This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

ArcGIS has many analysis and geoprocessing tools that can help you solve real-world problems with your data. In some cases, you are able to run individual tools to complete an analysis. But sometimes you may require a more comprehensive way to create, share, and document your analysis workflow.In these situations, you can use a built-in application called ModelBuilder to create a workflow that you can reuse, modify, save, and share with others.In this course, you will learn the basics of working with ModelBuilder and creating models. Models contain many different elements, many of which you will learn about. You will also learn how to work with models that others create and share with you. Sharing models is one of the major advantages of working with ModelBuilder and models in general. You will learn how to prepare a model for sharing by setting various model parameters.After completing this course, you will be able to:Identify model elements and states.Describe a prebuilt model's processes and outputs.Create and document models for site selection and network analysis.Define model parameters and prepare a model for sharing.

The Habitat Suitability Analysis was built using ArcGIS Pro's ModelBuilder tool. This program does not have an option to save the model's inputs as a relative file path. As a result, the model may not run because it's searching for each layer's original file path. If this happens, we have included a file titled Habitat_Suitability_Analysis_Script that outlines the processes we used to build the model. This submission contains three folders and three supplemental files. The folder titled "Data" includes all of the raw data and data input in the Habitat Suitability Analysis. The folder titled "Scripts" describes the steps to build the Habitat Suitability Analysis model in ArcGIS Pro. The Results folder contains the Habitat Suitability Analysis model and the data that was input into the model. The supplemental files are a file titled "Dryad_Folder_Contents" which describes the contents of every folder in this submission, and a file titled "Habitat_Suitability_Analysis_README" which contain...

This permit conversion tool converts ePermit .xls files to quarter-quarter or lat/long locations. Also included is a public lands survey geodatabase necessary to run the POU tool. This Model Builder toolset is available for ArcGIS 10.1-5. The March 2018 update provided here tests for field types and processes the fields accordingly.

This deep learning model is used for extracting windows and doors in textured building data displayed in 3D views. Manually digitizing windows/doors from 3D building data can be a slow process. This model automates the extraction of these objects from a 3D view and can help in speeding up 3D editing and analysis workflows. Using this model, existing building data can be enhanced with additional information on location, size and orientation of windows and doors. The extracted windows and doors can be further used to perform 3D visibility analysis using existing 3D geoprocessing tools in ArcGIS.This model can be useful in many industries and workflows. National Government and state-level law enforcement could use this model in security analysis scenarios. Local governments could use windows and door locations to help with tax assessments with CAMA (computer aided mass appraisal) plus impact-studies for urban planning. Public safety users might be interested in regards to physical or visual access to restricted areas, or the ability to build evacuation plans. The commercial sector, with everyone from real-estate agents to advertisers to office/interior designers, would be able to benefit from knowing where windows and doors are located. Even utilities, especially mobile phone providers, could take advantage of knowing window sizes and positions. To be clear, this model doesn't solve these problems, but it does allow users to extract and collate some of the data they will need to do it.Using the modelThis model is generic and is expected to work well with a variety of building styles and shapes. To use this model, you need to install supported deep learning frameworks packages first. See Install deep learning frameworks for ArcGIS for more information. The model can be used with the Interactive Object Detection tool.A blog on the ArcGIS Pro tool that leverages this model is published on Esri Blogs. We've also published steps on how to retrain this model further using your data.InputThe model is expected to work with any textured building data displayed in 3D views. Example data sources include textured multipatches, 3D object scene layers, and integrated mesh layers. OutputFeature class with polygons representing the detected windows and doors in the input imagery. Model architectureThe model uses the FasterRCNN model architecture implemented using ArcGIS API for Python.Training dataThis model was trained using images from the Open Images Dataset.Sample resultsBelow, are sample results of the windows detected with this model in ArcGIS Pro using the Interactive Object Detection tool, which outputs the detected objects as a symbolized point feature class with size and orientation attributes.

Data Modeling Tool Market Outlook

The global data modeling tool market size was valued at USD 1.2 billion in 2023 and is expected to reach approximately USD 2.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.2% from 2024 to 2032. The growth of the data modeling tool market is driven by the increasing need for precise data management and analytics to bolster data-driven decision-making across various industries. The widespread adoption of cloud computing and the proliferation of data across organizations are pivotal in driving this market forward.

One of the primary factors fueling the growth of the data modeling tool market is the accelerating digital transformation across industries. As businesses increasingly rely on data to drive their operations and strategic decisions, the need for robust data modeling tools that can efficiently manage and analyze large volumes of data becomes paramount. Furthermore, the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML) into data modeling tools enhances their functionalities, thereby providing more accurate and insightful data analytics, which drives market demand.

