This dataset comprises of a set of individual RDF files. It contains 3 data layers that cover Austria (namely Administrative data, Snow cover data, and Crop-type data), and each layer is also divided geospatially. Thus, each RDF file contains only one thematic layer and refers to a specific area.

This data can be used in the context of experimenting with federated query processors. Thus, if we deploy each RDF file in a separate GeoSPARQL endpoint, we will have a resulting federation of 34 GeoSPARQL source endpoints. The objective of this scenario is to evaluate the effectiveness of a source selection mechanism of a federation engine, and, in particular, if the source selector is aware of the geospatial nature of the source endpoints.

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Overview

GeoQuestions1089 is a crowdsourced geospatial question-answering dataset that targets the Knowledge Graph YAGO2geo. It contains 1089 triples of geospatial questions, their answers, and the respective SPARQL/GeoSPARQL queries.

It has been used to benchmark two state of the art Question Answering engines, GeoQA2 and the engine of Hamzei et al.

Repository information

The official code repository for this dataset is located on GitHub.

Dataset

The dataset is described in the following paper (also used to cite the dataset):

@inproceedings{10.1007/978-3-031-47243-5_15,
 title = {Benchmarking Geospatial Question Answering Engines Using the Dataset GeoQuestions1089},
 author = {Sergios-Anestis Kefalidis, Dharmen Punjani, Eleni Tsalapati, 
     Konstantinos Plas, Mariangela Pollali, Michail Mitsios, 
     Myrto Tsokanaridou, Manolis Koubarakis and Pierre Maret},
 booktitle = {The Semantic Web - {ISWC} 2023 - 22nd International Semantic Web Conference,
      Athens, Greece, November 6-10, 2023, Proceedings, Part {II}},
 year = {2023}
}

Shortly, the GeoQuestions1089 dataset consists of two parts, which we will refer to as GeoQuestions_c and GeoQuestions_w both of which target the union of YAGO2 and YAGO2geo.

GeoQuestions_c consits of 1017 entries and GeoQuestions_w of 72 entries. The difference between the two is that the natural language questions of GeoQuestions_w contain grammatical, syntactical and spelling mistakes.

Current version of the dataset

The aforementioned paper describes version 1.0. The latest available version is 1.1.

Version 1.1 includes several enhancements: - Uniform query format and variable naming - Fixes in natural language capitalization - Corrections in query categorization - Replacement of stSPARQL functions with GeoSPARQL functions where applicable - Minor improvements in query correctness of existing queries - A few triples that were erroneous (resulting from incorrect file modifications and text editing) have been replaced by correct ones.

These updates ensure greater consistency and accuracy in the dataset, making it a more reliable resource for geospatial QA research.

Categories

The questions of the dataset are split into 9 categories:

1. Asking for a thematic or a spatial attribute of a feature, e.g., Where is Loch Goil located?
2. Asking whether a feature is in a geospatial relation with another feature or features, e.g., Is Liverpool east of Ireland?
3. Asking for features of a given class that are in a geospatial relation with another feature, e.g., Which counties border county Lincolnshire?
4. Asking for features of a given class that are in a geospatial relation with any features of another class, e.g., Which churches are near castles?
5. Asking for features of a given class that are in a geospatial relation with an unspecified feature of another class, and either one or both, is/are in another geospatial relation with a feature specified explicitly, e.g., Which churches are near a castle in Scotland?
6. As in categories C, D and E above, plus more thematic and/or geospatial characteristics of the features expected as answers, e.g., Which mountains in Scotland have height more than 1000 meters?
7. Questions with quantities and aggregates, e.g., What is the total area of lakes in Monaghan? or How many lakes are there in Monaghan?
8. Questions with superlatives or comparatives, e.g., Which is the largest island in Greece?
9. Questions with quantities, aggregates, and superlatives/comparatives, e.g., Which city in the UK has the most hospitals?

