Best Management Practices (BMPs) are structural controls used to manage stormwater runoff. Examples include green roofs, rain gardens, and cisterns. BMPs reduce the effects of stormwater pollution and help restore the District’s waterbodies. The District’s stormwater regulations require that large construction or renovation projects install BMPs to manage stormwater runoff once construction is complete. The District also provides financial incentives for properties that install BMPs voluntarily. This dataset includes BMPs that were installed to comply with the District’s stormwater regulations, to participate in the Stormwater Retention Credit (SRC) trading program, to participate in the RiverSmart Homes program, to participate in the Green Roof Rebate program, or to participate in the RiverSmart Rewards stormwater fee discount program. These BMPs have been reviewed by the Department of Energy and Environment (DOEE) as part of these programs. This dataset is updated weekly with data from the District’s Stormwater Database.

Integrated geospatial infrastructure is the modern pattern for connecting organizations across borders, jurisdictions, and sectors to address shared challenges. Implementation starts with a strategy, followed by the pillars of collaborative governance, data and technology, capacity building, and engagement. It is inherently multi-organizational.Whether you call your initiative Open Data, Regional GIS, Spatial Data Infrastructure (SDI), Digital Twin, Knowledge Infrastructure, Digital Ecosystem, or otherwise, collaboration is key.This guide shares good practices for new and existing ArcGIS Administrators to get the most out of your 'OneMap' Hub. See also the complimentary Configure ArcGIS Online: 'OneMap' Good Practices and 'OneMap' Hub Template How-To Guide.

Dataset containing all nine metrics for the Utility Management program Best Practice Scores, including data collected by Rural Utility Business Advisor (RUBA).These scores are collected in collaboration with the Department of Environmental Conservation twice a year. Data collection started in 2016 and is currently on-going. Best Practice scores help to determine funding for water utility projects.

This layer shows the Best Management Practices for water pollution control located on West Chester University's campus. | Publication Date: April 2018, Last Updated: April 2018 | West Chester University’s Geography and Planning department upholds its mission to provide spatial analysis expertise in order to solve many problems regarding spatial applications that facilitates research, sustainability goals, planning and communal integration.This dataset was curated by West Chester University’s Department of Geography and Planning and presented using West Chester University's Open GIS Data.

MassGIS is working very closely with the State 911 Department in the state’s Executive Office of Public Safety and Security on the Next Generation 911 Emergency Call System. MassGIS developed and is maintaining the map and address information that are at the heart of this new system. Statewide deployment of this new 9-1-1 call routing system was completed in 2018.Address sources include the Voter Registration List from the Secretary of the Commonwealth, site addresses from municipal departments (primarily assessors), and customer address lists from utilities. Addresses from utilities were “anonymized” to protect customer privacy. The MAD was also validated for completeness using the Emergency Service List (a list of telephone land line addresses) from Verizon.The MAD contains both tabular and spatial data, with addresses being mapped as point features. At present, the MAD contains 3.2 million address records and 2.2 million address points. As the database is very dynamic with changes being made daily, the data available for download will be refreshed weekly.A Statewide Addressing Standard for Municipalities is another useful asset that has been created as part of this ongoing project. It is a best practices guide for the creation and storage of addresses for Massachusetts Municipalities.Points features with each point having an address to the building/floor/unit level, when that information is available. Where more than one address is located at a single location multiple points are included (i.e. "stacked points"). The points for the most part represent building centroids. Other points are located as assessor parcel centroids.Points will display at scales 1:75,000 and closer.MassGIS' service does not contain points for Boston; they may be accessed at https://data.boston.gov/dataset/live-street-address-management-sam-addresses/resource/873a7659-68b6-4ac0-98b7-6d8af762b6f1.More details about the MAD and Master Address Points.Map service also available.

Stormwater management, as a specialized area within the field of environmental engineering, emerged later in the 20th century, and practitioners have used the term BMP to describe both structural or engineered control devices and systems (e.g. retention ponds) to treat polluted stormwater, as well as operational or procedural practices (e.g. minimizing use of chemical fertilizers and pesticides).

