This dataset connects each subject matter associated with each municipal question. You can recreate the City Clerk's lobbyist database by downloading all lobbyist datasets on the data portal and view the complete data model of the lobbyist database here: https://services.austintexas.gov/edims/document.cfm?id=293053

Key-Value Store as a Service Market Outlook

According to our latest research, the Global Key-Value Store as a Service market size was valued at $1.2 billion in 2024 and is projected to reach $6.8 billion by 2033, expanding at a robust CAGR of 21.5% during the forecast period from 2025 to 2033. The major factor propelling this remarkable growth is the increasing demand for scalable, high-performance data storage solutions across industries adopting digital transformation. The proliferation of cloud computing, big data analytics, and real-time applications has made key-value store as a service a critical enabler for enterprises seeking low-latency, cost-effective, and flexible data management infrastructure. As organizations migrate from legacy systems to cloud-native architectures, the need for agile, managed database solutions that can handle dynamic workloads is accelerating the adoption of key-value store as a service platforms globally.

Regional Outlook

North America holds the largest share of the global Key-Value Store as a Service market, accounting for approximately 38% of the total market value in 2024. This dominance is underpinned by the region’s mature cloud ecosystem, high digital adoption rates, and the presence of leading technology giants such as Amazon Web Services, Microsoft Azure, and Google Cloud. Furthermore, North American enterprises are early adopters of next-generation data management technologies, driven by a focus on innovation, regulatory compliance, and the need for real-time analytics. The region’s robust IT infrastructure, favorable governmental policies supporting cloud adoption, and a thriving startup ecosystem further reinforce its leading position in the global market. The strong presence of BFSI, healthcare, and e-commerce sectors, which are among the largest consumers of key-value store as a service, also contributes to North America’s market leadership.

The Asia Pacific region is anticipated to be the fastest-growing market for Key-Value Store as a Service, with a projected CAGR exceeding 26% through 2033. This rapid growth is fueled by massive investments in digital infrastructure, the proliferation of cloud service providers, and the exponential rise in data generation from mobile and IoT devices. Countries like China, India, Japan, and South Korea are witnessing a surge in cloud adoption, driven by government initiatives, a booming e-commerce sector, and digital-first strategies among enterprises. The region’s burgeoning small and medium enterprise (SME) segment is increasingly embracing managed database services to overcome resource constraints and focus on core business activities. Additionally, the competitive landscape in Asia Pacific is intensifying as local and global providers expand their footprint, offering tailored solutions to meet diverse business requirements.

Emerging economies in Latin America, the Middle East, and Africa are gradually entering the key-value store as a service market, albeit at a slower pace due to challenges such as limited cloud infrastructure, regulatory uncertainties, and lower digital literacy. However, localized demand for scalable and affordable data storage solutions is rising, particularly in sectors like fintech, retail, and telecommunications. Governments in these regions are increasingly recognizing the importance of digital transformation and are introducing policies to foster cloud adoption and data localization. While the market faces hurdles related to connectivity and skilled workforce shortages, the long-term outlook remains positive as enterprises seek to modernize legacy systems and capitalize on the benefits of managed key-value store services.

Report Scope

Document Database Platform Market Outlook

According to our latest research, the Global Document Database Platform market size was valued at $7.8 billion in 2024 and is projected to reach $26.4 billion by 2033, expanding at a CAGR of 14.5% during the forecast period of 2025–2033. The primary driver behind this robust growth is the exponential surge in unstructured data generation across various industries, which has significantly increased the need for scalable, flexible, and high-performance document database platforms. As enterprises transition to digital-first operations and cloud-native architectures, document database platforms are becoming critical for efficient data management, real-time analytics, and seamless integration with next-generation applications. This market is further propelled by the increasing adoption of artificial intelligence and machine learning technologies, which demand sophisticated data storage and retrieval solutions capable of handling diverse and complex data types.

Regional Outlook

North America holds the largest share of the global Document Database Platform market, accounting for nearly 39% of the total market value in 2024. This dominance stems from the region’s mature IT infrastructure, high cloud adoption rates, and a strong presence of leading technology vendors such as MongoDB, Amazon Web Services, and Microsoft. The United States, in particular, has seen a rapid uptake of document database platforms in sectors like BFSI, healthcare, and retail, driven by stringent regulatory compliance requirements and the need for robust data security. Furthermore, North America’s innovation ecosystem, characterized by substantial investments in R&D and a vibrant startup culture, continues to foster advancements in database technologies, ensuring sustained market leadership throughout the forecast period.

