1
5. Umlauf, N., Klein, N., Simon, T., & Zeileis, A. (2019). bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond). arXiv preprint arXiv:1909.11784. https://arxiv.org/abs/1909.11784
6. Rodewald, A. D., Strimas-Mackey, M., Schuster, R., & Arcese, P. (2019). Tradeoffs in the value of biodiversity feature and cost data in conservation prioritization. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-52241-2
7. Næss, M. W. (2019). From hunter-gatherers to nomadic pastoralists: forager bands do not tell the whole story of the evolution of human cooperation. https://doi.org/10.31235/osf.io/9c8bm
8. Marshall, B. M., & Strine, C. T. (2019). Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media. PeerJ, 7, e8059. https://doi.org/10.7717/peerj.8059
9. Czernecki, B., Głogowski, A., & Nowosad, J. (2020). Climate: An R Package to Access Free In-Situ Meteorological and Hydrological Datasets For Environmental Assessment. Sustainability, 12(1), 394. https://doi.org/10.3390/su12010394
10. Rego, A., Sousa, A. G. G., Santos, J. P., Pascoal, F., Canário, J., Leão, P. N., & Magalhães, C. (2020). Diversity of Bacterial Biosynthetic Genes in Maritime Antarctica. Microorganisms, 8(2), 279. https://doi.org/10.3390/microorganisms8020279
11. Eastman, R. T., Roth, J. S., Brimacombe, K. R., Simeonov, A., Shen, M., Patnaik, S., & Hall, M. D. (2020). Remdesivir: A Review of Its Discovery and Development Leading to Emergency Use Authorization for Treatment of COVID-19. ACS Central Science, 6(5), 672–683. https://doi.org/10.1021/acscentsci.0c00489
12. Ozturk, R. C., & Altinok, I. (2020). Interaction of Plastics with Marine Species. Turkish Journal of Fisheries and Aquatic Sciences, 20(8). https://doi.org/10.4194/1303-2712-v20_8_07
13. Deconinck, D., Volckaert, F. A. M., Hostens, K., Panicz, R., Eljasik, P., Faria, M., … Derycke, S. (2020). A high-quality genetic reference database for European commercial fishes reveals substitution fraud of processed Atlantic cod (Gadus morhua) and common sole (Solea solea) at different steps in the Belgian supply chain. Food and Chemical Toxicology, 141, 111417. https://doi.org/10.1016/j.fct.2020.111417
14. Connors, B., Malick, M. J., Ruggerone, G. T., Rand, P., Adkison, M., Irvine, J. R., … Gorman, K. (2020). Climate and competition influence sockeye salmon population dynamics across the Northeast Pacific Ocean. Canadian Journal of Fisheries and Aquatic Sciences, 77(6), 943–949. https://doi.org/10.1139/cjfas-2019-0422
15. Runge, C. A., Hausner, V. H., Daigle, R. M., & Monz, C. A. (2020). Pan-Arctic analysis of cultural ecosystem services using social media and automated content analysis. Environmental Research Communications, 2(7), 075001. https://doi.org/10.1088/2515-7620/ab9c33
16. Swetnam, D. M., Stuart, J. B., Young, K., Maharaj, P. D., Fang, Y., Garcia, S., … Coffey, L. L. (2020). Movement of St. Louis encephalitis virus in the Western United States, 2014- 2018. PLOS Neglected Tropical Diseases, 14(6), e0008343. https://doi.org/10.1371/journal.pntd.0008343
17. Kurose, D., Pollard, K. M., & Ellison, C. A. (2020). Chloroplast DNA analysis of the invasive weed, Himalayan balsam (Impatiens glandulifera), in the British Isles. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-67871-0
18. Lawlor, J. A., & Arellano, S. M. (2020). Temperature and salinity, not acidification, predict near-future larval growth and larval habitat suitability of Olympia oysters in the Salish Sea. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-69568-w
19. Cahill, C. L., Anderson, S. C., Paul, A. J., MacPherson, L., Sullivan, M. G., van Poorten, B. T., … & Post, J. R. (2020). A spatial-temporal approach to modeling somatic growth across inland recreational fisheries landscapes. Canadian Journal of Fisheries and Aquatic Sciences, (ja). https://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2019-0434
