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

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.

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)

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/.

A Collection of Contextual data for USA

These are anonymized responses to a survey of 389 members of the UK public on their perceptions towards different maps about the social determinants of health. It was originally collected as part of a study described in the article 'Do personal narratives make thematic maps more persuasive? Integrating concrete examples into maps of the social determinants of health', in the Cartography and Geographic Information Science journal.The responses were collected in September 2024 on Qualtrics, via the recruitment platform Prolific.Participants were shown information on three social determinants of health (public transport, air pollution, youth services). For each topic, they were randomly shown one of three maps with varying levels of personal narratives presented. The type of map shown to each respondent can be found in columns 'transport_condition', 'pollution_condition', and 'youth_condition'. Most of the other variables refer to perceptions about those issues. For example, 'severity_pollution' refers to whether they deem air pollution a severe issue facing the country. Other variables include demographic information, chart literacy measured by four questions, and self-assessed confidence with charts.

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 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)

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 29 atolls of Australia as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). 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).

To deliver sample estimates provided with the necessary probability foundation to permit generalization from the sample data subset to the whole target population being sampled, probability sampling strategies are required to satisfy three necessary not sufficient conditions: (i) All inclusion probabilities be greater than zero in the target population to be sampled. If some sampling units have an inclusion probability of zero, then a map accuracy assessment does not represent the entire target region depicted in the map to be assessed. (ii) The inclusion probabilities must be: (a) knowable for nonsampled units and (b) known for those units selected in the sample: since the inclusion probability determines the weight attached to each sampling unit in the accuracy estimation formulas, if the inclusion probabilities are unknown, so are the estimation weights. This original work presents a novel (to the best of these authors' knowledge, the first) probability sampling protocol for quality assessment and comparison of thematic maps generated from spaceborne/airborne Very High Resolution (VHR) images, where: (I) an original Categorical Variable Pair Similarity Index (CVPSI, proposed in two different formulations) is estimated as a fuzzy degree of match between a reference and a test semantic vocabulary, which may not coincide, and (II) both symbolic pixel-based thematic quality indicators (TQIs) and sub-symbolic object-based spatial quality indicators (SQIs) are estimated with a degree of uncertainty in measurement in compliance with the well-known Quality Assurance Framework for Earth Observation (QA4EO) guidelines. Like a decision-tree, any protocol (guidelines for best practice) comprises a set of rules, equivalent to structural knowledge, and an order of presentation of the rule set, known as procedural knowledge. The combination of these two levels of knowledge makes an original protocol worth more than the sum of its parts. The several degrees of novelty of the proposed probability sampling protocol are highlighted in this paper, at the levels of understanding of both structural and procedural knowledge, in comparison with related multi-disciplinary works selected from the existing literature. In the experimental session the proposed protocol is tested for accuracy validation of preliminary classification maps automatically generated by the Satellite Image Automatic MapperTM (SIAMTM) software product from two WorldView-2 images and one QuickBird-2 image provided by DigitalGlobe for testing purposes. In these experiments, collected TQIs and SQIs are statistically valid, statistically significant, consistent across maps and in agreement with theoretical expectations, visual (qualitative) evidence and quantitative quality indexes of operativeness (OQIs) claimed for SIAMTM by related papers. As a subsidiary conclusion, the statistically consistent and statistically significant accuracy validation of the SIAMTM pre-classification maps proposed in this contribution, together with OQIs claimed for SIAMTM by related works, make the operational (automatic, accurate, near real-time, robust, scalable) SIAMTM software product eligible for opening up new inter-disciplinary research and market opportunities in accordance with the visionary goal of the Global Earth Observation System of Systems (GEOSS) initiative and the QA4EO international guidelines. Overlapping area matrices between:(A) the QuickBird-like Satellite Image Automatic MapperTM (Q-SIAMTM) preliminary classification maps at fine, intermediate, coarse semantic granularity (52, 28 and 12 spectral categories) generated from three very high resolution (VHR) test images: WorldView-2 T1, WorldView-2 T2, QuickBird-2 and(B) Reference thematic samples belonging to 7 land cover classes, selected by Michael Humber in the three VHR test images.Quality indicators of an Overlapping area matrix: Overall accuracy, Producer's accuracy, User's accuracy, Categorical Variable Pair Similarity Index.Files contain three test maps each: Q-SIAMTM at fine, intermediate, coarse granularity; One reference land cover class set: 7

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 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).