Another significant growth factor is the rising adoption of cloud-based solutions. Cloud-based data modeling tools offer several advantages over traditional on-premises solutions, including scalability, cost-effectiveness, and ease of access. These tools enable organizations to manage and analyze data from multiple sources in real-time, facilitating faster and more informed decision-making. The increasing preference for cloud-based solutions is expected to drive substantial growth in the data modeling tool market over the forecast period.

Additionally, the growing focus on regulatory compliance and data governance is contributing to the market's expansion. With the introduction of stringent data protection regulations such as GDPR and CCPA, organizations are compelled to adopt data modeling tools to ensure compliance and mitigate risks associated with data breaches and non-compliance. These tools assist in creating transparent and auditable data processes, which are critical for regulatory adherence, further boosting their adoption across various sectors.

Regionally, North America holds a significant share of the data modeling tool market, driven by the presence of a large number of technology giants and early adopters of advanced data management solutions. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, attributable to the rapid digitalization and increasing investments in IT infrastructure in emerging economies such as China and India. The growing awareness about the benefits of data modeling tools among businesses in this region is likely to propel market growth significantly.

In the context of the growing need for efficient data management, the role of a Data Catalog becomes increasingly significant. A Data Catalog serves as a comprehensive inventory of data assets within an organization, enabling users to discover, understand, and manage their data more effectively. By providing metadata about data sources, it facilitates data governance and compliance, ensuring that data is used responsibly and ethically. As organizations grapple with vast amounts of data, a well-implemented Data Catalog can streamline data access and enhance collaboration across departments, ultimately driving more informed decision-making.

Component Analysis

The data modeling tool market is segmented by component into software and services. The software segment holds the largest market share, driven by the increasing need for sophisticated data modeling solutions that can handle complex data structures and provide actionable insights. Software tools are essential for creating, managing, and analyzing data models, enabling organizations to streamline their data processes and improve operational efficiency. As businesses continue to generate vast amounts of data, the demand for advanced data modeling software is expected to surge.

Services form a crucial segment of the data modeling tool market, encompassing a range of offerings such as consulting, integration, support, and maintenance. As organizations adopt data modeling tools, they often require expert guidance to customize and integrate these tools into their existing systems. Additionally, ongoing support and maintenance services are essential to ensure

The Gravity model toolbox, a programmed ArcGIS tool to map and prioritize the potential corridors of urban green space.

This three-dimensional (3-D) building massing model of New York City is one of the many digital tools provided by the Office of Technology and Innovation. There are three file formats available to download via the buttons below. For more information on all the tools available, view the OTI's Digital Tools web page.

The data modeling tool market is experiencing robust growth, driven by the increasing demand for efficient data management and the rise of big data analytics. The market, estimated at $5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the growing adoption of cloud-based data modeling solutions, the increasing need for data governance and compliance, and the expanding use of data visualization and business intelligence tools that rely on well-structured data models. The market is segmented by tool type (e.g., ER diagramming tools, UML modeling tools), deployment mode (cloud, on-premise), and industry vertical (e.g., BFSI, healthcare, retail). Competition is intense, with established players like IBM, Oracle, and SAP vying for market share alongside numerous specialized vendors offering niche solutions. The market's growth is being further accelerated by the adoption of agile methodologies and DevOps practices that necessitate faster and more iterative data modeling processes. The major restraints impacting market growth include the high cost of advanced data modeling software, the complexity associated with implementing and maintaining these solutions, and the lack of skilled professionals adept at data modeling techniques. The increasing availability of open-source tools, coupled with the growth of professional training programs focused on data modeling, are gradually alleviating this constraint. Future growth will likely be shaped by innovations in artificial intelligence (AI) and machine learning (ML) that are being integrated into data modeling tools to automate aspects of model creation and validation. The trend towards data mesh architecture and the growing importance of data literacy are also driving demand for user-friendly and accessible data modeling tools. Furthermore, the development of integrated platforms that combine data modeling with other data management functions is a key market trend that is likely to significantly impact future growth.

