GEE-TED: A tsetse ecological distribution model for Google Earth Engine Please refer to the associated publication: Fox, L., Peter, B.G., Frake, A.N. and Messina, J.P., 2023. A Bayesian maximum entropy model for predicting tsetse ecological distributions. International Journal of Health Geographics, 22(1), p.31. https://link.springer.com/article/10.1186/s12942-023-00349-0 Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (2023): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (2023) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (2023) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/unsuitable) for day/night temperatures and ndvi var con =...

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

Digital geographic maps remain largely inaccessible to blind and low vision individuals (BLVIs), despite global legislation adopting the Web Content Accessibility Guidelines (WCAG). A critical gap exists in defining “equivalent purpose” for maps under WCAG Success Criterion 1.1.1, which requires that non-text content provide a text alternative that serves the "equivalent purpose”. This paper proposes a systematic framework for evaluating map accessibility, defining purpose through three items (Generalized, Spatial Information, and Spatial Relationships), and establishing 15 measurable criteria for equivalent information communication. Eight text map representations: turn-by-turn directions, tables, Nearby Address Searches, short text alternatives, Google Maps Interactive Alternate Text, Audiom Map Interactive Alternate Text, and audio descriptions, were evaluated against visual map baselines. Results show that legacy methods like tables and turn-by-turn directions fail to meet equivalent purpose, while Audiom Maps, MUD Maps, and Audio Descriptions satisfy the framework. The evaluation highlights the necessity of holistic, systematic approaches to ensure non-visual maps convey all generalized spatial information and relationships present in visual maps. This framework provides a replicable methodology for assessing digital map accessibility, clarifying WCAG’s equivalent purpose, and guiding compliant and usable map creation. Compliant maps will support BLVIs’ participation in map-dependent professions and civic engagement.

Geospatial raster data and vector data created in the frame of the study "Mapping Arctic Lake Ice Backscatter Anomalies using Sentinel-1 Time Series on Google Earth Engine" submitted to the journal "Remote Sensing" and Python code to reproduce the results.

In addition to the full repository (Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies.zip), two reduced alternatives of this repository are available due to large file size of the full repository:

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_without_IW_result_data.zip contains the same data and Python scripts as the full repository, but results based on IW data and tiled EW delta sigma0 images directly exported from Google Earth Engine have been removed. The merged data (from tiled EW delta sigma0 images) and all other results deduced thereof are included.

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_scripts_and_reference_data_only.zip contains only the Python scripts and reference data. The directory structure was retained for better reproducibility.

Please see the associated README-files for details.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. This dataset includes all 50 variables as available on CDS. ERA5-Land data is available from 1950 to three months from real-time. Please consult the ERA5-Land "Known Issues" section. In particular, note that three components of the total evapotranspiration have values swapped as follows: variable "Evaporation from bare soil" (mars parameter code 228101 (evabs)) has the values corresponding to the "Evaporation from vegetation transpiration" (mars parameter 228103 (evavt)), variable "Evaporation from open water surfaces excluding oceans (mars parameter code 228102 (evaow)) has the values corresponding to the "Evaporation from bare soil" (mars parameter code 228101 (evabs)), variable "Evaporation from vegetation transpiration" (mars parameter code 228103 (evavt)) has the values corresponding to the "Evaporation from open water surfaces excluding oceans" (mars parameter code 228102 (evaow)). The asset is a daily aggregate of ECMWF ERA5 Land hourly assets which includes both flow and non-flow bands. Flow bands are formed by collecting the first hour's data of the following day which holds aggregated sum of previous day and while the non-flow bands are created by averaging all hourly data of the day. The flow bands are labeled with the "_sum" identifier, which approach is different from the daily data produced by Copernicus Climate Data Store, where flow bands are averaged too. Daily aggregates have been pre-calculated to facilitate many applications requiring easy and fast access to the data. Precipitation and other flow (accumulated) bands might occasionally have negative values, which doesn't make physical sense. At other times their values might be excessively high. This problem is due to how the GRIB format saves data: it simplifies or "packs" the data into smaller, less precise numbers, which can introduce errors. These errors get worse when the data varies a lot. Because of this, when we look at the data for a whole day to compute daily totals, sometimes the highest amount of rainfall recorded at one time can seem larger than the total rainfall measured for the entire day. To learn more, Please see: "Why are there sometimes small negative precipitation accumulations"