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

As part of the U.S. Geological Survey's National Water-Quality Assessment Program, an investigation of the Yellowstone River Basin study unit is being conducted to document status and trends in surface- and ground-water quality. Surface-water samples are collected from streams (or lakes) at specific sampling stations. Water-quality characteristics at each station are influenced by the natural and cultural characteristics of the drainage area upstream from the sampling station. Efficient quantification of the drainage area characteristics requires a digital map of the drainage area boundary that may be processed, together with other digital thematic maps (such as geology or land use), in a geographic information system (GIS). Digital drainage-area data for 24 selected stream-sampling stations in the Yellowstone River Basin are included in this data release. The drainage divides were identified chiefly using 1:100,000-scale (50 m accuracy) hypsography. Drainage areas based on 1:100,000-scale hypsography data generally agree to within 5 percent with drainage areas measured at 1:24,000 scale, for areas larger than 50 km2.

The 2019 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. After each decennial census, the Census Bureau delineates Public Use Microdata Areas (PUMAs) for the tabulation and dissemination of decennial census Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) PUMS data, and ACS period estimates. Nesting within states, or equivalent entities, PUMAs cover the entirety of the United States, Puerto Rico, Guam, and the U.S. Virgin Islands. PUMA delineations are subject to population, building block geography, geographic nesting, and contiguity criteria. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name. Pumas are avialiable at the 1:500,000

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.

Nurses play a crucial role in suicide prevention, yet the integration of artificial intelligence and machine learning technologies into nursing practice remains understudied. This research examines how these technologies can enhance nurses’ ability to identify and intervene with at-risk patients. A systematic bibliometric analysis and thematic mapping approach was employed. The Web of Science database was searched for relevant publications from January 2019 to October 2024. The initial search yielded 883 publications, with 257 meeting the inclusion criteria after systematic screening. Analysis revealed six distinct research clusters, with machine learning-based behavioral prediction emerging as the dominant theme. Findings indicate significant potential for integrating artificial intelligence-supported tools into nursing workflows, particularly in risk assessment and early intervention. Natural language processing and ecological momentary assessment emerged as promising approaches for enhancing nurse-patient communication and monitoring. These findings suggest opportunities for nurses to leverage artificial intelligence technologies in suicide prevention while maintaining the essential human element of care. This study provides evidence-based guidance for nurses implementing artificial intelligence-supported suicide prevention tools while maintaining therapeutic relationships and professional judgment in clinical practice.

This dataset is designed to represent and identify the general locations of city and state maintained parks within Lexington-Fayette County. One private park, Triangle Park, is included in the dataset due to its integration into downtown Lexington. The dataset is programmatically created and updated by converting the polygon centroids of the LFUCG Park boundary polygon layer to a point layer . The park property inventory is maintained by the LFUCG Division of Parks and changes are conveyed to the GIS Office for inclusion. This dataset participates in a topology with the parcel dataset to assure coincident geometry during parcel editing.As part of the basemap data layers, the park point map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, and presentation output).

The 2019 cartographic boundary KMLs 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.

      After each decennial census, the Census Bureau delineates Public Use Microdata Areas (PUMAs) for the tabulation and dissemination of decennial census Public Use Microdata Sample (PUMS) data, American Community Survey (ACS) PUMS data, and ACS period estimates. Nesting within states, or equivalent entities, PUMAs cover the entirety of the United States, Puerto Rico, Guam, and the U.S. Virgin Islands. PUMA delineations are subject to population, building block geography, geographic nesting, and contiguity criteria. Each PUMA is identified by a 5-character numeric census code that may contain leading zeros and a descriptive name. Pumas are avialiable at the 1:500,000

Dataset accompanying the publication "Introduction to cross-section spatial econometric models with applications in R". This paper introduces the spatial component in cross-section econometric estimations and specifically, the spatial dependence effect inherent in some of the variables involved in the modelling process. First, the spatial structure of the data from thematic maps is observed and Moran's spatial autocorrelation indicators are presented. Subsequently, the spatial weights matrix is built under different specifications. Finally, several modelling specification strategies are shown and the interpretation of the estimated coefficients. The theoretical concepts are illustrated with examples and their corresponding R software codes. This code and databases are available in this repository. Exploratory Spatial Data Analysis (ESDA) and spatial econometrics.