This dataset was created to be used in my Capstone Project for the Google Data Analytics Professional Certificate. Data was web scraped from the state websites to combine the GIS information like FIPS, latitude, longitude, and County Codes by both number and Mailing Number.

RStudio was used for this web scrape and join. For details on how it was done you can go to the following link for my Github repository.

Feel free to follow my Github or LinkedIn profile to see what I end up doing with this Dataset.

Open-Source GIS plays a pivotal role in advancing GIS education, fostering research collaboration, and supporting global sustainability by enabling the sharing of data, models, and knowledge. However, the integration of big data, deep learning methods, and artificial intelligence deep learning in geospatial research presents significant challenges for GIS education. These include increasing software learning costs, higher computational power demand, and the management of fragmented information in the Web 2.0 context. Addressing these challenges while integrating emerging GIS innovations and restructuring GIS knowledge systems is crucial for the evolution of GIS Education 3.0. This study introduces a Visual Programming-based Geospatial Cyberinfrastructure (V-GCI) framework, integrated with the replicable and reproducible (R&R) framework, to enhance GIS function compatibility, learning scalability, and web GIS application interoperability. Through a case study on spatial accessibility using the generalized two-step floating catchment area method (G2SFCA), this paper demonstrates how V-GCI can reshape the GIS knowledge tree and its potential to enhance replicability and reproducibility within open-source GIS Education 3.0.

This software version has been superseded: please note a more recent version of the MCAS-S software is now available. See the ABARES website for details. \r \r MCAS-S version 3.2 \r The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a tool to view and combine mapped information. MCAS-S can inform spatial decision making and help with stakeholder engagement and communication. MCAS-S is powerful and easy to use. GIS (geographic information system) programming is not required, removing the usual technical obstacles to non-GIS users. \r \r MCAS-S projects are: \r • transparent - you can see all the inputs used to meet an objective and how these are combined \r • flexible - you can use MCAS-S to compare options and explore trade-offs. You can use your own input data \r • fast - you can immediately see changes to your objective when any input or combination method changes. \r The new version 3.2 has: • improved performance \r • a user guide incorporated into the software \r • live links to metadata \r • more options for processing and analysing time series data \r • simpler options for labelling and classifying data inputs. \r \r MCAS-S 3.2 is made freely available with the support of the MCAS-S development partners: ABARES, the NSW Office of Environment and Heritage, Barry Consulting, the Australian Collaborative Land Use and Management Program, the National Environmental Research Program Landscapes and Policy Hub at University of Tasmania and the Terrestrial Ecosystems Research Network.

de Rigo, D., Corti, P., Caudullo, G., McInerney, D., Di Leo, M., San Miguel-Ayanz, J., 2013. Toward open science at the European scale: Geospatial Semantic Array Programming for integrated environmental modelling. Geophysical Research Abstracts 15, 13245+. ISSN 1607-7962, European Geosciences Union (EGU).

This is the authors’ version of the work. The definitive version is published in the Vol. 15 of Geophysical Research Abstracts (ISSN 1607-7962) and presented at the European Geosciences Union (EGU) General Assembly 2013, Vienna, Austria, 07-12 April 2013http://www.egu2013.eu/

Toward open science at the European scale: Geospatial Semantic Array Programming for integrated environmental modelling

Daniele de Rigo ¹ ², Paolo Corti ¹ ³, Giovanni Caudullo ¹, Daniel McInerney ¹, Margherita Di Leo ¹, Jesús San-Miguel-Ayanz ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy ² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy ³ United Nations World Food Programme,Via C.G.Viola 68 Parco dei Medici, I-00148 Rome, Italy

