Datasets- simple_land_cover1.tif - an example land cover dataset presented in Figures 1 and 2- simple_landform1.tif - an example landform dataset presented in Figures 1 and 2- landcover_europe.tif - a land cover dataset with nine categories for Europe - landcover_europe.qml - a QGIS color style for the landcover_europe.tif dataset- landform_europe.tif - a landform dataset with 17 categories for Europe - landform_europe.qml - a QGIS color style for the landform_europe.tif dataset- map1.gpkg - a map of LTs in Europe constructed using the INCOMA-based method- map1.qml - a QGIS color style for the map1.gpkg dataset- map2.gpkg - a map of LTs in Europe constructed using the COMA method to identify and delineate pattern types in each theme separately- map2.qml - a QGIS color style for the map2.gpkg dataset- map3.gpkg - a map of LTs in Europe constructed using the map overlay method- map3.qml - a QGIS color style for the map3.gpkg dataset

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

Multispectral remote sensing data acquired by Landsat 8 Operational Land Imager (OLI) sensor were analyzed using an automated technique to generate surficial mineralogy and vegetation maps of the conterminous western United States. Six spectral indices (e.g. band-ratios), highlighting distinct spectral absorptions, were developed to aid in the identification of mineral groups in exposed rocks, soils, mine waste rock, and mill tailings across the landscape. The data are centered on the Western U.S. and cover portions of Texas, Oklahoma, Kansas, the Canada-U.S. border, and the Mexico-U.S. border during the summers of 2013 – 2014. Methods used to process the images and algorithms used to infer mineralogical composition of surficial materials are detailed in Rockwell and others (2021) and were similar to those developed by Rockwell (2012; 2013). Final maps are provided as ERDAS IMAGINE (.img) thematic raster images and contain pixel values representing mineral and vegetation group classifications. Rockwell, B.W., 2012, Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3190, 35 p. pamphlet, 5 map sheets, scale 1:100,000, http://doi.org/10.13140/RG.2.1.2769.9365. Rockwell, B.W., 2013, Automated mapping of mineral groups and green vegetation from Landsat Thematic Mapper imagery with an example from the San Juan Mountains, Colorado: U.S. Geological Survey Scientific Investigations Map 3252, 25 p. pamphlet, 1 map sheet, scale 1:325,000, http://doi.org/10.13140/RG.2.1.2507.7925. Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improved automated identification and mapping of iron sulfate minerals, other mineral groups, and vegetation from Landsat 8 Operational Land Imager Data: San Juan Mountains, Colorado, and Four Corners Region: U.S. Geological Survey Scientific Investigations Map 3466, scale 1:325,000, 51 p. pamphlet, https://doi.org/10.3133/sim3466/.

This dataset contains raster data, R scripts, and obtained results that are related to statistically rigorous methods for accuracy assessment and area estimation of forest change maps. These data can be used to run all simulations, comparisons, and examples described in RELATED MATERIALS 1. The R scripts can also be used for the accuracy assessment of thematic maps derived from other datasets.

The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 102 atolls of France (in the Pacific and Indian Oceans) as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). The data set provides one zip file per region of interest. Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent …Show full descriptionThis map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW. Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product. Soil data confidence is described using a 4 class system between high and very low as outlined below.: Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps. Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps. Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only. Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area. Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

Kenward-et-al_RADA_Buzzard_radio-tracking_dataData used to infer the resource needs of common buzzards (Buteo buteo) Dorset, southern UK. Inference was made by applying Resource-Area-Dependence Analysis (RADA) to a sample of 114 buzzard home ranges and a thematic map depicting resource distribution. The compressed archive contains the radio-tracking dataset, which consists of standardized 30 locations per home range obtained via VHF telemetry between 1990 and 1995. The thematic map, formed by using knowledge about buzzards to group 25 land-cover types of the Land Cover Map of Great Britain into 16 map classes, is available against permission at public site http://www.ceh.ac.uk/services/land-cover-map-1990. All coordinates are in UK National Grid format (EPSG 27700). The radio-tracking dataset is provided as: (i) .txt and (ii) .loc. The format in (ii) is native to the Ranges suite of software (http://www.anatrack.com/home.php) for the analysis of animal home ranging and habitat use. Sinc...

