This dataset is a sample from the TalkingData AdTracking competition. I kept all the positive examples (where is_attributed == 1), while discarding 99% of the negative samples. The sample has roughly 20% positive examples.

For this competition, your objective was to predict whether a user will download an app after clicking a mobile app advertisement.

File descriptions

train_sample.csv - Sampled data

Data fields

Each row of the training data contains a click record, with the following features.

• ip: ip address of click.
• app: app id for marketing.
• device: device type id of user mobile phone (e.g., iphone 6 plus, iphone 7, huawei mate 7, etc.)
• os: os version id of user mobile phone
• channel: channel id of mobile ad publisher
• click_time: timestamp of click (UTC)
• attributed_time: if user download the app for after clicking an ad, this is the time of the app download
• is_attributed: the target that is to be predicted, indicating the app was downloaded

Note that ip, app, device, os, and channel are encoded.

I'm also including Parquet files with various features for use within the course.

In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

The SWOT Level 2 Lake Single-Pass Vector Prior Data Product from the Surface Water Ocean Topography (SWOT) mission provides water surface elevation, area, storage change derived from the high rate (HR) data stream from the Ka-band Radar Interferometer (KaRIn). SWOT launched on December 16, 2022 from Vandenberg Air Force Base in California into a 1-day repeat orbit for the "calibration" or "fast-sampling" phase of the mission, which completed in early July 2023. After the calibration phase, SWOT entered a 21-day repeat orbit in August 2023 to start the "science" phase of the mission, which is expected to continue through 2025. Water surface elevation, area, and storage change are provided in three feature datasets covering the full swath for each continent-pass: 1) an observation-oriented feature dataset of lakes identified in the prior lake database (PLD), 2) a feature dataset of lakes identified in the PLD, and 3) a feature dataset containing unassigned features (i.e., not identified in PLD nor prior river database (PRD)). These data are generally produced for inland and coastal hydrology surfaces, as controlled by the reloadable KaRIn HR mask. The dataset is distributed in ESRI Shapefile format. This collection is a sub-collection of its parent: https://podaac.jpl.nasa.gov/dataset/SWOT_L2_HR_LakeSP_2.0 It contains feature datasets of lakes identified in the PLD.

This dataset contains features for 44 centralized and decentralized communication applications. In total, 77 different features identified in these 44 applications.

The BLM's VRI is a systematic process for:

Assessing and rating the intrinsic scenic quality of a particular tract of land, through the Scenic Quality Rating process; Measuring the public concern for the scenic quality of the tract, through the Sensitivity Level Analysis; and Classifying the distance from which the landscape is most commonly viewed, through delineation of distance zones. Based on the outcome of the VRI, BLM-administered lands are assigned to one of four VRI classes. VRI Class I lands have the greatest relative visual values, and VRI Class IV lands have the lowest relative visual values.

Guidance regarding BLM's VRI process is contained in BLM Manual 8410 - Visual Resource Inventory (Issued 1986, 28 pp). See the BLM VRI page within the Visual Resource Inventory section of this website for more detailed information on BLM's visual resource inventory process.

Abstract

This dataset was derived by the Bioregional Assessment Programme from the GEODATA TOPO 250K Series 3 dataset (GUID: a0650f18-518a-4b99-a553-44f82f28bb5f). The source dataset is identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This dataset is a copy of the original Geodata Topo 250k Series 3 data, converted from Personal (Microsoft Access) Databases, to ESRI File Geodatabases. This was done to ensure .mdb lock files would not restrict map makers from using the topographic data in their cartographic products. The data and folders are structured the same as the original dataset.

Dataset History

A new file geodatabase schema was created in the same structure as the original .mdb data (including database and feature dataset names and projections). Feature Classes were then copied from the .mdb format to the .gdb format, using ArcCatalog 10.0.

Dataset Citation

Bioregional Assessment Programme (2014) GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb). Bioregional Assessment Derived Dataset. Viewed 09 October 2018, http://data.bioregionalassessments.gov.au/dataset/96ebf889-f726-4967-9964-714fb57d679b.