Excerpt: Interfacing science and policy raises challenging issues when large spatial-scale (regional, continental, global) environmental problems need transdisciplinary integration within a context of modelling complexity and multiple sources of uncertainty. This is characteristic of science-based support for environmental policy at European scale, and key aspects have also long been investigated by European Commission transnational research. Approaches (either of computational science or of policy-making) suitable at a given domain-specific scale may not be appropriate for wide-scale transdisciplinary modelling for environment (WSTMe) and corresponding policy-making. In WSTMe, the characteristic heterogeneity of available spatial information and complexity of the required data-transformation modelling (D-TM) appeal for a paradigm shift in how computational science supports such peculiarly extensive integration processes. In particular, emerging wide-scale integration requirements of typical currently available domain-specific modelling strategies may include increased robustness and scalability along with enhanced transparency and reproducibility. This challenging shift toward open data and reproducible research (open science) is also strongly suggested by the potential - sometimes neglected - huge impact of cascading effects of errors within the impressively growing interconnection among domain-specific computational models and frameworks. Concise array-based mathematical formulation and implementation (with array programming tools) have proved helpful in supporting and mitigating the complexity of WSTMe when complemented with generalized modularization and terse array-oriented semantic constraints. This defines the paradigm of Semantic Array Programming (SemAP) where semantic transparency also implies free software use (although black-boxes - e.g. legacy code - might easily be semantically interfaced). A new approach for WSTMe has emerged by formalizing unorganized best practices and experience-driven informal patterns. The approach introduces a lightweight (non-intrusive) integration of SemAP and geospatial tools - called Geospatial Semantic Array Programming (GeoSemAP). GeoSemAP exploits the joint semantics provided by SemAP and geospatial tools to split a complex D-TM into logical blocks which are easier to check by means of mathematical array-based and geospatial constraints. Those constraints take the form of precondition, invariant and postcondition semantic checks. This way, even complex WSTMe may be described as the composition of simpler GeoSemAP blocks. GeoSemAP allows intermediate data and information layers to be more easily and formally semantically described so as to increase fault-tolerance, transparency and reproducibility of WSTMe. This might also help to better communicate part of the policy-relevant knowledge, often diffcult to transfer from technical WSTMe to the science-policy interface. [...]

The overall goal of this project is to address identified gaps in the area of violence prevention and mental health promotion programming for under-served populations. Specifically, we will evaluate and/or develop effective programming for high risk

Description: These Reference grids have been created for the NaturaConnect project and are based on an intersection of the European Coastline delineation and the GADM database. Thee reference grids have been created in a way so that they are fully consistent with the EEA reference grid (https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2), meaning that for example two 5km gridded cells fully match a 10km grid cell in width. Filestructure: ReferenceGrid_Europe_{format}_{grain} format is either "frac" for fractional data (which has been multiplied with 10000 to save in integer format) or binary (0,1). grain is provided as layers in 100m, 1000m, 5000m, 10000m, 50000m spatial resolution. Alternative aggregations can be provided on request. File format: The layers are gridded geoTiff files and can be loaded in any conventional Graphical Information System (GIS) or specific analytical programming languages (e.g. R or python). In addition external pyramids (.tfw) have been precreated to enable faster rendering. Geographic projection: We use the Lamberts-Equal-Area Projection by default for all layers in NaturaConnect. This is an equal-area (but distorted shape) projection and commonly used by European institution with a focus on the European continent. For global layers the equal-area World Mollweide projection is used. Sourcecode: The code to reproduce the layers has been made available in the "code" file.

This is the authors’ version of the work. It is based on a poster presented at the Wageningen Conference on Applied Soil Science, http://www.wageningensoilmeeting.wur.nl/UK/ Cite as: Bosco, C., de Rigo, D., Dewitte, O., Montanarella, L., 2011. Towards the reproducibility in soil erosion modeling: a new Pan-European soil erosion map. Wageningen Conference on Applied Soil Science “Soil Science in a Changing World”, 18 - 22 September 2011, Wageningen, The Netherlands. Author’s version DOI:10.6084/m9.figshare.936872 arXiv:1402.3847

Towards the reproducibility in soil erosion modeling:a new Pan-European soil erosion map

Claudio Bosco ¹, Daniele de Rigo ¹ ² , Olivier Dewitte ¹, Luca Montanarella ¹ ¹ European Commission, Joint Research Centre, Institute for Environment and Sustainability,Via E. Fermi 2749, I-21027 Ispra (VA), Italy² Politecnico di Milano, Dipartimento di Elettronica e Informazione,Via Ponzio 34/5, I-20133 Milano, Italy