The dataset presents the estimated occurrence of less common tree species (other than pine and spruce) in the form of thematic maps covering entire area of Finland. The maps series represent the following years: 1994, 2002, 2009 and 2015. The tree species maps are based on geostatistical interpolation of field measurements from national forest inventory sample plots and satellite image-based forest resource estimates. The occurrence data is presented as the average volume (m3/ha) of the tree species in forestry land. The tree species maps are available as ESRI polygon shapefiles where Finland is divided into 1 x 1 km2 square polygons for which the tree species data is estimated. Koordinaattijärjestelmä: ETRS89 / ETRS-TM35FIN (EPSG:3067)

For this dataset, we used 50k soybean samples collected from thematic maps produced by the Global Land Analysis & Discovery group (GLAD).

This dataset contains two .zip files that hold supplementary data and information related to a review article "Area estimation and accuracy assessment for forest change maps derived from satellite data" by Shimizu (2023) in Journal of the Japanese Forestry Society.

The first .zip file contains supplementary figures and tables, which are almost identical to those in the review article.

The second .zip file includes raster data, R scripts, and obtained results. These data can be used to run all simulations, comparisons, and examples described in the review article. The R scripts can also be used for the accuracy assessment of thematic maps derived from other datasets.

This map provides a guide to the data confidence of DPIE's soil related thematic map products in NSW. Examples of products this map supports includes Land and Soil Capability mapping, Inherent fertility of soils in NSW and Great Soil Group soil types in NSW.

Confidence classes are determined based on the data scale, type of mapping and information collected, accuracy of the attributes and quality assurance on the product.

Soil data confidence is described using a 4 class system between high and very low as outlined below.:

• Good (1) - All necessary soil and landscape data is available at a catchment scale (1:100,000 & 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Moderate (2) - Most soil and landscape data is available at a catchment scale (1:100,000 - 1:250,000) to undertake the assessment of LSC and other soil thematic maps.

• Low (3) - Limited soil and landscape data is available at a reconnaissance catchment scale (1:100,000 & 1:250,000) which limits the quality of the assessment of LSC and other soil thematic maps.

• Very low (4) - Very limited soil and landscape data is available at a broad catchment scale (1:250,000 - 1:500,000) and the LSC and other soil thematic maps should be used as a guide only.

Online Maps: This dataset can be viewed using eSPADE (NSW’s soil spatial viewer), which contains a suite of soil and landscape information including soil profile data. Many of these datasets have hot-linked soil reports. An alternative viewer is the SEED Map; an ideal way to see what other natural resources datasets (e.g. vegetation) are available for this map area.

Reference: Department of Planning, Industry and Environment, 2020, Soil Data Confidence map for NSW, Version 4, NSW Department of Planning, Industry and Environment, Parramatta.