In contrast, the Asia Pacific region is projected to be the fastest-growing market for document database platforms, with a forecasted CAGR of 17.2% from 2025 to 2033. The surge in digital transformation initiatives across countries such as China, India, and Japan is fueling unprecedented demand for scalable data management solutions. Rapid urbanization, the proliferation of e-commerce, and the expansion of fintech and healthcare sectors are key contributors to this growth. Governments in the region are actively promoting digital infrastructure development, which, coupled with increasing investments from global cloud service providers, is accelerating the adoption of document database platforms. Notably, the region’s large population base and growing internet penetration present significant opportunities for market expansion, particularly among small and medium enterprises seeking cost-effective and agile database solutions.

Emerging economies in Latin America and the Middle East & Africa are also witnessing gradual adoption of document database platforms, albeit at a slower pace compared to mature markets. Localized challenges such as limited access to advanced IT infrastructure, budget constraints, and data sovereignty concerns hinder widespread implementation. However, increasing awareness about the benefits of cloud-based database solutions and supportive government policies aimed at digitalization are gradually mitigating these barriers. In Latin America, countries like Brazil and Mexico are experiencing a rise in demand from the retail and government sectors, while in the Middle East & Africa, the focus is on leveraging document databases for smart city initiatives and enhancing public sector efficiency.

Report Scope

Email Security for Public Sector Market Outlook

According to our latest research, the Email Security for Public Sector market size was valued at $2.6 billion in 2024 and is projected to reach $7.1 billion by 2033, expanding at a robust CAGR of 11.8% during the forecast period from 2025 to 2033. The primary growth driver for the global Email Security for Public Sector market is the surge in sophisticated cyber threats targeting public sector infrastructures, which necessitates advanced email security solutions to safeguard sensitive governmental, educational, and healthcare data. Increasing digital transformation initiatives, coupled with the proliferation of cloud-based communication platforms, are further propelling the adoption of comprehensive email security frameworks across public sector organizations worldwide.

Regional Outlook

North America continues to dominate the Email Security for Public Sector market, accounting for the largest share with an estimated market value exceeding $1.1 billion in 2024. This region’s leadership position is attributed to its mature cybersecurity ecosystem, high adoption of advanced technologies, and stringent regulatory frameworks such as FISMA and HIPAA that mandate robust data protection measures for public sector entities. The presence of key market players, significant investments in cybersecurity infrastructure, and a proactive approach to threat intelligence have further solidified North America’s position. Government agencies and educational institutions in the United States and Canada are increasingly prioritizing email security to counteract the rising tide of ransomware, phishing attacks, and data breaches, thereby fueling sustained market growth in the region.

The Asia Pacific region is anticipated to witness the fastest growth in the Email Security for Public Sector market, registering an impressive CAGR of 14.3% through 2033. This surge is primarily driven by rapid digitalization across public sector organizations in countries like China, India, Japan, and Australia. Significant government investments in digital infrastructure, coupled with rising incidences of cyberattacks targeting public databases and communication channels, are compelling public sector agencies to upgrade their email security frameworks. Furthermore, the implementation of stringent data protection regulations such as India’s Digital Personal Data Protection Act and Japan’s Act on the Protection of Personal Information are accelerating market expansion. The region’s burgeoning population, increasing internet penetration, and growing awareness about cybersecurity risks are further catalyzing demand for advanced email security solutions.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing email security solutions for the public sector, although adoption rates remain comparatively lower due to budgetary constraints and limited technical expertise. In these regions, public sector organizations often face challenges related to legacy IT infrastructure, fragmented regulatory environments, and a lack of comprehensive cybersecurity policies. However, growing governmental initiatives to modernize public services, rising cyber threat levels, and partnerships with global technology providers are beginning to bridge the gap. Localized demand is also being shaped by region-specific threats and compliance requirements, prompting a gradual but steady increase in the deployment of both on-premises and cloud-based email security solutions.

Report Scope

State Government Buildings in the United States This dataset is comprised of buildings or properties that are owned or leased by state level governments. It includes buildings occupied by the headquarters of cabinet level state government executive departments, legislative office buildings outside of the capitol building, offices and court rooms associated with the highest level of the judicial branch of the state government, and large multi-agency state office buildings. Because the research to create this dataset was primarily keyed off of the headquarters of cabinet level state government agencies, some state office buildings that don't house a headquarters for such an agency may have been excluded. Intentionally excluded from this dataset are government run institutions (e.g., schools, colleges, prisons, and libraries). Also excluded are state capitol buildings, as these entities are represented in other HSIP layers. State owned or leased buildings whose primary purpose is not to house state offices have also been intentionally excluded from this dataset. Examples of these include "Salt Domes", "Park Shelters", and "Highway Garages". All entities that have been verified to have no building name have had their [NAME] value set to "NO NAME". If the record in the original source data had no building name and TGS was unable to verify the building name, the [NAME] value was set to "UNKNOWN". All phone numbers in this dataset have been verified by TGS to be the main phone for the building. If the building was verified not to have a main phone number, the [TELEPHONE] field has been left blank. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 11/27/2007 and the newest record dates from 05/28/2008.

