The data provides a summary of the state of development practice for Geographic Information Systems (GIS) software (as of August 2017). The summary is based on grading a set of 30 GIS products using a template of 56 questions based on 13 software qualities. The products range in scope and purpose from a complete desktop GIS systems, to stand-alone tools, to programming libraries/packages.

The template used to grade the software is found in the TabularSummaries.zip file. Each quality is measured with a series of questions. For unambiguity the responses are quantified wherever possible (e.g.~yes/no answers). The goal is for measures that are visible, measurable and feasible in a short time with limited domain knowledge. Unlike a comprehensive software review, this template does not grade on functionality and features. Therefore, it is possible that a relatively featureless product can outscore a feature-rich product.

A virtual machine is used to provide an optimal testing environments for each software product. During the process of grading the 30 software products, it is much easier to create a new virtual machine to test the software on, rather than using the host operating system and file system.

The raw data obtained by measuring each software product is in SoftwareGrading-GIS.xlsx. Each line in this file corresponds to between 2 and 4 hours of measurement time by a software engineer. The results are summarized for each quality in the TabularSummaries.zip file, as a tex file and compiled pdf file.

Item details page: https://giscorps.maps.arcgis.com/home/item.html?id=ebc9a5056d4c40b6bc52d0cdbe4db435This view is inlcudes testing sites added to the GISCorps dataset int he last 24 hours, which should not be interpreted to mean that the test sites opened in the last 24 hours. This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and on findcovidtesting.com. States and counties are encouraged to include this feature service in their own testing site locator apps as well.Please submit new testing sites or updated testing site information via this form: https://arcg.is/10S1ib. Including this link on your organization's testing site finder web app will allow testing providers to add their own sites directly to the map, improving the accuracy and completeness of the dataset. GISCorps volunteers verify each submission prior to including it in this public view. You can also add your sites in bulk by completing a copy of this template and emailing it to admin@giscorps.org. This dataset is updated daily. All information is sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated by GISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document: https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups: https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

Announcement: Project Ended on October 15, 2021After over 18 months of collaboration between hundreds of GISCorps volunteers, Esri's Disaster Response Program, Coders Against COVID, HERE Technologies, dozens of government agencies, and hundreds of testing providers, GISCorps has decided to end our COVID-19 Testing and Vaccination Sites Data Creation Project as of October 15th, 2021. Our data will remain available for use by researchers and analysts, but it should not be considered a reliable source of current testing and vaccination site location information after October 15th. We are grateful for the support we have received by so many throughout the life of this monumental undertaking. Read more about this effort https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-data.Item details page: https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cbThis view is the original COVID-19 Testing Locations in the United States - public dataset. A backup copy also exists: https://giscorps.maps.arcgis.com/home/item.html?id=11fe8f374c344549815a716c8472832f. The parent hosted feature service is the same. This version is symbolized by type of test (molecular, antibody, antigen, or combinations thereof).This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and on findcovidtesting.com. All information was sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated by GISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document: https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups: https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

Read about this volunteer-driven effort, access data and apps, and contribute your own testing site data:https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-dataItem details page:https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cbThis view is the originalCOVID-19 Testing Locations in the United States - public dataset. A backup copy also exists:https://giscorps.maps.arcgis.com/home/item.html?id=11fe8f374c344549815a716c8472832f. The parent hosted feature service is the same.This version is symbolized by type of test (molecular, antibody, antigen, or combinations thereof).This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and onfindcovidtesting.com. States and counties are encouraged to include this feature service in their own testing site locator apps as well.Please submit new testing sites or updated testing site information via this form:https://arcg.is/10S1ib. Including this link on your organization's testing site finder web app will allow testing providers to add their own sites directly to the map, improving the accuracy and completeness of the dataset.GISCorps volunteers verify each submission prior to including it in this public view. You can also add your sites in bulk by completing a copy ofthis templateand emailing it to admin@giscorps.org.This dataset is updated daily. All information is sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated byGISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Data dictionary document:https://docs.google.com/document/d/1HlFmtsT3GzibixPR_QJiGqGOuia9r-exN3i5UK8c6h4/edit?usp=sharingArcade code for popups:https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

This resource contains the test data for the GeoServer OGC Web Services tutorials for various GIS applications including ArcGIS Pro, ArcMap, ArcGIS Story Maps, and QGIS. The contents of the data include a polygon shapefile, a polyline shapefile, a point shapefile, and a raster dataset; all of which pertain to the state of Utah, USA. The polygon shapefile is of every county in the state of Utah. The polyline is of every trail in the state of Utah. The point shapefile is the current list of GNIS place names in the state of Utah. The raster dataset covers a region in the center of the state of Utah. All datasets are projected to NAD 1983 Zone 12N.

