The size of the Italy Geospatial Analytics market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 8.17% during the forecast period.Geospatial analytics is the act of applying geospatial data to understand spatial data patterns, relation, and trends. The method utilizes numerous types of sources ranging from satellite imaging, GPS signals, and sensor-generated data in constructing interactive maps as well as different forms of visualization. Geospatial analytics becomes a utility across most industries from urban planning and agriculture, to transportation, to environmental monitoring. It can, for instance, optimize the routes for the transportation of products, monitor environmental pollution, and assess the impacts of climate changes along the coasts. The industry is driven by increased government spending on infrastructure construction, growing interest in precision agriculture, and the wide adoption of high-tech solutions such as artificial intelligence and machine learning in the geospatial world. Recent developments include: March 2023: The Italian space agency and NASA have collaborated to build and launch the Multi-Angle Imager for Aerosols mission, an effort to investigate the health impacts of tiny airborne particles polluting the cities through analyzing data by collecting data from the satellite-based observatories, which would fuel the demand for geospatial analytics market in the country., January 2023: EDB, an open-source database service provider in Italy, announced its partnership with Esri to certify EDB Postgres Advanced Server with Esri ArcGIS Pro and Esri ArcGIS Enterprise, which work together to form Esri's Geospatial analytic solutions, operating in many countries, including Italy. After this partnership, users can connect their EDB Postgres Advanced Server to explore, visualize and analyze their geospatial data and share their work with an Esri ArcGIS Enterprise portal. In addition, EDB customers, especially those in the public sector, can use their database with Esri ArcGIS software to transform their data into something that improves workflows and processes and shapes policies and engagement within their communities.. Key drivers for this market are: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Potential restraints include: High Costs and Operational Concerns, Lack of Standardization for Data Integration. Notable trends are: The Increase in the Number of Smart Cities in The Country Fuels the Market Growth.

This dashboard monitors the latest earthquake events around the world. It automatically updates when new events come in to show you where they occurred, how significant they were, and if any there were any resulting tsunamis. The real-time earthquake data, provided by the Living Atlas, was used to create a web map that was then used in this dashboard.To learn about the creation of this dashboard, read the blog: Making an Auto-Focusing Real-Time Dashboard. Feel free to make a copy and see how it is configured.

The ArcGIS Monitor Excel report task summarizes statistics for a specified time range (eg. last 7 days). The user can edit the config.json parameters to determine what timeframe, report path, and modules (tabs) to be included. Long-running reports over 7 days are best scheduled as a task and may avoid web time-out conditions in some environments.For more information on configuring the extension, see the PDF included in the download.

Hourly air quality data provided for various dashboards based on hourly averages of qualified raw values from the various air quality censors.The table shown below includes the common names for each of the stations as well as the pod and serial numbers assigned by the manufacturer.
Dallas Station Name Pod Number Pod Serial Number

Larry Johnson Recreation Center Monitor 2977 2450952

Mill Creek Batch Plant Monitor 2978 2450953

South Central Park (Joppa Neighborhood) Monitor 2979 2450954

West Dallas Multipurpose Center Monitor 2983 2450958

Polk Recreation Center Monitor 3020 2450995

Water Quality Monitoring Site identifies locations across the state of Vermont where water quality data has been collected, including habitat, chemistry, fish and/or macroinvertebrates. Currently the layer is not maintained as site locations are provided through another means to the ANR Natural Resources Atlas.

Monitor COVID-19 at a glance.ArcGIS Dashboards enables users to convey information by presenting location-based analytics using intuitive and interactive data visualizations on a single screen. This video series will help you learn about ArcGIS Dashboards and how to leverage them for COVID-19 Emergency Management. Enroll in this plan to learn how to bring your data into ArcGIS Online, then configure and design your own dashboards, and make them interactive._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Growth data for housing units and employment for the growth areas, urban centers and villages, for the City of Seattle Comprehensive Plan. This is a stand alone table that includes non-spatial records.Housing unit growth is reported quarterly from the city's permitting system while employment change is reported annually from the State of Washington QCEW data.