Dataset Ancestors

• Derived From GEODATA TOPO 250K Series 3

24K Hydro File Geodatabase, including bank lines, flow lines, junction points, hydro lines, water bodies, hydro points, and a network. Access the user guide, data dictionaries, and metadata below.The DNR Hydrography database was developed statewide using several 1:24,000-scale sources. This data layer includes information about surface water features represented on the USGS 1:24,000-scale topographic map series such as perennial and intermittent streams, lakes, etc. Because the sources of the Hydrography data span many years and originate from several sources, the data may reflect areas of transition from one source to another. As a result, the water features as represented in the Hydrography data may not always match what you see on a particular USGS quad or Digital Raster Graphic (DRG). General source information is presented on this map: Wisconsin Hydrography Source Information. Note: Wetlands delineations are not included in the DNR Hydrography data layer. For information about DNR Wetlands data, see the Wisconsin Wetland Inventory web page.Report errors in this data to Dennis Wiese (dennis.wiese@wisconsin.gov) with the following information:HYDROID of the feature in question; OR if the feature is missing, a location coordinate or description (e.g. latitude/longitude, Public Land Survey System Township, Range, and Section identifier) that identifies the area in question.Optional but very helpful: a screen capture of the area in question, or the Water Body Identification Code (WBIC) of the feature in question.DNR staff can access the hydrography database in the agency's central GIS data repository. The hydrography feature classes are stored in the feature dataset "W23324.WD_HYDRO_DATA_24K".USER GUIDES AND DOCUMENTATION: WDNR_HYDRO_24k_GETTING STARTED WDNR HYDRO 24K UPDATES DOCUMENT 24K HYDRO DECISION RULESData Dictionaries and Metadata WDNR_HYDRO_24k_waterbody_data_dict WDNR_HYDRO_24k_waterbody_metadata WDNR_HYDRO_24k_flowline_data_dict WDNR_HYDRO_24k_flowline_metadata WDNR_HYDRO_24k_bank_data_dict WDNR_HYDRO_24k_bank_metadata WDNR_HYDRO_24k_junction_data_dict WDNR_HYDRO_24k_junction_metadata WDNR_HYDRO_24k_line_data_dict WDNR_HYDRO_24k_line_metadata WDNR_HYDRO_24k_flowline_wbic_data_dict WDNR_HYDRO_24k_flowline_wbic_metadata WDNR_HYDRO_24k_waterbody_wbic_data_dict WDNR_HYDRO_24k_waterbody_wbic_metadataArcMap Layer (.lyr) Files 24k Hydro Flowline Duration 24k Hydro Bank Lines 24k Hydro Flowline Streams 24k Hydro Waterbody Open Water

