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 dataset provides the usage statistics (covering both the number of downloads and the number of API requests) of open data (spatial data included) of the Open Data Portal per data provider in a specific time period

This tutorial series is designed to provide an accessible introduction to techniques for handling, analysing and visualising spatial data in R. R is an open source software environment for statistical computing and graphics. It has a range of bespoke packages which provide additional functionality for handling spatial data and performing complex spatial analysis operations. The practical series uses open data which has been made readily available for each tutorial and demonstrates a range of techniques that are useful for social science research including multivariate analysis, mapping and spatial interpolation.

The tutorials and their associated data are freely available, although users are required to register for an account on this website to access them. For any questions or concerns please see the contact information below.

The Geospatial Data Gateway (GDG) provides access to a map library of over 100 high resolution vector and raster layers in the Geospatial Data Warehouse. It is the one stop source for environmental and natural resource data, available anytime, from anywhere. It allows a user to choose an area of interest, browse and select data, customize the format, then download or have it shipped on media. The map layers include data on: Public Land Survey System (PLSS), Census data, demographic statistics, precipitation, temperature, disaster events, conservation easements, elevation, geographic names, geology, government units, hydrography, hydrologic units, land use and land cover, map indexes, ortho imagery, soils, topographic images, and streets and roads. This service is made available through a close partnership between the three Service Center Agencies (SCA): Natural Resources Conservation Service (NRCS), Farm Service Agency (FSA), and Rural Development (RD). Resources in this dataset:Resource Title: Geospatial Data Gateway. File Name: Web Page, url: https://gdg.sc.egov.usda.gov This is the main page for the GDG that includes several links to view, download, or order various datasets. Find additional status maps that indicate the location of data available for each map layer in the Geospatial Data Gateway at https://gdg.sc.egov.usda.gov/GDGHome_StatusMaps.aspx

The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III) data set contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Geospatial Data Fusion market is experiencing remarkable growth as industries increasingly leverage spatial data for decision-making and operational efficiency. Defined as the process of integrating multiple data sources to produce a comprehensive, accurate representation of a geographic area, geospati...

The dataset comprises the following fields: 1. RegistrationID: This represents the registration id that is created when any new location is visited. 2. SubscriberID: This ID is linked with the customer who visits the location. 3. Date: This field shows the date of making the visit to the location. For our simulation, we have considered the date to be in the last 1 week i.e. from 1st December 2018 to 7th December 2018. 4. Latitude: This represents the latitude of the location. 5. Longitude: This represents the longitude of the location.

This dataset represents the frequency of geospatial data where person visits multiple locations in a week and his data is captured/simulated to compile this dataset.

This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

Based on open access data, 79 Mediterranean passenger ports are analyzed to compare their infrastructure, hinterland accessibility and offered multi-modality categories. Comparative Geo-spatial analysis is also carried out by using the data normalization method in order to visualize the ports' performance on maps. These data driven comprehensive analytical results can bring added value to sustainable development policy and planning initiatives in the Mediterranean Region. The analyzed elements can be also contributed to the development of passenger port performance indicators. The empirical research methods used for the Mediterranean passenger ports can be replicated for transport nodes of any region around the world to determine their relative performance on selected criteria for improvement and planning.

The Mediterranean passenger ports were initially categorized into cruise and ferry ports. The cruise ports were identified from the member list of the Association for the Mediterranean Cruise Ports (MedCruise), representing more than 80% of the cruise tourism activities per country. The identified cruise ports were mapped by selecting the corresponding geo-referenced ports from the map layer developed by the European Marine Observation and Data Network (EMODnet). The United Nations (UN) Code for Trade and Transport Locations (LOCODE) was identified for each of the cruise ports as the common criteria to carry out the selection. The identified cruise ports not listed by the EMODnet were added to the geo-database by using under license the editing function of the ArcMap (version 10.1) geographic information system software. The ferry ports were identified from the open access industry initiative data provided by the Ferrylines, and were mapped in a similar way as the cruise ports (Figure 1).

Based on the available data from the identified cruise ports, a database (see Table A1–A3) was created for a Mediterranean scale analysis. The ferry ports were excluded due to the unavailability of relevant information on selected criteria (Table 2). However, the cruise ports serving as ferry passenger ports were identified in order to maximize the scope of the analysis. Port infrastructure and hinterland accessibility data were collected from the statistical reports published by the MedCruise, which are a compilation of data provided by its individual member port authorities and the cruise terminal operators. Other supplementary sources were the European Sea Ports Organization (ESPO) and the Global Ports Holding, a cruise terminal operator with an established presence in the Mediterranean. Additionally, open access data sources (e.g. the Google Maps and Trip Advisor) were consulted in order to identify the multi-modal transports and bridge the data gaps on hinterland accessibility by measuring the approximate distances.