This dataset includes five files developed using the procedures described in the article 'Developing County-level Data of Nitrogen Fertilizer and Manure Inputs for Corn Production in the United States' and Supplemental Information published in the Journal of Cleaner Production in 2021. Citation: Xia, Yushu, Hoyoung Kwon, and Michelle Wander. "Developing county-level data of nitrogen fertilizer and manure inputs for corn production in the United States." Journal of Cleaner Production 309 (2021): e126957. Brief method: The fertilizer and manure inputs for corn were generated with a top-down approach by assigning county-level total N inputs reported by USGS to different crops using state- and county-level survey data. The corn N needs were estimated using empirical extension-based equations coupled with soil and environmental covariates. The estimates of fertilizer N inputs were further refined for corn grain and silage production at the county level and gap-filling (using state-level averages) was carried out to generate final files for U.S. county-level N inputs. The dataset is provided in an alternative format in Google Earth Engine: https://code.earthengine.google.com/13a0078e7ee727bc001e045ad0e8c6fc

Abstract: We present a detailed geomorphological map (1:5000-scale) of a middle mountainous area in Jena, Germany. To overcome limitations associated with traditional field-based approaches and to extend the possibility of manually digital mapping in a structural way, we propose an approach using geographic information systems (GIS) and high-resolution digital data. The geomorphological map features were extracted by manually interpreting and analyzing the combination of different data sources using light detection and ranging (LiDAR) data. A combination of topographic and geological maps, digital orthophotos (DOPs), Google Earth images, field investigations, and derivatives from digital terrain models (DTMs) revealed that it is possible to generate and present the geomorphologic features involved in classical mapping approaches. We found that LiDAR-DTM and land surface parameters (LSPs) can provide better results when incorporating the visual interpretation of multidirectional hillshade and LSP composite maps. The genesis of landforms can be readily identified, and findings enabled us to systematically delineate landforms and geomorphological process domains. Although our approach provides a cost effective, objective, and reproducible alternative for the classical approach, we suggest that further use of digital data should be undertaken to support analysis and applications.

The Open Buildings 2.5D Temporal Dataset contains data about building presence, fractional building counts, and building heights at an effective1 spatial resolution of 4m (rasters are provided at 0.5m resolution) at an annual cadence from 2016-2023. It is produced from open-source, low-resolution imagery from the Sentinel-2 collection. The dataset is …

LinkedIn Data for Talent Acquisition, CRM Enrichment & Company Insights

LinkedIn data is one of the most essential sources of alternative business intelligence — enabling real-time visibility into company growth, hiring behavior, and lead signals. Canaria’s LinkedIn data product delivers structured insights across LinkedIn company data, LinkedIn job postings, and job market trends, verified and enriched with Google Maps metadata.

This LinkedIn data product is specifically designed for use cases including talent acquisition, business development, CRM data enrichment, HR intelligence, and company analysis. It empowers organizations to track high-growth companies, hiring signals, employee trends, and location-level expansions — all sourced from LinkedIn and optimized for integration.

Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn data helps transform fragmented online business profiles into clean, analyzable signals. Whether you need to enrich CRM records, build a talent pipeline, or score leads by job activity, this product enables precision targeting and faster decision-making.

LinkedIn Company Analysis • Identify company size, industry tags, and employee count trends using LinkedIn company data • Monitor company presence through LinkedIn follower growth, location footprints, and job post activity • Benchmark similar companies using standardized LinkedIn data fields and activity metrics • Track changes in business strategy based on real-time LinkedIn job and company page updates

Talent Acquisition & HR Intelligence • Discover which companies are actively hiring through LinkedIn job postings • Analyze job title demand, skill trends, and role seniority using normalized job market LinkedIn data • Track employer branding and recruiting momentum across key locations • Use LinkedIn data to identify companies hiring for specific departments or regions

Risk Detection & Workforce Insights • Spot slowing hiring momentum using LinkedIn job volume trends • Detect early signs of restructuring or regional downsizing • Cross-reference LinkedIn data with Google Maps to validate real-world branch activity • Compare declared headcount with public-facing recruiting behavior

CRM Enrichment & B2B Lead Generation • Enrich company records with verified LinkedIn company data (industry, size, hiring) • Score accounts based on hiring momentum and LinkedIn engagement • Use job postings data to find companies actively hiring for the roles you serve • Identify ideal B2B lead targets based on title trends and LinkedIn hiring signals

Why This LinkedIn Data Product Is Different Matchable Across Systems • Our LinkedIn data is designed to integrate with your job market data, CRM tools, BI platforms, and talent dashboards