The 2023 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The generalized BG boundaries in this release are based on those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Dataset last updated: 8th January 2025 This dataset provides indicative areas of biodiversity hotspots in Greater London, identified by research and data analysis using methods derived from the Greater London Authority’s (GLA) “Planning for Biodiversity?” report (2016). The dataset has been created by Greenspace Information for Greater London CIC (GiGL). GiGL mobilises, curates and shares data that underpin our knowledge of London’s natural environment. We provide impartial evidence to enable informed discussion and decision-making in policy and practice. The dataset is based on GiGL partnership data which are continuously updated. The underlying data for the dataset may have been subject to changes since the current version was modelled. Subsequent versions will provide updated information from the GiGL database annually. The dataset is a coarse-resolution presentation of high-resolution data. To access data at their original resolution, please contact GiGL or visit www.gigl.org.uk for more information. Research for this dataset has been assisted by London and South East England Local Records Centres (LaSER) and the London Boroughs Biodiversity Forum (LBBF), and is based on advice provided by the Open Data Institute (ODI). Description To meet Policy G6 D of The London Plan (2021), the capital’s spatial development strategy, " Development proposals should manage impacts on biodiversity and aim to secure net biodiversity gain. This should be informed by the best available ecological information and addressed from the start of the development process". The Biodiversity Hotspots for Planning (BHP) dataset provides developers, homeowners and LPAs an indication of areas, where data are available, that have potential impacts on biodiversity and are likely to be relevant to local planning decisions by applying biodiversity criteria developed by GiGL, based on the original “Planning for Biodiversity?” research. ‘Hotspot’ areas indicate a detected presence of sensitive biodiversity that could potentially be affected by development. Original records can be accessed from GiGL to assist the decision-making process. N.B. 1: Areas without these biodiversity indicator records may still have undetected biodiversity so should also be considered for biodiversity potential on a case-by-case basis. N.B. 2: The dataset is purely indicative and an ecological data search report must still be commissioned as evidence for planning applications. See here for help on this. Specification The GIS file shows London as 100m hexagon tiles. Each tile is scored for the known presence of protected species, sites and habitats impact areas based on the impact buffer size as specified in the criteria table below, giving a cumulative score range of 0 to 3. Tiles are considered a hotspot where impact areas overlap the tile by more than 10%. Tiles with a score of 0 indicate that there are currently no known protected species, sites or habitats impact areas present in that area based on the criteria table, which excludes some protected species. Tiles with a score of 3 indicate the presence of impact areas for all three categories. Intermediate scores indicate the presence of impact areas for one or more of the categories without specifying which are present. The scores can be used in a thematic map to colour the tiles and visually indicate areas with greater presence of impact areas. A sample thematic map is provided. The dataset will be updated annually using the latest protected species, sites and habitats data available to GiGL at time of creation. This dataset is licenced under CC-BY (Creative Commons Attribution Licence). Please give GiGL appropriate credit when using, adapting or sharing the dataset following the guidance below: In-text citation: GiGL, [dataset creation date] Reference: "Biodiversity Hotspots for Planning" Greenspace Information for Greater London CIC, [dataset creation date] Where data is used in maps: Map displays GiGL data [dataset creation date] Where data is summarised but not mapped: Data provided by Greenspace Information for Greater London CIC [dataset creation date]

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

Two vector (.shp) files are provided. The first, (macro assemblages.shp) shows the modelled (random forest) macrofaunal assemblage type based on a clustering of abundance data from the OneBenthic database (see https://sway.office.com/HM5VkWvBoZ86atYP?ref=Link). The second file, (macro assemblages confidence.shp) shows associated confidence in the modelled output, with darker shades (high values) indicating higher confidence and lighter shades (lower values) indicating lower confidence. Both layers can be viewed in the OneBenthic Layers tool (https://rconnect.cefas.co.uk/onebenthic_layers/), together with further details of the methodology used to produce them.

The modelled layer for macrofaunal assemblage is based on a random forest modelling of point sample data from the OneBenthic (OB, https://rconnect.cefas.co.uk/onebenthic dashboard/) dataset, largely following the methodology in Cooper et al. (2019), but with an expanded dataset covering the Greater North Sea and including data from the EurOBIS (https://www.eurobis.org/) data repository. Of the 44,407 samples within OB, we selected a subset of 31,845 for which data were considered comparable (i.e. sample acquired using a 0.1 m2 grab or core, processed using a 1 mm sieve and not taken from a known impacted site). Colonial taxa were included and given a value of one. To take account of potential differences in taxonomic resolution between surveys, macrofaunal data were aggregated to family level using the taxonomic hierarchy provided by the World Register of Marine Species (https://www.marinespecies.org/). This reduced the number of taxa from 3,659 to 750. To address spatial autocorrelation in the data, and in keeping with the previous approach, samples closer than 50 m were removed from the dataset, reducing the overall number to 18,348. A fourth-root transformation was then applied to the data to down weight the influence of highly abundant taxa. Data were then subjected to clustering using k-means. A species distribution modelling approach, based on random forest, was then used to model cluster group (i.e. macrofaunal assemblage or biotope) identity across the study area (Greater North Sea). Cross-validation via repeated sub-sampling was done to evaluate the robustness of the model estimate and predictions to data sub-setting and to extract additional information from the model outputs to produce maps of confidence in the predicted distribution, following the approach described in Mitchell et al. (2018). The cross-validation was done on 10 split sample data sets with 75% used to train and 25% to test models, randomly sampled within the levels of the response variable to maintain the class balance. The final model output was plotted as the cluster class with the majority vote of all 10 model runs. An associated confidence map was produced by multiplying map layers for 1) the frequency of the most common class and ii) the average probability of the most common class. Model outputs are used in the OneBenthic Layers Tool (https://rconnect.cefas.co.uk/onebenthic_layers/).