The data sources overall score is a composity measure of whether countries have data available from the following sources: Censuses and surveys, administrative data, geospatial data, and private sector/citizen generated data. The data sources (input) pillar is segmented by four types of sources generated by (i) the statistical office (censuses and surveys), and sources accessed from elsewhere such as (ii) administrative data, (iii) geospatial data, and (iv) private sector data and citizen generated data. The appropriate balance between these source types will vary depending on a country’s institutional setting and the maturity of its statistical system. High scores should reflect the extent to which the sources being utilized enable the necessary statistical indicators to be generated. For example, a low score on environment statistics (in the data production pillar) may reflect a lack of use of (and low score for) geospatial data (in the data sources pillar). This type of linkage is inherent in the data cycle approach and can help highlight areas for investment required if country needs are to be met.

This ONS Geography Linked Data (http://statistics.data.gov.uk) site is the access point for information on statistical geographies required to support the use of official statistics. It is designed to allow users to discover, view and use geospatial data. This site is complementary to the ONS Open Geography Portal (http://geoportal.statistics.gov.uk/). This dataset contains definitions of the different types of statistical geography areas.

It allows access directly to data within the geography products, in machine-readable form and using an Application Programming Interface.

This ONS Geography Linked Data site is the access point for information on statistical geographies required to support the use of official statistics. It is designed to allow users to discover, view and use geospatial data. This site is complementary to the ONS Open Geography Portal.

This dataset contains definitions of the different types of statistical geography areas.

It allows access directly to data within the geography products, in machine-readable form and using an Application Programming Interface.

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 polygons on the plastic overlays were then corrected using photogrammetric procedures and converted to vector format for use in creating a geographic information system (GIS) database for each park. In addition, high resolution color orthophotographs were created from the original aerial photographs for use in the GIS. Upon completion of the GIS database (including vegetation, orthophotos and updated roads and hydrology layers), both hardcopy and softcopy maps were produced for delivery. Metadata for each database includes a description of the vegetation classification system used for each park, summary statistics and documentation of the sources, procedures and spatial accuracies of the data. At the time of this writing, an accuracy assessment of the vegetation mapping has not been performed for most of these parks. Thus, those procedures and results are not included in this report.

The Geospatial Data Services market has emerged as a critical component across various industries, leveraging location-based data to provide insightful solutions that enhance decision-making and operational efficiency. With an estimated market size currently valued at approximately $XX billion, this sector...

Discover the booming Spatial Analysis Software market! This in-depth analysis reveals key trends, drivers, and restraints, including the rise of AI, cloud-based solutions, and the impact of drone technology. Explore market size, CAGR, and regional breakdowns to gain strategic insights for 2025-2033.

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.

The Geospatial Analytics Software market has emerged as a vital component in various industries, harnessing the power of location-based data to drive informed decision-making. As organizations increasingly recognize the value of integrating geospatial data into their operations, the market for such softwar...

Aircraft Landing Facilities in the US

Geospatial data on US aircraft landing facilities and operational statistics

By Homeland Infrastructure Foundation [source]

About this dataset

The Aircraft Landing Facilities dataset provides comprehensive geospatial data on aircraft landing facilities across the United States. This dataset contains valuable information on the location, ownership, facility use, elevation, and operational statistics of these landing facilities.

Each landing facility is uniquely identified by a site number and is classified based on its type, such as whether it caters to general aviation or military operations. The dataset includes details about the number of aircraft based at each facility, including single-engine general aviation planes, jet engine planes, multi-engine general aviation aircraft, and helicopters.

Ownerships of the landing facilities vary and can be categorized into different types. The dataset also indicates if a facility has a control tower or customs landing rights. It provides insight into whether a facility offers commercial services or air taxi services.

Geospatial coordinates (latitude and longitude) allow for accurate location mapping of each landing facility. Additionally, information about each facility's proximity to the central business district (CBD) is included in terms of direction and distance.

Operational statistics offer insights into the activity level at each landing facility. It includes data on itinerant operations (takeoffs and landings), arrivals/departures count, enplanements (passengers boarded), passengers count overall at the establishment.