🇦🇺 Australia English Export Data Access API NSW Features of Interest Category - Place Area Please Note WGS 84 = GDA94 service This dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS84 = GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that these original services will adopt the new multiCRS functionality. Metadata Portal Metadata InformationContent TitleNSW Features of Interest Category - Place AreaContent TypeHosted Feature LayerDescriptionPlace Area is a polygon feature class defining a named place.Themes included in the Place Area include:Region - A region is a relatively large tract of land distinguished by certain common characteristics, natural or cultural. Natural unifying features could include same drainage basin, similar landforms, or climatic conditions, a special flora or fauna, or the like. This polygon feature dataset is part of Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the region dataset align to Spatial Services Defined Administrative Data Sets.Locality - A bounded area within the landscape that has a rural character. This polygon feature dataset is part of Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the locality dataset align to Spatial Services Defined Administrative Data Sets.City - A centre of population, commerce and culture with all essential services; a town of significant size and importance, generally accorded the legal right to call itself a city under, either, the Local Government Act, the Crown Lands Act or other instruments. This polygon feature dataset is part of Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the city dataset align to Spatial Services Defined Administrative Data Sets.Village - A cohesive populated place in a rural landscape, which may provide a limited range of services to the local area. Residential subdivisions are in urban lot sizes. This polygon feature dataset is part of Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the village dataset align to Spatial Services Defined Administrative Data SetsTown - A commercial nucleus offering a wide range of services and a large number of shops, often several of the same type. Depending on size, the residential area can be relatively compact or (in addition) dispersed in clusters on the periphery. This polygon feature dataset is Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the town dataset align to the Spatial Services Defined Administrative Data Sets.Suburb - A gazetted boundary of a suburb or locality area as defined by the Geographical Names Board of NSW. This polygon feature dataset is part of Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the suburb dataset align to Spatial Services Defined Administrative Data Sets.Urban Place - A place, site or precinct in an urban landscape, the name of which is in current use, but the limits of which have not been defined under the address locality program. This polygon feature dataset is part of Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the urban place dataset align to Spatial Services Defined Administrative Data Sets.Rural Place - A place, site or precinct in a rural landscape, generally of small extent, the name of which is in current use. This polygon feature dataset is part of Spatial Services Defined Administrative Data Sets. Where possible, polygon geometries of the rural place dataset align to Spatial Services Defined Administrative Data Sets.Initial Publication Date06/02/02020Data Currency01/01/3000Data Update FrequencyOtherContent SourceData provider filesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.auData Theme, Classification or Relationship to other DatasetsNSW Features of Interest Category.AccuracyThe dataset maintains a positional relationship to, and alignment with, a range of themes from the NSW FSDF including, transport, imagery, positioning, water and land cover. This dataset was captured by utilising the best available source at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:3857WGS84 Equivalent ToGDA94Spatial ExtentFull StateContent LineageFor additional information, please contact us via the Spatial Services Customer HubData ClassificationUnclassifiedData Access PolicyOpenData QualityFor additional information, please contact us via the Spatial Services Customer HubTerms and ConditionsCreative CommonsStandard and SpecificationOpen Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Data CustodianDCS Spatial Services346 Panorama Ave Bathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama Ave Bathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama Ave Bathurst NSW 2795Additional Supporting InformationData DictionariesTRIM Number

As part of the National Fish and Wildlife Foundation (NFWF)-funded Monitoring Hurricane Sandy Beach and Marsh Resilience in New York and New Jersey project (NFWF project ID 2300.16.055110), the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) is using remotely-sensed data and targeted in-situ observations to monitor the post-restoration evolution of beaches, dunes, vegetative cover, and sediment budgets at seven post-Hurricane Sandy beach and marsh restoration sites in New York and New Jersey. These data and derived ecological resilience metrics will be used to assess the cost-effectiveness and ecological benefits of the restoration techniques and evaluate how the restored parts of the coast have changed through time. This dataset links to coastal land-cover and feature data derived from Landsat satellite imagery acquired between 2008 and 2022 from Delaware Bay, New Jersey to Shinnecock Bay, New York.

This line feature class contains Deschutes County, Oregon roads with measures for locating information along the routes based on a mileage from a starting point on the road.

Dataset

This dataset was created by Ankit Sharma

Contents

Anatomical-multi-view data, where each brain anatomical structure is described with multiple feature sets.

SLIDO-4.5 is an Esri ArcGIS version 10.7 file geodatabase which can be downloaded here: https://www.oregon.gov/dogami/slido/Pages/data.aspx The geodatabase contains two feature datasets (a group of datasets within the geodatabase) containing six feature classes total, as well as two raster data sets, one individual table, and two individual feature classes. The original studies vary widely in scale, scope and focus which is reflected in the wide range of accuracy, detail, and completeness with which landslides are mapped. In the future, we propose a continuous update of SLIDO. These updates should take place: 1) each time DOGAMI publishes a new GIS dataset that contains landslide inventory or susceptibility data or 2) at the end of each winter season, a common time for landslide occurrences in Oregon, which will include recent historic landslide point data. In order to keep track of the updates, we will use a primary release number such as Release 4.0 along with a decimal number identifying the update such as 4.5.

