Discover the booming interactive map creation tools market! This in-depth analysis reveals a $2.5 billion market in 2025, projected to reach $8 billion by 2033, driven by cloud-based solutions and growing data visualization needs. Learn about key players, market segmentation, and regional trends shaping this exciting sector.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $7.8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of location-based services (LBS) and geographic information systems (GIS) across industries like real estate, tourism, logistics, and urban planning is a major catalyst. Businesses are increasingly leveraging interactive maps to enhance customer engagement, improve operational efficiency, and gain valuable insights from geospatial data. Furthermore, advancements in mapping technologies, including the integration of AI and machine learning for improved data analysis and visualization, are contributing to market growth. The accessibility of user-friendly tools, coupled with the decreasing cost of cloud-based solutions, is also making interactive map creation more accessible to a wider range of users, from individuals to large corporations. However, the market also faces certain challenges. Data security and privacy concerns surrounding the use of location data are paramount. The need for specialized skills and expertise to effectively utilize advanced mapping technologies may also hinder broader adoption, particularly among smaller businesses. Competition among established players like Mapbox, ArcGIS StoryMaps, and Google, alongside emerging innovative solutions, necessitates constant innovation and differentiation. Nevertheless, the overall market outlook remains positive, with continued technological advancements and rising demand for data visualization expected to propel growth in the coming years. Specific market segmentation data, while unavailable, can be reasonably inferred from existing market trends, suggesting a strong dominance of enterprise-grade solutions, but with substantial growth expected from simpler, more user-friendly tools designed for individuals and small businesses.

NDS is an interactive, web-based system, for the visualization of multidimensional neighborhood dynamics across the 50 largest US Metropolitan Statistical Areas (MSAs) from 1980 to 2010 (http://neighborhooddynamics.dreamhosters.com). Four different visualization tools are developed: (1) an interactive time slider to show neighborhood classification changes for different years; (2) multiple interactive bar charts for each variables of each neighborhood; (3) an animated neighborhood’s trajectory and sequence cluster on a self-organizing map (SOM) output space; and (4) a synchronized visualization tool showing maps for four time stamps at once. The development of this interactive online platform for visualizing dynamics overcomes many of the challenges associated with communicating changes for multiple variables, across multiple time stamps, and for a large geographic area when relying upon static maps. The system enables users to select and dive into details on particular neighborhoods and explore their changes over time.

This study focuses on the use of citizen science and GIS tools for collecting and analyzing data on Rose Swanson Mountain in British Columbia, Canada. While several organizations collect data on wildlife habitats, trail mapping, and fire documentation on the mountain, there are few studies conducted on the area and citizen science is not being addressed. The study aims to aggregate various data sources and involve citizens in the data collection process using ArcGIS Dashboard and ArcGIS Survey 123. These GIS tools allow for the integration and analysis of different kinds of data, as well as the creation of interactive maps and surveys that can facilitate citizen engagement and data collection. The data used in the dashboard was sourced from BC Data Catalogue, Explore the Map, and iNaturalist. Results show effective citizen participation, with 1073 wildlife observations and 3043 plant observations. The dashboard provides a user-friendly interface for citizens to tailor their map extent and layers, access surveys, and obtain information on each attribute included in the pop-up by clicking. Analysis on classification of fuel types, ecological communities, endangered wildlife species presence and critical habitat, and scope of human activities can be conducted based on the distribution of data. The dashboard can provide direction for researchers to develop research or contribute to other projects in progress, as well as advocate for natural resource managers to use citizen science data. The study demonstrates the potential for GIS and citizen science to contribute to meaningful discoveries and advancements in areas.

The primary GIS platform, accessible through the City of Allen GIS portal, integrates hardware, software, and data to capture, manage, analyze, and display geographically referenced information. This system enables users to visualize and interpret data in various forms, such as maps and reports, revealing relationships, patterns, and trends that support planning and development efforts. Key FeaturesZoning and Land Use Maps: Interactive maps display current zoning boundaries, land use classifications, and related ordinances, assisting in understanding development regulations. Development Projects Map: Users can explore proposed, approved, under-construction, or recently completed development projects, with details on each project's status and related information. City Limits and ETJ Map: This map delineates the official city boundaries and extraterritorial jurisdiction (ETJ) areas, providing context for municipal planning and services.Aerial Imagery Viewer: An interactive aerial map offers high-resolution imagery of the city, useful for detailed site analysis and visualization. Open Data Hub: The Allen GIS Open Data Hub allows users to discover, analyze, and download various datasets in multiple formats, supporting transparency and data-driven decision-making.These tools are integral to the City of Allen's commitment to providing accessible and comprehensive geographic data, facilitating effective planning, development, and community engagement.Metadata updated: 05/2025

Cristy Parsons · Geospatial Portfolio is a dynamic online platform that highlights my expertise and passion for geospatial technologies. This portfolio features a variety of GIS projects I've worked on, showcasing my skills in spatial analysis, mapping, and data visualization. Each project demonstrates the use of GIS tools to address real-world problems, from community art mapping to land use analysis. The site includes interactive maps, embedded StoryMaps, web mapping applications, and other geospatial content, offering visitors an in-depth look at my professional capabilities and projects. It's also a space where I can continue to grow, share new work, and connect with the geospatial community.