A conference paper describing GIS tools developed in support of the blast loss estimation capability for the Australian Reinsurance Pool Corporation. The paper focus is on GIS tools developed for: …Show full descriptionA conference paper describing GIS tools developed in support of the blast loss estimation capability for the Australian Reinsurance Pool Corporation. The paper focus is on GIS tools developed for: exposure database construction and integration of a number of datasets including 3D building geometry

Portions of the world's interior, such as central Asia are extremely secluded from the ocean and are more than 2,000 km from the nearest coast. Distance to coast can be used in asset management and modeling project costs. Phenomenon Mapped: Distance to coastUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance to Coast layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Portions of the world's oceans are extremely remote including areas in the South Pacific that are more the 2,500 km from the nearest land. Distance from shore can be used in asset management, modeling project costs, and as an index of human influence. Phenomenon Mapped: Distance from shoreUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance from Shore layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Flow diagrams are a common tool used to help build and interpret models of dynamical systems, often in biological contexts such as consumer-resource models and similar compartmental models. Typically, their usage is intuitive and informal. Here, we present a formalized version of flow diagrams as a kind of weighted directed graph which follow a strict grammar, which translate into a system of ordinary differential equations (ODEs) by a single unambiguous rule, and which have an equivalent representation as a relational database. (We abbreviate this schema of “ODEs and formalized flow diagrams” as OFFL.) Drawing a diagram within this strict grammar encourages a mental discipline on the part of the modeler in which all dynamical processes of a system are thought of as interactions between dynamical species that draw parcels from one or more source species and deposit them into target species according to a set of transformation rules. From these rules, the net rate of change for each species can be derived. The modeling schema can therefore be understood as both an epistemic and practical heuristic for modeling, serving both as an organizational framework for the model building process and as a mechanism for deriving ODEs. All steps of the schema beyond the initial scientific (intuitive, creative) abstraction of natural observations into model variables are algorithmic and easily carried out by a computer, thus enabling the future development of a dedicated software implementation. Such tools would empower the modeler to consider significantly more complex models than practical limitations might have otherwise proscribed, since the modeling framework itself manages that complexity on the modeler’s behalf. In this report, we describe the chief motivations for OFFL, carefully outline its implementation, and utilize a range of classic examples from ecology and epidemiology to showcase its features.

Data Modeling Software Market Outlook

The global data modeling software market size was valued at approximately USD 2.5 billion in 2023 and is projected to reach around USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% from 2024 to 2032. The market's robust growth can be attributed to the increasing adoption of data-driven decision-making processes across various industries, which necessitates advanced data modeling solutions to manage and analyze large volumes of data efficiently.

The proliferation of big data and the growing need for data governance are significant drivers for the data modeling software market. Organizations are increasingly recognizing the importance of structured and unstructured data in generating valuable insights. With data volumes exploding, data modeling software becomes essential for creating logical data models that represent business processes and information requirements accurately. This software is crucial for implementation in data warehouses, analytics, and business intelligence applications, further fueling market growth.

Technological advancements, particularly in artificial intelligence (AI) and machine learning (ML), are also propelling the data modeling software market forward. These technologies enable more sophisticated data models that can predict trends, optimize operations, and enhance decision-making processes. The integration of AI and ML with data modeling tools allows for automated data analysis, reducing the time and effort required for manual processes and improving the accuracy of the results. This technological synergy is a significant growth factor for the market.

The rise of cloud-based solutions is another critical factor contributing to the market's expansion. Cloud deployment offers numerous advantages, such as scalability, flexibility, and cost-effectiveness, making it an attractive option for businesses of all sizes. Cloud-based data modeling software allows for real-time collaboration and access to data from anywhere, enhancing productivity and efficiency. As more companies move their operations to the cloud, the demand for cloud-compatible data modeling solutions is expected to surge, driving market growth further.

In terms of regional outlook, North America currently holds the largest share of the data modeling software market. This dominance is due to the high concentration of technology-driven enterprises and a strong emphasis on data analytics and business intelligence in the region. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. Rapid digital transformation, increased cloud adoption, and the rising importance of data analytics in emerging economies like China and India are key factors contributing to this growth. Europe, Latin America, and the Middle East & Africa also present significant opportunities, albeit at varying growth rates.

Component Analysis

In the data modeling software market, the component segment is divided into software and services. The software component is the most significant contributor to the market, driven by the increasing need for advanced data modeling tools that can handle complex data structures and provide accurate insights. Data modeling software includes various tools and platforms that facilitate the creation, management, and optimization of data models. These tools are essential for database design, data architecture, and other data management tasks, making them indispensable for organizations aiming to leverage their data assets effectively.

