The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

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)

The Digital City Map (DCM) data represents street lines and other features shown on the City Map, which is the official street map of the City of New York. The City Map consists of 5 different sets of maps, one for each borough, totaling over 8000 individual paper maps. The DCM datasets were created in an ongoing effort to digitize official street records and bring them together with other street information to make them easily accessible to the public. The Digital City Map (DCM) is comprised of seven datasets; Digital City Map, Street Center Line, City Map Alterations, Arterial Highways and Major Streets, Street Name Changes (areas), Street Name Changes (lines), and Street Name Changes (points). All of the Digital City Map (DCM) datasets are featured on the Streets App All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of of additional information.Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes describing the basic properties of soil derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1).Attributes in this layer include:Soil Phase 1 and Soil Phase 2 - Phases identify characteristics of soils important for land use or management. Soils may have up to 2 phases with phase 1 being more important than phase 2.Other Properties - provides additional information important for agriculture.Additionally, 3 class description fields were added by Esri based on the document Harmonized World Soil Database Version 1.2 for use in web map pop-ups:Soil Phase 1 DescriptionSoil Phase 2 DescriptionOther Properties DescriptionThe layer is symbolized with the Soil Unit Name field.The document Harmonized World Soil Database Version 1.2 provides more detail on the soil properties attributes contained in this layer.Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant componentMore information on the Harmonized World Soil Database is available here.Other layers created from the Harmonized World Soil Database are available on ArcGIS Online:World Soils Harmonized World Soil Database - Bulk DensityWorld Soils Harmonized World Soil Database – ChemistryWorld Soils Harmonized World Soil Database - Exchange CapacityWorld Soils Harmonized World Soil Database – HydricWorld Soils Harmonized World Soil Database – TextureThe authors of this data set request that projects using these data include the following citation:FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Living Atlas Discussion GroupSoil Data Discussion GroupThe Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The National Forest Climate Change Maps project was developed by the Rocky Mountain Research Station (RMRS) and the Office of Sustainability and Climate to meet the needs of national forest managers for information on projected climate changes at a scale relevant to decision making processes, including forest plans. The maps use state-of-the-art science and are available for every national forest in the contiguous United States with relevant data coverage. Currently, the map sets include variables related to precipitation, air temperature, snow (including snow residence time and April 1 snow water equivalent), and stream flow.\Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the contiguous United States are ensemble mean values across 20 global climate models from the CMIP5 experiment (https://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1), downscaled to a 4 km grid. For more information on the downscaling method and to access the data, please see Abatzoglou and Brown, 2012 (https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312) and the Northwest Knowledge Network (https://climate.northwestknowledge.net/MACA/). We used the MACAv2- Metdata monthly dataset; monthly precipitation values (mm) were summed over the season of interest (annual, winter, or summer). Absolute and percent change were then calculated between the historical and future time periods.Historical (1975-2005) and future (2071-2090) precipitation and temperature data for the state of Alaska were developed by the Scenarios Network for Alaska and Arctic Planning (SNAP) (https://snap.uaf.edu). These datasets have several important differences from the MACAv2-Metdata (https://climate.northwestknowledge.net/MACA/) products, used in the contiguous U.S. They were developed using different global circulation models and different downscaling methods, and were downscaled to a different scale (771 m instead of 4 km). While these cover the same time periods and use broadly similar approaches, caution should be used when directly comparing values between Alaska and the contiguous United States.Raster data are also available for download from RMRS site (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/categories/us-raster-layers.html), along with pdf maps and detailed metadata (https://www.fs.usda.gov/rm/boise/AWAE/projects/NFS-regional-climate-change-maps/downloads/NationalForestClimateChangeMapsMetadata.pdf).

ABSTRACT The considerable volume of data generated by sensors in the field presents systematic errors; thus, it is extremely important to exclude these errors to ensure mapping quality. The objective of this research was to develop and test a methodology to identify and exclude outliers in high-density spatial data sets, determine whether the developed filter process could help decrease the nugget effect and improve the spatial variability characterization of high sampling data. We created a filter composed of a global, anisotropic, and an anisotropic local analysis of data, which considered the respective neighborhood values. For that purpose, we used the median to classify a given spatial point into the data set as the main statistical parameter and took into account its neighbors within a radius. The filter was tested using raw data sets of corn yield, soil electrical conductivity (ECa), and the sensor vegetation index (SVI) in sugarcane. The results showed an improvement in accuracy of spatial variability within the data sets. The methodology reduced RMSE by 85 %, 97 %, and 79 % in corn yield, soil ECa, and SVI respectively, compared to interpolation errors of raw data sets. The filter excluded the local outliers, which considerably reduced the nugget effects, reducing estimation error of the interpolated data. The methodology proposed in this work had a better performance in removing outlier data when compared to two other methodologies from the literature.