Development of a data policy and ensuring its uptake is not a trivial task within any organisation. There are many surrounding factors that may help or hinder the acceptance and imbedding of policies. Preparation and development of Geoscience Australia’s (GA) Data Strategy and Data Stewardship Policy required a combined understanding and knowledge of political, stakeholder, geoinformatics and technological landscapes external to the organisation, and an internal understanding of a vast amount of multi-disciplinary data assets and their champions within GA. Externally, from an international perspective, any data policy needs to take into account: - Regulations and compliance requirements (FAIR Principles and Trusted repositories), - Supporting data interoperability geoinformatics developments (common ontological information models, vocabularies and content standards (ISO, OGC, W3C)); - Technology trends (semantic web, machine learning, block chain); and - How these may interrelate to each other. From an Australian perspective, any GA data policy must: - Maintain a high level awareness of changes in Government priorities and policies (Australian Government Data Policy, Digital Continuity 2020); - Similar developments within other Government organisations; - Understand GA stakeholders and their roles in supporting delivery of GA goals and outcomes: the influencers, partners and consumers and how GA can communicate its Data Policy to them. Internally, to ensure the Strategy implementation, GA needs to: - Build a strong support base from executives, managers and data champions to ensure adoption of the strategy and funding; - Develop an architecture to sustain the implementation; - Ensure technological support through expert geoinformatics and Multi-Disciplinary-Teams; - Educate staff to ensure they have adequate competencies to comply with the policy. The GA Data Strategy is accompanied by a three year roadmap, which includes developing methodologies and frameworks to: - Streamline data processes, systems and tools; - Embed best practice data management; - Encourage and reward data management; - Develop data capabilities; - Strengthen and embed Data Governance. Realisation of this work is essential for GA to achieve its main goal of maximising geoscientific data potential to serve Australia.

Key Management Service Market size was valued at USD 10.07 Billion in 2023 and is projected to reach USD 47.71 Billion by 2031, growing at a CAGR of 23.71% from 2024 to 2031.

Global Key Management Service Market Drivers

The market drivers for the Key Management Service Market can be influenced by various factors. These may include:

Increasing Data Breaches and Cyber Threats: The frequency and sophistication of cyber-attacks are on the rise, prompting organizations to enhance their data security measures. Effective key management is crucial for protecting sensitive data.
Rising Compliance and Regulatory Requirements: Regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA) require stringent data protection measures, including key management. Compliance with these regulations is a significant driver for adopting KMS solutions.
Proliferation of Cloud Services: As businesses adopt cloud computing, they need effective key management to secure data in the cloud. Cloud service providers often offer integrated KMS solutions, driving market growth.
Growth of IoT Devices: The expansion of the Internet of Things (IoT) introduces numerous connected devices that require secure communication channels. Proper key management is essential to maintain the security of IoT ecosystems.
Adoption of Encryption Across Industries: Industries such as finance, healthcare, and retail are increasingly adopting encryption to protect sensitive information. Effective key management is essential for handling the encryption and decryption processes.
Digital Transformation Initiatives: Organizations undergoing digital transformation are focusing on securing their digital assets, which includes managing encryption keys effectively.
Advancement in Encryption Technologies: Innovations in encryption technologies and the increasing use of advanced cryptographic methods necessitate robust key management systems to handle complex encryption keys.
Remote Work and BYOD Trends: The rise of remote work and Bring Your Own Device (BYOD) policies have increased the need for secure data access and transmission, driving the demand for effective key management solutions.
Awareness and Education: Growing awareness about the significance of data security and encryption best practices among enterprises is propelling the adoption of KMS solutions.
Vendor Innovations and Offerings: The continuous innovations and diversified offerings by KMS vendors to address various industry needs and challenges contribute to the market’s growth.

A webpage intended to highlight the RUBA program and how to connect with its resources. This includes introducing to the Local Government Specialists (LGSs) at DCRA and which LGS services which communities, and an overview of different RUBA programs, grants, publications and trainings. Includes embeds or links to the following:LGS Headshots and Bios: LGS Headshots and Bios - Overview (arcgis.com)DCRA Local Government Assistance App: DCRA Local Government Assistance / RUBA Program (arcgis.com)RUBA Utility Management Training Courses Storymap: RUBA Utility Management Training Courses (arcgis.com)RUBA Publications Storymap: RUBA Publications (arcgis.com)RUBA Grant Report Summary Storymap: RUBA Grant Report Summary (arcgis.com)Best Practices Storymap: Best Practices (arcgis.com)