Serverless NoSQL Database Market Outlook

According to our latest research, the Global Serverless NoSQL Database market size was valued at $2.8 billion in 2024 and is projected to reach $13.6 billion by 2033, expanding at a robust CAGR of 19.2% during the forecast period of 2025–2033. The primary driver fueling this remarkable growth is the surging demand for highly scalable, low-latency data management solutions that can seamlessly support modern, cloud-native applications across diverse industries. As organizations increasingly migrate to digital-first strategies, the need for flexible, cost-efficient, and maintenance-free database architectures is accelerating the adoption of serverless NoSQL databases worldwide. This transformation is further amplified by the proliferation of real-time analytics, IoT deployments, and mobile-first services, all of which require agile data storage and retrieval capabilities that traditional database models struggle to deliver.

Regional Outlook

North America currently dominates the serverless NoSQL database market, commanding the largest share with over 38% of global revenues in 2024. This leadership is largely attributed to the region’s mature cloud ecosystem, early adoption of serverless technologies, and the presence of tech giants such as Amazon Web Services, Google, and Microsoft, who are continuously innovating within this space. Favorable regulatory frameworks, a robust startup culture, and significant investments in digital transformation initiatives across industries like BFSI, healthcare, and retail further bolster the region’s dominance. Moreover, North American enterprises are at the forefront of leveraging advanced analytics, artificial intelligence, and IoT, all of which necessitate the high availability and scalability offered by serverless NoSQL databases.

Asia Pacific is emerging as the fastest-growing region in the serverless NoSQL database market, projected to expand at a remarkable CAGR of 23.5% through 2033. Rapid digitization, government-led smart city initiatives, and the exponential growth of mobile and IoT applications are key factors propelling market expansion in countries such as China, India, Japan, and South Korea. Additionally, the increasing penetration of cloud computing and the rising number of tech-savvy SMEs are driving demand for flexible, cost-effective database solutions. Major cloud service providers are also ramping up their investments and partnerships in the region, making advanced database technologies more accessible to a broader spectrum of enterprises.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual but steady uptake of serverless NoSQL database solutions. While these regions face challenges such as limited cloud infrastructure, skills shortages, and regulatory uncertainties, localized demand for digital services, especially in e-commerce, fintech, and media, is driving adoption. Governments are beginning to recognize the economic potential of digital transformation and are implementing supportive policies and incentives. However, organizations in these regions must navigate issues related to data sovereignty, connectivity, and vendor lock-in, which may moderate the pace of market penetration compared to more developed regions.

Report Scope

Serverless NoSQL Market Outlook

According to our latest research, the Global Serverless NoSQL market size was valued at $2.3 billion in 2024 and is projected to reach $13.7 billion by 2033, expanding at a robust CAGR of 21.8% during the forecast period of 2024 to 2033. The principal driver behind this remarkable growth is the increasing demand for scalable, flexible, and cost-efficient data management solutions, especially as organizations accelerate digital transformation initiatives and embrace cloud-native architectures. The proliferation of IoT devices, real-time analytics, and mobile applications is amplifying the need for serverless NoSQL databases, which offer low-latency access, seamless scaling, and reduced operational overhead compared to traditional database models. As businesses strive for agility and rapid innovation, the adoption of serverless NoSQL solutions is expected to surge across diverse industry verticals worldwide.

Regional Outlook

North America currently commands the largest share of the global Serverless NoSQL market, accounting for nearly 38% of the total market value in 2024. This dominance is attributed to the region’s mature cloud ecosystem, high concentration of technology giants, and early adoption of advanced data management technologies. The presence of leading cloud service providers such as Amazon Web Services, Google Cloud, and Microsoft Azure has accelerated the deployment of serverless NoSQL solutions across various sectors, including BFSI, healthcare, and retail. Furthermore, supportive regulatory frameworks, significant investments in R&D, and a robust startup culture have fostered innovation and market expansion. The region’s enterprises are quick to leverage serverless architectures to gain a competitive edge, driving sustained demand for NoSQL platforms.