Location Intelligence Included • All LinkedIn company profiles include verified HQs and branches, cross-validated with Google Maps metadata

Weekly Updates • Stay current with weekly-updated LinkedIn data streams, covering new companies, job postings, and hiring shifts

Taxonomy-Mapped & Clean • Data is normalized into standard LinkedIn company data fields, ready for matching across systems and teams

Who Benefits from LinkedIn Data • Talent acquisition platforms and recruiting teams • CRM and RevOps teams enriching lead and account records • Strategy and BI teams monitoring workforce and hiring dynamics • Investment teams tracking company growth and hiring behavior • B2B marketers building lead scoring and account targeting models • HR tech tools offering benchmarking and job market insight

Summary Canaria’s LinkedIn Data product delivers enriched, structured, and match-ready business intelligence sourced directly from LinkedIn. By combining LinkedIn company data, LinkedIn job postings, and job market data with location validation via Google Maps, this product enables confident execution across talent acquisition, HR intelligence, CRM enrichment, and company analysis.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligence — such as addresses, coordinates, hours, categories, and ratings — which is fully matchable with our company datasets for powerful geospatial analysis and location-based enrichment. Our AI-powered pipeline is developed by a seasoned team of machine learning experts from Google, Meta, and Amazon, and by alumni of Stanford, Caltech, and Columbia — combining cutting-edge research with enterprise-grade engineering. This foundation enables us to deliver precise, reliable insights that power corporate intelligence, market research, and lead generation at scale. With insights drawn from ...

LinkedIn Company Data for Company Analysis, Valuation & Portfolio Strategy LinkedIn company data is one of the most powerful forms of alternative data for understanding company behavior, firmographics, business dynamics, and real-time hiring signals. Canaria’s enriched LinkedIn company data provides detailed company profiles, including hiring activity, job postings, employee trends, headquarters and branch locations, and verified metadata from Google Maps. This LinkedIn corporate data is updated weekly and optimized for use in company analysis, startup scouting, private company valuation, and investment monitoring. It supports BI dashboards, risk models, CRM enrichment, and portfolio strategy.

Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn company insights transform opaque business landscapes into structured, analyzable data. Whether you’re conducting M&A due diligence, tracking high-growth companies, or benchmarking performance, this dataset empowers fast, confident decisions.

Company Analysis • Identify a company’s size, industry classification, and headcount signals using LinkedIn firmographic data • Analyze social presence through LinkedIn follower metrics and employee engagement • Understand geographic expansion through branch locations and hiring distribution • Benchmark companies using LinkedIn profile activity and job posting history • Monitor business changes with real-time LinkedIn updates

Company Valuation & Financial Benchmarking • Feed LinkedIn-based firmographics into comps and financial models • Use hiring velocity from LinkedIn job data as a proxy for business growth • Strengthen private market intelligence with verified non-financial signals • Validate scale, structure, and presence via LinkedIn and Google Maps footprint

Company Risk Analysis • Detect red flags using hiring freezes or drop in profile activity • Spot market shifts through location downsizing or organizational changes • Identify distressed companies with decreased LinkedIn job posting frequency • Compare stated presence vs. active behavior to identify risk anomalies

Business Intelligence (BI) & Strategic Planning • Segment companies by industry, headcount, growth behavior, and hiring activity • Build BI dashboards integrating LinkedIn job trends and firmographic segmentation • Identify geographic hiring hotspots using Maps and LinkedIn signal overlays • Track job creation, title distribution, and skill demand in near real-time • Export filtered LinkedIn corporate data into CRMs, analytics tools, and lead scoring systems

Portfolio Management & Investment Monitoring • Enhance portfolio tracking with LinkedIn hiring data and firmographic enrichment • Spot hiring surges, geographic expansions, or restructuring in real-time • Correlate LinkedIn growth indicators with strategic outcomes • Analyze competitors and targets using historical and real-time LinkedIn data • Generate alerts for high-impact company changes in your portfolio universe

What Makes This LinkedIn Company Data Unique

Includes Real-Time Hiring Signals • Gain visibility into which companies are hiring, at what scale, and for which roles using enriched LinkedIn job data

Verified Location Intelligence • Confirm branch and HQ locations with Google Maps coordinates and public company metadata

Weekly Updates • Stay ahead of the market with fresh, continuously updated LinkedIn company insights

Clean & Analysis-Ready Format • Structured, deduplicated, and taxonomy-mapped data that integrates with CRMs, BI platforms, and investment models

