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 =...

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.

Results from time-series analysis of Landsat images in characterizing global forest extent and change. The 'first' and 'last' bands are reference multispectral imagery from the first and last available years for Landsat spectral bands corresponding to red, NIR, SWIR1, and SWIR2. Reference composite imagery represents median observations from a set of quality-assessed growing-season observations for each of these bands. Please see the User Notes for this update, as well as the associated journal article: Hansen, Potapov, Moore, Hancher et al. "High-resolution global maps of 21st-century forest cover change." Science 342.6160 (2013): 850-853.

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.

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"

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 ...

The National Census of Ferry Operators (NCFO) Terminals dataset was collected through December 31, 2020 and compiled on October 16, 2024 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 2020 National Census of Ferry Operators. Areas covered by the dataset include the 50 states as well as the territories of Puerto Rico, the United States Virgin Island, 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, longitude 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, termina 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, in addition to geographic location and transportation connections, whenever operators provided this information.

The global Cloud Native GIS Platform market is poised for significant expansion, projected to reach an estimated $12,500 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 18.5% through 2033. This upward trajectory is propelled by an increasing demand for scalable, flexible, and accessible geospatial data solutions across diverse industries. The inherent advantages of cloud-native architectures – such as enhanced data processing capabilities, real-time analytics, and simplified deployment – are making these platforms indispensable for modern geographic information system (GIS) applications. Key drivers fueling this growth include the burgeoning need for advanced land surveying and mapping, environmental monitoring initiatives, sophisticated traffic management systems, and enhanced public safety applications. The transition from traditional on-premise GIS solutions to cloud-native alternatives is a defining trend, enabling organizations to leverage powerful GIS functionalities without the burden of substantial infrastructure investments. This shift democratizes access to sophisticated geospatial tools, fostering innovation and efficiency in how spatial data is managed, analyzed, and visualized. The market is characterized by a dynamic interplay of technological advancements and evolving user needs. While the server computing type continues to hold a significant share, the rapid emergence and adoption of serverless computing type present a compelling growth opportunity. Serverless architectures offer unparalleled scalability and cost-effectiveness, making them increasingly attractive for handling fluctuating workloads and dynamic data processing requirements. Despite the promising outlook, certain restraints such as data security concerns, integration complexities with legacy systems, and the need for specialized cloud-native GIS expertise may pose challenges. However, the market is actively addressing these through continuous innovation in security protocols, development of comprehensive integration tools, and increased training programs. Major players like ESRI, Mapbox, and Google Maps are at the forefront, investing heavily in R&D to offer cutting-edge cloud-native GIS solutions, further solidifying the market's growth trajectory. The Asia Pacific region, driven by rapid digitalization and smart city initiatives in countries like China and India, is expected to exhibit the fastest growth, closely followed by North America and Europe. This report delves into the dynamic realm of Cloud Native GIS Platforms, a technology revolutionizing how organizations harness location-based data. From sophisticated land surveying to real-time traffic management, these platforms are becoming indispensable. Our analysis spans the Historical Period (2019-2024), the Base Year (2025), and forecasts future trajectory through 2033. The market is witnessing significant growth, estimated to reach hundreds of millions of dollars by the Estimated Year (2025), with projections soaring into the billions of dollars by 2033. This comprehensive report provides in-depth insights into market dynamics, key players, emerging trends, and future opportunities within this critical technology sector.

Manufacturing Company Data for Market Share, Risk & B2B Intelligence Manufacturing company data is a critical resource for understanding how industrial businesses operate, expand, and compete. Canaria’s enriched manufacturing company data delivers verified, structured intelligence on thousands of US manufacturing companies, including firmographics, operational scale, geographic footprint, and public metadata.

Sourced from trusted platforms like Google Maps and Indeed, this manufacturing company dataset includes branch-level details, industry classifications, headcount estimates, and location intelligence. It’s designed to support workflows in company analysis, competitive intelligence, market share modeling, B2B lead generation, and company risk monitoring.

Use Cases: What Problems This Manufacturing Company Data Solves Canaria’s manufacturing company data transforms fragmented industrial records into match-ready, analyzable insights. Whether you’re targeting manufacturing companies for sales outreach or tracking competitors across regions, this dataset offers powerful real-world coverage.