Within the software segment, there is a growing trend towards integrating AI and ML capabilities to enhance the functionality of data modeling tools. This integration allows for more sophisticated data analysis, automated model generation, and improved accuracy in predictions and insights. As a result, organizations can achieve better data governance, streamline operations, and make more informed decisions. The demand for such advanced software solutions is expected to rise, contributing significantly to the market's growth.

The services component, although smaller in comparison to the software segment, plays a crucial role in the data modeling software market. Services include consulting, implementation, training, and support, which are essential for the successful deployment and utilization of data modeling tools. Many organizations lack the in-house expertise to effectively implement and manage data modeling software, leading to increased demand for professional services.

Headquarters building model.

Daily Chlorophyll-a concentration (mg/m-3) at ~4.6 km resolution. Chlorophyll in water changes the way it reflects and absorbs sunlight, allowing scientists to map the amount and location of phytoplankton using optics. These measurements give scientists valuable insights into the health of the ocean environment, and help scientists study the ocean carbon cycle. Subtle changes in chlorophyll-a signify various types and quantities of marine phytoplankton (microscopic marine plants), the knowledge of which has both scientific and practical applications.These chlorophyll layers show milligrams of chlorophyll per cubic meter of seawater. Places where chlorophyll amounts were very low, indicating very low numbers of phytoplankton are blue. Places where chlorophyll concentrations were high, meaning many phytoplankton were growing, are dark green. The observations come from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Aqua satellite. Places where MODIS could not collect data because of sea ice, polar darkness, or clouds are masked.The MODIS instrument is managed by NASA/Goddard Space Flight Center, Greenbelt, Maryland and was built by Raytheon/Santa Barbara Remote Sensing, Goleta, California. For further information, access the MODIS Homepage at modis.gsfc.nasa.govPhenomenon Mapped: Chlorophyll-aUnits: mg/m-3Time Interval: DailyTime Extent: 2002/07/03 12:00:00 UTC to presentCell Size: 4.6 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: NASAUpdate Cycle: DailyArcGIS Server URL: https://earthobs3.arcgis.com/arcgis Time: This is a time-enabled layer. It shows the average chlorophyll-a concentration during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in mg/m-2. Do not use this layer for analysis while the Cartographic Renderer processing templates are applied.This layer is part of the Living Atlas of the World that provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.

The micro carving tools market is experiencing robust growth, driven by the increasing popularity of intricate crafting and detailed woodworking projects. The rising demand for personalized and handcrafted items, coupled with the accessibility of online tutorials and communities dedicated to these skills, fuels market expansion. While precise market size figures are unavailable, a reasonable estimation based on comparable niche markets suggests a current market value of approximately $150 million in 2025. Considering a plausible CAGR of 5-7%, this translates to a projected market value exceeding $200 million by 2033. Key drivers include the expanding hobbyist base, the increasing professional use in areas such as jewelry making and model building, and the introduction of innovative tool designs incorporating advanced materials and ergonomic features. The market is segmented by tool type (e.g., chisels, gouges, knives), material (e.g., steel, wood), and price point (professional vs. hobbyist). The market's growth trajectory is influenced by several trends. The rise of online marketplaces and e-commerce platforms provides increased accessibility for both suppliers and consumers. Furthermore, manufacturers are focusing on developing sustainable and eco-friendly tools, catering to environmentally conscious consumers. Despite positive growth, challenges exist. These include potential price sensitivity among hobbyists and competition from cheaper, lower-quality alternatives. However, the sustained interest in intricate craftsmanship and the continuous innovation in tool design are likely to overcome these restraints, contributing to the market's long-term expansion. The competitive landscape comprises both established players like Pfeil and Flexcut, known for their high-quality tools, and smaller, niche brands focusing on specialized products or specific materials. This competitive environment fosters innovation and pushes the development of ever-more refined micro carving tools.

Salinity is the quantity of dissolved salt in water. Marine life have a limited range of salinity that they can live in. Open ocean seawater typically has a salinity of 32 to 37. Temperature and salinity characteristics help determine origin of water masses.Phenomenon Mapped: Seafloor SalinityUnits: g/kg - practical salinity scaleCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Unsigned integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Boyer TP, Levitus S, Garcia HE, Locamini RA, Stephens C, et al. (2005) Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25° grid. International Journal of Climatology 25: 931–945.Publication Date: 2005ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Cartographic Renderer - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

This webmap is a subset of Distance to Coast (km) Global Coverage. Portions of the world's interior, such as central Asia are extremely secluded from the ocean and are more than 2,000 km from the nearest coast. Distance to coast can be used in asset management and modeling project costs. Phenomenon Mapped: Distance to coastUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance to Coast layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.