This layer presents the best-known point and perimeter locations of wildfire occurrences within the United States over the past 7 days. Points mark a location within the wildfire area and provide current information about that wildfire. Perimeters are the line surrounding land that has been impacted by a wildfire.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment. When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source:  Wildfire points are sourced from Integrated Reporting of Wildland-Fire Information (IRWIN) and perimeters from National Interagency Fire Center (NIFC). Current Incidents: This layer provides a near real-time view of the data being shared through the Integrated Reporting of Wildland-Fire Information (IRWIN) service. IRWIN provides data exchange capabilities between participating wildfire systems, including federal, state and local agencies. Data is synchronized across participating organizations to make sure the most current information is available. The display of the points are based on the NWCG Fire Size Classification applied to the daily acres attribute.Current Perimeters: This layer displays fire perimeters posted to the National Incident Feature Service. It is updated from operational data and may not reflect current conditions on the ground. For a better understanding of the workflows involved in mapping and sharing fire perimeter data, see the National Wildfire Coordinating Group Standards for Geospatial Operations.Update Frequency:  Every 15 minutes using the Aggregated Live Feed Methodology based on the following filters:Events modified in the last 7 daysEvents that are not given a Fire Out DateIncident Type Kind: FiresIncident Type Category: Prescribed Fire, Wildfire, and Incident Complex

Area Covered: United StatesWhat can I do with this layer? The data includes basic wildfire information, such as location, size, environmental conditions, and resource summaries. Features can be filtered by incident name, size, or date keeping in mind that not all perimeters are fully attributed.Attribute InformationThis is a list of attributes that benefit from additional explanation. Not all attributes are listed.Incident Type Category: This is a breakdown of events into more specific categories.Wildfire (WF) -A wildland fire originating from an unplanned ignition, such as lightning, volcanos, unauthorized and accidental human caused fires, and prescribed fires that are declared wildfires.Prescribed Fire (RX) - A wildland fire originating from a planned ignition in accordance with applicable laws, policies, and regulations to meet specific objectives.Incident Complex (CX) - An incident complex is two or more individual incidents in the same general proximity that are managed together under one Incident Management Team. This allows resources to be used across the complex rather than on individual incidents uniting operational activities.IrwinID: Unique identifier assigned to each incident record in both point and perimeter layers.

Acres: these typically refer to the number of acres within the current perimeter of a specific, individual incident, including unburned and unburnable islands.Discovery: An estimate of acres burning upon the discovery of the fire.Calculated or GIS:  A measure of acres calculated (i.e., infrared) from a geospatial perimeter of a fire.Daily: A measure of acres reported for a fire.Final: The measure of acres within the final perimeter of a fire. More specifically, the number of acres within the final fire perimeter of a specific, individual incident, including unburned and unburnable islands.

Dates: the various systems contribute date information differently so not all fields will be populated for every fire.FireDiscovery: The date and time a fire was reported as discovered or confirmed to exist. May also be the start date for reporting purposes.

Containment: The date and time a wildfire was declared contained. Control: The date and time a wildfire was declared under control.ICS209Report: The date and time of the latest approved ICS-209 report.Current: The date and time a perimeter is last known to be updated.FireOut: The date and time when a fire is declared out.ModifiedOnAge: (Integer) Computed days since event last modified.DiscoveryAge: (Integer) Computed days since event's fire discovery date.CurrentDateAge: (Integer) Computed days since perimeter last modified.CreateDateAge: (Integer) Computed days since perimeter entry created.

GACC: A code that identifies one of the wildland fire geographic area coordination centers. A geographic area coordination center is a facility that is used for the coordination of agency or jurisdictional resources in support of one or more incidents within a geographic coordination area.Fire Mgmt Complexity: The highest management level utilized to manage a wildland fire event.Incident Management Organization: The incident management organization for the incident, which may be a Type 1, 2, or 3 Incident Management Team (IMT), a Unified Command, a Unified Command with an IMT, National Incident Management Organization (NIMO), etc. This field is null if no team is assigned.Unique Fire Identifier: Unique identifier assigned to each wildland fire. yyyy = calendar year, SSUUUU = Point Of Origin (POO) protecting unit identifier (5 or 6 characters), xxxxxx = local incident identifier (6 to 10 characters)RevisionsJan 4, 2021: Added Integer fields 'Days Since...' to Current_Incidents point layer and Current_Perimeters polygon layer. These fields are computed when the data is updated, reflecting the current number of days since each record was last updated. This will aid in making 'age' related, cache friendly queries.Mar 12, 2021: Added second set of 'Age' fields for Event and Perimeter record creation, reflecting age in Days since service data update.Apr 21, 2021: Current_Perimeters polygon layer is now being populated by NIFC's newest data source. A new field was added, 'IncidentTypeCategory' to better distinguish Incident types for Perimeters and now includes type 'CX' or Complex Fires. Five fields were not transferrable, and as a result 'Comments', 'Label', 'ComplexName', 'ComplexID', and 'IMTName' fields will be Null moving forward.Apr 26, 2021: Updated Incident Layer Symbology to better clarify events, reduce download size and overhead of symbols. Updated Perimeter Layer Symbology to better distingish between Wildfires and Prescribed Fires.May 5, 2021: Slight modification to Arcade logic for Symbology, refining Age comparison to Zero for fires in past 24-hours.Aug 16, 2021: Enabled Time Series capability on Layers (off by default) using 'Fire Discovery Date' for Incidents and 'Creation Date' for Perimeters.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