These is an examples to test Data Processing Kernel in CyberGIS-Jupyter for water. The 2_map_visualization folder is an example of an interactive map visualization which is the high-level visualization using PyViz tools as post-processing of environmental modeling. For this example, we used the following PyViz tools: - geopandas (https://geopandas.org/), cartopy (https://scitools.org.uk/cartopy/), geoviews (https://geoviews.org/), and holoviews (https://holoviews.org/)

The global Virtual Globe market is poised for significant expansion, projected to reach approximately USD 4,500 million in 2025 and grow at a robust Compound Annual Growth Rate (CAGR) of 12% through 2033. This surge is primarily driven by the increasing integration of virtual globes into diverse applications, from enhanced geographical information systems (GIS) and urban planning to immersive educational experiences and advanced gaming environments. The proliferation of high-resolution satellite imagery and the rapid advancements in cloud computing and big data analytics are providing the foundational technology for more sophisticated and accessible virtual globe platforms. Furthermore, the growing demand for real-time data visualization and simulation for sectors like disaster management, environmental monitoring, and logistics is a critical catalyst for market growth. The market is seeing a strong shift towards web-based editions due to their accessibility and ease of deployment, alongside sophisticated software versions catering to specialized professional needs. Key trends shaping the Virtual Globe market include the rise of augmented reality (AR) and virtual reality (VR) integrations, which promise to revolutionize how users interact with and experience geographical data. The incorporation of artificial intelligence (AI) for data analysis and pattern recognition within virtual globes is also gaining traction, enabling predictive capabilities and intelligent insights. While the market benefits from widespread adoption across individual, family, school, enterprise, and government sectors, certain restraints such as the high initial investment for developing highly detailed and interactive virtual globes and the ongoing need for data privacy and security considerations can temper the growth trajectory. However, the continuous innovation in visualization technologies and the expanding use cases across industries are expected to propel the market forward, with North America and Asia Pacific emerging as dominant regions due to significant investments in technology and infrastructure. This report provides an in-depth analysis of the global Virtual Globe market, examining its current state, future projections, and key influencing factors. The market, valued at an estimated $1.5 billion in 2023, is experiencing robust growth driven by advancements in geospatial technology, increasing demand for immersive visualization, and the expanding applications across diverse sectors.

ArcGIS DashboardsUse ArcGIS Dashboards to present location-based analytics in Microsoft Teams using intuitive and interactive data visualizations on a single screen.ArcGIS Dashboards enables users to convey information by presenting location-based analytics using intuitive and interactive data visualizations on a single screen. Every organization using the ArcGIS platform can take advantage of ArcGIS Dashboards to help make decisions, visualize trends, monitor status in real time, and inform their communities. Tailor dashboards to your audiences, giving them the ability to slice the data to get the answers they need. Dashboards are essential information products, like maps and apps, providing a critical component to your geospatial infrastructure.Strategic DashboardsStrategic dashboards help executives track key performance indicators (KPIs) and make strategic decisions by evaluating performance based on their organization's goals.Explore this dashboardTactical DashboardsTactical dashboards help analysts and line-of-business managers analyze historical data and visualize trends to gain deeper understanding.Explore this dashboardOperational DashboardsOperational dashboards help operations staff understand events, projects, or assets by monitoring their status in real time.Explore this dashboardInformational Dashboards Informational dashboards help organizations inform and engage their audiences through community outreach.Explore this dashboard

The Digital Geologic-GIS Map of San Miguel Island, California is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (smis_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (smis_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (smis_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) this file (chis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (chis_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (smis_geology_metadata_faq.pdf). Please read the chis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: American Association of Petroleum Geologists. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (smis_geology_metadata.txt or smis_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

These are examples to test Data Processing Kernel in CyberGIS-Jupyter for water. The 1_watershed_delineation folder is an example of a watershed delineation which is the basic step to analyze an interesting watershed. We used GRASS GIS 7.8 version and shell script to apply GRASS GIS library. The 2_map_visualization folder is an example of an interactive map visualization which is the high-level visualization using PyViz tools as post-processing of environmental modeling. For this example, we used the following PyViz tools: - geopandas (https://geopandas.org/), cartopy (https://scitools.org.uk/cartopy/), geoviews (https://geoviews.org/), and holoviews (https://holoviews.org/)

The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

EAGLE-I, an interactive geographic information system (GIS) that allows users to view and map the nation's energy infrastructure and obtain near real-time informational updates concerning the electric, petroleum and natural gas sectors within one visualization platform.