These are the full-resolution boundary zip code tabular areas (ZCTA), derived from the US Census Bureau's TIGER/Line Shapefiles. The dataset contains polygons that roughly approximate each of the USPS 5-digit zip codes. It is one of many geography datasets available in BigQuery through the Google Cloud Public Dataset Program to support geospatial analysis. You can find more information on the other datasets at the US Geographic Boundaries Marketplace page . Though they do not continuously cover all land and water areas in the United States, ZCTAs are a great way to visualize geospatial data in an understandable format with excellent spatial resolution. This dataset gives the area of land and water within each zip code, as well as the corresponding city and state for each zip code. This makes the dataset an excellent candidate for JOINs to support geospatial queries with BigQuery’s GIS capabilities. Note: BQ-GIS is in public beta, so your GCP project will need to be whitelisted to try out these queries. You can sign up to request access here . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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. The project geodatabase links the spatial vegetation data layer to vegetation classification, plot photos, project boundary extent, AA points, and the PLOTS database. The geodatabase includes USNVC hierarchy tables allowing for spatial queries of data associated with a vegetation polygon or sample point. All geospatial products are projected using North American Datum 1983 (NAD83) in Universal Transverse Mercator (UTM) Zone 16 N. The final GUIS vegetation map consists of 1,268 polygons totaling 35,769.0 ha (88,387.2 ac). Mean polygon size is 28.2 ha (69.7 ac). Mean polygon size, excluding water, is 3.6 ha (8.9 ac).

Refined DataCo Smart Supply Chain Geospatial Dataset

Optimized for Geospatial and Big Data Analysis

This dataset is a refined and enhanced version of the original DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS dataset, specifically designed for advanced geospatial and big data analysis. It incorporates geocoded information, language translations, and cleaned data to enable applications in logistics optimization, supply chain visualization, and performance analytics.

Key Features

1. Geocoded Source and Destination Data

• Accurate latitude and longitude coordinates for both source and destination locations.
• Facilitates geospatial mapping, route analysis, and distance calculations.

2. Supplementary GeoJSON Files

• src_points.geojson: Source point geometries.
• dest_points.geojson: Destination point geometries.
• routes.geojson: Line geometries representing source-destination routes.
• These files are compatible with GIS software and geospatial libraries such as GeoPandas, Folium, and QGIS.

3. Language Translation

• Key location fields (countries, states, and cities) are translated into English for consistency and global accessibility.

4. Cleaned and Consolidated Data

• Addressed missing values, removed duplicates, and corrected erroneous entries.
• Ready-to-use dataset for analysis without additional preprocessing.

5. Routes and Points Geometry

• Enables the creation of spatial visualizations, hotspot identification, and route efficiency analyses.

Applications

1. Logistics Optimization

• Analyze transportation routes and delivery performance to improve efficiency and reduce costs.

2. Supply Chain Visualization

• Create detailed maps to visualize the global flow of goods.

3. Geospatial Modeling

• Perform proximity analysis, clustering, and geospatial regression to uncover patterns in supply chain operations.

4. Business Intelligence

• Use the dataset for KPI tracking, decision-making, and operational insights.

Dataset Content

Files Included

1. DataCoSupplyChainDatasetRefined.csv

   • The main dataset containing cleaned fields, geospatial coordinates, and English translations.

2. src_points.geojson

   • GeoJSON file containing the source points for easy visualization and analysis.

3. dest_points.geojson

   • GeoJSON file containing the destination points.

4. routes.geojson

   • GeoJSON file with LineStrings representing routes between source and destination points.

Attribution

This dataset is based on the original dataset published by Fabian Constante, Fernando Silva, and António Pereira:
Constante, Fabian; Silva, Fernando; Pereira, António (2019), “DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS”, Mendeley Data, V5, doi: 10.17632/8gx2fvg2k6.5.

Refinements include geospatial processing, translation, and additional cleaning by the uploader to enhance usability and analytical potential.

Tips for Using the Dataset

• For geospatial analysis, leverage tools like GeoPandas, QGIS, or Folium to visualize routes and points.
• Use the GeoJSON files for interactive mapping and spatial queries.
• Combine this dataset with external datasets (e.g., road networks) for enriched analytics.

This dataset is designed to empower data scientists, researchers, and business professionals to explore the intersection of geospatial intelligence and supply chain optimization.