Soil erosion by water is a widespread phenomenon throughout Europe and has the potentiality, with his on-site and off-site effects, to affect water quality, food security and floods. Despite the implementation of numerous and different models for estimating soil erosion by water in Europe, there is still a lack of harmonization of assessment methodologies. Often, different approaches result in soil erosion rates significantly different. Even when the same model is applied to the same region the results may differ. This can be due to the way the model is implemented (i.e. with the selection of different algorithms when available) and/or to the use of datasets having different resolution or accuracy. Scientific computation is emerging as one of the central topic of the scientific method, for overcoming these problems there is thus the necessity to develop reproducible computational method where codes and data are available. The present study illustrates this approach. Using only public available datasets, we applied the Revised Universal Soil loss Equation (RUSLE) to locate the most sensitive areas to soil erosion by water in Europe. A significant effort was made for selecting the better simplified equations to be used when a strict application of the RUSLE model is not possible. In particular for the computation of the Rainfall Erosivity factor (R) the reproducible research paradigm was applied. The calculation of the R factor was implemented using public datasets and the GNU R language. An easily reproducible validation procedure based on measured precipitation time series was applied using MATLAB language. Designing the computational modelling architecture with the aim to ease as much as possible the future reuse of the model in analysing climate change scenarios is also a challenging goal of the research.