GISCorps quickly marshaled its members to build a nationwide map of COVID-19 testing sites.Key TakeawaysGISCorps rallies to provide quick, expert mapping help in times of crisis.Volunteers aggregate data on testing sites to create an authoritative national map.Additional map project memorializes victims and survivors of COVID-19._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...

This paper presents an integrative and interdisciplinary approach to the study of ancient agricultural terraces and food production systems. Our approach consists of (1) a resource dependency theoretical framework and (2) the application of a variety of archaeological and geoscientific methods, including archaeological and geomorphological surveys, archaeological excavations, drone surveys, mapping based on satellite imagery and high-resolution digital elevation models (DEMs), geographic information system (GIS) applications, soil testing, phytolith analysis, radiocarbon dating, and calculations of food supply capacity and labor requirements. We apply these to the prehispanic site of Cutamalla (3,300 m asl) in the southern Peruvian Andes, which serves as an ideal and pioneering case study. Previous research has focused primarily on the settlement of Cutamalla, particularly through large-scale archaeological excavations, but less attention has been paid to the extensive farming terraces surrounding the settlement and the close relationship between agricultural and settlement activities. By analyzing both the terrace and settlement levels, we take a new perspective and introduce the term agricultural terrace-settlement system for such complexes. Our results show that the residential occupation of Cutamalla and the use of the surrounding farming terraces coincided: the agricultural terrace-settlement system was intensively used for a relatively short period of about 200 years (~250–40 BCE) during the Formative Late Paracas and transitional Initial Nasca periods, long before the famous Inka terrace agricultural systems. There is no evidence of reoccupation of the site and subsequent reuse of the agricultural system. Our data also document the large extent of agricultural terraces around Cutamalla (221 ha) and that maize was likely a major crop grown there. Finally, we place these findings in their broader socio-economic and ecological context. Cutamalla was an important regional center and economic hub during a very dynamic period characterized by significant population growth and increased violence. Not only a more humid climate, but probably also forced collective labor were cornerstones of substantial agricultural production in Cutamalla and the region.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

An application used by the public to locate the nearest Coronavirus testing or screening location. Read more about this project here: https://covid-19-giscorps.hub.arcgis.com/pages/contribute-covid-19-testing-sites-dataAbout the app:The GISCorps Testing Sites Locator can be used by the general public to locate screening and testing sites in the community and obtain instructional information and operational hours. The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers. GISCorps does not share any screening or testing site location information not previously made public by one of those entities.Adjust the slider to the desired buffer distance. Search for an address or place in the search bar, or click a location directly on the map to define the location to be buffered. Click a layer in the list of results and click the facility you are interested in. Select the Directions tab to view driving directions to the facility from the defined location.To submit updated information for one of our testing sites or to suggest a new one, please fill out and submit this form. To access our public feature service, click here. GISCorps can also provide a spreadsheet template for bulk data uploads; please contact us at info@giscorps.org to find out more about that option.All information is sourced from the websites of local governments and healthcare providers by volunteers from GISCorps and Coders Against COVID and is not complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements. If you are experiencing extreme COVID-19 symptoms such as trouble breathing, please seek medical attention immediately.

Tags

   soil survey, soils, Soil Survey Geographic, SSURGO




   Summary


   SSURGO depicts information about the kinds and distribution of

   soils on the landscape. The soil map and data used in the SSURGO

   product were prepared by soil scientists as part of the National

   Cooperative Soil Survey.


   Description


   This data set is a digital soil survey and generally is the most

   detailed level of soil geographic data developed by the National

   Cooperative Soil Survey. The information was prepared by digitizing

   maps, by compiling information onto a planimetric correct base

   and digitizing, or by revising digitized maps using remotely

   sensed and other information.


   This data set consists of georeferenced digital map data and

   computerized attribute data. The map data are in a 3.75 minute

   quadrangle format and include a detailed, field verified inventory

   of soils and nonsoil areas that normally occur in a repeatable

   pattern on the landscape and that can be cartographically shown at

   the scale mapped. A special soil features layer (point and line

   features) is optional. This layer displays the location of features

   too small to delineate at the mapping scale, but they are large

   enough and contrasting enough to significantly influence use and

   management. The soil map units are linked to attributes in the

   National Soil Information System relational database, which gives

   the proportionate extent of the component soils and their properties.


   Credits

   There are no credits for this item.