The Asia Pacific region is poised to be the fastest-growing market for Serverless NoSQL databases, with an anticipated CAGR exceeding 25% over the forecast period. Rapid digitalization, increasing cloud adoption, and government-led initiatives promoting smart infrastructure are key growth catalysts in countries such as China, India, Japan, and South Korea. Enterprises in this region are investing heavily in modernizing their IT infrastructure to support burgeoning e-commerce, fintech, and IoT ecosystems. The rise of local cloud providers and the entry of global technology firms have intensified competition, further propelling innovation and adoption. Additionally, the region’s vast population and growing internet penetration are generating enormous volumes of unstructured data, necessitating scalable and efficient NoSQL database solutions.

Emerging economies in Latin America, the Middle East, and Africa are gradually embracing serverless NoSQL technologies, albeit at a slower pace due to infrastructural constraints and limited technical expertise. However, increasing investments in digital transformation, coupled with the expansion of cloud service offerings, are creating new growth avenues. Localized demand for real-time analytics, mobile applications, and digital banking is driving adoption, particularly among small and medium enterprises seeking cost-effective data solutions. Policy reforms aimed at improving data security and cloud readiness are expected to further boost market penetration, despite challenges related to network reliability and workforce skill gaps.

Report Scope

LLM Grounding with DB Constraints Market Outlook

According to our latest research, the LLM Grounding with DB Constraints market size reached USD 1.54 billion in 2024 globally, and is projected to expand at a robust CAGR of 28.7% from 2025 to 2033. By the end of 2033, the market is expected to achieve a value of USD 13.57 billion. This impressive growth is primarily driven by the increasing adoption of large language models (LLMs) integrated with database (DB) constraints to ensure more accurate, reliable, and context-aware AI-driven solutions across diverse industries.

The rapid expansion of artificial intelligence applications, particularly those leveraging large language models, is a key driver behind the growing demand for LLM Grounding with DB Constraints. Organizations are increasingly seeking advanced AI solutions that can not only understand and generate human-like language but also adhere to strict data integrity and compliance requirements. By grounding LLMs with database constraints, businesses can ensure that AI-generated outputs are both contextually relevant and compliant with organizational rules or regulatory standards. This is particularly vital in sectors such as finance, healthcare, and manufacturing, where data accuracy and adherence to industry-specific regulations are non-negotiable. The growing complexity of enterprise data landscapes and the rising need for trustworthy AI are thus fueling the market’s growth trajectory.

Another significant growth factor is the acceleration of digital transformation initiatives worldwide. Enterprises are investing heavily in modernizing their IT infrastructure, which includes the integration of AI-powered solutions with existing databases and business processes. The deployment of LLMs grounded with DB constraints allows companies to automate complex workflows, enhance decision-making, and drive operational efficiencies while maintaining control over data governance. This integration is also enabling organizations to unlock new value from their structured and unstructured data, supporting advanced analytics, personalized customer experiences, and improved risk management. The trend towards AI democratization, where even non-technical users can leverage the power of LLMs safely, is further propelling demand for these solutions.

Moreover, the rise of regulatory scrutiny concerning AI outputs and data usage is compelling organizations to adopt solutions that provide transparent and auditable results. LLMs grounded with DB constraints offer the ability to trace AI-generated answers back to authoritative data sources, ensuring accountability and compliance. This is particularly attractive to industries dealing with sensitive or mission-critical data, such as banking, insurance, and public sector organizations. As regulatory frameworks around AI continue to evolve, the importance of incorporating database constraints into LLM deployments will only increase, positioning this market for sustained long-term growth.

From a regional perspective, North America currently leads the LLM Grounding with DB Constraints market due to its advanced AI ecosystem, high concentration of technology providers, and early adoption across key verticals. However, Asia Pacific is anticipated to witness the fastest growth rate in the coming years, driven by rapid digitalization, expanding enterprise IT budgets, and strong government support for AI innovation. Europe, with its stringent data protection regulations and emphasis on trustworthy AI, is also emerging as a significant market for LLM-grounded solutions. Meanwhile, Latin America and the Middle East & Africa are gradually gaining traction, supported by increasing awareness and pilot deployments in sectors such as finance and healthcare.