References [1] Rusco, E., Montanarella, L., Bosco, C., 2008. Soil erosion: a main threats to the soils in Europe. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 37-45 [2] Casagrandi, R. and Guariso, G., 2009. Impact of ICT in Environmental Sciences: A citation analysis 1990-2007. Environmental Modelling & Software 24 (7), 865-871. DOI:10.1016/j.envsoft.2008.11.013 [3] Stallman, R. M., 2005. Free community science and the free development of science. PLoS Med 2 (2), e47+. DOI:10.1371/journal.pmed.0020047 [4] Waldrop, M. M., 2008. Science 2.0. Scientific American 298 (5), 68-73. DOI:10.1038/scientificamerican0508-68 [5] Heineke, H. J., Eckelmann, W., Thomasson, A. J., Jones, R. J. A., Montanarella, L., and Buckley, B., 1998. Land Information Systems: Developments for planning the sustainable use of land resources. Office for Official Publications of the European Communities, Luxembourg. EUR 17729 EN [6] Farr, T. G., Rosen, P A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., Alsdorf, D., 2007. The Shuttle Radar Topography Mission. Review of Geophysics 45, RG2004, DOI:10.1029/2005RG000183 [7] Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., and New, M., 2008. A European daily high-resolution gridded dataset of surface temperature and precipitation. Journal of Geophysical Research 113, (D20) D20119+ DOI:10.1029/2008jd010201 [8] Renard, K. G., Foster, G. R., Weesies, G. A., McCool, D. K., and Yoder, D. C., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture handbook 703. US Dept Agric., Agr. Handbook, 703 [9] Bosco, C., Rusco, E., Montanarella, L., Panagos, P., 2009. Soil erosion in the alpine area: risk assessment and climate change. Studi Trentini di scienze naturali 85, 119-125 [10] Bosco, C., Rusco, E., Montanarella, L., Oliveri, S., 2008. Soil erosion risk assessment in the alpine area according to the IPCC scenarios. In: Tóth, G., Montanarella, L., Rusco, E. (Eds.), Threats to Soil Quality in Europe. No. EUR 23438 EN in EUR - Scientific and Technical Research series. Office for Official Publications of the European Communities, pp. 47-58 [11] de Rigo, D. and Bosco, C., 2011. Architecture of a Pan-European Framework for Integrated Soil Water Erosion Assessment. IFIP Advances in Information and Communication Technology 359 (34), 310-31. DOI:10.1007/978-3-642-22285-6_34 [12] Bosco, C., de Rigo, D., Dewitte, O., and Montanarella, L., 2011. Towards a Reproducible Pan-European Soil Erosion Risk Assessment - RUSLE. Geophys. Res. Abstr. 13, 3351 [13] Bollinne, A., Laurant, A., and Boon, W., 1979. L’érosivité des précipitations a Florennes. Révision de la carte des isohyétes et de la carte d’erosivite de la Belgique. Bulletin de la Société géographique de Liége 15, 77-99 [14] Ferro, V., Porto, P and Yu, B., 1999. A comparative study of rainfall erosivity estimation for southern Italy and southeastern Australia. Hydrolog. Sci. J. 44 (1), 3-24. DOI:10.1080/02626669909492199 [15] de Santos Loureiro, N. S. and de Azevedo Coutinho, M., 2001. A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal. J. Hydrol. 250, 12-18. DOI:10.1016/S0022-1694(01)00387-0 [16] Rogler, H., and Schwertmann, U., 1981. Erosivität der Niederschläge und Isoerodentkarte von Bayern (Rainfall erosivity and isoerodent map of Bavaria). Zeitschrift fur Kulturtechnik und Flurbereinigung 22, 99-112 [17] Nearing, M. A., 1997. A single, continuous function for slope steepness influence on soil loss. Soil Sci. Soc. Am. J. 61 (3), 917-919. DOI:10.2136/sssaj1997.03615995006100030029x [18] Morgan, R. P C., 2005. Soil Erosion and Conservation, 3rd ed. Blackwell Publ., Oxford, pp. 304 [19] Šúri, M., Cebecauer, T., Hofierka, J., Fulajtár, E., 2002. Erosion Assessment of Slovakia at regional scale using GIS. Ecology 21 (4), 404-422 [20] Cebecauer, T. and Hofierka, J., 2008. The consequences of land-cover changes on soil erosion distribution in Slovakia. Geomorphology 98, 187-198. DOI:10.1016/j.geomorph.2006.12.035 [21] Poesen, J., Torri, D., and Bunte, K., 1994. Effects of rock fragments on soil erosion by water at different spatial scales: a review. Catena 23, 141-166. DOI:10.1016/0341-8162(94)90058-2 [22] Wischmeier, W. H., 1959. A rainfall erosion index for a universal Soil-Loss Equation. Soil Sci. Soc. Amer. Proc. 23, 246-249 [23] Iverson, K. E., 1980. Notation as a tool of thought. Commun. ACM 23 (8), 444-465. DOI:10.1145/358896.358899 [24] Quarteroni, A., Saleri, F., 2006. Scientific Computing with MATLAB and Octave. Texts in Computational Science and Engineering. Milan, Springer-Verlag [25] The MathWorks, 2011. MATLAB. http://www.mathworks.com/help/techdoc/ref/ [26] Eaton, J. W., Bateman, D., and Hauberg, S., 2008. GNU Octave Manual Version 3. A high-level interactive language for numerical computations. Network Theory Limited, ISBN: 0-9546120-6-X [27] de Rigo, D., 2011. Semantic Array Programming with Mastrave - Introduction to Semantic Computational Modeling. The Mastrave project. http://mastrave.org/doc/MTV-1.012-1 [28] de Rigo, D., (exp.) 2012. Semantic array programming for environmental modelling: application of the Mastrave library. In prep. [29] Bosco, C., de Rigo, D., Dewitte, O., Poesen, J., Panagos, P.: Modelling Soil Erosion at European Scale. Towards Harmonization and Reproducibility. In prep. [30] R Development Core Team, 2005. R: A language and environment for statistical computing. R Foundation for Statistical Computing. [31] Stallman, R. M., 2009. Viewpoint: Why “open source” misses the point of free software. Commun. ACM 52 (6), 31–33. DOI:10.1145/1516046.1516058 [32] de Rigo, D. 2011. Multi-dimensional weighted median: the module "wmedian" of the Mastrave modelling library. Mastrave project technical report. http://mastrave.org/doc/mtv_m/wmedian [33] Shakesby, R. A., 2011. Post-wildfire soil erosion in the Mediterranean: Review and future research directions. Earth-Science Reviews 105 (3-4), 71-100. DOI:10.1016/j.earscirev.2011.01.001 [34] Zuazo, V. H., Pleguezuelo, C. R., 2009. Soil-Erosion and runoff prevention by plant covers: A review. In: Lichtfouse, E., Navarrete, M., Debaeke, P Véronique, S., Alberola, C. (Eds.), Sustainable Agriculture. Springer Netherlands, pp. 785-811. DOI:10.1007/978-90-481-2666-8_48

This data set is a harmonized collection of existing data from GBIF, the EU-Forest project and the LUCAS survey. It has about 3 million observations and is supplemented by variables (e.g. location accuracy, land cover type, canopy height, etc.) which enable precise filtering for specific user applications.