This is a polygon dataset of the strategic noise mapping of rail, which were identified as those rail exceeding the flow threshold of 30,000 vehicle passages per year, in the form of noise contours for the Lden (day, evening, night) period for Dublin and Cork agglomerations and the major roads outside of the agglomerations. The dB value represents the average decibel value during the Lden time. Any direct comparison of the Round 3 versus Round 2 results should be carefully considered, as changes to the model input datasets used between these rounds may be significant. This may especially apply to the terrain model used, while there may be improved building height data, & improved traffic flow data with fewer assumed flows. There may also be some revisions to the actual road network modelled in Round 3. The noise maps are the product of assimilating a collection of digital datasets, and over the last 10 years there has been significant improvements to the quality of the digital datasets describing the natural and built environment in Ireland. This has led to the strategic noise models giving much more reliable noise results with much less tendency to over predict the impact.

Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.The Farmland Mapping and Monitoring Program (FMMP) provides data to decision makers for use in planning for the present and future use of California's agricultural land resources. The data is a current inventory of agricultural resources. This data is for general planning purposes and has a minimum mapping unit of ten acres.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

Data includes reviews of different restaurants on Google Maps. There are 1100 comments in total and pictures of each comment in the data set. The data is labeled according to 4 classes (Taste, Menu, Indoor atmosphere, Outdoor atmosphere) for the artificial intelligence to predict. The dataset has been prepared in a way that can be used in both text processing and image processing fields.

The dataset contains the following columns: business_name, author_name, text, photo, rating, rating_category

IMPORTANT: The rating_category column is related to the photo of the review. If you want to use this dataset for NLP, you need to label it yourself. I will label it for you when I am available.

European Directive 2002/49/EC of 25 June 2002 on the assessment and management of environmental noise entails for EU Member States an assessment of environmental noise in the vicinity of major transport infrastructure (land and air) and in large agglomerations. This assessment is carried out in particular through the development of ‘so-called’ noise maps, the first series of which were drawn up in 2007 (1st deadline of the Directive) and 2012 (2nd deadline). Article L572-5 of the Environmental Code states that these maps are “reviewed, and if necessary revised, at least every five years”. Thus, the implementation of this review leads, in 2017 and as appropriate, to revise or renew the maps previously developed.

Strategic Noise Maps (CBS) are designed to allow for the overall assessment of exposure to noise and to forecast its evolution.

CBS are required in particular for road infrastructure with annual traffic of more than 3 million vehicles per year. For major road and rail transport infrastructure, the CBS are established, decided and approved under the authority of the prefect of the department.

Noise maps are developed according to the indicators established by the European Directive, namely Lden (Day Evening Night Level) and Ln (Night Level). • Day/day: [6h-18h] • Evening/evening: [18h-22h] • Night/night: [22h-6h] The Lden and Ln indicators correspond to a defined energy average over the periods (Day/Black/Night) for Lden and (Night) for Ln. The corresponding results are expressed in A or dB(A) weighted decibels.

Type C maps represent areas where noise limit values are exceeded for residential, educational and health buildings. For road and high-speed railway lines, the limit values are 68 dB(A) in Lden and 62 dB(A) in Ln.

For a list of pathways, please refer to the non-technical summary (page 9).

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

This is a polygon dataset of the strategic noise mapping of rail, which were identified as those rail exceeding the flow threshold of 30,000 vehicle passages per year, in the form of noise contours for the Lnight (night) period for Dublin and Cork agglomerations and the major rail outside of the agglomerations. The dB value represents the average decibel value during the Lnight time. Any direct comparison of the Round 3 versus Round 2 results should be carefully considered, as changes to the model input datasets used between these rounds may be significant. This may especially apply to the terrain model used, while there may be improved building height data, & improved traffic flow data with fewer assumed flows. There may also be some revisions to the actual road network modelled in Round 3. The noise maps are the product of assimilating a collection of digital datasets, and over the last 10 years there has been significant improvements to the quality of the digital datasets describing the natural and built environment in Ireland. This has led to the strategic noise models giving much more reliable noise results with much less tendency to over predict the impact.

This Story Map locates some of the important geographical sites that were part of the many years Ernest Hemingway spent in Idaho's Wood River Valley and the surrounding regions. This project is an ongoing collaboration between the North Idaho College English Department, University of Idaho Library Data & Digital Services Department, and the Ketchum Community Library. Our map is intended to visualize the cartography of Hemingway's Idaho travels and provide the locations of several Hemingway legacy sites, such as his grave and the Hemingway Memorial. We hope to provide students, Hemingway aficionados, and scholars new perspectives on the diversity and range of his time spent in Idaho.

Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.

Established in 1982, Government Code Section 65570 mandates FMMP to biennially report on the conversion of farmland and grazing land, and to provide maps and data to local government and the public.