Who Benefits from LinkedIn Company Data • Hedge funds, VCs, and PE firms analyzing startup and private company activity • Portfolio managers and financial analysts tracking operational shifts • Market research firms modeling sector momentum and firmographics • Strategy teams calculating market size using LinkedIn company footprints • BI and analytics teams building company-level dashboards • Compliance and KYC teams enriching company identity records • Corp dev teams scouting LinkedIn acquisition targets and expansion signals

Summary Canaria’s LinkedIn company data delivers high-frequency, high-quality insights into U.S. companies, combining job posting trends, location data, and firmographic intelligence. With real-time updates and structured delivery formats, this alternative dataset enables powerful workflows across company analysis, financial modeling, investment research, market segmentation, and business strategy.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our AI-powered pipeline is developed by a seasoned team of machine learning experts from Google, Meta, and Amazon, and by alumni of Stanford, Caltech, and Columbia ...

LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. Department of the Interior's Geological Survey, and The Nature Conservancy. LANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees. Existing Vegetation Height (EVH) represents the average height of the dominant vegetation for a 30-m cell. Canopy height is generated separately for tree, shrub, and herbaceous lifeforms using training data and other geospatial layers. EVH is determined by the average height weighted by species cover and based on the Existing Vegetation Type (EVT) lifeform. Decision tree models using field reference data, lidar, Landsat, and ancillary data are developed separately for each lifeform. Decision tree relationships are used to generate lifeform specific height class layers, which are merged into a single composite EVH layer. Disturbance data were used to develop LF Remap products for LFRDB plot filtering and to ensure 2015 and 2016 disturbances were included that were not visible in the source imagery. The EVC product is then reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Cover and EVT products. LF uses EVH in several subsequent layers, including the development of the fuel products. The LANDIFRE Vegetation datasets include: Biophysical Settings (BPS) Environmental Site Potential (ESP) Existing Vegetation Canopy Cover (EVC) Existing Vegetation Height (EVH). Existing Vegetation Type (EVT) These layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.

The IndieWeb is a people-focused alternative to the "corporate" web. Participants use their own personal web sites to post, reply, share, organize events and RSVP, and interact in online social networking in ways that have otherwise been limited to centralized silos like Facebook and Twitter.

The Indie Map dataset includes:

• Social network of the 2300 most active IndieWeb sites, including all connections between sites and number of links in each direction, broken down by type.
• 5.8M web pages, including raw HTML, parsed microformats2, and extracted links with metadata.
• 380GB of HTML and HTTP requests in WARC format.

The complete dataset of 5.8M HTML pages is available in a publicly accessible Google BigQuery dataset.

The raw sites and pages data can be downloaded as JSON files, one per site, and also as raw WARC files. They're hosted on Google Cloud Storage: https://console.cloud.google.com/storage/browser/indie-map/

More details in the full documentation.

This data contains the dataset for the Master’s thesis “Use Cases and Limitations of Webcam Eye Tracking for Cartography Research” by Rahimeh Gharibpour, under supervisor Dr. Merve Keskin, submitted to the University of Salzburg, UNIGIS program. Full Thesis PDF. The thesis explores webcam-based eye tracking as a low-cost, scalable alternative to traditional lab-based systems for studying map cognition in cartographic research. The experiment replicated a subset of tasks from the CartoGAZE (2023) study, available at here, focusing on spatial memory and recognition of road and hydrographic features on 2D static Google road maps. A total of 30 map stimuli were used, divided into three blocks: * Block 1: 10 map stimuli focusing on the memorability of main roads and road junctions. * Block 2: 10 map stimuli focusing on the memorability of major water bodies and rivers. * Block 3: 10 map stimuli combining elements from the first two blocks to assess their collective memorability. The study involved 35 participants in an online experiment, accessible via here, with data from 28 participants analyzed after applying a 70% calibration accuracy threshold. Cognitive load was assessed using both behavioral metrics (response times, success rates) and eye-tracking metrics (average fixation duration, fixations per second, average saccade length). Results suggest that webcam-based eye tracking can replicate general attentional patterns observed in lab-based studies, but with reduced precision due to lower sampling rates (15–20 Hz vs. 250 Hz), environmental variability, and technical factors such as device differences and participant movement. See the GitHub repository for the source code dataset: https://github.com/rahgh/WebcamET_CartoGAZE-data-set