Component Analysis

The LLM Grounding with DB Constraints market by component is segmented into software, hardware, and services. The software segment dominates the market, accounting for the largest share in 2024, as organizations prioritize investments in advanced platforms, APIs, and middleware that facilitate seamless integration of LLMs with database systems. Software solutions enable enterprises to manage, monitor, and optimize LLM interactions while enforcing business rules and data constraints. The evolution of low-code and no-code platforms is also democratizing access to LLM-powered automation, allowing a broader range of users to leverage these capabilities without deep technical expertise. As sof

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

State Government Buildings in the United States This dataset is comprised of buildings or properties that are owned or leased by state level governments. It includes buildings occupied by the headquarters of cabinet level state government executive departments, legislative office buildings outside of the capitol building, offices and court rooms associated with the highest level of the judicial branch of the state government, and large multi-agency state office buildings. Because the research to create this dataset was primarily keyed off of the headquarters of cabinet level state government agencies, some state office buildings that don't house a headquarters for such an agency may have been excluded. Intentionally excluded from this dataset are government run institutions (e.g., schools, colleges, prisons, and libraries). Also excluded are state capitol buildings, as these entities are represented in other HSIP layers. State owned or leased buildings whose primary purpose is not to house state offices have also been intentionally excluded from this dataset. Examples of these include "Salt Domes", "Park Shelters", and "Highway Garages". All entities that have been verified to have no building name have had their [NAME] value set to "NO NAME". If the record in the original source data had no building name and TGS was unable to verify the building name, the [NAME] value was set to "UNKNOWN". All phone numbers in this dataset have been verified by TGS to be the main phone for the building. If the building was verified not to have a main phone number, the [TELEPHONE] field has been left blank. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 11/27/2007 and the newest record dates from 05/28/2008.

OverviewThe Jurisdictional Units dataset outlines wildland fire jurisdictional boundaries for federal, state, and local government entities on a national scale and is used within multiple wildland fire systems including the Wildland Fire Decision Support System (WFDSS), the Interior Fuels and Post-Fire Reporting System (IFPRS), the Interagency Fuels Treatment Decision Support System (IFTDSS), the Interagency Fire Occurrence Reporting Modules (InFORM), the Interagency Reporting of Wildland Fire Information System (IRWIN), and the Wildland Computer-Aided Dispatch Enterprise System (WildCAD-E).In this dataset, agency and unit names are an indication of the primary manager’s name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIID=null, JurisdictionalKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).AttributesField NameDefinitionGeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. Not populated for Census Block Groups.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available in the Unit ID standard.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons except for Census Blocks Group and for PAD-US polygons that did not have an associated name.LocalNameLocal name for the polygon provided from agency authoritative data, PAD-US, or other source.JurisdictionalKindDescribes the type of unit jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, Other, and Private. A value is not populated for Census Block Groups.JurisdictionalCategoryDescribes the type of unit jurisdiction using the NWCG Landowner Category data standard. Valid values include: BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, State, OtherLoc (other local, not in the standard), Private, and ANCSA. A value is not populated for Census Block Groups.LandownerKindThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Kind data standard. A value is populated for all polygons.LandownerCategoryThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. Legal values align with the NWCG Landowner Category data standard. A value is populated for all polygons.LandownerDepartmentFederal department information that aligns with a unit’s landownerCategory information. Legal values include: Department of Agriculture, Department of Interior, Department of Defense, and Department of Energy. A value is not populated for all polygons.DataSourceThe database from which the polygon originated. An effort is made to be as specific as possible (i.e. identify the geodatabase name and feature class in which the polygon originated).SecondaryDataSourceIf the DataSource field is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if DataSource is "PAD-US 4.0", then for a TNC polygon, the SecondaryDataSource would be " TNC_PADUS2_0_SA2015_Public_gdb ".SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.DataSourceYearYear that the source data for the polygon were acquired.MapMethodControlled vocabulary to define how the geospatial feature was derived. MapMethod will be Mixed Methods by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; Other.DateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using the 24-hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature.JoinMethodAdditional information on how the polygon was matched to information in the NWCG Unit ID database.LegendJurisdictionalCategoryJurisdictionalCategory values grouped for more intuitive use in a map legend or summary table. Census Block Groups are classified as “No Unit”.LegendLandownerCategoryLandownerCategory values grouped for more intuitive use in a map legend or summary table.Other Relevant NWCG Definition StandardsUnitA generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc.) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Protecting Unit; LandownerData SourcesThis dataset is an aggregation of multiple spatial data sources: • Authoritative land ownership records from BIA, BLM, NPS, USFS, USFWS, and the Alaska Fire Service/State of Alaska• The Protected Areas Database US (PAD-US 4.0)• Census Block-Group Geometry BIA and Tribal Data:BIA and Tribal land management data were aggregated from BIA regional offices. These data date from 2012 and were reviewed/updated in 2024. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The spatial data coverage is a consolidation of the best available records/data received from each of the 12 BIA Regional Offices. The data are no better than the original sources from which they were derived. Care was taken when consolidating these files. However, BWFM cannot accept any responsibility for errors, omissions, or positional accuracy in the original digital data. The information contained in these data is dynamic and is continually changing. Updates to these data will be made whenever such data are received from a Regional Office. The BWFM gives no guarantee, expressed, written, or implied, regarding the accuracy, reliability, or completeness of these data.Alaska:The state of Alaska and Alaska Fire Service (BLM) co-manage a process to aggregate authoritative land ownership, management, and jurisdictional boundary data, based on Master Title Plats. Data ProcessingTo compile this dataset, the authoritative land ownership records and the PAD-US data mentioned above were crosswalked into the Jurisdictional Unit Polygon schema and aggregated through a series of python scripts and FME models. Once aggregated, steps were taken to reduce overlaps within the data. All overlap areas larger than 300 acres were manually examined and removed with the assistance of fire management SMEs. Once overlaps were removed, Census Block Group geometry were crosswalked to the Jurisdictional Unit Polygon schema and appended in areas in which no jurisdictional boundaries were recorded within the authoritative land ownership records and the PAD-US data. Census Block Group geometries represent areas of unknown Landowner Kind/Category and Jurisdictional Kind/Category and were assigned LandownerKind and LandownerCategory values of "Private".Update FrequencyThe Authoritative land ownership records and PAD-US data used to compile this dataset are dynamic and are continually changing. Major updates to this dataset will be made once a year, and minor updates will be incorporated throughout the year as needed. New to the Latest Release (1/15/25)Now pulling from agency authoritative sources for BLM, NPS, USFS, and USFWS (instead of getting this data from PADUS).