The RDS file is created from an sf-object and suitable for fast reading in the R-programming environment. The CSV.GZ file contains records as a table with Easting and Northing in Coordinate Reference System ETRS89 / LAEA Europe (= EPSG code 3035) and can be fed in a GIS after being unzipped.

The code producing this data set is publicly available on GitLab.

Variables:

• id = unique point identifier
• easting = x coordinate
• northing = y coordinate
• country = ISO country code
• species = Latin species name
• genus = genus name
• scientific_name = long species name
• gbif_taxon_key = taxon key from GBIF
• gbif_genus_key = genus key from GBIF
• taxon_rank = species or genus
• year = year of observation
• accessed_through = database through which data was accessed (GBIF, LUCAS, EU-Forest)
• dataset_info = data set name (individual sub-data-set)
• citation = DOI citation of the individual data set
• license = distribution license
• location_accuracy = spatial accuracy of observation (meters)
• flag_location_issue = known location issues present
• flag_date_issue = known date issues present
• eoo = Extent of occurrence (applying the concept of natural geographical range used for the EU-Forest data set (Mauri et al., 2017) to all other data points. 1 = point inside species range; 0 = point outside; NA = EOO polygon not available for this species)
• dbh = Diameter Breast Height (only recorded for observations from the EU-Forest data set (Mauri et al., 2017))
• lc1 = LUCAS land cover type 1 (only recorded for observations from LUCAS data)
• lc2 = LUCAS land cover type 2 (only recorded for observations from LUCAS data)
• landmask_country = land mask overlay 30 meters (NA = not on land)
• corine = CORINE 2018 land cover type (extracted from the 100 meter raster data set)
• nightlights = light pollution observed by VIIRS (proxy for remoteness / distance to human structures)
• canopy_height = canopy height derived from GEDI waveform LiDAR point data
• natura_2000 = Natura 2000 site code (if a point falls inside a protected area (GIS-layer) this variable contains the site identification code; all sites can be explored on an interactive map)
• freq_location = number of points with identical location (in some cases one location has multiple observation, differing in species and/or year. This may lead to difficulties in certain modeling tasks)
• geometry = point geometry in ETRS89 / LAEA Europe

See this detailed documentation for more insights into each variable.

If you would like to know more about the creation of this data set, see

1. the R-Markdown documenting the process (GitLab repository)
2. the talk at OpenGeoHub Summer School 2020 (Youtube)

Some advice: This data set is a puzzle with pieces from many different sources. Take some time to explore before including it in your work. Use summary statistics to see which variables have NAs and how many. Choose your filtering criteria wisely. For example, some points with the highest location accuracy have no record for the year of observations. You would exclude these, if "year > 1990" was your criteria.

This work has received funding from the European Union's the Innovation and Networks Executive Agency (INEA) under Grant Agreement Connecting Europe Facility (CEF) Telecom project 2018-EU-IA-0095 (https://ec.europa.eu/inea/en/connecting-europe-facility/cef-telecom/2018-eu-ia-0095).

The data can be opened in GIS programs such as Esri ArcGIS, QGIS, or GRASS GIS (or others) or in statistical programming software such as R (or others).