   Use limitations


   The U.S. Department of Agriculture, Natural Resources Conservation

   Service, should be acknowledged as the data source in products

   derived from these data.


   This data set is not designed for use as a primary regulatory tool

   in permitting or citing decisions, but may be used as a reference

   source. This is public information and may be interpreted by

   organizations, agencies, units of government, or others based on

   needs; however, they are responsible for the appropriate

   application. Federal, State, or local regulatory bodies are not to

   reassign to the Natural Resources Conservation Service any

   authority for the decisions that they make. The Natural Resources

   Conservation Service will not perform any evaluations of these maps

   for purposes related solely to State or local regulatory programs.


   Photographic or digital enlargement of these maps to scales greater

   than at which they were originally mapped can cause misinterpretation

   of the data. If enlarged, maps do not show the small areas of

   contrasting soils that could have been shown at a larger scale. The

   depicted soil boundaries, interpretations, and analysis derived from

   them do not eliminate the need for onsite sampling, testing, and

   detailed study of specific sites for intensive uses. Thus, these data

   and their interpretations are intended for planning purposes only.

   Digital data files are periodically updated. Files are dated, and

   users are responsible for obtaining the latest version of the data.


   Extent



   West -76.713689  East -76.526117 

   North 39.374398  South 39.194856 




   Scale Range

   There is no scale range for this item.

Airborne platforms used for testing the app and the various sites where flight tests were performed.

This data is associated with the following layers: Chronic Wasting Disease Sampling Stations - 2023-2024 - CDFW ds3182, Participating Meat Processors and Taxidermists - 2025 - CDFW ds3155, Participating Meat Processors and Taxidermists - 2023-2024 - CDFW ds3188, Approved Deer Carcass Disposal Sites - CDFW ds3156, Chronic Wasting Disease Surveys - CDFW ds3157. These layers include CWD Testing Sites, Participating Meat Processors and Taxidermists (MPT), Carcass Disposal Sites, and comprehensive data on CWD surveillance spanning from 1999 to 2025. This compilation of layers provides a comprehensive view of CWD-related activities, facilitating informed decision-making and strategic planning for wildlife management and disease prevention efforts. For the latest information, please visit https://wildlife.ca.gov/CWD.

The global soil environmental quality monitoring system market is experiencing robust growth, driven by increasing awareness of soil degradation and its impact on food security, environmental sustainability, and human health. Government regulations mandating soil quality monitoring, coupled with advancements in sensor technology and data analytics, are key catalysts for market expansion. The market size in 2025 is estimated at $2.5 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This growth trajectory projects a market value exceeding $4.5 billion by 2033. Several factors contribute to this positive outlook. The rising adoption of precision agriculture techniques, requiring detailed soil analysis for optimized crop management, is a major driver. Furthermore, the development of more affordable and portable monitoring devices, alongside sophisticated data management and interpretation software, is expanding the market's accessibility to a wider range of users, from large agricultural enterprises to smallholder farmers. While potential restraints like high initial investment costs for some systems and the need for skilled personnel to operate and interpret data exist, the overall market trend indicates sustained and significant growth fueled by increasing environmental concerns and technological advancements. Market segmentation includes various technologies (e.g., sensors, spectroscopy, GIS), application areas (e.g., agriculture, environmental remediation), and service types (e.g., testing, consulting, software). The United States and other developed nations currently dominate the market, but developing economies are witnessing increasing adoption rates.

Kuwait's arid desert landscape, geological formations, and extreme climate conditions make it a potential site for establishing a terrestrial Mars analog, as this research presents a new GIS-based methodology. The Analog Conjunctive Method (ACM) was specifically developed to identify a suitable location in Kuwait to hold a terrestrial Mars analog using a geographic information system (GIS) and remote sensing techniques. Analogs play a crucial role in simulating different Martian conditions, supporting astronaut training, testing various exploration technologies, and doing different types of scientific research on these environments. The ACM method integrates GIS and remote sensing techniques to evaluate the study area, resulting in potential sites for analog. The analysis employs two stages to finalize the best location. In stage one, the newly developed ACM is applied; it systematically eliminates unstable areas while allowing minimal flexibility for real-world environmental adjustment, particularly in regions with natural wind barriers. ACM is used to process the buffers created for the seven criteria (urban areas and farms, coastal areas, streets, airports, oil fields, natural reserves, and country borders) in QGIS to exclude unsuitable areas. Stage two screens the stage one map locations using different data (STRM, Copernicus sentinel-2, and field visits) to polish the selection based on other criteria (water bodies, dust rate, vegetation cover, and topography). The result shows nine locations in Jal Al-Zor as potential analog sites where a random location is selected for a 3D model creation to visualize the analog. Java Mission-planning and Analysis for Remote Sensing (JMARS) software was used to identify similarities between specific areas, such as the Jal Al-Zor escarpment and Huwaimllyah sand dunes in the Kuwait desert, and comparable terrains on Mars. The research concluded that Jal Al-Zor holds substantial potential as a terrestrial Mars analog site due to its geological and topographical similarities to Martian landscapes. This makes it an ideal location for crew training, Mars equipment testing, and further research in Mars analog studies, providing valuable insights for future planetary exploration.