The Foundation Spatial Data Framework (FSDF) is a framework of ten national authoritative geographic data themes that supports evidence-based social-economic decision making across multiple levels of Australian and New Zealand government agencies, industry, research and the community. The AAA data management principles (Authoritative, Accurate and Accessible), articulated for FSDF, are easily translatable to the FAIR Principles and applied to ensure: - Ability to Find data through rich and consistently implemented metadata; - Access to metadata and data by humans and machines while practicing federated data management within trusted data repositories; - Interoperability of metadata and data through adoption of common standards and application of best practices; and - Reusability of data by capturing licencing constraints and information about its quality and provenance. The Location Information Knowledge Platform (LINK) was developed in 2016 as a digital catalogue of FSDF content. This governed, online, dynamic, analysis and discovery tool was designed to enhance the discovery of FSDF datasets, support work planning and indicate the legal frameworks, agency priorities and use case associated with FSDF data. More than 73 Australian government agencies and commercial organisations use this Platform. Current work includes: - Building common high-level and individual lower-level information models (ontologies) for the FSDF and each dataset; - Development of a new architecture for persistent identifiers and identifier incorporation in the datasets; - The ISO 19115-1-based Australian and New Zealand Metadata profile and best practices user guides; and - Testing new workflows for metadata and data governance and integration utilising a set of common cloud-based infrastructure. On realisation, the FSDF will become a necessary component of spatial socio-economic decision making across Australian and New Zealand government agencies and the private sector. FSDF will encourage cross-sector partnerships and enable seamless access to authoritative spatial data across organisational and jurisdictional boundaries, thus contributing to economic growth, improved public safety, meeting legal and policy obligations and sustaining business needs.

This layer is part of Hamilton City Council's Freshwater Dataset.If you wish to download and consume this entire dataset - click on the link for the file format(s) of your choosing: CAD (DWG)

Please note that the links above may change at any time. For best practice, please refer to this page for the correct links.

If any of the links are above are not functioning, please let us know at gis@hcc.govt.nz.

This Water (Freshwater) dataset contains the following layers:

Water Valve (A tap on a main that controls the flow of water along that main) Water Service Valve (A tap on a service line that controls the flow of water along that line) Water Service Line/Connection (A pipe that delivers water from the main to a building for consumption) Water Meter (A device that measures and displays the amount of water passing through the associated main or service line) Water Main Offset (A point along a main indicating the distance of the main from another known point such as the property boundary or kerb) Water Main Crossover Junction (The junction of one or more pipes where the pipes do not intersect - aka crossover junction) Water Main Abandoned (A water main that is still in the ground, but is now disused and no longer forms part of the active network) Water Hydrant (A tap supplying access to high-pressure water to fight fires, flush pipes and fill water trucks) Water Chamber MH (An opening/structure in a water chamber for the purpose of allowing operators or equipment access to the inside of the chamber) Water Chamber (A chamber on a water main (except bulk mains) containing operational or monitoring devices such as valves or flow meters) Water BM Chamber (A chamber on a water bulk main containing operational devices such as valves or flow meters) Water BM AV Chamber (A chamber on a water bulk main containing an air valve) Water Backflow Device (A device which prevents the accidental backflow of contaminated water into the water system) Water Asbuilts (Plans showing the location and alignment of basic water infrastructure as it was constructed on site, as provided by the contractor or their representatives. Data has not yet been fully incorporated into the Council GIS or asset management system)

Hamilton City Council 3 Waters data is derived from the Council’s GIS (ArcGIS) dataset. The GIS dataset is synchronised with asset data contained in the Council’s Asset Management (IPS) database. A subset of the GIS dataset has been made available for download.