These aquaculture suitability raster datasets (in GeoTIFF format) indicate areas of potential suitability for freshwater and marine aquaculture species in earthen or lined ponds. A multi-criteria analysis, involved the integration of soil data and biophysical characteristics within a GIS spatial analysis environment to predict potential sites to inform decision making. A set of limitations and rules were adapted from (McLeod et al., 2002) to determine suitability. These aquaculture datasets were generated within the ‘Land suitability’ activity in consultation with the agricultural viability activity of the Northern Australia Water Resource Assessment (NAWRA). The aquaculture suitability analysis is described in full in the CSIRO NAWRA published report ‘Aquaculture viability. A technical report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments. CSIRO, Australia.’ Irvin S, Coman G, Musson D and Doshi A (2018). There are five suitability classes coded 1-5. 1 – Highly suitable land with negligible limitations 2 – Suitable land with minor limitations 3 – Moderately suitable land with considerable limitations 4 – Currently unsuitable land with severe limitations 5 – Unsuitable land with extreme limitations. Each drop in suitability implies that more management input (and cost) is required to achieve incremental increases in production. The soil and land characteristics considered for all configurations include; clay content, sodicity and rockiness; and mainly refer to geotechnical considerations (e.g. construction and stability of pond walls). Other limitations, including slope, and the likely presence of gilgai microrelief and acid sulfate soils, infer more difficult, expensive and therefore less suitable development environments, and a greater degree of land preparation effort. Key considerations for earthen ponds included soil properties preventing pond leakage and soil acidity (pH); the latter taking into account negative growth responses of species from unfavourable pH values (i.e. biological limitation) as well as engineering, as pH may affect the structural integrity of earthen walls. Proximity to sea water was considered for marine species although the characteristics of tides and their suitability for marine aquaculture have not been applied in this analysis therefore the full inland distance of tidal waters has not been explored. The aquaculture suitability rules, including the limitation classes and suitability subclasses for each species by pond configuration, is provided in the above referenced publication. It is important to emphasize that this is a regional-scale assessment: further data collection and analyses would be required to plan development at a scheme, enterprise or property scale. Lineage: These aquaculture suitability raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO NAWRA published reports and in particular ‘Aquaculture viability. A technical report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments. CSIRO, Australia.’ 1. Collated existing data. 2. Selection of additional soil and land attribute site data locations. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attributes to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data. 6. Create Digital Soil Mapping (DSM) attribute raster datasets. 7. Aquaculture suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets. 9. Final suitability data created for aquaculture options. 10. Quality assessment of these aquaculture data was conducted by on-ground and expert (qualitative) examination of outputs.

Description:
This dataset contains 538 ecogeographical variables as raster grids with 100 m cell size. Every layer matches template raster grids (CRS, pixel locations, extent, pixels with values), they cover the whole terrestrial territory of Latvia and are consistent with each other.

Files:

• "HiQBioDiv_EGVs_2024.csv" names ecogeographical variable layers in "HiQBioDiv_EGVlayers_2024.zip";
• "HiQBioDiv_EGVs_2024_README.csv" explains fields in "HiQBioDiv_EGVs_2024.csv";
• "HiQBioDiv_EGVs_FlowChart_2024.png" visualises relationships between ecogeographical variables in "HiQBioDiv_EGVlayers_2024.zip";
• "HiQBioDiv_EGVlayers_2024.zip" contains geoTiff layers with ecogeographical variables.

EGV layer's naming convention:

{part1}_{part2}_{part3}.tif

• part1 is a name of the group of variables;
• part2 is an abbreviated name of the specific variable;
• part3 is a categorised spatial resolution:
  • "cell" - information from within the cell only;
  • "r500" - a summary of information from an area with 500 m radius around the cell's centre;
  • "r1250" - a summary of information from an area with 1250 m radius around the cell's centre;
  • "r3000" - a summary of information from an area with 3000 m radius around the cell's centre;
  • "r10000" - a summary of information from an area with 10000 m radius around the cell's centre.

File format:
The layers are gridded geoTiff files and can be loaded in any conventional geographical information system (GIS) or analytical programming languages (e.g. R or Python).

Geographic projection:
We use the LKS-92 / Latvia TM by default for all layers in HiQBioDiv.

Sourcecode:
Detailed description of geodata used and workflows in production of every layer is available in an online document. Related work is available at the project's GitHub repository.