GIS professionals can search for, analyze, store, and visualize large amounts of geospatial data in one platform. Aerial imagery such as Landsat is available through this interface. EOS Platform allows data users access to data, including aerial imagery such as Landsat, and the ability to perform online image processing in a web browser. It is a set of mutually integrated cloud products for searching, analyzing, storing, and visualizing geospatial data. GIS professionals can search for, analyze, store, and visualize large amounts of geospatial data in one platform. GIS users have access to an ecosystem of four mutually integrated EOS products, which together provide a powerful toolset for geospatial analysts. Image data is stored in cloud-based storage and is available for image processing or remote sensing analysis at any time; this can be a raw user file, an imagery obtained from their LandViewer data portal, or an output file from their online EOS Processing tools. The EOS Platform is currently available for free during an open Beta. The LandViewer tool has been freely available for some time and will continue to be.

3D GIS Market Outlook

According to our latest research, the global 3D GIS market size reached USD 6.8 billion in 2024, and it is expected to grow at a robust CAGR of 15.2% from 2025 to 2033. By the end of 2033, the market is projected to achieve a value of USD 24.3 billion. This remarkable growth is primarily driven by increasing urbanization, the rapid adoption of smart city initiatives, and the demand for advanced spatial analytics across various sectors. The proliferation of digital transformation in infrastructure and the integration of geospatial data with emerging technologies such as IoT and AI are further fueling the expansion of the 3D GIS market globally.

One of the primary growth factors for the 3D GIS market is the accelerating pace of urbanization worldwide. As cities grow and evolve, urban planners and government agencies are increasingly relying on advanced geospatial tools to manage complex urban landscapes. The ability of 3D GIS to provide immersive, accurate, and interactive representations of urban environments enables more effective planning, zoning, and infrastructure development. Additionally, the integration of real-time data with 3D visualization enhances decision-making processes for city officials, architects, and engineers. This trend is particularly pronounced in regions with rapidly expanding metropolitan areas, where the need for efficient land use and resource management is critical. The ongoing push for smart cities, coupled with investments in digital infrastructure, is expected to sustain the demand for 3D GIS solutions in the coming years.

Another significant driver for the 3D GIS market is the increasing adoption of these technologies in the transportation and utility sectors. Transportation agencies are leveraging 3D GIS for route optimization, traffic management, and infrastructure monitoring, while utility companies utilize it for asset management, network planning, and predictive maintenance. The ability to visualize underground assets, model complex networks, and simulate disaster scenarios provides substantial operational efficiencies and cost savings. Moreover, the integration of 3D GIS with Building Information Modeling (BIM) and IoT devices enhances the accuracy and timeliness of critical data, enabling proactive responses to potential issues. These advancements are not only improving service delivery but also contributing to the overall safety and resilience of urban infrastructure.

The surge in environmental monitoring and disaster management applications is further propelling the growth of the 3D GIS market. Governments and organizations are increasingly utilizing 3D GIS to monitor environmental changes, assess risks, and develop mitigation strategies for natural disasters such as floods, earthquakes, and wildfires. The ability to visualize terrain, simulate disaster impacts, and analyze spatial data in three dimensions allows for more effective emergency planning and response. Furthermore, the adoption of cloud-based 3D GIS platforms is making these capabilities more accessible to a broader range of users, from local municipalities to international organizations. This democratization of geospatial intelligence is expected to drive continued innovation and adoption across multiple sectors.

Regionally, North America holds the largest share of the 3D GIS market, driven by substantial investments in smart infrastructure, technological advancements, and a strong presence of key market players. Europe follows closely, with significant growth observed in urban planning and environmental monitoring initiatives. The Asia Pacific region, however, is experiencing the fastest growth, fueled by rapid urbanization, government-led smart city projects, and increasing adoption of advanced geospatial technologies in countries like China, Japan, and India. Meanwhile, the Middle East & Africa and Latin America are gradually embracing 3D GIS solutions, particularly in the context of infrastructure development and disaster management. The global landscape is characterized by a dynamic interplay of technological innovation, policy initiatives, and evolving user requirements, all of which are shaping the future trajectory of the 3D GIS market.

this graphs the maps was created the : https://experience.arcgis.com/experience/b296879cc1984fda833a8acc93e31476/ https://www.ncei.noaa.gov/maps/daily/

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Climate data is a vital resource for understanding and addressing the complexities of climate change. With the advent of digital technology, accessing and utilizing climate datasets has become increasingly important for researchers, policymakers, and the general public alike. In this era of data-driven decision-making, the availability of comprehensive climate datasets empowers stakeholders to analyze trends, assess risks, and develop informed strategies for climate resilience and mitigation.