This GIS dataset is currently updated weekly which in turn dynamically updates to the WLASS open data site. Any questions pertaining to this data should be directed to the City Waters Asset Information Team at CityWatersAssetInfo@hcc.govt.nz

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

This layer is part of Hamilton City Council's Freshwater Dataset.If you wish to download and consume this entire dataset - click on the link for the file format(s) of your choosing: FGDB/File Geodatabase Shapefile Excel CAD (DWG) Please note that the links above may change at any time. For best practice, please refer to this page for the correct links. If any of the links are above are not functioning, please let us know at gis@hcc.govt.nz. This Water (Freshwater) dataset contains the following layers: Water Valve (A tap on a main that controls the flow of water along that main) Water Service Valve (A tap on a service line that controls the flow of water along that line) Water Service Line/Connection (A pipe that delivers water from the main to a building for consumption) Water Meter (A device that measures and displays the amount of water passing through the associated main or service line) Water Main Offset (A point along a main indicating the distance of the main from another known point such as the property boundary or kerb) Water Main Crossover Junction (The junction of one or more pipes where the pipes do not intersect - aka crossover junction) Water Main Abandoned (A water main that is still in the ground, but is now disused and no longer forms part of the active network) Water Hydrant (A tap supplying access to high-pressure water to fight fires, flush pipes and fill water trucks) Water Chamber MH (An opening/structure in a water chamber for the purpose of allowing operators or equipment access to the inside of the chamber) Water Chamber (A chamber on a water main (except bulk mains) containing operational or monitoring devices such as valves or flow meters) Water BM Chamber (A chamber on a water bulk main containing operational devices such as valves or flow meters) Water BM AV Chamber (A chamber on a water bulk main containing an air valve) Water Backflow Device (A device which prevents the accidental backflow of contaminated water into the water system) Water Asbuilts (Plans showing the location and alignment of basic water infrastructure as it was constructed on site, as provided by the contractor or their representatives. Data has not yet been fully incorporated into the Council GIS or asset management system) Hamilton City Council 3 Waters data is derived from the Council’s GIS (ArcGIS) dataset. The GIS dataset is synchronised with asset data contained in the Council’s Asset Management (IPS) database. A subset of the GIS dataset has been made available for download. This GIS dataset is currently updated weekly which in turn dynamically updates to the WLASS open data site. Any questions pertaining to this data should be directed to the City Waters Asset Information Team at CityWatersAssetInfo@hcc.govt.nz Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works. Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data. While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data: ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

This data release includes the data used to generate histograms that compared total watershed pollutant removal efficiency (TWPRE) in the two study watersheds Crystal Rock (traditional watershed) and Tributary (Trib.) 104 low impact development (LID watershed) to determine if LID BMP design offered an improved water quality benefit. Input/calibrants data used in the model (Monte Carlo) are described in the manuscript as mentioned in the list below: -BMP Name and Type: references in the manuscript -BMP Connectivity: Proprietary (derived from Montgomery County GIS Data) -BMP Drainage Areas: Proprietary (derived from Montgomery County GIS Data) -BMP Efficiency Ranges: referenced in manuscript -Baseline Pollutant Loadings: referenced in manuscript Stormwater runoff and associated pollutants from urban areas in the Chesapeake Bay Watershed represent a serious impairment to local streams and downstream ecosystems, despite urbanized land comprising only 7% of the Bay watershed area. Excess nitrogen, phosphorus, and sediment affect local streams in the Bay watershed by causing problems ranging from eutrophication and toxic algal blooms to reduced oxygen levels and loss of biodiversity. Traditional management of urban stormwater has primarily focused on directing runoff away from developed areas as quickly as possible. More recently, stormwater best management practices (BMPs) have been implemented in a low impact development (LID) manner on the landscape to treat stormwater runoff closer to its source.The objective of this research was to use a modeling approach to compare total watershed pollutant removal efficiency (TWPRE) of two watersheds with differing spatial patterns of SW BMP design (traditional and LID), and determine if LID SW BMP design offered an improved water quality benefit.

Direct download link for WIHMP dataset. Infiltration based geospatial analysis of land use, ownership, and other constraints impacting preferred locations of Best Management Practices (BMPs) to address water quality.