As of October 2025, Google represented ***** percent of the global online search engine referrals on desktop devices. Despite being much ahead of its competitors, this represents a modest increase from the previous months. Meanwhile, its longtime competitor Bing accounted for ***** percent, as tools like Yahoo and Yandex held shares of over **** percent and **** percent respectively. Google and the global search market Ever since the introduction of Google Search in 1997, the company has dominated the search engine market, while the shares of all other tools has been rather lopsided. The majority of Google revenues are generated through advertising. Its parent corporation, Alphabet, was one of the biggest internet companies worldwide as of 2024, with a market capitalization of **** trillion U.S. dollars. The company has also expanded its services to mail, productivity tools, enterprise products, mobile devices, and other ventures. As a result, Google earned one of the highest tech company revenues in 2024 with roughly ****** billion U.S. dollars. Search engine usage in different countries Google is the most frequently used search engine worldwide. But in some countries, its alternatives are leading or competing with it to some extent. As of the last quarter of 2023, more than ** percent of internet users in Russia used Yandex, whereas Google users represented little over ** percent. Meanwhile, Baidu was the most used search engine in China, despite a strong decrease in the percentage of internet users in the country accessing it. In other countries, like Japan and Mexico, people tend to use Yahoo along with Google. By the end of 2024, nearly half of the respondents in Japan said that they had used Yahoo in the past four weeks. In the same year, over ** percent of users in Mexico said they used Yahoo.

The National Census of Ferry Operators (NCFO) Terminals dataset was collected through December 31, 2022 and compiled on October 27, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Ferry Terminals dataset represents all ferry terminals from operators that provided responses to the 2022 National Census of Ferry Operators. Areas covered by the dataset include the 50 states as well as the territories of Puerto Rico, the U.S. Virgin Islands, and American Samoa. The terminals represent departure and arrival locations for ferry segments in the NCFO. As part of the NCFO questionnaire, respondents were asked terminal name, city, and state. From this information, longitudes and latitudes were obtained through open, online searches that include operator websites and map platforms such as Google Maps and Open Street Maps. As a result, terminals sometimes do not represent the exact location where ferry vessels dock but may represent other locations such as the operator's business location or alternative docking locations. However, whenever possible, the terminal locations represent departure and arrival points for that ferry segment. Each terminal contains information about its operation and ownership status and transportation connections, in addition to geographic locations, whenever operators provided this information. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529043

LinkedIn Job Postings Data - Comprehensive Professional Intelligence for HR Strategy & Market Research

LinkedIn Job Postings Data represents the most comprehensive professional intelligence dataset available, delivering structured insights across millions of LinkedIn job postings, LinkedIn job listings, and LinkedIn career opportunities. Canaria's enriched LinkedIn Job Postings Data transforms raw LinkedIn job market information into actionable business intelligence—normalized, deduplicated, and enhanced with AI-powered enrichment for deep workforce analytics, talent acquisition, and market research.

This premium LinkedIn job postings dataset is engineered to help HR professionals, recruiters, analysts, and business strategists answer mission-critical questions: • What LinkedIn job opportunities are available in target companies? • Which skills are trending in LinkedIn job postings across specific industries? • How are companies advertising their LinkedIn career opportunities? • What are the salary expectations across different LinkedIn job listings and regions?

With real-time updates and comprehensive LinkedIn job posting enrichment, our data provides unparalleled visibility into LinkedIn job market trends, hiring patterns, and workforce dynamics.

Use Cases: What This LinkedIn Job Postings Data Solves

Our dataset transforms LinkedIn job advertisements, market information, and career listings into structured, analyzable insights—powering everything from talent acquisition to competitive intelligence and job market research.

Talent Acquisition & LinkedIn Recruiting Intelligence • LinkedIn job market mapping • LinkedIn career opportunity intelligence • LinkedIn job posting competitive analysis • LinkedIn job skills gap identification

HR Strategy & Workforce Analytics • Organizational network analysis • Employee mobility tracking • Compensation benchmarking • Diversity & inclusion analytics • Workforce planning intelligence • Skills evolution monitoring

Market Research & Competitive Intelligence • Company growth analysis • Industry trend identification • Competitive talent mapping • Market entry intelligence • Partnership & business development • Investment due diligence

LinkedIn Job Market Research & Economic Analysis • Regional LinkedIn job analysis • LinkedIn job skills demand forecasting • LinkedIn job economic impact assessment • LinkedIn job education-industry alignment • LinkedIn remote job trend analysis • LinkedIn career development ROI

What Makes This LinkedIn Job Postings Data Unique

AI-Enhanced LinkedIn Job Intelligence • LinkedIn job posting enrichment with advanced NLP • LinkedIn job seniority classification • LinkedIn job industry expertise mapping • LinkedIn job career progression modeling