A geospatial interface will be developed using ArcIMS software. The interface will provide a means of accessing information stored in the SOFIA database and the SOFIA data exchange web site through a geospatial query. The spatial data will be served using the ArcSDE software, which provides a mechanism for storing spatial data in a relational database. A spatial database will be developed from existing data sets, including national USGS data sets, the Florida Geographic Digital Library, and other available data sets. Additional data sets will be developed from the published data sets available from PBS and other projects.

Abstract : The search for the most appropriate GIS data model to integrate, manipulate and analyse spatio-temporal data raises several research questions about the conceptualisation of geographic spaces. Although there is now a general consensus that many environmental phenomena require field and object conceptualisations to provide a comprehensive GIS representation, there is still a need for better integration of these dual representations of space within a formal spatio-temporal database. The research presented in this paper introduces a hybrid and formal dual data model for the representation of spatio-temporal data. The whole approach has been fully implemented in PostgreSQL and its spatial extension PostGIS, where the SQL language is extended by a series of data type constructions and manipulation functions to support hybrid queries. The potential of the approach is illustrated by an application to underwater geomorphological dynamics oriented towards the monitoring of the evolution of seabed changes. A series of performance and scalability experiments are also reported to demonstrate the computational performance of the model.Data Description : The data set used in our research is a set of bathymetric surveys recorded over three years from 2009 to 2011 as Digital Terrain Models (DTM) with 2m grid spacing. The first survey was carried out in February 2009 by the French hydrographic office, the second one was recorded on August-September 2010 and the third in July 2011, both by the “Institut Universitaire Européen de la Mer”.

The California State Places Boundary data.

This dataset offers high-resolution boundary definitions, which allow users to analyze and visualize California’s state limits within mapping and spatial analysis projects.

The shapefile is part of a ZIP archive containing multiple related files that together define and support the boundary data. These files include:

.shp (Shape): This is the core file containing the vector data for California’s Places boundaries, representing the geographic location and geometry of the state outline.

.shx (Shape Index): A companion index file for the .shp file, allowing for quick spatial queries and efficient data access.

.dbf (Attribute Table): A database file that stores attribute data linked to the geographic features in the .shp file, such as area identifiers or classification codes, in a tabular format compatible with database applications.

.prj (Projection): This file contains projection information, specifying the coordinate system and map projection used for the data, essential for aligning it accurately on maps.

.cpg (Code Page): This optional file indicates the character encoding for the attribute data in the .dbf file, which is useful for ensuring accurate text representation in various software.

.sbn and .sbx (Spatial Index): These files serve as a spatial index for the shapefile, allowing for faster processing of spatial queries, especially for larger datasets.

.xml (Metadata): A metadata file in XML format, often following FGDC or ISO standards, detailing the dataset’s origin, structure, and usage guidelines, providing essential information about data provenance and quality.

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

This geospatial dataset delivers high-accuracy GPS event streams from millions of connected devices across Asia, enabling advanced mobility, mapping, and location intelligence applications. Sourced from tier-1 app developers and trusted suppliers, it provides granular insights for commercial, government, and research use.

Each record includes: Latitude & Longitude coordinates Event timestamp (epoch & date) Mobile Advertising ID (IDFA/GAID) Horizontal accuracy (~85% fill rate) Country code (ISO3) Optional metadata: IP address, carrier, device model

Access & Delivery API with polygon queries (up to 10,000 tiles) Formats: JSON, CSV, Parquet Delivery via API, AWS S3, or Google Cloud Storage Hourly or daily refresh options Historical backfill from September 2024 Credit-based pricing for scalability

Compliance Fully compliant with GDPR and CCPA, with clear opt-in/out mechanisms and transparent privacy policies.

Use Cases Advanced mapping and GIS solutions Urban mobility and infrastructure planning Commercial site selection and market expansion Geofencing and targeted advertising Disaster response planning and risk assessment Transportation and logistics optimization

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

Detailed description about how to create a customised vocabulary and data type in Blazegraph to upload UK land use data to the JPS knowledge graph.