Cooper, K.M.; Bolam, S.G.; Downie, A.-L.; Barry, J. 2019. Biological-based habitat classification approaches promote cost-efficient monitoring: An example using seabed assemblages. J. Appl. Ecol. 56:1085–1098. https://doi.org/10.1111/1365-2664.13381

Mitchell, P.J., Downie, A.-L., Diesing, M. How good is my map? 2018. A tool for semi-automated thematic mapping and spatially explicit confidence assessment. Env. Model. Softw. 108, 111–122. https://doi.org/10.1016/j.envsoft.2018.07.014

This layer is a georeferenced image of a map of the Ottoman Empire originally produced in 1893. "This is a key milestone in the history of Ottoman cartography, being the very first distance-time-route map of the entire Ottoman Empire. Published in Istanbul in 1893, it was produced by the General Staff (Fourth Division) of the Ottoman Army, predicated upon exhaustive highway surveys and itinerary records compiled over recent years. The map captures the scene during the middle of the rule of Sultan Abdul Hamid II (reigned 1876-1909), during which the empire still controlled vast territories in Europe, Asia and Africa, extending from Albania to Yemen and from Libya to the Persian Gulf. The Hamidian Era also marked a period of rapid modernization of the empire, including the creation of macadamized roads (highways), railways and modern ports. It also hailed the rise of highly sophisticated scientific and thematic cartography of the all regions of the realm created by Ottoman subjects, as opposed to Westerners. The main part of the map encompasses a great area, centred upon Anatolia, but taking in all the core regions of the Ottoman Empire, with its coverage extending from Bosnia, in the northwest, all the way down to Kuwait City and the head of the Persian Gulf, in southwest, and from Crimea and Baku, in the north and east, down to include Lower Egypt in the southwest. The scope is extended by insets that depict the extremities of the empire; in the lower right corner is an inset capturing the western Persian Gulf, including Kuwait, Bahrain, and Qatar; the inset above details the Red Sea, including Hejaz, Asir and Yemen; while the large inset in the lower felt depicts Ottoman Libya, as well as parts of French Tunisia and Algeria. Exclusively employing text in Ottoman Turkish, the map is traversed by hundreds of lines that connect every city and town of importance in the empire, representing the main land travel routes between these centres. Each segment is accompanied by a number that corresponds to the estimated average travel times between the points in hours (assuming travel by foot while marching, or travel with a horse at a slow trott). The travel times in hours roughly correspond to the distance in the Ottoman unit of a firsah (or league), which is equivalent to 5.685 km (3.532 miles). In the lower right, the map features a chart quantifying the routes between the most important centres. For instance, the map reveals that, on average, it took 18 hours to travel from the Red Sea port of Jeddah to the holy city of Mecca (a journey that would normally be divided into at least two, if not three, days). The present work is the first ever map to display the distances between all significant travel points in the Ottoman Empire, and for this reason it would have been vitally useful for soldiers, merchants and government bureaucrats when planning their itineraries. It was also one of the only maps to give an approximately accurate notion of the times and distance along several of the most important Hajj Routes, including the famous Syrian Hajj Road, being the 1307 km-long route from Damascus to Mecca, which is here measured out on the present map. The route itself is of such great historical significance that it is being considered by UNESCO for World Heritage Status, an unusual distinction for an itinerary, as opposed to a single, distinct place. Transportation had always been one of the great challenges confronting the Ottoman Empire. An astoundingly vast realm, spanning parts of three continents, and traversing some of the World’s most rugged and forbidding terrain, overland travel was especially difficult. Traditionally, the condition of the empire’s roads was deplorable; many places were connected only by crude caravan trails. For instance, before the introduction of railways, it took 14-16 days for a horse cart laden with produce to travel from Ankara to Istanbul, while the routes between centres even further part could take months to traverse. Throughout the 19th Century the territorial integrity of the empire was continually threatened and reduced by the Sublime Porte’s foreign and domestic enemies. The inability of the Ottoman Army to quickly deploy to military theatres severely limited the Sultan’s authority. Moreover, the extreme travel times between centres was hindering the empire’s ability to develop a modern industrial national economy, one of the government’s ultimate goals. Moreover, the empire was also home to Mecca and Medina, the two holiest sites of Islam, the latter of which was the destination of the Hajj, the world’s greatest pilgrimage. The Ottoman Sultan’s legitimacy rested upon his clam to being the Caliph of Islam, or the Defender of the Faith, which included a responsibility for the protection of pilgrims. As the routes to Mecca were often arduous, if not