Comprehensive LinkedIn Job Market Intelligence • Real-time LinkedIn job postings with salary, requirements, and company insights • LinkedIn recruiting activity tracking • LinkedIn job application analytics • LinkedIn job skills demand analysis • LinkedIn compensation intelligence

Company & Organizational Intelligence • Company growth indicators • Cultural & values intelligence • Competitive positioning

LinkedIn Job Data Quality & Normalization • Advanced LinkedIn job deduplication • LinkedIn job skills taxonomy standardization • LinkedIn job geographic normalization • LinkedIn job company matching • LinkedIn job education standardization

Who Uses Canaria's LinkedIn Data

HR & Talent Acquisition Teams • Optimize recruiting pipelines • Benchmark compensation • Identify talent pools • Develop data-driven hiring strategies

Market Research & Intelligence Analysts • Track industry trends • Build competitive intelligence models • Analyze workforce dynamics

HR Technology & Analytics Platforms • Power recruiting tools and analytics solutions • Fuel compensation engines and dashboards

Academic & Economic Researchers • Study labor market dynamics • Analyze career mobility trends • Research professional development

Government & Policy Organizations • Evaluate workforce development programs • Monitor skills gaps • Inform economic initiatives

Summary

Canaria's LinkedIn Job Postings Data delivers the most comprehensive LinkedIn job market intelligence available. It combines job posting insights, recruiting intelligence, and organizational data in one unified dataset. With AI-enhanced enrichment, real-time updates, and enterprise-grade data quality, it supports advanced HR analytics, talent acquisition, job market research, and competitive intelligence.

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ArcGIS geological map of Heard Island created using legacy field and sample data together with satellite imagery and published in Fox, Jodi M., et al. "Construction of an intraplate island volcano: The volcanic history of Heard Island." Bulletin of Volcanology 83.5 (2021): 37. The geological map was created in ArcMap 10.0 using satellite imagery, aerial photography, and historical maps and data. An initial map was generated by outlining geological features observed in the remote sensing images and the aerial photographs. This map was then cross-referenced with all available published and unpublished data to verify rock type, stratigraphic unit, and contact relationships. Where uncertainty in rock type or composition existed, the feature has been assigned to the stratigraphic unit without using a rock type label. In addition to published data, we collated and reviewed legacy unpublished maps, rock collections and unpublished data including hand-drawn sketches and notebooks.

Criteria for allocation of rocks to formations were not changed from previous work (Barling 1990; Barling 1994; Lambeth 1948; Lambeth 1952; Stephenson 1964).

Summary of the stratigraphy of Heard Island is as follows:

1. Unconsolidated Deposits (Recent) - Moraines, beach pebble, gravel and sand deposits.
2. Coastal Volcanic Cones (less than 15 ka) - Basaltic ash and scoria cones and associated small lavas.
3. Newer Lavas (~750 ka- Present) - Comprises the Laurens Peninsula Group and the Big Ben Group.
3a. Laurens Peninsula Group - Trachyte, tephrite, trachyandesite and basanite porphyritic lavas. Phenocrysts include clinopyroxene, olivine, plagioclase, kaersutite, magnetite, ilmenite and apatite. High TiO2 and P2O5 content.
3b. Big Ben Group - Basalt-trachybasalt and basanite porphyritic lavas. Basalt-trachybasalt phenocryts include olivine, clinopyroxene, plagioclase and Fe-Ti oxides. Basanite phenocrysts and megacrysts include olivine and clinopyroxene
4. Drygalski Formation (3.63-2.5 Ma) - Subhorizontal. Volcaniclastic breccia, conglomerate, sandstone and mudstone. Conglomerates are clast and matrix supported. Clasts are mainly basalt with minor trachyte, limestone and chert. Pillow lavas. Tillite. Microfossils include foraminifera and palynomorphs. Macrofossil - Austrochlamys heardensis
5. Laurens Peninsula Limestone (Middle Eocene-Middle Oligocene) - Thin, white, grey and blue styolitic carbonate interbedded with thin, soft tuffaceous shales. Lense of chert. Microfossils include foraminifera, coccoliths and palynomorphs. Intruded by trachybasalt and dolerite dykes (5 cm-2 m thick) and dolerite and gabbro sills. Folded and tilted.