Entity Management Solutions Market size was valued at USD 12.7 Billion in 2023 and is projected to reach USD 38.6 Billion by 2030, growing at a CAGR of 15.0% during the forecast period 2024-2030.

Global Entity Management Solutions Market Drivers

The market drivers for the Entity Management Solutions Market can be influenced by various factors. These may include:

Raising the Bar for Regulatory Compliance: Organisations must efficiently manage entities to ensure compliance with legal and regulatory norms, given the growing complexity of regulatory compliance across numerous industries and geographies.
Globalisation and Business Expansion: Managing entities across several jurisdictions becomes increasingly difficult as organisations grow internationally. Multinational companies that deal with various legal and regulatory contexts might benefit from the streamlining of processes provided by entity management systems.Effective risk management and corporate governance are areas that businesses are focusing on more and more. Maintaining accurate and current business records is essential for risk reduction and governance, and entity management solutions can help with this.
Technological Developments: The effectiveness and capacities of entity management solutions are improved by technological developments, such as cloud computing and artificial intelligence. Workflows can be enhanced by automation and integration with other enterprise systems.
Demand for Centralised Data Management: In order to cut down on errors, improve data accuracy, and remove redundancies, organisations are looking for centralised systems for managing entity data. Solutions for entity administration offer a centralised location for handling and preserving company data.
Acquisitions and Mergers Activity: A more complicated corporate structure is frequently the result of the growing number of acquisitions and mergers across a range of industries. Solutions for entity management can help ensure compliance during these kinds of transactions and facilitate the smooth integration of organisations.
Emphasis on Operational Efficiency: Businesses are always looking for methods to increase their operational efficiency. Entity management solutions improve overall corporate entity management efficiency by streamlining administrative operations and cutting paperwork.
Demand for Real-time Reporting: The adoption of entity management solutions is fueled by the demand for real-time reporting and analytics. Companies need to know their ownership, compliance status, and corporate structure in real time.
Legal and Regulatory Changes: Regular revisions to business records are required in response to modifications in laws and regulations. Organisations may stay up to date with legal requirements and rapidly adjust to changes in the regulatory landscape with the aid of entity management systems.
Growing Recognition of Governance Best Practices: The significance of governance best practices is being recognised by organisations. Adopting entity management solutions guarantees an organised approach to corporate governance and is in line with these best practices.