The Climate Data Online platform serves as a gateway to a wealth of climate datasets, offering users the opportunity to explore, analyze, and extract valuable insights from a diverse array of environmental data sources. By providing access to a wide range of datasets encompassing various climatic variables, geographic regions, and temporal scales, Climate Data Online facilitates interdisciplinary research, fosters collaboration, and supports evidence-based decision-making in climate science and related fields.

One of the key features of Climate Data Online is its user-friendly interface, which allows users to easily navigate through different datasets and access detailed information about each dataset. By clicking on the name of a dataset, users can expand and view comprehensive descriptions, including metadata, data formats, temporal coverage, spatial resolution, and relevant links to related tools and resources. This intuitive interface enhances the usability of the platform, enabling users to quickly find and retrieve the data they need for their specific research or analysis purposes.

Moreover, Climate Data Online offers various download options, including FTP access and downloadable samples, enabling users to obtain the data in the format and resolution that best suits their requirements. Whether users need raw data for advanced analysis or pre-processed data for visualization and modeling purposes, Climate Data Online provides the flexibility and scalability to meet diverse data needs.

One of the strengths of Climate Data Online is its extensive coverage of different climatic variables, ranging from temperature and precipitation to atmospheric pressure and wind speed. By aggregating data from multiple sources, including weather stations, satellites, and climate models, Climate Data Online offers a comprehensive view of the Earth's climate system, enabling users to explore spatial and temporal patterns, identify trends, and detect anomalies.

For example, researchers studying the impact of climate change on agriculture may utilize temperature and precipitation datasets to assess changes in growing season length, drought frequency, and crop yields. Similarly, urban planners may use data on temperature and air quality to evaluate heat island effects, assess health risks, and design resilient infrastructure. By providing access to such diverse datasets, Climate Data Online facilitates interdisciplinary research and supports evidence-based decision-making across various sectors.

In addition to its rich collection of climate datasets, Climate Data Online also serves as a valuable repository of tools and resources for data analysis and visualization. From interactive maps and charting tools to statistical analysis software and programming libraries, Climate Data Online offers a variety of options for exploring and interpreting the data. Moreover, the platform provides documentation, tutorials, and user support to help users navigate the datasets and leverage the available tools effectively.

Furthermore, Climate Data Online encourages collaboration and knowledge sharing among users by facilitating community forums, workshops, and collaborative projects. By connecting researchers, practitioners, and policymakers with shared interests in climate data analysis and interpretation, Climate Data Online fosters a vibrant community of practice, where ideas are exchanged, best practices are shared, and innovative solutions are developed.

Overall, Climate Data Online plays a crucial role in advancing climate science and supporting evidence-based decision-making in response to the challenges of climate change. By providing access to comprehensive climate datasets, user-friendly tools, and a supportive community, Climate Data Online empowers stakeholders to explore, analyze, and ...