For creation of the Heard Island geological map limestone and carbonate rocks were allocated to the Laurens Peninsula Limestones. Fresh, unaltered basalts were allocated to the Newer Lavas (Barling 1990). The Drygalski Formation includes all noncarbonate sedimentary rocks, clastic facies, and basalts between the Laurens Peninsula Limestones and the Newer Lavas (Barling 1990). Defining the boundary between the Drygalski Formation and the Newer Lavas is problematic, here we used the absence of chlorite as a criterion for allocating basalts to the Newer Lavas and the presence of basaltic pillows to allocate rocks to the Drygalski Formation consistent with Barling (1990). Although not ideal, these criteria were retained in the absence of more robust alternatives. Ridges of sediment in front of or adjacent to glaciers (current or since retreated) were mapped as moraines. Glacial retreat has been significant since the 1940s (~20 vol.% reduction), and locations where glaciers have been observed but have since retreated are relatively well known (Ruddell 2006). Ridges of unconsolidated sediment that have unclear relationships with glaciers and that could have been produced by aeolian and/or alluvial processes were mapped as unconsolidated sediment.

Remote Sensing Resources Utilised:
1. Mosaic of QuickBird satellite images of Heard Island (0.6m resolution) collected between 2006 and 2009 provided by the Australian Antarctic Division Data Centre (AADC).
2. Satellite imagery from Google™ Earth. Images collected 1984-2016.
3. Landsat 8 imagery from NASA via the USGS EarthExplorer online platform. Images collected 2013-2020.
4. Analogue aerial photographs collected in 1987 and held at the AADC

Published Resources Utilised
1. Barling J (1990) The petrogenesis of the Newer Lavas on Heard Island unpublished thesis. Department of Earth Sciences, Monash University, Melbourne
2. Barling J (1994) Origin and evolution of a high-Ti ocean island basalt suite; the Laurens Peninsula Series. Heard Island, Indian Ocean Mineralogical Magazine 58A:49–50
3. Barling J, Goldstein SJ,Wheller GE, Nicholls IA (1988) Heard Island; an example of large isotopic variations on a small oceanic island. Chemical Geology 70:46–46
4. Barling J, Goldstein SL, Nicholls IA (1994) Geochemistry of Heard Island (southern Indian Ocean); characterization of an enriched mantle component and implications for enrichment of the sub-Indian Ocean mantle, Journal of Petrology. 35:1017–1053
5. Clarke I (1979) Petrogenesis of basic and ultrabasic lavas on Heard Island. J Geol Soc Aust 26:272–272
6. Clarke I, McDougall I, Whitford DJ (1983) Volcanic evolution of Heard and McDonald islands, southern Indian Ocean. In: Oliver RL, James PR, Jago JB (eds) Antarctic earth science. Cambridge University, Cambridge, United Kingdom (GBR), pp 631–635
7. Collerson KD, Regelous M, Frankland RA, Wendt JI, Wheller G, Anonymous (1998) 1997 eruption of McDonald Island (southern Indian Ocean); new trace element and Th-Sr-Pb-Nd isotopic constraints on Heard-McDonald island magmatism Abstracts. Geological Society of Australia 49:87
8. Duncan RA, Quilty PG, Barling J, Fox JM (2016b) Geological development of Heard Island. Central Kerguelen Plateau. Aust J Earth Sci 63:81–89
9. Fox JM (2014) Heard Island up-date LAVA news. Geological Society of Australia 25:6–7
10. JonkersHA (2003) Late Cenozoic - recent pectinidae (mollusca: bivalvia) of the Southern Ocean and neighbouring regions. Monographs of marine Mollusca no.5. Backhuys Publishers BV, Leiden
11. Kiernan K, McConnell A, Yates T (1998) Tube-fed pahoehoe lava-flow features of Azorella Peninsula, Heard Island, southern Indian Ocean. Polar Record 34:225–236
12. Lambeth AJ (1952) A geological account of Heard Island. Journal and Proceedings of the Royal Society of New South Wales 86 Part 1:14–19
13. Orth K, Carey RJ, Wright R (2013) Heard Island volcanic eruption. September-October, November 2012 LAVA News 24:3–4
14. Patrick M (2013) Heard (Australia): Satellite imagery reveals lava flows in December 2012 Bulletin of the Global Volcanism Network 38:1
15. Patrick MR, Smellie JL (2013) Synthesis: A spaceborne inventory of volcanic activity in Antarctica and southern oceans, 2000–10. Antarctic Science 25:475–500
16. Quilty PG, Wheller G (2000) Heard Island and the McDonald Islands; a window into the Kerguelen Plateau. Papers and Proceedings of the Royal Society of Tasmania 133 Part 2:1–12
17. Quilty PG, Shafik S, McMinn A, Brady H, Clarke I (1983) Microfossil evidence for the age and environment of deposition of sediments of Heard and McDonald Islands. In: Oliver RL, James PR, Jago JB (eds) Antarctic Earth Science. Cambridge University, Cambridge, pp 636–639
18. Quilty PG, Murray-Wallace CV, Whitehead JM (2004) Austrochlamys heardensis (Fleming, 1957) (bivalvia, pectinidae) from Central Kerguelen plateau, Indian Ocean; palaeontology and possible tectonic significance. Antarctic Science 16:329–338. https://doi.org/10.1017/S0954102004002160
19. Ruddell A (2006) An inventory of present glaciers on Heard Island and their historical variation. In: Green K, Woehler EJ (eds) Heard Island; Southern Ocean Sentinel. Surrey Beatty, Chipping Norton, New South Wales (AUS), pp 28–51
20. Stephenson PJ (1964) Some geological observations on Heard Island. In: Adie RJ (ed) Antarctic Geology - Proceedings of the first international symposium on Antarctic geology. North-Holland Publishing Company, Amsterdam, pp 14–24
21. Stephenson PJ (1972) Geochemistry of some Heard Island igneous rocks. In: Adie RJ (ed) Antarctic Geology and Geophysics. Scandinavian University Books, Oslo, pp 793–801
22. Stephenson PJ, Barling J, Wheller G, Clarke I (2006) The geology and volcanic geomorphology of Heard Island. In: Green K, Woehler EJ (eds) Heard Island; Southern Ocean Sentinel. Surrey Beatty, Chipping Norton, Australia, pp 10–27
23. Truswell EM, Quilty PG, McMinn A, MacPhail MK, Wheller GE (2005) Late Miocene vegetation and palaeoenvironments of the Drygalski Formation, Heard Island, Indian Ocean; evidence from palynology. Antarctic Science 17:427–442. https://doi.org/10.1017/S0954102005002865
24. Tyrrell GW (1937) The petrology of Heard Island BANZARE reports 2part 3:27-56