The OGC GeoSPARQL standard supports representing and querying geospatial data on the Semantic Web. GeoSPARQL defines a vocabulary for representing geospatial data in RDF, and it defines an extension to the SPARQL query language for processing geospatial data. In addition, GeoSPARQL is designed to accommodate systems based on qualitative spatial reasoning and systems based on quantitative spatial computations.

Dataset contains queries for Problog database of facts about USA geography. Taken from this source

This paper provides an abstract analysis of parallel processing strategies for spatial and spatio-temporal data. It isolates aspects such as data locality and computational locality as well as redundancy and locally sequential access as central elements of parallel algorithm design for spatial data. Furthermore, the paper gives some examples from simple and advanced GIS and spatial data analysis highlighting both that big data systems have been around long before the current hype of big data and that they follow some design principles which are inevitable for spatial data including distributed data structures and messaging, which are, however, incompatible with the popular MapReduce paradigm. Throughout this discussion, the need for a replacement or extension of the MapReduce paradigm for spatial data is derived. This paradigm should be able to deal with the imperfect data locality inherent to spatial data hindering full independence of non-trivial computational tasks. We conclude that more research is needed and that spatial big data systems should pick up more concepts like graphs, shortest paths, raster data, events, and streams at the same time instead of solving exactly the set of spatially separable problems such as line simplifications or range queries in manydifferent ways.

The Idaho Rangeland Atlas is a collaboration of the University of Idaho Library and the University of Idaho Rangeland Center. Its purpose is to provide simple, clear information about Idaho's rangelands using open, accessible web technologies. Leveraging the University of Idaho's investements in geospatial data and infrastructure enable us to present this information. We believe that if an Idaho citizen wants to understand the basic facts of rangeland ecology and space in our state, those facts should be available without the need to engage in advanced analysis or obtain new skills.The lack of an aggregating resource, like a statistical abstract, adds time to process of discovery and delays the ability of users to move on, either to advanced research questions, as they have to answer and prove more fundamental ones first, or to other tasks based on the information that they now have. Given the increasing accessibility of web-based geospatial processing, and the improvement in technology to provide rich, informative, web-based queries of spatial data, the opportunity exists to re-invent the statistical abstract for natural resource and agricultural questions, providing a simple interface to gather facts about the state of Idaho’s rangelands.

Web Soil Survey & Geospatial Data Gateway These requirements include:Provide a way to request data for an adhoc area of interest of any size.Provide a way to obtain data in real-time.Provide a way to request selected tabular and spatial attributes.Provide a way to return tabular and spatial data where the organization of that data doesn't hate to mirror that of the underlying source database.Provide a way to bundle results by request, rather tan by survey area.Click on Submit a custom request for soil tabular data, to input a query to extract data. For help click on:Creating my own custom database queries Index to SQL Library - Sample Scripts Using Soil Data Access website Using Soil Data Access web services

In this course, you will learn to work within the free and open-source R environment with a specific focus on working with and analyzing geospatial data. We will cover a wide variety of data and spatial data analytics topics, and you will learn how to code in R along the way. The Introduction module provides more background info about the course and course set up. This course is designed for someone with some prior GIS knowledge. For example, you should know the basics of working with maps, map projections, and vector and raster data. You should be able to perform common spatial analysis tasks and make map layouts. If you do not have a GIS background, we would recommend checking out the West Virginia View GIScience class. We do not assume that you have any prior experience with R or with coding. So, don't worry if you haven't developed these skill sets yet. That is a major goal in this course. Background material will be provided using code examples, videos, and presentations. We have provided assignments to offer hands-on learning opportunities. Data links for the lecture modules are provided within each module while data for the assignments are linked to the assignment buttons below. Please see the sequencing document for our suggested order in which to work through the material. After completing this course you will be able to: prepare, manipulate, query, and generally work with data in R. perform data summarization, comparisons, and statistical tests. create quality graphs, map layouts, and interactive web maps to visualize data and findings. present your research, methods, results, and code as web pages to foster reproducible research. work with spatial data in R. analyze vector and raster geospatial data to answer a question with a spatial component. make spatial models and predictions using regression and machine learning. code in the R language at an intermediate level.

The Geospatial Gold Standard dataset contains 200 place-related questions collected for translating questions into GeoSPARQL queries.