This project aims to identify areas in Los Angeles that are at high risk of crime in the future and to propose optimal locations for new police stations in those areas. By applying machine learning to post-COVID-19 crime data and various socioeconomic indicators, we predict crime risk at the ZIP Code level. Using a location-allocation model, we then determine suitable locations for new police stations to improve coverage of high-risk zones. The results of our analysis can support the efficient allocation of public safety resources in response to growing demand and budget constraints, helping city officials optimize law enforcement services. The content of the archive- Jupyter Notebook- Data (GeoJSON, CSV)- Summary report PDF FileThe platform on which the notebook should be run.This notebook is designed to run on Datahub.Project materials - Project Material we created on AGOL 1 Los Angeles Crime Hotspothttps://ucsdonline.maps.arcgis.com/home/item.html?id=4bddbae65c164f2d9b0285e09cb2820e 2 Choropleth Map of Predicted Crime Levels by ZIP Codehttps://ucsdonline.maps.arcgis.com/home/item.html?id=e47abb448f0a411ab77c6ac754ba0c34 3. Optimizing LA Police Station: A Location Allocation Analysishttps://ucsdonline.maps.arcgis.com/home/item.html?id=2409da85c3fe410e9578a0eaaed8471e - ArcGIS StoryMaphttps://ucsdonline.maps.arcgis.com/home/item.html?id=cfbd4fc27a3b400296e4e31555951d27 Software dependencies - pandas: Used for loading, formatting, and performing matrix operations on tabular data.- geopandas: Used for loading and processing spatial data, including spatial joins and coordinate transformations.- shapely.geometry.Point: Used to create spatial point objects from latitude and longitude coordinates.- arcgis.gis, arcgis.features, arcgis.geometry, arcgis.geoenrichment: Used to retrieve and manipulate geographic data from ArcGIS Online and to extract population statistics using the GeoEnrichment module.- numpy: Used for feature matrix formatting and numerical computations prior to model training.- IPython.display (display, Markdown, Image): Used to format and display Markdown text, data tables, and images within Jupyter Notebooks.- scikit-learn: Used for building and evaluating machine learning models. Specifically, it was used for data preprocessing (StandardScaler), splitting data (train_test_split), model selection and tuning (GridSearchCV, cross_val_score), training various regressors (e.g.,LinearRegression, RandomForestRegressor, KNeighborsRegressor), and assessing performance using metrics such as R², RMSE, and MAE.Other Components we used - ArcGIS Online: Used to create and host interactive web maps for spatial visualization and public presentation purposes.- Flourish: Used to create interactive graphs and charts for visualizing trends and supporting the analysis.

In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore Fort Ross map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore Fort Ross map area data layers. Data layers are symbolized as shown on the associated map sheets.

GIS Online Moisture Sensor Market Outlook

According to our latest research, the global GIS online moisture sensor market size reached USD 1.12 billion in 2024, reflecting robust adoption across key sectors such as agriculture, environmental monitoring, and industrial process control. The market is projected to grow at a CAGR of 8.7% from 2025 to 2033, reaching an estimated value of USD 2.43 billion by 2033. This impressive growth is primarily driven by the increasing demand for precision agriculture, advancements in sensor technologies, and the growing need for real-time environmental data to support sustainable resource management.

One of the primary growth factors fueling the GIS online moisture sensor market is the surging adoption of precision agriculture techniques worldwide. Farmers and agribusinesses are increasingly leveraging advanced moisture sensing technologies integrated with GIS platforms to monitor soil conditions, optimize irrigation schedules, and enhance crop yields. The ability to access real-time moisture data remotely has transformed traditional farming practices, allowing for data-driven decisions that conserve water and reduce operational costs. This trend is further supported by government initiatives and subsidies promoting smart farming solutions, particularly in regions facing water scarcity or climate variability. As a result, the integration of GIS and online moisture sensors has become a cornerstone in the modernization of agricultural operations, driving sustained market expansion.

Another significant driver for the GIS online moisture sensor market is the escalating focus on environmental monitoring and industrial process control. Industries such as construction, mining, and manufacturing are increasingly required to adhere to stringent environmental regulations, necessitating continuous monitoring of moisture levels in soil, air, and materials. GIS-enabled online moisture sensors provide accurate, location-based data that supports compliance, risk management, and process optimization. In addition, the proliferation of smart city initiatives and the expansion of IoT infrastructure have amplified the deployment of these sensors in urban planning, flood prediction, and infrastructure maintenance. The convergence of GIS and online sensor technologies enables seamless data visualization and analysis, making them indispensable tools for both public and private sector stakeholders.

Technological advancements in sensor design and connectivity are also playing a pivotal role in the market's growth trajectory. Innovations such as wireless and cloud-connected moisture sensors, improved accuracy through advanced materials, and miniaturization have broadened the scope of applications. These advancements have resulted in more cost-effective, durable, and easy-to-deploy solutions, fostering adoption across diverse end-user segments. Furthermore, the integration of AI and machine learning algorithms with GIS platforms is enabling predictive analytics and automated decision-making, further enhancing the value proposition of online moisture sensors. As the demand for actionable insights and real-time monitoring continues to rise, the GIS online moisture sensor market is poised for sustained innovation and expansion.

Regionally, North America and Europe are leading the market, driven by early adoption of precision agriculture, robust regulatory frameworks, and substantial investments in R&D. Asia Pacific, however, is emerging as the fastest-growing region, propelled by rapid urbanization, increasing awareness of sustainable agricultural practices, and government support for smart farming initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as industries in these regions recognize the benefits of GIS-enabled moisture monitoring for resource optimization and environmental management. Overall, the global market is characterized by dynamic regional trends, with each geography contributing uniquely to the market's evolution.

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