Unpublished Resources Utilised:

1. H.O. Fletcher, 1929 Rock Collection Australian Museum, Sydney.
2. A.J. Lambeth, 1948-1949 Rock collection, hand drawn outcrop sketches and maps, field notebooks, Australian Museum, Sydney
3. P. Blaxland, 1948 Rock Collection Australian Museum, Sydney.
4. G.C Compton, 1951 Personal letter outlining geological observations with sketches made during survey of Heard Island, Australian Museum Sydney.
5. P.G. Law and T. Burstall, 1953 ANARE Interim Report 7 Heard Island, Australian Antarctic Museum Library.
6. I. Clarke, 1982 Technical Report - Expedition to the Australian Territory of Heard Island and McDonald Island, Australian Antarctic Museum Library.
7. R. Vining, 1983 A report of activities by

Abstract This study carried out a mapping procedure focusing on apple orchards considering the planted area, spatial location, altitude range, slope interval, and presence of anti-hail nets in the city of São Joaquim (Southern Santa Catarina Plateau, Brazil). Spectral images from the Sentinel-2 orbital platform acquired in August 2018 and an enhanced digital elevation model from the Shuttle Radar Topography Mission (SRTM) were used. In a GIS application configured with the SIRGAS 2000,4 reference system and UTM cartographic projection, Sentinel-2 constellation images and digital elevation models from the SRTM mission and more recently refined with sensor data Phased Array type L-band Synthetic Aperture Radar (PALSAR) were added. All images were resampled to a spatial resolution of 10m. The results were validated based on high spatial resolution images available from Google Earth. The results show that São Joaquim has a planted area of 7,974.80 ha, and only 12% use an anti-hail coverage system. The majority of the orchards range from one to five ha and belong to small producers. More than 50% of the orchards are between 1,200 and 1,400 m in altitude, with 45% of orchards located in areas with slopes between 8 to 20%. Interestingly, most of the orchards are concentrated in a radius of up to 20km from the urban center of São Joaquim, where industries and cooperatives are located for packaging, processing, and logistics. This study demonstrated that orbital data from Sentinel-2 can effectively quantify the distribution of apple orchards, being a viable and effective alternative for collecting information for agricultural monitoring. In this way, it enables efficient planning of apple production, such as technical assistance, marketing with producers, and production flow.

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