The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

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.

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

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).

Core Based Statistical AreasThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U.S. Census Bureau, depicts Core Based Statistical Areas (CBSA). Per the USCB, "Metropolitan and Micropolitan Statistical Areas are together termed CBSAs and are defined by the Office of Management and Budget (OMB) and consist of the county or counties or equivalent entities associated with at least one urban core of at least 10,000 population, plus adjacent counties having a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. Categories of CBSAs are: Metropolitan Statistical Areas, based on urban areas of 50,000 or more population; and Micropolitan Statistical Areas, based on urban areas of at least 10,000 population but less than 50,000 population."Kill Devil Hills, NC (Micro Area)Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal sources (Metropolitan Statistical Areas & Micropolitan Statistical Areas) and will support mapping, analysis, data exports and OGC API – Feature access.NGDAID: 84 (Series Information for Core-Based Statistical Areas National TIGER/Line Shapefiles, Current)OGC API Features Link: (Core Based Statistical Areas - OGC Features) copy this link to embed it in OGC Compliant viewersFor more information, please visit: Delineation FilesFor feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

Assets:data tables each for city of Indianapolis (IN) and Baltimore (MD) in MS Excel and MS Word, ditto shapefiles in ESRI format; all files ZIPPEDContext – abstract of report published in a blog at the serial Geospatial World:This case study called into question police conduct and policy injustice discovered in two American big cities. My path to learn about Indianapolis (IN) and Baltimore (MD) policing patterns and crime events was due to availability of open data focused on use-of-force (UOF). My specific goal was to conduct geospatial data analytics aimed at these two cities using location and other key variables. Two spreadsheets captured small data that laid acceptable statistical groundwork for iterations of exploratory spatial data analysis (ESDA). Bivariate scatterplots revealed possible police misconduct. Parallel coordinate plotting – an innovative multivariate tool – was then used to display co-occurrences of plotted UOF and racial variables associated with key police districts in Indianapolis and Baltimore. A final summary visualization sought to cartographically and dramatically compare force and race variables by way of comparative plots, graphs, and maps. I closed with three action items pertaining to a “social-justice” framework for future data-visualization, to the heightening of standards for law enforcement reform, and for a need to make a hypothetical “citizen’s arrest” of police misconduct.

Consolidated CitiesThis feature layer, utilizing National Geospatial Data Asset (NGDA) data from the U. S. Census Bureau, displays consolidated cities within the United States. Per the USCB, "consolidated cities are a unit of government for which the functions of an incorporated place and its county or MCD have merged. The legal aspects of this action may result in both the primary incorporated place and the county or MCD continuing to exist as legal entities, even though the county or MCD performs few or no governmental functions. Where one or more other incorporated places within the consolidated government continue to function as separate governmental units, the primary incorporated place is referred to as a 'consolidated city'."Data currency: This cached Esri federal service is checked weekly for updates from its enterprise federal source (Consolidated Cities) and will support mapping, analysis, data exports and OGC API – Feature access.Data.gov: TIGER/Line Shapefile, 2019, Series Information for the Current Consolidated City State-based ShapefileGeoplatform: TIGER/Line Shapefile, 2019, Series Information for the Current Consolidated City State-based ShapefileFor more information, please visit: Frequently Asked Questions (FAQs)For feedback please contact: Esri_US_Federal_Data@esri.comNGDA Data SetThis data set is part of the NGDA Governmental Units, and Administrative and Statistical Boundaries Theme Community. Per the Federal Geospatial Data Committee (FGDC), this theme is defined as the "boundaries that delineate geographic areas for uses such as governance and the general provision of services (e.g., states, American Indian reservations, counties, cities, towns, etc.), administration and/or for a specific purpose (e.g., congressional districts, school districts, fire districts, Alaska Native Regional Corporations, etc.), and/or provision of statistical data (census tracts, census blocks, metropolitan and micropolitan statistical areas, etc.). Boundaries for these various types of geographic areas are either defined through a documented legal description or through criteria and guidelines. Other boundaries may include international limits, those of federal land ownership, the extent of administrative regions for various federal agencies, as well as the jurisdictional offshore limits of U.S. sovereignty. Boundaries associated solely with natural resources and/or cultural entities are excluded from this theme and are included in the appropriate subject themes."For other NGDA Content: Esri Federal Datasets

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.

Water samples were collected by the USGS National Water Quality Program (NWQP) Southeastern Stream Quality Assessment (SESQA) from 76 perennial, wadeable (less than 10 m width and 1 m depth at base-flow) headwater stream sites in watersheds with varying degrees of urban land use in four states. Dataset includes sample site locations and information, water sample nutrient concentrations and statistics analyses, and corresponding watershed land-use-land-cover data and data dictionary.

This data set provides an estimate of the surface air temperature. 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.

Presentation of statistical data in digital map format using data from the Household Socio-Economic Survey.

Gini coefficient of income refers to a value indicating the difference in household income, ranging from 0 to 1. A value of 0 indicates no income difference, while a value of 1 indicates the maximum income difference.

** * Do not use the map data layer as a reference for administrative boundaries. **

The data sources overall score is a composite 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.

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

Geospatial data available at 1st Admin Level. We recognize that this data source provides only limited coverage but consider that it does at least provide some indication of the ability of the national statistical system to produce geospatial data. A major research and data collection effort is needed via GGIM to fill in this information, so that a more comprehensive picture of geospatial data capability at the national level can be produced. Until this is done, it we cannot even assess the scale of the data gaps in a comparable way.

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.

Geospatial data about Rainfall trend statistics xlsx. Export to CAD, GIS, PDF, CSV and access via API.

Geospatial data about Information about the data 2018 census private dwellings by SA22018.pdf. Export to CAD, GIS, PDF, CSV and access via API.

Presentation of statistical data in digital map format using data from the Labor Force Survey.

Total Labor Force: Refers to all persons aged 15 and above in the reference week who are currently in the labor force or classified as seasonally unemployed according to the definitions stated above.

** * Do not use the map layer as a reference for administrative boundaries **

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...