20. Chugunkova, A. V., & Pyzhev, A. I. (2020). Impacts of Global Climate Change on Duration of Logging Season in Siberian Boreal Forests. Forests, 11(7), 756. https://doi.org/10.3390/f11070756
21. Kakioka, R., Mori, S., Kokita, T., Hosoki, T. K., Nagano, A. J., Ishikawa, A., … Kitano, J. (2020). Multiple waves of freshwater colonization of the three-spined stickleback in the Japanese Archipelago. https://doi.org/10.21203/rs.3.rs-59443/v1
22. Yeşilkanat, C. M. (2020). Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm. Chaos, Solitons & Fractals, 140, 110210. https://doi.org/10.1016/j.chaos.2020.110210
23. Obradovich, N., Özak, Ö., Martín, I., Ortuño-Ortín, I., Awad, E., Cebrián, M., … Cuevas, Á. (2020). Expanding the Measurement of Culture with a Sample of Two Billion Humans. National Bureau of Economic Research. https://doi.org/10.3386/w27827
24. Rycyk, A. M., Tyson Moore, R. B., Wells, R. S., McHugh, K. A., Berens McCabe, E. J., & Mann, D. A. (2020). Passive acoustic listening stations (PALS) show rapid onset of ecological effects of harmful algal blooms in real time. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-74647-z
25. Wenndt, A., Sudini, H. K., Pingali, P., & Nelson, R. (2020). Exploring aflatoxin contamination and household-level exposure risk in diverse Indian food systems. PLOS ONE, 15(10), e0240565. https://doi.org/10.1371/journal.pone.0240565
26. Abbas, H. K., Zablotowicz, R. M., Bruns, H. A., & Abel, C. A. (2006). Biocontrol of aflatoxin in corn by inoculation with non-aflatoxigenicAspergillus flavusisolates. Biocontrol Science and Technology, 16(5), 437–449. https://doi.org/10.1080/09583150500532477
27. Jacobs, E., Bittig, H. C., Gräwe, U., Graves, C. A., Glockzin, M., Müller, J. D., … Rehder, G. (2020). Upwelling-induced trace gas dynamics in the Baltic Sea inferred from 8 years of autonomous measurements on a ship of opportunity. https://doi.org/10.5194/bg-2020-365
28. Hotez, P., Bottazzi, M. E., Strub-Wourgaft, N., Sosa-Estani, S., Torrico, F., Pajín, L., … Sancho, J. (2020). A new patient registry for Chagas disease. PLOS Neglected Tropical Diseases, 14(10), e0008418. https://doi.org/10.1371/journal.pntd.0008418
29. Raut, S. (2020). A computer vision approach to assess wood variability from whole-disk images of longleaf pine (Order No. 28023614). https://search.proquest.com/docview/2446699035
30. Cramer, M. T., Fidler, R. Y., Penrod, L. M., Carroll, J., & Turingan, R. G. (2020). A spatiotemporal comparison of length-at-age in the coral reef fish Acanthurus nigrofuscus between marine reserves and fished reefs. PLOS ONE, 15(9), e0239842. https://doi.org/10.1371/journal.pone.0239842
31. Winter-Billington, A., Moore, R. D., & Dadic, R. (2020). Evaluating the transferability of empirical models of debris-covered glacier melt. Journal of Glaciology, 1–18. https://doi.org/10.1017/jog.2020.57
32. Pellowe, K. E., & Leslie, H. M. (2020). Ecosystem service lens reveals diverse community values of small-scale fisheries. Ambio. https://doi.org/10.1007/s13280-020-01405-w
33. Stresman, G., Whittaker, C., Slater, H. C., Bousema, T., & Cook, J. (2020). Quantifying Plasmodium falciparum infections clustering within households to inform household-based intervention strategies for malaria control programs: An observational study and meta-analysis from 41 malaria-endemic countries. PLOS Medicine, 17(10), e1003370. https://doi.org/10.1371/journal.pmed.1003370
34. Lockley, E. C., Fouda, L., Correia, S. M., Taxonera, A., Nash, L. N., Fairweather, K., … Eizaguirre, C. (2020). Long-term survey of sea turtles (Caretta caretta) reveals correlations between parasite infection, feeding ecology, reproductive success and population dynamics. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-75498-4
35. Hirons, A. D., Watkins, J. H. R., Baxter, T. J., Miesbauer, J. W., Male‐Muñoz, A., Martin, K. W. E., … Sjöman, H. (2020). Using botanic gardens and arboreta to help identify urban trees for the future. PLANTS, PEOPLE, PLANET. https://doi.org/10.1002/ppp3.10162