At the US Centers for Disease Control and Prevention (CDC), a new effort is under way to assist states in creating or enhancing localized COVID-19 dashboards and maps for the public. This effort with states has an external focus, aiming to help the them deliver data to residents, civic leaders, and public health administrators. Armed with this information, states and localities will be better equipped to monitor the impacts and mitigate risks, and federal resources can go where they are needed most, because everyone will be working from the same data._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

ArcGIS Dashboards useful links (GeoNet). ArcGIS Dashboards is a configurable web app that provides location-aware data visualization and analytics for a real-time operational view of people, services, assets, and events. You can monitor the activities and key performance indicators that are vital to meeting your organization’s objectives within a dynamic dashboard._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...Edi

The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The Water quantity in Canadian rivers indicators provide information about the state of the amount of surface water in Canada and its change through time to support water resource management. They are used to provide information about the state and trends in water quantity in Canada. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated. See Local Water quantity in Canadian rivers - Water quantity at monitoring stations, Canada for more information on data formats, interactive indicator map, web services, and contact information.

This product is part of the Landscape Change Monitoring System (LCMS) data suite. It shows LCMS change attribution classes for each year. See additional information about change in the Entity_and_Attribute_Information or Fields section below.LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS change, land cover, and land use maps offer a holistic depiction of landscape change across the United States over the past four decades.Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).Outputs fall into three categories: change, land cover, and land use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), fast loss (which also includes hydrologic changes such as inundation or desiccation), and gain. These values are predicted for each year of the time series and serve as the foundational products for LCMS. References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. http://doi.org/10.1016/j.rse.2017.03.026Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830).Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10mWeiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CAZhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Progress photo that was taken of an air quality monitor. Photo was taken by the Office of Environmental Quality & Sustainability.

California least tern colonies are monitored during nesting season along the California Coast and data is reported to California Department of Fish and Wildlife. The colonies in this dataset range from the Tijuana river mouth near the Mexico border to Sacramento. Locations and data include both present and historic sites. Locations are approximate and can have variation over time. These approximate locations were overlaid with a statewide hexagon layer (ds675) and then a buffer was created in order to mask exact nesting locations. Some additional sites without location information can be found in the related table. The related table includes nesting data by year from 1990 - 2023. It's anticipated to update this dataset to include earlier and newer records. Monitoring data includes estimated fledglings, estimated breeding pairs, total number of nests, and predator information when included in site reports. Annual reports 2017 and earlier can also be found by searching in California Department of Fish and Wildlife's document library (nrm.dfg.ca.gov/documents/docViewer.aspx). A version of this data, summarized by region, has been made available in BIOS as California Least Tern Monitoring Sites by Region [ds3147]. This is a publicly available dataset.

[Metadata] Description: This data shows the location of air quality monitoring sites used by the Hawaii Department of Health, as of April 2025.Source: State of Hawaii, Department of Health (DOH), Clean Air Branch, April 14, 2025.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/AQ_Sites.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This is a point feature class of environmental monitoring stations maintained in the California Department of Water Resources’ (hereafter the Department) Hydstra continuous database management system used for collection of high frequency continuous timeseries data for groundwater, surface water, water quality and tidal station types. The QA/QC data timeseries data associated with these stations is published through the Departments Water Data Library web application. This dataset is comprised of a “Stations Table” and a related “Period of Record Table”. Stations table is the primary feature class and contains basic information about each station including Station Name, Latitude, Longitude and Description. The Period of Record Table is a related feature class that contains a list of parameters (i.e. stage, flow, depth to groundwater, water temperature, turbidity, pH, etc.) collected at each station along with the start date and end date (period of record) for each parameter and the number of data points collected.