Company Analysis • Profile manufacturing companies by size, industry sector, location, and facility count • Map HQ and branch locations using Google Maps-enriched manufacturing company profiles • Track workforce and operational structure using public metadata from Indeed company pages • Benchmark industrial clusters or strategic suppliers with firmographic manufacturing company insights

Market Share & Competitive Intelligence • Compare manufacturing companies by production presence, regional scale, and sector focus • Identify dominant players and rising competitors in key U.S. industrial zones • Track market penetration using branch data across manufacturing company datasets • Model sector fragmentation using enriched manufacturing firmographics and location patterns

Company Risk Analysis • Spot site closures or contraction trends across manufacturing companies • Analyze Indeed signals and Maps metadata for early signs of distress • Track operational risk by comparing listed locations with verified manufacturing company activity • Identify companies that pose supply chain exposure or regional over-concentration

Business Intelligence & Strategic Planning • Segment manufacturing companies by size bands and physical presence • Build BI dashboards using real-world manufacturing company data and Google Maps overlays • Support go-to-market strategy using granular insights from manufacturing company profiles • Integrate into your CRM or lead scoring system with structured, matchable attributes

B2B Manufacturing Lead Generation • Generate targeted B2B manufacturing leads with validated firmographic and geographic data • Filter manufacturing companies by size, state, industry, or proximity to target regions • Identify fast-growing or undercovered manufacturing companies using enriched metadata • Match B2B records with up-to-date manufacturing company data to boost targeting precision

What Makes This Manufacturing Company Data Unique Match-Ready for Enterprise Workflows • Seamlessly integrates with internal data pipelines, CRMs, BI tools, and enrichment APIs

Verified Location Intelligence • Every manufacturing company entry is linked to Google Maps coordinates, reviews, and working hours

Indeed Company Enrichment • Public descriptions, ratings, categories, and signals from Indeed company profiles

Clean & Normalized Format • Structured manufacturing company data with taxonomy-mapped fields ready for real-time use

Frequently Updated • New manufacturing companies and branch locations are refreshed weekly or monthly to ensure accuracy

Who Benefits from Manufacturing Company Data • B2B sales teams targeting U.S. manufacturing companies by size, sector, or region • Strategy and BI teams modeling manufacturing market share and competitive presence • Consultants analyzing facility-level distribution of industrial companies • Risk teams evaluating exposure to underperforming manufacturing companies • RevOps and ABM teams integrating high-quality manufacturing company leads into CRM platforms • M&A analysts scouting fragmented manufacturing segments for consolidation

Summary Canaria’s Manufacturing Company Data provides deep visibility into the U.S. manufacturing economy. Enriched with Google Maps and Indeed company data, this structured dataset supports actionable insights across company analysis, B2B lead generation, risk assessment, and market share modeling.

With verified manufacturing company profiles, matchable records, and regular updates, this product helps teams move from static firmographics to high-resolution industrial intelligence — powering decisions in sales, strategy, finance, and analytics.

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, structure...

The National Census of Ferry Operators (NCFO) Terminals dataset was collected through December 31, 2020 and compiled on October 16, 2024 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 2020 National Census of Ferry Operators. Areas covered by the dataset include the 50 states as well as the territories of Puerto Rico, the United States Virgin Island, 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, longitude 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, termina 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, in addition to geographic location and transportation connections, whenever operators provided this information. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529043

Blue Cart Residential Recycling zones and schedule. Each recycling zone is divided into two sections – A (Orange) and B (Yellow). Collection is done on an alternating-week basis. For 2012, B/Yellow sections were collected the week of 1/2/2012 and A/Orange sections were collected the week of 1/9/2012, continuing to alternate from there. See http://bit.ly/wssHIB or https://data.cityofchicago.org/d/eb3b-ehmq for the full schedule. For more about Recycling in Chicago, visit http://bit.ly/IcfLHr.

To view or use these files, special GIS software, such as Google Earth is required.

Indeed Data Company & Job Postings Data for Talent Acquisition, HR Analytics, Business Development & Market Research

Indeed Data Company and Job Postings Data from Canaria provide one of the most comprehensive and actionable alternative data sources for understanding U.S. workforce demand, employer hiring behavior, salary trends, and regional labor market signals. Our enriched Indeed Data combines millions of detailed job postings with verified company data, salary intelligence, and Google Maps metadata—delivered in a clean, normalized, and deduplicated format that is matchable with trusted company profiles.