dangerous, this somewhat undercut the Sultan’s effectiveness as the ‘protector’, a matter which Abdul Hamid II would go to extraordinary efforts to ameliorate. Abdul Hamid II’s government relied heavily upon foreign capital and technical expertise to improve the country’s ports, build macadamized roads, and, most importantly, to create a comprehensive railway network. The present map depicts the rapidly expanding Ottoman railway system, just after a wave of development had revolutionized travel in the empire’s European domains, but just before an unprecedented boom in railway construction would do the same for Ottoman Asia. As shown, the Balkans are traversed by several railways; most notably as of 1888 the great port of Salonika (Thessaloniki) was connected to the rest of Europe by rail, while Istanbul was linked to the European system for the first time that same year, providing the direct route for the famed Orient Express, which commenced in 1889. One will also notice the first great leg of the Anatolian Railway that connected Istanbul to Ankara on December 31, 1892, completed only a matter of weeks before the present map was issued. The Anatolian Railway would subsequently be expanded with the ambition of reaching Iraq, creating the Baghdad Railway (a project which would become one of the great factors of World War I). The present map, however, predates the great railway boom that would occur in the Levant and Arabia, whereby from 1895 to 1908, major centres in Syria, Lebanon and Palestine would be linked, while the legendary Hejaz Railway would connect Damascus to Medina (within relatively close proximity to Mecca). The railways had a revolutionary effect upon the Ottoman Empire, spurring economic development, improving governance and facilitating military movement. The empire’s infrastructure projects and related economic development, administrative and military ventures were a catalyst leading to the creation of advanced thematic cartography in Istanbul. The Sublime Porte’s various organs (notably the War Ministry) provided generous funding for the creation of maps to assist the modernization of the country and the graphic recording of data. This dovetailed into the rise of a vibrant private publishing scene that enjoyed government patronage. Ottoman cartographers were initially schooled in the world’s most advanced cartographic methods by French and German instructors (while some Ottoman mapmakers even apprenticed in European geographic publishing houses), although by the late 1880s many Ottoman cartographers had gained the skills and experience to develop their own unique works with an Ottoman flair, well beyond duplicating Western methods. Ottoman cartographers were producing topographic and thematic maps of the highest sophistication and diversity, every bit as impressive as those of the best German and French and British mapmakers. However, these works, such as the present map, are today not nearly as well-known as they deserve to be. First, Ottoman thematic maps tend to be very rare today. They were almost invariably issued in only small print runs, while maps intended for practical use in the field, such as the present work, tended to perish, leaving few survivors. Second, Turkey’s switch from using Arabic-based script to Latin script, in 1928, ensured that many of the surviving Ottoman maps were discarded, as they could no longer be understood my most people. Third, the academic study of late Ottoman cartography, even in Turkey, has been haphazard, leaving many important realms of the subject almost completely untouched by modern authors. Hopefully, the present rise in interest in Ottoman cartography will lead to these maps receiving the attention they deserve viz. better known Western works. The present map is rare. While encountered another example a few years ago, the map only rarely appears on the market. We cannot trace any examples in institutions outside of Turkey. The library of the Harita Genel Müdürlüğü (General Command of Mapping) of the Turkish Army, in Ankara, holds an example that that has appeared as part of exhibitions." (Alexander Johnson, 2020)

Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This land cover dataset for Canada is produced using observation from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensors. An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities. - Land Cover of Canada - Cartographic Product Collection

This dataset contains the address points of the Grand-Duchy of Luxembourg. The dataset is structured according to the INSPIRE Annex I Theme - Addresses. The data has been derived from the Cadastral database.

For every parcel, where an address is known, the address point is located on the entrance of the main building(s).

The syntax of the addresses is compliant with the "CACLR" - database (https://www.services-publics.lu/caclr/index.action)

These addresses are used for the geolocalisation in the Geoportal (http://map.geoportal.lu)

They have been transposed into the INSPIRE data model and coordinate system.

The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 52 atolls of the Indian Ocean and Red Sea as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). The data set provides one zip file per country or region of interest. Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.