DownloadA daily extract of the NPDES Structures dataset is available for download as a zipped file geodatabase.BackgroundAs a government agency that owns and maintains separate storm sewer systems, the Maryland State Highway Administration (SHA) is mandated to file a National Pollutant Discharge Elimination System (NPDES) permit with the Maryland Department of the Environment (MDE). The permit requires the inventory, inspection, and maintenance of SHA stormwater infrastructure. SHA is responsible for maintaining storm drain infrastructure on more than 5,000 miles of roadway statewide. SHA has developed a program consisting of SHA personnel, data managers, and subject matter experts to support the permit requirements and maintain these roadways. The tasks involved in the SHA NPDES data collection program are often completed by engineering consultants for SHA. The data are organized into a series of drainage systems with stormwater management facilities that are interconnected, allowing for flow-tracing function through distinct systems. A drainage system is defined as a series of storm drain structures or point features (i.e., manholes, inlets, endwalls) that connect hydraulically through conveyance features such as pipes and / or ditches. Closed and open storm drain structures are connected by pipe and ditch conveyance to create the drainage system. Stormwater management facilities (SWMF), also known as stormwater best management practices (BMP) are inventoried with the storm drain system. A system can include both open and closed storm drain features. StructuresPhysical stormwater structures to be identified and inventoried include headwalls, endwalls, cross culverts, pumping stations, stormwater risers and weirs, inlets, pipe connections, and manholes. Storm drain structures are represented as point features in the database. Several database features are included that are not existing physical structures, but are employed to facilitate connection of drainage systems in the database. For detailed descriptions of each feature, refer to the SHA Book of Standard for Highway & Incidental Structures, Category 3 “Drainage.” Storm drain structures within SHA ROW are inventoried. Information on private storm drain structures will need to be collected if a private system ties into SHA-owned storm drain features. The only structures that are not inventoried within SHA ROW are single residential driveway culvert end structures (See below for more details), bridge inlets, under drains, roof drainage, or other private tie-ins with the exception of the first or last structure from a private storm drain system and curb opening. If an under-drain pipe has an end structure (such as an endwall), then the structure is inventoried. Curb openings are only inventoried when affecting the drainage area for a BMP or major outfalls. If it is deemed necessary to include a curb cut in the database, the curb cut is captured as an inlet feature with comments identifying the feature as a curb opening. A curb opening is not a COG or COS inlet with an open back, but simply a cut in the curb where sheet flow is exiting impervious. The following are brief discussions of the structures in the data. See Chapter 2 of the Maryland SHA Stormwater NPDES Program SOP for more information, figures, and descriptions of each field. End / Head StructuresAn end / head structure is any structure at the upstream or downstream end of a culvert or pipe. These can include headwalls, endwalls, end sections, and projection pipes. Often the end / head structure is designated on the contract sheets and field verified. When contract plans are not available for a roadway, the SHA Book of Standard for Highway & Incidental Structures should be referenced if structure types are unfamiliar with field teams. Outfall areas are not to be inventoried, but will be analyzed during the inspection process. Headwalls (HW) are structures that are placed at the upstream end of pipes and culverts to provide a stable or hydraulically desirable entrance to the conveyance. Headwalls are usually concrete but can be constructed of wood or masonry, such as brick or concrete block. Wall structures on the upstream side of a culvert or pipe are inventoried as headwalls. Plan sheets may designate the upstream end of a pipe or culvert as an endwall, but these structures should be inventoried as headwalls. All wall-end structures at the upstream end of a pipe or culvert should be inventoried as headwalls. Endwalls (EW) are structures that are placed at the downstream end of pipes and culverts to provide a stable or hydraulically desirable exit to the conveyance. Endwalls are usually concrete, but can be constructed of wood or masonry such as brick or concrete block. All wall structures on the downstream side of a culvert or pipe are inventoried as endwalls. Plan sheets may designate the downstream end of pipe or culvert as a headwall, but these structures should be inventoried as endwalls. All wall-end structures at the downstream end of a pipe or culvert should be inventoried as endwalls. End Sections (ES) are structures that transition the ends of pipes into slopes and provide stability to the pipe entrances and outflows. End sections do not affect the hydraulic capacity or efficiency of the pipes. End sections can be constructed of concrete, metal, or plastic (HDPE). End sections can either be inventoried at the upstream or downstream end of a pipe. Projection Pipes (PP) are not physical structures but represent the upstream and downstream end of a pipe if an end structure on a pipe does not exist. Projection pipes are captured spatially as a feature and represent the ends of pipes. Inlet StructuresInlets are structures that collect storm drain runoff. Inlets convey the runoff to closed storm drain systems, open conveyance, or outfalls. There are many different types of inlet structures, and all are discussed in the SHA Standard Design Manual and should be reviewed prior to conducting an inventory. Spring heads are also inventoried as inlets. Inlets (IN) are hydraulic structure chambers below surface grade that collect storm drain runoff. An inlet either has a grate or open sides / curb to allow runoff to enter the storm drain system. Inlets are often constructed of concrete, masonry brick, or concrete block. Spring Heads (SH) are inventoried as inlets. Spring heads are inventoried only where they emerge and are connected to a storm drain system. Spring heads are inventoried because they provide evidence for the presence of ground water for dry weather flows during illicit discharge field screening operation. Spring heads may be identified from contract drawings or identified during the field inventory. Spring heads are mostly found in rural areas. Connection StructuresA connection structure is a storm drain structure that connects conveyance (pipes and ditches) within a system and is not an inlet, riser, weir, or pumping station. These can include manholes, ditch intersections, junction boxes, pipe connections, wye connections, capped inlets, pipe bends, and pipe directions. Because field crews are not required to open manhole lids and enter closed storm drain structures, no designation type is necessary for connection structures. All of the attribute data for these structures will be collected from contract drawings, including connection material and top of manhole elevations. The existence of connection structures should be field verified for spatial accuracy, even though the attributed data will be collected from contract drawings. For structures that are buried or paved over, a GPS point is to be recorded at the best estimated location in the field based on contract plan sheets. The verification of attribute table data for structures that cannot be verified in the field will be completed based on plan sheet information. This also holds true for structures that are buried or cannot be accessed; the attribute data should be obtained from plan sheets. Manholes (MH) are hydraulic structures that connect pipes through a system. They are used as access points to a system, to change direction or invert elevations for pipes, as a junction to change pipe size and / or material, and as a junction of multiple pipes to a single pipe. Manholes are frequently paved over or buried, but are still inventoried. Unless it is certain that the manhole does not exist, the manhole is inventoried. Manholes with lids that have designed holes to allow runoff to enter are inventoried as manholes and not inlets. Ditch Intersections (ID) are geographic representations of where ditches meet, begin, or end a system and are captured as point features. These features are used to define the extents of ditches. Junction Boxes (JB) are underground hydraulic structures that connect pipes through a system. They are used to change direction or invert elevations for pipes, to change pipe size and / or material, and to connect multiple pipes to a single pipe. Identifying junction boxes in the field is difficult because these structures are usually buried with no part of the structure exposed to the surface. Junction boxes are only inventoried from contract drawings and should never be assumed in the field, unless the field crew is certain the structure is a junction box. If the field crew suspects that pipes are merging together and no contract plans are available to confirm this, the connection should be inventoried as a pipe connection and not a junction box. Pipe Connections (PC) are locations throughout the conveyance of a system where two or more pipes connect. A pipe connection is also captured at the location where a closed storm drain pipe connects to a culvert or stream crossing. Wye Connections (YC) are hydraulic structures that join two pipes together within a system’s conveyance. Wye connections will be identified from contract drawings and should not be assumed in the field. Instead of assuming a wye