This Indeed Data product empowers HR, recruiting, sales, and strategy teams to answer critical questions: Who is hiring? Which roles and skills are in demand? What are the salary benchmarks? Where are talent hotspots? Updated weekly with AI-driven enrichment and deep field-level granularity, Canaria’s Indeed Data offers a high-resolution, actionable view of the U.S. labor market for industries and regions.

Use Cases: Unlocking Value with Indeed Data Company & Job Postings Data Our enriched Indeed Data transforms raw job postings into structured, analyzable insights—enabling critical workflows in:

Talent Acquisition & HR Analytics • Identify hiring trends by industry, company, location, and role specialization using Indeed Data • Benchmark competitor job descriptions, salary ranges, and benefits with Indeed Data insights • Optimize recruiting based on in-demand skills, certifications, and seniority levels derived from Indeed Data • Prioritize outreach to companies expanding hiring using real-time Indeed Data signals • Detect emerging roles, skill clusters, and shifts in talent demand through Indeed Data’s job title taxonomies

Business Development & CRM Data Enrichment • Enrich CRM and sales platforms with up-to-date hiring activity signals from Indeed Data • Identify growth and expansion opportunities by linking Indeed Data job postings with firmographics and Google Maps • Enhance lead scoring and sales targeting using Indeed Data hiring momentum and salary intelligence • Build dynamic marketing and outreach cohorts based on Indeed Data hiring behavior and labor market activity

Labor Market Research & Workforce Strategy • Analyze labor demand trends by geography and sector with Indeed Data job postings • Correlate hiring and salary trends with company size, industry, and location using Indeed Data insights • Monitor occupational growth or decline via Indeed Data’s normalized job titles and enriched fields • Support workforce planning, consulting, and government research with high-quality Indeed Data labor signals • Build predictive models and skills gap analyses powered by AI-enhanced Indeed Data

Why Choose Canaria’s Indeed Data & Job Postings Data? AI-Powered Data Enrichment & Normalization • Extracts and normalizes hard skills, soft skills, certifications, education levels, and employment type from Indeed Data job descriptions • Predicts job seniority, modality (remote, onsite, hybrid), and contract/full-time status with advanced NLP applied to Indeed Data • Maps millions of raw job titles from Indeed Data into a comprehensive taxonomy of 50,000+ standardized categories • Estimates salary ranges using parsed salaries and market benchmarks to fill gaps in Indeed Data

Deduplication & Matchability • Achieves approximately 60% deduplication using semantic similarity and metadata, ensuring clean, reliable Indeed Data • Normalizes job titles, company names, locations, and employment types for consistent Indeed Data integration • Matches every Indeed Data job posting to normalized, verified company profiles enriched with firmographics and Google Maps metadata • Enables employer-level aggregation, benchmarking, and hiring activity profiling based on Indeed Data

Who Benefits from Canaria’s Indeed Data & Job Postings Data? • HR & Talent Acquisition Teams leveraging Indeed Data to optimize recruiting pipelines, compensation benchmarking, and talent market positioning • Business Development & Sales Teams using Indeed Data hiring signals and enriched company data for lead generation and account targeting • Workforce Planners & Labor Market Analysts building predictive models and economic forecasts powered by Indeed Data insights • Consultants & Researchers performing labor market studies, competitive benchmarking, and skills gap analyses with Indeed Data • B2B Platforms & HR Tech Companies powering salary tools, recruitment analytics, and talent intelligence dashboards with Indeed Data • Government Agencies & Think Tanks monitoring employment trends and informing workforce policy using Indeed Data • BI & Analytics Teams visualizing hiring velocity, talent clusters, and compensation patterns with Indeed Data

Summary Canaria’s Indeed Data Company & Job Postings Data provides an AI-enhanced, normalized, and deduplicated view of the U.S. labor market—blending millions of job postings, salary data, company ...

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

Images composites de radiance moyenne mensuelle utilisant les données nocturnes de la bande jour/nuit (DNB) de la suite radiométrique Visible Infrared Imaging Radiometer (VIIRS). Comme ces données sont composées mensuellement, il est impossible d'obtenir une couverture de données de bonne qualité pour ce mois dans de nombreuses régions du monde. Cela peut être dû à…