With this extensive dataset compiled from trusted sources like FAA's National Airspace System Resource Aeronautical Data Product and others mentioned in its source link provided with this dataset on Kaggle platform makes it an essential resource for various analyses related to aviation infrastructure planning, transportation management studies as well as market research within various sectors connected with air travel industry

How to use the dataset

How to Use the Aircraft Landing Facilities Dataset

The Aircraft Landing Facilities dataset provides geospatial data on aircraft landing facilities across the United States. This dataset includes information on location, ownership, facility use, elevation, and operational statistics. Here is a guide on how you can effectively use this dataset:

1. Familiarize Yourself with the Columns

The dataset contains numerous columns with various types of information. Before diving into the analysis, make sure you understand what each column represents. Below are some important columns to pay attention to:

• SITE_NO: The unique identifier for each landing facility.
• LAN_FA_TY: The type of landing facility.
• OWNER_TYPE: The type of owner of the landing facility.
• COUNTY_NAM: The name of the county where the landing facility is located.
• CITY_NAME: The name of the city where the landing facility is located.
• FULLNAME: The full name of the landing facility.
• CERT_TYPE: The type of certification for the landing facility.
• LATITUDE and LONGITUDE: Coordinates representing each landing facility's location.

2. Explore Location-based Information

One interesting aspect of this dataset is its geospatial nature. You can explore different locations based on a variety of parameters:

Distance from Central Business District (CBD)

Use the column CBD_DIST to analyze how far each airport or heliport is from its respective city's central business district (CBD). You can also utilize this data to study patterns in aviation infrastructure development around major cities.

Direction from CBD

The column CBD_DIR provides information about which direction an aircraft needs to travel from a given airport or heliport to reach its respective city's central business district (CBD). Analyzing this data might provide insights into flight path planning or geographical considerations for establishing airports.

Elevation

Use column ELEV to analyze the elevation of each landing facility. You can compare elevations between different airports to identify variations in topography and how they might impact aviation operations.

Coordinates

Latitude (LATITUDE) and longitude (LONGITUDE) values are provided for each landing facility. You can plot these coordinates on maps or perform spatial analysis to study patterns related to location, such as clustering of facilities or proximity to other landmarks.

3. Analyze Ownership and Use

Understanding the ownership and use of landing fa...

Central to our investigation is the Building Energy Rating (BER) dataset of Ireland, sourced from GeoDirectory data. This dataset provides a foundational resource, encompassing a comprehensive range of building-scale parameters such as detailed addresses, geocode data (latitude and longitude), area, height, number of floors, roof type, construction age, radon emission, HVAC system details, and bedroom and bathroom counts. To enrich this dataset, we incorporated additional features from the Digital Landscape Models (DLM) Core Data from Tailte Éireann Surveying (PRIME2 Dataset). These enhancements include metrics such as nearest neighbor buildings, the density of the built environment surrounding each building, and district attributes like the ratio of green area to non-green area in the urban vicinity. Dataset Compilation: After carefully considering various data acquisition methods to capture building-scale and neighborhood-scale features, including both geometric and non-geometric attributes, as well as information about the surrounding built environment, we compiled a comprehensive dataset. Due to data protection policies, specific spatial details such as latitude, longitude, and addresses were omitted. However, the remaining data, aligned with our data acquisition objectives, is structured into a CSV file available in the mentioned repository. Key Features: 1. Building-Scale Attributes: o Identification and Location: Building ID, detailed address (excluding specific spatial details for privacy), geocode data. o Structural Details: Area, height, number of floors, roof type, construction age. o Energy and Environmental Data: BER rating, radon emission, HVAC system details. o Additional Metrics: Estimated number of bedrooms and bathrooms, water heating source, space heating source. 2. Neighborhood-Scale Attributes: o Environmental Context: Soil composition, presence of water bodies and green spaces, built-up areas. o Urban Infrastructure: Roads, pathways, networks, and other land uses. o Spatial Relationships: Built environment density, district green and non-green area, and land cover specification. Acknowledgment: We would like to thank the funder of the project, Science Foundation Ireland (SFI) under the NexSys SFI/21/SPP/3756 programme. We also thank to CEO, Tailte Éireann (acting on behalf of the Government of Ireland) to reproduce Tailte Éireann – Surveying maps and data for the annual copyright licence, CYAL50402364, © Tailte Éirean.n – Surveying.

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

Each tutorial video is also accompanied by a written script, providing a step-by-step reference that users can follow alongside the video or consult afterwards.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 2.1 protection status for various land unit boundaries (Protected Areas Database of the United States (PAD-US) Summary Statistics by GAP Status Code) as well as summaries of public access status (Public Access Statistics), provided in Microsoft Excel readable workbooks, the vector GIS analysis files and scripts used to complete the summaries, and raster GIS analysis files for combination with other raster data. The PAD-US 2.1 Combined Fee, Designation, Easement feature class in the full inventory (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.