This view is a copy of the original COVID-19 Testing Locations in the United States - public dataset (https://giscorps.maps.arcgis.com/home/item.html?id=d7d10caf1cec43e0985cc90fbbcf91cb). The parent hosted feature service is the same. This version is symbolized by site status.This feature layer view contains information about COVID-19 screening and testing locations. It is made available to the public using the GISCorps COVID-19 Testing Site Locator app (https://giscorps.maps.arcgis.com/apps/webappviewer/index.html?id=2ec47819f57c40598a4eaf45bf9e0d16) and on findcovidtesting.com. States and counties are encouraged to include this feature service in their own testing site locator apps as well.Please submit new testing sites or updated testing site information via this form: https://arcg.is/10S1ib. Including this link on your organization's testing site finder web app will allow testing providers to add their own sites directly to the map, improving the accuracy and completeness of the dataset. GISCorps volunteers verify each submission prior to including it in this public view. You can also add your sites in bulk by completing a copy of this template and emailing it to admin@giscorps.org. This dataset is updated daily. All information is sourced from public information shared by health departments, local governments, and healthcare providers. The data are aggregated by GISCorps volunteers in collaboration with volunteers from Coders Against COVID and should not be considered complete or authoritative. Please contact testing sites or your local health department directly for official information and testing requirements.The objective of this application is to aggregate and facilitate the public communications of local governments, health departments, and healthcare providers with regard to testing site locations. GISCorps does not share any screening or testing site location information not previously made public or provided to us by one of those entities.Latest Arcade code for popups available here: https://docs.google.com/document/d/1PDOq-CxUX9fuC2v3N8muuuxN5mLMinWdf7fiwUt1lOM/edit?usp=sharing

Hydrant Mapping and Testing Apps Market Outlook

According to our latest research, the global Hydrant Mapping and Testing Apps market size reached USD 1.12 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.6% observed over recent years. The market is projected to continue its upward trajectory, reaching USD 3.48 billion by 2033 as per CAGR calculations. This impressive growth is primarily driven by the increasing need for efficient water management, regulatory compliance, and the adoption of digital solutions in municipal and industrial sectors worldwide.

One of the fundamental growth factors propelling the Hydrant Mapping and Testing Apps market is the escalating demand for real-time data and analytics in water infrastructure management. Municipal water utilities and fire departments are under mounting pressure to ensure that hydrants are functional, accessible, and compliant with safety regulations. The integration of advanced mapping and testing apps enables these entities to streamline hydrant inspections, automate reporting, and enhance response times during emergencies. Additionally, the proliferation of Internet of Things (IoT) devices and Geographic Information System (GIS) technology has further bolstered the adoption of these solutions, making hydrant monitoring more precise, efficient, and cost-effective.

Another significant driver is the growing emphasis on public safety and disaster preparedness. Fire departments and emergency services are increasingly leveraging hydrant mapping and testing apps to maintain up-to-date records, track maintenance schedules, and optimize firefighting strategies. The ability to access hydrant status and location data via mobile devices or cloud-based platforms has revolutionized operational workflows, reducing manual errors and improving accountability. Furthermore, stringent government regulations regarding water infrastructure maintenance and the need for periodic hydrant testing have compelled both public and private sector organizations to invest in robust digital solutions.

The rapid digital transformation in industrial facilities and commercial enterprises also plays a pivotal role in the expansion of the Hydrant Mapping and Testing Apps market. Industrial facilities, in particular, are adopting these applications to mitigate risks associated with fire hazards, ensure regulatory compliance, and safeguard assets. The trend towards smart cities and the integration of digital water management systems are creating new opportunities for market players, as urban planners and facility managers seek scalable, user-friendly, and interoperable solutions. The convergence of cloud computing, mobile technology, and data analytics is expected to further accelerate market growth in the coming years.