This feature class is updated every business day using Python scripts and the WellNet database. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates. The NDWR's water monitoring database contains information related to sites for groundwater measurements. These data are used by NDWR to assess the condition of the groundwater and surface water systems over time and are available to the public on NDWR’s website. Groundwater measurement sites are chosen based on physical location and access considerations, permit terms, and to maximize the distribution of measurement points in a given basin.Groundwater monitoring sites are typically chosen based on spatial location, access, and period of record considerations. When possible NDWR tries to have a distribution of monitoring locations within a given hydrographic area. The entity who does the monitoring depends on the site – for example, some mines have well fields where they collect data and submit those data to NDWR as a condition of their monitoring plan – and some sites are monitored by NDWR staff annually or more frequently. While people can volunteer to have their well monitored, more often the NDWR staff who measure water levels recommend an additional site or staff in the office recommend alternate sites. The Chief of the Hydrology Section will review the recommendations and make a final decision on adding/changing a site. This dataset is updated every business day from a non-spatial SQL Server database using lat/long coordinates to display location. This feature class participates in a relationship class with a groundwater measure table joined using the sitename field. This dataset contains both active and inactive sites. Measurement data is provided by reporting agencies and by regular site visits from NDWR staff. For website access, please see the Water Levels site at water.nv.gov/WaterLevelData.aspx

Sentinel-2 Level-1C imagery with on-the-fly renderings for visualization. This imagery layer pulls directly from theSentinel-2 on AWScollection and is updated daily with new imagery.Sentinel-2 imagery can be applied across a number of industries, scientific disciplines, and management practices. Some applications include, but are not limited to, land cover and environmental monitoring, climate change, deforestation, disaster and emergency management, national security, plant health and precision agriculture, forest monitoring, watershed analysis and runoff predictions, land-use planning, tracking urban expansion, highlighting burned areas and estimating fire severity. Geographic Coverage GlobalContinental land masses from65.4° South to 72.1° North, with these special guidelines:All coastal waters up to 20 km from the shoreAll islands greater than 100 km2All EU islandsAll closed seas (e.g. Caspian Sea)The Mediterranean Sea Temporal Coverage This layer includes a rolling collection of Sentinel-2 imagery acquired within the past 14 months. This layer is updated daily with new imagery. The revisit time for each point on Earth is every 5 days. The number of images available will vary depending on location. Product Level This service provides Level-1C Top of Atmosphere imagery.Alternatively,Sentinel-2 Level-2A is also available. Image Selection/Filtering The most recent and cloud free images are displayed by default. Any image available within the past 14 months can be displayed via custom filtering. Filtering can be done based on attributes such as Acquisition Date, Estimated Cloud Cover, and Tile ID. Tile_ID is computed as [year][month][day]T[hours][minutes][seconds]_[UTMcode][latitudeband][square]_[sequence].More… Visual Rendering Default rendering is Natural Color (bands 4,3,2) with Dynamic Range Adjustment (DRA). The DRA version of each layer enables visualization of the full dynamic range of the images. Rendering (or display) of band combinations and calculated indices is done on-the-fly from the source images via Raster Functions. Various pre-defined Raster Functions can be selected or custom functions created. Available renderings include: Agriculture with DRA,Bathymetric with DRA,Color-Infrared with DRA,Natural Color with DRA,Short-wave Infrared with DRA,Geology with DRA,NDMI Colorized,Normalized Difference Built-Up Index (NDBI),NDWI Raw,NDWI - with VRE Raw,NDVI – with VRE Raw (NDRE),NDVI - VRE only Raw,NDVI Raw,Normalized Burn Ratio,NDVI Colormap. Multispectral Bands BandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020Additional Notes Overviews exist with a spatial resolution of 150m and are updated every quarter based on the best and latest imagery available at that time.To work with source images at all scales, the ‘Lock Raster’ functionality is available. NOTE: ‘Lock Raster’ should only be used on the layer for short periods of time, as the imagery and associated record Object IDs may change daily.This ArcGIS Server dynamic imagery layer can be used in Web Maps and ArcGIS Desktop as well as Web and Mobile applications using the REST based Image services API.Images can be exported up to a maximum of 4,000 columns x 4,000 rows per request.Data SourceSentinel-2 imagery is the result of close collaboration between the (European Space Agency) ESA, the European Commission and USGS. Data is hosted by the Amazon Web Services as part of theirRegistry of Open Data. Users can access the imagery fromSentinel-2 on AWS, or alternatively accessEarthExploreror theCopernicus Data Space Ecosystemto download the scenes.For information on Sentinel-2 imagery, seeSentinel-2.

For further information about air quality monitoring - see the City of York Council website

See areas of drought in the U.S. Updated weekly.