Field Name Changes

These data were generated as part of a two-and-a-half-year ESRC-funded research project examining the digitalisation of higher education (HE) and the educational technology (Edtech) industry in HE. Building on a theoretical lens of assetisation, it focused on forms of value in the sector, and governance challenges of digital data. It followed three groups of actors: UK universities, Edtech companies, and investors in Edtech. The researchers first sought to develop an overview of the Edtech industry in HE by building three databases on Edtech companies, investors in Edtech, and investment deals, using data downloaded from Crunchbase, a proprietary platform. Due to Crunchbase’s Terms of Service, only parts of one database are allowed to be submitted to this repository, i.e. a list of companies with the project’s classification. A report offering descriptive analysis of all three databases was produced and is submitted as well. A qualitative discursive analysis was conducted by analysing seven documents in depth. In the second phase, researchers conducted interviews with participants representing three groups of actors (n=43) and collected documents on their organisations. Moreover, a list of documents collected from Big Tech (Microsoft, Amazon, and Salesforce) were collected to contextualise the role of global digital infrastructure in HE. Due to commercial sensitivity, only lists of documents collected about investors and Big Tech are submitted to the repository. Researchers then conducted focus groups (n=6) with representatives of universities (n=19). The dataset includes transcripts of focus groups and outputs of writing by participants during the focus group. Finally, a public consultation was held via a survey, and 15 participants offered qualitative answers.

The higher education (HE) sector has been marketised for decades; but the speed, scope, and extent of marketisation has led key education scholars to conceptualise it as a global industry (Verger, Lubienski, & Steiner-Khamsi, 2016). Further, the use of technology to transform teaching and learning, as well as the profound digitalisation of universities more broadly, has led universities to collect and process an unprecedented amount of digital data. Education technology (EdTech) companies have become one of the key players in the HE industry and the UK has made EdTech one of its key pillars in its recent international education strategy (HM Government, 2019). EdTech companies are reporting unprecedented growth. In 2019, Coursera became a 'unicorn' (i.e. a company worth over $1 billion), while British-based FutureLearn secured £50 million investment by selling 50% shares of the company. Investment in EdTech is growing at an impressive rate and reached $16.3bn in 2018 (ET, 2019). While EdTech start-up companies strive to become 'unicorns' and profit from HE, so too might universities increasingly look for new ways of profiting from the wealth of digital data they produce.

The study of HE markets has so far focused on service-commodities. However, data and data products do not act like commodities. Commodities are consumed once used, but data is reproducible at almost zero marginal cost. New products and services can be created from data and monetised through subscription fees, an app, or a platform that does not transfer ownership, control, or reproduction rights to the user. Furthermore, data use creates yet more data, and the network effects increase the value of these platforms. Therefore, there is a new quality at play in the monetisation and marketisation of these digital HE products and services: 'assetization'. We are witnessing a widespread change from creating value via market exchange towards extracting value via the ownership and control of assets.