This orange layer shows a 250-meter turbidity buffer of the blue 45-meter growth buffer (blue layer called "SF Bay Eelgrass 45m Buffer") adjacent to the maximum extent eelgrass survey in the San Francisco Bay. When a dredging project’s footprint overlaps with this 250-meter buffer, indirect impacts to eelgrass are assessed and best management practices are required per the National Marine Fisheries Service's LTMS Programmatic Essential Fish Habitat consultation. Methods for creating this layer are as follows: Downloaded Bay-wide Eelgrass Surveys for 2003, 2009, and 2014 by Merkel & Associates, Inc. (Merkel) from SFEI. Obtained Richardson Bay 2019 eelgrass survey from Merkel. Loaded all layers into ArcGIS Pro © ESRI and re-projected all data to NAD 1983 UTM Zone 10N. Used Buffer tool to develop a single multipart shapefile with a 45-meter buffer of the 2003, 2009, 2014, and 2019 survey data . Imported the Pacific Marine and Estuarine Fish Habitat Partnership (PMEP) Estuary Extent layer and clipped the 45-meter buffer over terrestrial areas based on the PEMP Estuary Extent (this represents the 45-meter eelgrass buffer layer also found in this Web Application). To create the 250-meter turbidity buffer from there, the same methods were used as follows. Used Buffer tool to develop a single multipart shapefile with a 250-meter buffer from the 45-meter buffer layer. Clipped the 250-meter turbidity buffer over terrestrial areas based on the PEMP Estuary Extent. Some minor adjustments were made where the 250-meter turbidity buffer layer resulted in fragments on land or behind levees.

This project will construct a knowledge base using data on management practices, health, and production of honeybee colonies, supplied by beekeepers themselves, mainly via text messaging. These data, combined with GIS technology to map hive locations and foraging landscapes, will be used to identify the best management practices and most productive landscapes for honeybees.

Green Stormwater Infrastructure (GSI) project data from Seattle Public Utilities (SPU), Seattle Department of Transportation (SDOT), Seattle Parks and Recreation, Seattle Department of Construction and Inspection, and King County Department of Natural Resources and Parks are collected into one comprehensive summary overview data set. If BMP level data are available, each point represents one type of GSI feature with the count by project. There may be many points for one project, each plotted on top of one another. If BMP data are not available such as the SDCI data, there will be point for each project.This grouped layerfile displays City-wide Green Stormwater Infrastructure Best Management Practices(City-wide GSI BMP) and City-wide Green Stormwater Infrastructure projects(City-wide GSI Project). GSI BMP is sourced from CARTO.GSI_BMP_PT_PV. Labels are based on the attribute BMP. City-wide GSI Project displays the data from CARTO.GSI_PT_PV. The labels are based on the attribute PROJECT NAME. This data will not display when zoomed out beyond 1:3,000.Seattle Executive Order 2013-01 and City Council Resolution 31459 direct City departments to coordinate to develop an implementation strategy for managing 700 million gallons of stormwater annually with green stormwater infrastructure approaches by 2025. These data on the location, purpose, funder, install year, and best managed practices of GSI installations in Seattle are gathered and integrated for comprehensive City-wide tracking and reporting at the project level.