HISTORECO is a comprehensive database that includes more than 45 geographic, climatic, hydrological, demographic, and economic variables (64 columns of data apart from the 7 first of identification of the municipality in "Historeco.csv") spanning the 20th and 21st centuries, covering all 8,122 municipalities in Spain each of which has one value per decade. It is a unique dataset that integrates data from various sources, facilitating the analysis of long-term temporal and spatial trends across multiple disciplines such as climate, geography, and socio-economic development.The dataset combines information from twenty sources (databases/articles), harmonizing and downscaling them to the municipal level using GIS and programming tools (mainly QGIS, R, and Python). This is the most extensive dataset of its kind in terms of temporal depth and spatial granularity available for Spain.This project has been developed thanks to funding from the Ramón Areces Foundation, without which it would not have been possible.

In this study, we investigated: Are water quality monitoring stations proportionally distributed across communities of varying social vulnerability? We specifically focus on nutrient monitoring of surface waters in the South Atlantic-Gulf region, a water-rich area with diverse land uses and communities spanning the social vulnerability spectrum. We used 2018-2022 data from the U.S. Geological Survey (USGS) National Water Information System and U.S. Environmental Protection Agency Storage and Retrieval database to compare station locations to census tract-scale metrics from the U.S. Center for Disease Control Social Vulnerability Index (SVI) and hydrography from the USGS. Statistical analyses revealed a significant disparity in the distribution of active monitoring station placements, with more monitoring stations in lower vulnerability areas and fewer in highly vulnerable areas. Stations were also clustered in areas of similar SVI values; areas were less likely to be monitored if they w..., , , # Dataset: Surface waters in socially vulnerable areas are disproportionately under-monitored for nutrients in the U.S. South Atlantic-Gulf Region

https://doi.org/10.5061/dryad.7d7wm3858

Files and variables:

The zipped files contain all of the individual files that are required to open, project, and manipulate shapefiles. All of the shape (.shp) files used in this study contain the geometry and attributes of geospatial features (e.g., points, lines, polylines, polygons). The zipped file bundle contains the main file .shp and companion files including: .cpg, .dbf, .prj, .qmd, and .shx.Â

The main .shp file can be opened and analyzed by Python, R, and many other programming languages, and open-source geospatial software such as QGIS, SAGA GIS, GRASS GIS, GeoDa, etc. These .shp files were the base of much of this study’s analysis.

merged.zip: contains data from both the Centers for Disease Control and Prevention Social Vulnerability Ind...,

This dataset consists of two GIS maps of the British Army brigade and divisional districts across the island of Ireland circa 1920-21. This is a digital reproduction of an original hand-marked Ordnance Survey map within the The National Archives, UK collection W078/4712. The map files were created using a geocomputational framework using the programming language R. There are two versions released, ESRI Shapefiles and a GeoPackage (GPKG) file.

Soil type is a composite of various soil properties, and soil type maps serve as representations of the distribution and characteristics of different soils. Regional soil type maps are presented in a cartographic format, delineating the surveyed area into multiple polygons, with each polygon describing the soil characteristics of the corresponding map unit. Each polygon encompasses one or more soil types. These soil types encapsulate, to a certain extent, the physicochemical properties of the soil as well as geographical environmental information. Based on the definitions of type names, one can quickly obtain fundamental information about the soils corresponding to spatial locations on the map, such as color, soil development degree, fertility status, and local temperature and humidity. This information plays a significant role in decision-making related to land development and ecological environmental protection, while also providing essential foundational data for research in regional agriculture, environment, ecology, and climate. This dataset comprises a 1:500,000 scale soil type map of the Qinghai-Tibet Plateau, with soil types classified into four categories: Order, suborder, group, and subgroup. The soil classification system is based on the Chinese Soil Taxonomy (CST), which differs from traditional soil genesis classification systems by quantitatively describing the differences between soil types, thereby addressing the phenomena of "different names for the same soil" or "same name for different soils" that can occur at large scales in genesis classification systems. The map files within this dataset are in shapefile format and can be accessed using standard GIS software such as QGIS, ArcGIS, ArcGIS Pro, or programming languages like R, Python, or MATLAB. Given the numerous data polygons, it is recommended to utilize ArcGIS Pro for optimal performance.