36. Alhajeri, B. H. (2020). A Geometric Morphometric Analysis of Geographic Mandibular Variation in the Dwarf Gerbil Gerbillus nanus (Gerbillinae, Rodentia). Journal of Mammalian Evolution. https://doi.org/10.1007/s10914-020-09530-9
37. Tchokponhoué, D. A., Achigan-Dako, E. G., N’Danikou, S., Nyadanu, D., Kahane, R., Houéto, J., … Sibiya, J. (2020). Phenotypic variation, functional traits repeatability and core collection inference in Synsepalum dulcificum (Schumach & Thonn.) Daniell reveals the Dahomey Gap as a centre of diversity. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-76103-4
38. Chung, M., Jørgensen, K. M., Trueman, C. N., Knutsen, H., Jorde, P. E., & Grønkjær, P. (2020). First measurements of field metabolic rate in wild juvenile fishes show strong thermal sensitivity but variations between sympatric ecotypes. Oikos. https://doi.org/10.1111/oik.07647
39. Evdokimova, E. V., Gladkov, G. V., Kuzina, N. I., Ivanova, E. A., Kimeklis, A. K., Zverev, A. O., … Andronov, E. E. (2020). The difference between cellulolytic “culturomes” and microbiomes inhabiting two contrasting soil types. PLOS ONE, 15(11), e0242060. https://doi.org/10.1371/journal.pone.0242060
40. Hobson, K. A., Jinguji, H., Ichikawa, Y., Kusack, J. W., & Anderson, R. C. (2020). Long-Distance Migration of the Globe Skimmer Dragonfly to Japan Revealed Using Stable Hydrogen (δ 2H) Isotopes. Environmental Entomology. https://doi.org/10.1093/ee/nvaa147
41. Bennie, J. A., De Cocker, K., Smith, J. J., & Wiesner, G. H. (2020). The epidemiology of muscle-strengthening exercise in Europe: A 28-country comparison including 280,605 adults. PLOS ONE, 15(11), e0242220. https://doi.org/10.1371/journal.pone.0242220
42. Rose Vineer, H., Morgan, E. R., Hertzberg, H., Bartley, D. J., Bosco, A., Charlier, J., … Rinaldi, L. (2020). Increasing importance of anthelmintic resistance in European livestock: creation and meta-analysis of an open database. Parasite, 27, 69. https://doi.org/10.1051/parasite/2020062
43. Farooq, H., Azevedo, J. A. R., Soares, A., Antonelli, A., & Faurby, S. (2020). Mapping Africa’s Biodiversity: More of the Same Is Just Not Good Enough. Systematic Biology. https://doi.org/10.1093/sysbio/syaa090

The web service at https://www.geonames.org/ provides a number of spatial data queries, including administrative area hierarchies, city locations and some country postal code queries. A (free) username is required and rate limits exist.

1. Harsch, M. A., & HilleRisLambers, J. (2016). Climate Warming and Seasonal Precipitation Change Interact to Limit Species Distribution Shifts across Western North America. PLOS ONE, 11(7), e0159184. https://doi.org/10.1371/journal.pone.0159184
2. Ummel, K. (2012). CARMA revisited: an updated database of carbon dioxide emissions from power plants worldwide. Center for Global Development Working Paper, (304). http://www.cgdev.org/publication/carma-revisited-updated-database-carbon-dioxide-emissions-power-plants-worldwide-working
3. Kolb, J.-P. (2016). Visualizing GeoData with R. Austrian Journal of Statistics, 45(1), 45. https://doi.org/10.17713/ajs.v45i1.88
4. Kevin Ummel. 2012. “CARMA Revisited: An Updated Database of Carbon Dioxide Emissions from Power Plants Worldwide.” CGD Working Paper 304. Washington, D.C.: Center for Global Development. http://www.cgdev.org/content/publications/detail/1426429
5. Holzmeyer, L., Hartig, A.-K., Franke, K., Brandt, W., Muellner-Riehl, A. N., Wessjohann, L. A., & Schnitzler, J. (2020). Evaluation of plant sources for antiinfective lead compound discovery by correlating phylogenetic, spatial, and bioactivity data. Proceedings of the National Academy of Sciences, 117(22), 12444–12451. https://doi.org/10.1073/pnas.1915277117
6. Grattarola, F., González, A., Mai, P., Cappuccio, L., Fagúndez-Pachón, C., Rossi, F., … Pincheira-Donoso, D. (2020). Biodiversidata: A novel dataset for the vascular plant species diversity in Uruguay. Biodiversity Data Journal, 8. https://doi.org/10.3897/bdj.8.e56850