This research project aims to investigate these new processes of value creation and extraction in an HE sector that is digitalising its operations and introducing new digital solutions premised on the expansion of service fees. By introducing a focus on assets, and economic rents, this project offers a theoretically and empirically transformative approach to understand emerging HE markets and their implications for the HE sector. The assetization of HE is consequential because of the legal and technical implications for its regulation. It is also crucial to examine in any discussion about the legitimate and socially just arrangement and distribution of assets, their ownership, and their uses. The project employs an innovative, comparative, and participatory mixed-methods research design. It combines digital methods, interviews, observation, document analysis, deliberative focus groups, knowledge exchange and co-production with stakeholders, and public consultation. Data analysis will include quantitative and qualitative analysis of investment trends, comparative case studies of investors, EdTech companies and universities, and social network analysis.

The application of this research project is fourfold. First, it will help universities understand the emerging processes of assetization so they can develop policies and practices for protecting their rights. Second, it will assist entrepreneurs in finding ways to incorporate ethical and sustainable considerations in their innovation processes. Third, it will mediate between the financial interests of investors and the social function of universities. Here, it will provide evidence for policymakers on how to include assets in HE sector regulation. Finally, it will unpack potential forms of inequality that assetization might bring into the HE sector.

Digital Terrain Database Market Outlook

According to our latest research, the global Digital Terrain Database market size in 2024 stands at USD 2.54 billion, with a robust year-on-year growth trajectory. The market is expected to expand at a CAGR of 9.2% from 2025 to 2033, reaching a forecasted value of USD 5.67 billion by 2033. This growth is primarily driven by the increasing adoption of advanced geospatial technologies across various sectors, including defense, civil engineering, and urban planning, as organizations seek to leverage high-precision terrain data for enhanced decision-making and operational efficiency.

The Digital Terrain Database market is experiencing significant momentum due to the rising demand for accurate topographical information in mission-critical applications. The integration of digital terrain data in aerospace and defense operations, such as flight simulation, mission planning, and navigation, is a key growth factor. These sectors require precise elevation models to ensure safety, optimize routes, and enhance situational awareness. Furthermore, the proliferation of unmanned aerial vehicles (UAVs) and autonomous systems has intensified the need for real-time, high-resolution terrain data, propelling the adoption of sophisticated digital terrain databases. As defense budgets continue to prioritize geospatial intelligence, the market is poised for sustained expansion.

Another pivotal growth driver for the Digital Terrain Database market is the rapid urbanization and infrastructure development observed globally. Civil engineering and urban planning sectors are increasingly relying on detailed terrain models for designing resilient infrastructure, mitigating natural hazards, and optimizing land use. The surge in smart city initiatives, particularly in emerging economies, necessitates the deployment of advanced geospatial solutions. Digital terrain databases enable planners and engineers to simulate various scenarios, assess environmental impacts, and streamline construction processes. The integration of terrain data with Building Information Modeling (BIM) and Geographic Information Systems (GIS) further amplifies its value, fostering market growth across public and private sectors.

Technological advancements and the growing accessibility of cloud-based geospatial solutions are also catalyzing market expansion. Cloud deployment models are democratizing access to high-quality terrain data, enabling organizations of all sizes to leverage these resources without significant upfront investments in hardware or infrastructure. The evolution of data acquisition methods, such as LiDAR, satellite imagery, and photogrammetry, has enhanced the accuracy and granularity of digital terrain databases. This, coupled with the increasing emphasis on environmental monitoring, disaster management, and agricultural optimization, is broadening the application landscape and stimulating demand for digital terrain databases across diverse verticals.

From a regional perspective, North America currently dominates the Digital Terrain Database market, attributed to the presence of leading technology providers, robust defense spending, and widespread adoption of geospatial technologies. Europe follows closely, driven by stringent regulatory frameworks and substantial investments in infrastructure modernization. The Asia Pacific region is anticipated to exhibit the fastest growth during the forecast period, fueled by rapid urbanization, government-led smart city projects, and expanding applications in agriculture and environmental monitoring. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit from a lower base, as digital transformation initiatives gain traction across these regions.