Regionally, North America currently dominates the global market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate of advanced digital solutions, strong regulatory frameworks, and significant investments in water infrastructure modernization are key factors contributing to North America's market leadership. Meanwhile, emerging economies in Asia Pacific and Latin America are witnessing rapid adoption due to urbanization, increasing awareness about water conservation, and government initiatives to upgrade municipal utilities. The Middle East and Africa, although smaller in market size, are expected to experience notable growth, driven by infrastructure development and the need for efficient water resource management.

Component Analysis

The Hydrant Mapping and Testing Apps market is segmented by component into software and services, each playing a crucial role in shaping the market landscape. The software segment comprises standalone applications, integrated platforms, and mobile apps designed to facilitate hydrant mapping, inspection, and testing. These solutions leverage GIS, real-time data analytics, and cloud connect

This dashboard provides insights into the state of COVID-19 testing in the United States. While some testing site data is provided directly by state and local governments and healthcare providers, much of this data was sourced by GISCorps volunteers from the websites of local governments and healthcare providers and is not authoritative or comprehensive. Please contact testing sites or state and local agencies directly for official information and testing requirements.Find the COVID-19 Testing Sites in the United States public ArcGIS REST service at https://services.arcgis.com/8ZpVMShClf8U8dae/arcgis/rest/services/TestingLocations_public2/FeatureServer.

The global soil erosion testing market is experiencing robust growth, driven by increasing awareness of soil degradation's impact on agriculture, environmental sustainability, and infrastructure. The market's value is estimated at $1.5 billion in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several factors: rising government initiatives promoting sustainable land management practices, stringent environmental regulations demanding accurate soil erosion assessment, and the increasing adoption of precision agriculture techniques. Furthermore, advancements in testing methodologies, including remote sensing and GIS technologies, are enhancing the efficiency and accuracy of soil erosion assessments, contributing to market expansion. Key players like TRI Environmental, AgSource Laboratories, and SGS SA are driving innovation and market penetration through advanced testing services and strategic partnerships. However, the market faces certain restraints. High testing costs, particularly for advanced techniques, can limit accessibility, especially for small-scale farmers in developing regions. The variability in soil types and climatic conditions across different geographical locations also poses challenges in standardizing testing protocols. Despite these challenges, the long-term outlook for the soil erosion testing market remains positive, driven by the increasing need for effective land management and the growing adoption of sustainable agricultural practices worldwide. The market segmentation will likely see a rise in demand for specialized tests catering to specific soil types and agricultural applications. This presents opportunities for companies to develop tailored solutions and expand their market share.

The global portable partial discharge (PD) monitor market is experiencing robust growth, projected to reach a market size of $337.5 million in 2025. While the exact CAGR isn't provided, considering the strong industry drivers such as the increasing demand for reliable power grids, stringent regulations for grid maintenance, and the growing adoption of smart grids, a conservative estimate of the CAGR for the forecast period (2025-2033) would be between 7-9%. This growth is fueled by the need for proactive maintenance and condition monitoring of high-voltage equipment like transformers, gas-insulated switchgear (GIS), and power cables to prevent costly outages and ensure grid stability. Key trends include the increasing adoption of advanced analytics and digital twin technologies integrated into PD monitors, leading to improved diagnostics and predictive maintenance capabilities. Furthermore, miniaturization and improved portability are driving market expansion, allowing for easier deployment and use in diverse field conditions. However, high initial investment costs and the need for specialized expertise to operate and interpret the data from these monitors pose some restraints to market penetration. The market segmentation highlights a significant share for the permanent type PD monitors due to their continuous monitoring capabilities crucial for critical infrastructure. However, the temporary type segment is also showing strong growth driven by the need for periodic testing and diagnostics in various applications. Among applications, GIS and transformers account for a substantial market share, followed by power cables. Leading players such as Rugged Monitoring, Qualitrol, Megger, and Doble Engineering are driving innovation through product development and strategic partnerships, while the emergence of several smaller players indicates a dynamic and competitive landscape. The regional breakdown shows significant demand in North America and Europe, attributed to the developed power infrastructure and stringent regulatory frameworks. However, Asia Pacific is expected to witness faster growth during the forecast period, driven by the rapid expansion of power grids and increasing investments in renewable energy infrastructure in regions like China and India. This report provides a detailed analysis of the global portable partial discharge (PD) monitor market, projecting significant growth in the coming years. We delve into market segmentation, key players, emerging trends, and challenges, offering invaluable insights for industry stakeholders. The market size is estimated to be in the hundreds of millions of dollars, with a projected Compound Annual Growth Rate (CAGR) exceeding 5% driven by factors detailed below.

Explore the burgeoning Portable Partial Discharge Monitor market, driven by robust CAGR and essential for safeguarding electrical infrastructure. Discover key applications, trends, and regional growth in this vital industry report.