The data release (Bernier, 2021) associated with this metadata record serves as an archive of coastal land-cover and feature datasets derived from Landsat satellite imagery at the northern Chandeleur Islands, Louisiana. To minimize the effects of tidal water-level variations, 75 cloud-free, low-water images acquired between 1984 and 2019 were analyzed. Water, bare earth (sand), vegetated, and intertidal land-cover classes were mapped from Hewes Point to Palos Island using successive thresholding and masking of the modified normalized difference water index (mNDWI), the normalized difference bare land index (NBLI), and the normalized difference vegetation index (NDVI). Vector shoreline, sand, and vegetated feature extents were extracted for each image by contouring the spectral indices using the calculated threshold values. The geographic information system (GIS) data files with accompanying formal Federal Geographic Data Committee (FGDC) metadata can be downloaded from https://doi.org/10.5066/P9HY3HOR.

Machining Feature Recognition (MFR) is the process of grouping geometric and topological elements from CAD models from the perspective of manufacturing requirements to obtain machining features with specific engineering semantics, such as holes, slots, steps and fillets. This dataset contains 100,000 synthetic CAD models with machining feature labels, supporting data-driven deep learning methods for machining feature recognition. The CADSynth dataset provides three types of data files for each CAD model. These include a STEP file, which records the three-dimensional shape of the CAD model (compliant with ISO 10303-21 and GB/T 16656 standards), a JSON file that records the machining feature labels corresponding to each topological face (compliant with ECMA-404 and ISO/IEC 21778:2017 standards), and a B-rep data graph representation file (with a .bin extension), which can be used for deep learning training (formatted for the Deep Graph Library, an open-source graph neural network framework).

Interstates are a definition query from RoadInvReadOnly. Map Service & Feature Service is a line feature class from the Transportation feature dataset. Intended for use in any desktop maps or visual display in Portal web maps and web applications. Scale range displaying max out beyond map scale of 1:2,000,000 and the label class scale range max out to 24,000. Definition Query by ST_CODE field used to publish the service. No the Time zone property set.

This feature class is part of the Cadastral National Spatial Data Infrastructure (NSDI) CADNSDI publication data set for rectangular and non-rectangular Public Land Survey System (PLSS) data set. The metadata description in the Cadastral Reference System Feature Data Set more fully describes the entire data set. This feature class is the second division of the PLSS is quarter, quarter-quarter, sixteenth or government lot divisions of the PLSS. The second and third divisions are combined into this feature class as an intentional de-normalization of the PLSS hierarchical data. The polygons in this feature class represent the smallest division to the sixteenth that has been defined for the first division. For example In some cases sections have only been divided to the quarter. Divisions below the sixteenth are in the Special Survey or Parcel Feature Class.

SLIDO-4.5 is an Esri ArcGIS version 10.7 file geodatabase which can be downloaded here: https://www.oregon.gov/dogami/slido/Pages/data.aspx The geodatabase contains two feature datasets (a group of datasets within the geodatabase) containing six feature classes total, as well as two raster data sets, one individual table, and two individual feature classes. The original studies vary widely in scale, scope and focus which is reflected in the wide range of accuracy, detail, and completeness with which landslides are mapped. In the future, we propose a continuous update of SLIDO. These updates should take place: 1) each time DOGAMI publishes a new GIS dataset that contains landslide inventory or susceptibility data or 2) at the end of each winter season, a common time for landslide occurrences in Oregon, which will include recent historic landslide point data. In order to keep track of the updates, we will use a primary release number such as Release 4.0 along with a decimal number identifying the update such as 4.5.