Component Analysis

The Digital Terrain Database market by component is segmented into Software, Hardware, and Services, each playing a vital role in the overall ecosystem. Software solutions form the backbone

The Database as a Service (DaaS) platform market is experiencing robust growth, driven by the increasing adoption of cloud computing, the need for scalable and cost-effective database solutions, and the rising demand for real-time data processing. Let's assume, for illustrative purposes, a 2025 market size of $50 billion with a Compound Annual Growth Rate (CAGR) of 15% for the forecast period of 2025-2033. This implies significant expansion, reaching an estimated market value exceeding $150 billion by 2033. This growth is fueled by several key trends including the proliferation of big data analytics, the expanding adoption of serverless architectures, and the growing preference for managed services that reduce operational overhead for businesses. Major players like AWS, Microsoft Azure, Google Cloud Platform, and others are heavily investing in enhancing their DaaS offerings, fostering competition and innovation. However, challenges remain, including security concerns related to data stored in the cloud, vendor lock-in, and the complexity of migrating existing databases to a DaaS environment. The competitive landscape is intensely dynamic, with established tech giants alongside specialized DaaS providers vying for market share. The segmentation of the market is likely based on deployment model (public, private, hybrid), database type (SQL, NoSQL), and industry vertical. Future growth will be influenced by factors such as advancements in database technologies (e.g., graph databases, in-memory databases), increasing adoption of artificial intelligence and machine learning for database management, and the growing demand for data sovereignty and compliance solutions. The market's continued expansion is assured, but the precise trajectory will depend on the evolution of cloud technologies, regulatory changes, and the ability of providers to address security and scalability challenges effectively. This robust growth presents significant opportunities for both established and emerging players within the DaaS landscape.

The dataset included with this article contains three files describing and defining the sample and variables for VAT impact, and Excel file 1 consists of all raw and filtered data for the variables for the panel data sample. Excel file 2 depicts time-series and cross-sectional data for nonfinancial firms listed on the Saudi market for the second and third quarters of 2019 and the third and fourth quarters of 2020. Excel file 3 presents the raw material of variables used in measuring the company's profitability of the panel data sample

Distributed SQL Database as a Service Market Outlook

According to our latest research, the global Distributed SQL Database as a Service market size reached USD 1.12 billion in 2024, reflecting robust momentum in cloud-native database adoption. The market is poised for substantial growth, projected to expand at a CAGR of 25.6% from 2025 to 2033. By the end of 2033, the market is expected to achieve a value of approximately USD 8.8 billion. This remarkable growth trajectory is primarily driven by enterprises’ increasing demand for high-availability, scalable, and globally distributed data management solutions, as well as the proliferation of cloud infrastructure and digital transformation initiatives across all major industries.

A key growth factor for the Distributed SQL Database as a Service market is the rapid shift towards cloud-native architectures and microservices-based applications. Enterprises are increasingly realizing the limitations of traditional relational databases in handling globally distributed workloads and mission-critical, real-time transactional data. The need for elastic scalability, continuous availability, and seamless geo-distribution has propelled organizations to adopt distributed SQL databases delivered as a service. This shift is further reinforced by the growing adoption of hybrid and multi-cloud strategies, which require databases capable of operating efficiently across diverse cloud and on-premises environments. As organizations prioritize agility and business continuity, the demand for Distributed SQL Database as a Service is expected to accelerate over the forecast period.

Another significant driver is the surge in data volumes generated by digital business processes, IoT devices, and customer-facing applications. Modern enterprises, especially those in sectors such as BFSI, retail, e-commerce, and telecommunications, require robust data platforms that can process, analyze, and store massive amounts of structured and semi-structured data in real time. Distributed SQL Database as a Service solutions offer horizontal scaling, strong consistency, and automated failover, making them ideal for supporting high-throughput transaction management and analytics workloads. Furthermore, the integration of advanced security features, compliance capabilities, and automated management tools has made these solutions attractive for organizations seeking to reduce operational complexity and total cost of ownership.

The market’s expansion is also fueled by the increasing focus on digital transformation and modernization of legacy IT systems. As enterprises embark on cloud migration journeys, they are leveraging Distributed SQL Database as a Service to modernize their data infrastructure, enhance application performance, and improve customer experiences. The proliferation of SaaS, mobile, and edge computing applications necessitates databases that can operate seamlessly across geographies and deliver low-latency access to data. Additionally, the availability of flexible deployment models, including public, private, and hybrid clouds, allows organizations to tailor their database strategies to meet regulatory, security, and performance requirements. These factors collectively contribute to the sustained growth of the Distributed SQL Database as a Service market.

From a regional perspective, North America continues to dominate the Distributed SQL Database as a Service market, accounting for the largest revenue share in 2024, owing to the early adoption of cloud technologies and the presence of leading technology vendors. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid digitalization, increased cloud investments, and expanding IT infrastructure in countries such as China, India, and Japan. Europe also demonstrates strong growth potential, supported by stringent data protection regulations and the rising adoption of cloud-based database solutions among enterprises. Latin America and the Middle East & Africa are gradually catching up, with increasing awareness and investments in cloud-native data platforms. The regional landscape is expected to evolve further as organizations worldwide embrace distributed database technologies to gain competitive advantage.