The Geodatabase to Shapefile Warning Tool examines feature classes in input file geodatabases for characteristics and data that would be lost or altered if it were transformed into a shapefile. Checks include:
1) large files (feature classes with more than 255 fields or over 2GB), 2) field names longer than 10 characters
string fields longer than 254 characters, 3) date fields with time values 4) NULL values, 5) BLOB, guid, global id, and raster field types, 6) attribute domains or subtypes, and 7) annotation or topology

The results of this inspection are written to a text file ("warning_report_[geodatabase_name]") in the directory where the geodatabase is located. A section at the top provides a list of feature classes and information about the geodatabase as a whole. The report has a section for each valid feature class that returned a warning, with a summary of possible warnings and then more details about issues found.

The tool can process multiple file geodatabases at once. A separate text file report will be created for each geodatabase. The toolbox was created using ArcGIS Pro 3.7.11.

For more information about this and other related tools, explore the Geospatial Data Curation toolkit

Export Data Access API NSW Features of Interest Category - Education FacilitiesPlease NoteWGS 84 service aligned to GDA94This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments.In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionality. Metadata Portal Metadata InformationContent TitleNSW Features of Interest Category - Education FacilitiesContent TypeHosted Feature LayerDescription The Features of Interest category Education Services is part of the Building Complex feature class and is represented as a community facility.Features that make up the NSW Features of interest Category - Education services include:Primary School - An education facility that caters for 5- 12 year olds, private and public. This point feature dataset is part of the Features of interest Category. Primary school data points are positioned within the cadastral parcel in which they are located.High School - An education facility that caters for 12 – 18 year olds, private and public. This point feature dataset is part of the Features of interest Category. High school data points are positioned within the cadastral parcel in which they are located.Combined Primary and Secondary School - A facility used for full-time primary and secondary instruction of children, typically aged 5 to 17. This point feature dataset is part of the Features of interest Category. Combined primary and secondary school data points are positioned within the cadastral parcel in which they are located.Pre School - A facility used for the tuition of young children prior to school age, usually children under the age of five. This point feature dataset is part of the Features of interest Category. Pre-school data points are positioned within the cadastral parcel in which they are located.Technical College - Post secondary (TAFEs) education excluding University. This point feature dataset is part of the Features of interest Category. Technical college data points are positioned within the cadastral parcel in which they are located.University - An educational institution for both instruction and examination in the higher branches of knowledge with the power to confer degrees. This point feature dataset is part of the Features of interest Category. University data points are positioned within the cadastral parcel in which they are located.These features do not fit within one of the ten foundation spatial data themes and are therefore classified as a category. They have historically been captured by Spatial Services as part of the NSW topographic mapping program and therefore warrant inclusion.nitial Publication Date04/02/02020Data Currency01/01/3000Data Update FrequencyOtherContent SourceData provider filesFile TypeESRI File Geodatabase (*.gdb)Attribution© State of New South Wales (Spatial Services, a business unit of the Department of Customer Service NSW). For current information go to spatial.nsw.gov.auData Theme, Classification or Relationship to other DatasetsNSW Features of Interest Category.AccuracyThe dataset maintains a positional relationship to, and alignment with, a range of themes from the NSW FSDF including, transport, imagery, positioning, water and land cover. This dataset was captured by utilising the best available source at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program to upgrade the spatial location and accuracy of data is ongoing.Spatial Reference System (dataset)GDA94Spatial Reference System (web service)EPSG:3857WGS84 Equivalent ToGDA94Spatial ExtentFull StateContent LineageFor additional information, please contact us via the Spatial Services Customer HubData ClassificationUnclassifiedData Access PolicyOpenData QualityFor additional information, please contact us via the Spatial Services Customer HubTerms and ConditionsCreative CommonsStandard and SpecificationOpen Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Data CustodianDCS Spatial Services346 Panorama Ave Bathurst NSW 2795Point of ContactPlease contact us via the Spatial Services Customer HubData AggregatorDCS Spatial Services346 Panorama Ave Bathurst NSW 2795Data DistributorDCS Spatial Services346 Panorama Ave Bathurst NSW 2795Additional Supporting InformationData DictionariesTRIM Number